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  <front>
    <journal-meta>
      <journal-id journal-id-type="nlm-ta">Arq Bras Cardiol</journal-id>
      <journal-id journal-id-type="publisher-id">abc</journal-id>
      <journal-title-group>
        <journal-title>Arquivos Brasileiros de Cardiologia</journal-title>
        <abbrev-journal-title abbrev-type="publisher">Arq. Bras. Cardiol.</abbrev-journal-title>
      </journal-title-group>
      <issn pub-type="ppub">0066-782X</issn>
      <issn pub-type="epub">1678-4170</issn>
      <publisher>
        <publisher-name>Sociedade Brasileira de Cardiologia - SBC</publisher-name>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id specific-use="scielo-v3" pub-id-type="publisher-id">VhK7qhwZpfKHXT4bN8NgB3x</article-id>
      <article-id specific-use="scielo-v2" pub-id-type="publisher-id">S0066-782X2026000400608</article-id>
      <article-id pub-id-type="other">00608</article-id>
      <article-id pub-id-type="doi">10.36660/abc.20250765</article-id>
      <article-categories>
        <subj-group subj-group-type="heading">
          <subject>Artigo Original</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>iCardio: Aplica&#231;&#227;o de Business Intelligence na Avalia&#231;&#227;o da Disparidade Regional da Assist&#234;ncia Cardiovascular com Dados do Mundo Real</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <contrib-id contrib-id-type="orcid">0009-0005-8879-1306</contrib-id>
          <name>
            <surname>Souza</surname>
            <given-names>Let&#237;cia Cunha Pereira de</given-names>
          </name>
          <role>An&#225;lise e interpreta&#231;&#227;o dos dados e Reda&#231;&#227;o do manuscrito</role>
          <xref ref-type="aff" rid="aff1">
            <sup>1</sup>
          </xref>
          <xref ref-type="aff" rid="aff2">
            <sup>2</sup>
          </xref>
        </contrib>
        <contrib contrib-type="author">
          <contrib-id contrib-id-type="orcid">0000-0001-5333-3173</contrib-id>
          <name>
            <surname>Marcolino</surname>
            <given-names>Miriam Allein Zago</given-names>
          </name>
          <role>Concep&#231;&#227;o e desenho da pesquisa</role>
          <role>Obten&#231;&#227;o de dados</role>
          <role>Revis&#227;o cr&#237;tica do manuscrito quanto ao conte&#250;do intelectual importante</role>
          <xref ref-type="aff" rid="aff1">
            <sup>1</sup>
          </xref>
          <xref ref-type="aff" rid="aff3">
            <sup>3</sup>
          </xref>
        </contrib>
        <contrib contrib-type="author">
          <contrib-id contrib-id-type="orcid">0009-0002-0659-8878</contrib-id>
          <name>
            <surname>Engster</surname>
            <given-names>Pedro Henrique</given-names>
          </name>
          <role>An&#225;lise e interpreta&#231;&#227;o dos dados e Reda&#231;&#227;o do manuscrito</role>
          <xref ref-type="aff" rid="aff1">
            <sup>1</sup>
          </xref>
          <xref ref-type="aff" rid="aff4">
            <sup>4</sup>
          </xref>
        </contrib>
        <contrib contrib-type="author">
          <contrib-id contrib-id-type="orcid">0000-0003-4762-4569</contrib-id>
          <name>
            <surname>Casiraghi</surname>
            <given-names>Yohan</given-names>
          </name>
          <role>An&#225;lise e interpreta&#231;&#227;o dos dados e Reda&#231;&#227;o do manuscrito</role>
          <xref ref-type="aff" rid="aff1">
            <sup>1</sup>
          </xref>
          <xref ref-type="aff" rid="aff5">
            <sup>5</sup>
          </xref>
        </contrib>
        <contrib contrib-type="author">
          <contrib-id contrib-id-type="orcid">0009-0003-4558-4097</contrib-id>
          <name>
            <surname>Linn</surname>
            <given-names>Thiago Brusca da Costa</given-names>
          </name>
          <role>An&#225;lise e interpreta&#231;&#227;o dos dados e Reda&#231;&#227;o do manuscrito</role>
          <xref ref-type="aff" rid="aff1">
            <sup>1</sup>
          </xref>
          <xref ref-type="aff" rid="aff5">
            <sup>5</sup>
          </xref>
        </contrib>
        <contrib contrib-type="author">
          <contrib-id contrib-id-type="orcid">0009-0009-4909-0741</contrib-id>
          <name>
            <surname>Bairos</surname>
            <given-names>Jerry Eduardo de Almeida de</given-names>
          </name>
          <role>An&#225;lise e interpreta&#231;&#227;o dos dados e Reda&#231;&#227;o do manuscrito</role>
          <xref ref-type="aff" rid="aff1">
            <sup>1</sup>
          </xref>
          <xref ref-type="aff" rid="aff5">
            <sup>5</sup>
          </xref>
        </contrib>
        <contrib contrib-type="author">
          <contrib-id contrib-id-type="orcid">0000-0002-1934-1003</contrib-id>
          <name>
            <surname>Lara</surname>
            <given-names>Luciana Rodrigues de</given-names>
          </name>
          <role>Obten&#231;&#227;o de dados</role>
          <xref ref-type="aff" rid="aff1">
            <sup>1</sup>
          </xref>
          <xref ref-type="aff" rid="aff3">
            <sup>3</sup>
          </xref>
          <xref ref-type="aff" rid="aff6">
            <sup>6</sup>
          </xref>
        </contrib>
        <contrib contrib-type="author">
          <contrib-id contrib-id-type="orcid">0000-0003-0985-6510</contrib-id>
          <name>
            <surname>Schneider</surname>
            <given-names>Nay&#234; Balzan</given-names>
          </name>
          <role>Revis&#227;o cr&#237;tica do manuscrito quanto ao conte&#250;do intelectual importante</role>
          <xref ref-type="aff" rid="aff1">
            <sup>1</sup>
          </xref>
          <xref ref-type="aff" rid="aff3">
            <sup>3</sup>
          </xref>
          <xref ref-type="aff" rid="aff6">
            <sup>6</sup>
          </xref>
        </contrib>
        <contrib contrib-type="author">
          <contrib-id contrib-id-type="orcid">0000-0002-6411-3480</contrib-id>
          <name>
            <surname>Etges</surname>
            <given-names>Ana Paula Beck da Silva</given-names>
          </name>
          <role>Concep&#231;&#227;o e desenho da pesquisa</role>
          <role>Revis&#227;o cr&#237;tica do manuscrito quanto ao conte&#250;do intelectual importante</role>
          <xref ref-type="aff" rid="aff1">
            <sup>1</sup>
          </xref>
          <xref ref-type="aff" rid="aff3">
            <sup>3</sup>
          </xref>
          <xref ref-type="aff" rid="aff6">
            <sup>6</sup>
          </xref>
        </contrib>
        <contrib contrib-type="author">
          <contrib-id contrib-id-type="orcid">0000-0002-2447-2577</contrib-id>
          <name>
            <surname>Polanczyk</surname>
            <given-names>Carisi Anne</given-names>
          </name>
          <role>Concep&#231;&#227;o e desenho da pesquisa</role>
          <role>Obten&#231;&#227;o de financiamento</role>
          <role>Revis&#227;o cr&#237;tica do manuscrito quanto ao conte&#250;do intelectual importante</role>
          <xref ref-type="aff" rid="aff1">
            <sup>1</sup>
          </xref>
          <xref ref-type="aff" rid="aff3">
            <sup>3</sup>
          </xref>
          <xref ref-type="aff" rid="aff5">
            <sup>5</sup>
          </xref>
          <xref ref-type="aff" rid="aff6">
            <sup>6</sup>
          </xref>
          <xref ref-type="aff" rid="aff7">
            <sup>7</sup>
          </xref>
        </contrib>
      </contrib-group>
      <aff id="aff1">
        <label>1</label>
        <institution content-type="orgname">Hospital de Cl&#237;nicas de Porto Alegre</institution>
        <addr-line>
          <named-content content-type="city">Porto Alegre</named-content>
          <named-content content-type="state">RS</named-content>
        </addr-line>
        <country country="BR">Brasil</country>
        <institution content-type="original">Hospital de Cl&#237;nicas de Porto Alegre (HCPA), Porto Alegre, RS &#8211; Brasil</institution>
      </aff>
      <aff id="aff2">
        <label>2</label>
        <institution content-type="orgdiv2">Departamento de Bioqu&#237;mica</institution>
        <institution content-type="orgdiv1">Instituto de Ci&#234;ncias B&#225;sicas da Sa&#250;de</institution>
        <institution content-type="orgname">Universidade Federal do Rio Grande do Sul</institution>
        <addr-line>
          <named-content content-type="city">Porto Alegre</named-content>
          <named-content content-type="state">RS</named-content>
        </addr-line>
        <country country="BR">Brasil</country>
        <institution content-type="original">Programa de P&#243;s-gradua&#231;&#227;o em Ci&#234;ncias Biol&#243;gicas: Bioqu&#237;mica, Departamento de Bioqu&#237;mica, Instituto de Ci&#234;ncias B&#225;sicas da Sa&#250;de, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS &#8211; Brasil</institution>
      </aff>
      <aff id="aff3">
        <label>3</label>
        <institution content-type="orgname">Instituto Nacional de Avalia&#231;&#227;o de Tecnologia em Sa&#250;de</institution>
        <addr-line>
          <named-content content-type="city">Porto Alegre</named-content>
          <named-content content-type="state">RS</named-content>
        </addr-line>
        <country country="BR">Brasil</country>
        <institution content-type="original">Instituto Nacional de Avalia&#231;&#227;o de Tecnologia em Sa&#250;de INCT/IATS, Porto Alegre, RS &#8211; Brasil</institution>
      </aff>
      <aff id="aff4">
        <label>4</label>
        <institution content-type="orgdiv1">Faculdade de Medicina</institution>
        <institution content-type="orgname">Universidade Federal de Ci&#234;ncias da Sa&#250;de de Porto Alegre</institution>
        <addr-line>
          <named-content content-type="city">Porto Alegre</named-content>
          <named-content content-type="state">RS</named-content>
        </addr-line>
        <country country="BR">Brasil</country>
        <institution content-type="original">Faculdade de Medicina, Universidade Federal de Ci&#234;ncias da Sa&#250;de de Porto Alegre, Porto Alegre, RS &#8211; Brasil</institution>
      </aff>
      <aff id="aff5">
        <label>5</label>
        <institution content-type="orgdiv1">Faculdade de Medicina</institution>
        <institution content-type="orgname">Universidade Federal do Rio Grande do Sul</institution>
        <addr-line>
          <named-content content-type="city">Porto Alegre</named-content>
          <named-content content-type="state">RS</named-content>
        </addr-line>
        <country country="BR">Brasil</country>
        <institution content-type="original">Faculdade de Medicina, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS &#8211; Brasil</institution>
      </aff>
      <aff id="aff6">
        <label>6</label>
        <institution content-type="orgdiv1">Programa de P&#243;s-Gradua&#231;&#227;o em Epidemiologia</institution>
        <institution content-type="orgname">Universidade Federal do Rio Grande do Sul</institution>
        <addr-line>
          <named-content content-type="city">Porto Alegre</named-content>
          <named-content content-type="state">RS</named-content>
        </addr-line>
        <country country="BR">Brasil</country>
        <institution content-type="original">Programa de P&#243;s-Gradua&#231;&#227;o em Epidemiologia, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS &#8211; Brasil</institution>
      </aff>
      <aff id="aff7">
        <label>7</label>
        <institution content-type="orgdiv1">Programa de P&#243;s-Gradua&#231;&#227;o em Cardiologia e Ci&#234;ncias Cardiovasculares</institution>
        <institution content-type="orgname">Universidade Federal do Rio Grande do Sul</institution>
        <addr-line>
          <named-content content-type="city">Porto Alegre</named-content>
          <named-content content-type="state">RS</named-content>
        </addr-line>
        <country country="BR">Brasil</country>
        <institution content-type="original">Programa de P&#243;s-Gradua&#231;&#227;o em Cardiologia e Ci&#234;ncias Cardiovasculares, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS &#8211; Brasil</institution>
      </aff>
      <author-notes>
        <corresp id="c01"><label>Correspond&#234;ncia</label>: Carisi Anne Polanczyk &#8226; Hospital de Clinicas de Porto Alegre &#8211; Rua Ramiro Barcelos, 2350, Sala 21507, Centro de Pesquisa Cl&#237;nica. CEP 90035-900, Porto Alegre, RS &#8211; Brasil E-mail: <email>carisi.anne@gmail.com</email> </corresp>
        <fn fn-type="edited-by">
          <label>Editor respons&#225;vel pela revis&#227;o:</label>
          <p> Gl&#225;ucia Maria Moraes de Oliveira</p>
        </fn>
        <fn fn-type="coi-statement">
          <label>Potencial Conflito de Interesse:</label>
          <p> Declaro n&#227;o haver conflito de interesses pertinentes.</p>
        </fn>
      </author-notes>
      <pub-date date-type="pub" publication-format="electronic">
        <day>06</day>
        <month>07</month>
        <year>2026</year>
      </pub-date>
      <pub-date date-type="collection" publication-format="electronic">
        <month>04</month>
        <year>2026</year>
      </pub-date>
      <volume>123</volume>
      <issue>4</issue>
      <elocation-id>e20250765</elocation-id>
      <history>
        <date date-type="received">
          <day>28</day>
          <month>11</month>
          <year>2025</year>
        </date>
        <date date-type="rev-recd">
          <day>05</day>
          <month>03</month>
          <year>2026</year>
        </date>
        <date date-type="accepted">
          <day>25</day>
          <month>03</month>
          <year>2026</year>
        </date>
      </history>
      <permissions>
        <license license-type="open-access" xlink:href="https://creativecommons.org/licenses/by/4.0/" xml:lang="pt">
          <license-p>Este &#233; um artigo de acesso aberto distribu&#237;do sob os termos da licen&#231;a de atribui&#231;&#227;o pelo Creative Commons</license-p>
        </license>
      </permissions>
      <abstract>
        <title>Resumo</title>
        <sec>
          <title>Fundamento</title>
          <p> A avalia&#231;&#227;o da assist&#234;ncia cardiovascular no Brasil &#233; desafiada pela extens&#227;o territorial e pelo grande volume de dados. Ferramentas de <italic>Business Intelligence</italic> (BI) podem facilitar a visualiza&#231;&#227;o e o uso dessas informa&#231;&#245;es na tomada de decis&#245;es em sa&#250;de p&#250;blica.</p>
        </sec>
        <sec>
          <title>Objetivos</title>
          <p> Demonstrar a aplica&#231;&#227;o pr&#225;tica do painel interativo <italic>iCardio</italic> na an&#225;lise de desigualdades regionais da assist&#234;ncia cardiovascular de alta complexidade no Brasil.</p>
        </sec>
        <sec>
          <title>M&#233;todos</title>
          <p> Estudo descritivo, baseado em dados secund&#225;rios disponibilizados pelo painel <italic>iCardio</italic>, que integra informa&#231;&#245;es assistenciais do Sistema &#218;nico de Sa&#250;de (SUS). Analisaram-se vari&#225;veis de 2019, por localidade. Consideraram-se o volume de atendimentos, as taxas de mortalidade, as reinterna&#231;&#245;es e o deslocamento.</p>
        </sec>
        <sec>
          <title>Resultados</title>
          <p> As an&#225;lises dos procedimentos de cirurgias cardiovasculares e cardiologia intervencionista indicaram que o Sudeste apresentou as menores taxas de mortalidade (hospitalar: 5,65%, at&#233; 30 dias ap&#243;s o procedimento: 5,73% e at&#233; 30 dias ap&#243;s a alta: 6,77%). O Norte concentrou as taxas de mortalidade mais altas (hospitalar: 7,56%, at&#233; 30 dias ap&#243;s o procedimento: 7,7% e at&#233; 30 dias ap&#243;s a alta: 8,7%). Sul e Centro-Oeste registraram as maiores taxas de reinterna&#231;&#227;o em at&#233; 30 dias do procedimento (9,79% e 10,3% respectivamente) e em at&#233; 30 dias da alta (11,7% e 11,97%). O Sul se destacou ainda por apresentar a maior taxa padronizada de procedimentos (10,54), quase o dobro do Sudeste que teve a segunda maior taxa (6,02). O Nordeste destacou-se pelo maior deslocamento m&#233;dio dos pacientes (94,59 km).</p>
        </sec>
        <sec>
          <title>Conclus&#245;es</title>
          <p> O uso de BI por meio do painel <italic>iCardio</italic> permite uma an&#225;lise &#225;gil e acess&#237;vel de dados assistenciais, com potencial de apoiar decis&#245;es baseadas em evid&#234;ncias, otimiza&#231;&#227;o de recursos e identifica&#231;&#227;o de desigualdades regionais na assist&#234;ncia cardiovascular.</p>
        </sec>
      </abstract>
      <abstract abstract-type="graphical">
        <p>
          <fig id="f01">
            <label>Figura Central</label>
            <caption>
              <title>: iCardio: Aplica&#231;&#227;o de Business Intelligence na Avalia&#231;&#227;o da Disparidade Regional da Assist&#234;ncia Cardiovascular com Dados do Mundo Real</title>
              <p>Foram consideradas as regi&#245;es com maiores e menores valores para aspecto.</p>
            </caption>
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          </fig>
        </p>
      </abstract>
      <kwd-group xml:lang="pt">
        <title>Palavras-chave:</title>
        <kwd>Cardiologia</kwd>
        <kwd>Indicadores de Qualidade em Assist&#234;ncia &#224; Sa&#250;de</kwd>
        <kwd>Dados de Sa&#250;de Coletados Rotineiramente</kwd>
      </kwd-group>
      <funding-group>
        <award-group>
          <funding-source>Minist&#233;rio da S&#225;ude</funding-source>
          <award-id>44/2022</award-id>
        </award-group>
        <funding-statement>Fontes de Financiamento: O presente estudo foi financiado pelo Conselho Nacional de Desenvolvimento Cient&#237;fico e Tecnol&#243;gico (CNPq) e Minist&#233;rio da S&#225;ude por meio do edital CNPq/Decit/SCTIE/MS/DESID/SE/MS Economia as Sa&#250;de &#8211; Pesquisas para a Melhoria do Gasto P&#250;blico em Sa&#250;de (No.44/2022) e recebeu apoio financeiro do Hospital de Cl&#237;nicas de Porto Alegre (HCPA), Porto Alegre, Brasil (ROR: https://ror.org/010we4y38).</funding-statement>
      </funding-group>
      <counts>
        <fig-count count="2"/>
        <table-count count="6"/>
        <equation-count count="0"/>
        <ref-count count="41"/>
      </counts>
    </article-meta>
  </front>
  <body>
    <sec sec-type="intro">
      <title>Introdu&#231;&#227;o</title>
      <p>Atualmente, as doen&#231;as cardiovasculares (DCV) representam uma carga significativa para os sistemas de sa&#250;de em todo o mundo. No Brasil, as DCV se destacam tanto em termos de mortalidade quanto de custos financeiros, com mais de R$1 bilh&#227;o sendo gastos anualmente em procedimentos cardiovasculares pelo Sistema &#218;nico de Sa&#250;de (SUS).<sup><xref ref-type="bibr" rid="B1">1</xref></sup> Al&#233;m disso, a literatura em cardiologia aponta desigualdades regionais na oferta de servi&#231;os de alta complexidade,<sup><xref ref-type="bibr" rid="B2">2</xref></sup> no volume de atendimentos em centros especializados,<sup><xref ref-type="bibr" rid="B3">3</xref></sup> na cobertura da sa&#250;de suplementar<sup><xref ref-type="bibr" rid="B4">4</xref></sup> e na distribui&#231;&#227;o de profissionais de sa&#250;de no territ&#243;rio brasileiro.<sup><xref ref-type="bibr" rid="B5">5</xref></sup> Dada a magnitude do impacto desses fatores, a busca por maior efici&#234;ncia nos investimentos em sa&#250;de &#233; de interesse p&#250;blico. Nesse contexto, o uso de painel de indicadores emerge como uma ferramenta promissora, simplificando o acesso e a compreens&#227;o das informa&#231;&#245;es e potencialmente melhorando a gest&#227;o de recursos e a tomada de decis&#245;es.<sup><xref ref-type="bibr" rid="B6">6</xref>-<xref ref-type="bibr" rid="B8">8</xref></sup></p>
      <p>Apesar de seu potencial, h&#225; uma escassez de estudos sobre a aplica&#231;&#227;o de pain&#233;is com dados espec&#237;ficos de DCV no Brasil, o que limita a explora&#231;&#227;o sistem&#225;tica de informa&#231;&#245;es j&#225; dispon&#237;veis para subsidiar decis&#245;es em sa&#250;de p&#250;blica. Nesse contexto, este estudo se prop&#245;e a contribuir com esse campo ao analisar dados do mundo real dispon&#237;veis no painel interativo iCardio,<sup><xref ref-type="bibr" rid="B9">9</xref></sup> que representa uma iniciativa de organiza&#231;&#227;o e visualiza&#231;&#227;o de dados de cardiologia intervencionista e cirurgia cardiovascular referentes aos anos de 2019 e 2020. O painel foi desenvolvido com base em dados p&#250;blicos do Sistema &#218;nico de Sa&#250;de (SUS), especificamente do Sistema de Informa&#231;&#245;es Hospitalares (SIH) e do Sistema de Informa&#231;&#227;o sobre Mortalidade (SIM).</p>
      <p>O <italic>iCardio</italic> fornece par&#226;metros epidemiol&#243;gicos detalhados, incluindo o perfil dos pacientes, locais de atendimento, tempo de interna&#231;&#227;o, mortalidade e recursos investidos nos procedimentos. Assim, o objetivo deste estudo foi explorar a plataforma <italic>iCardio</italic> como ferramenta para avaliar a variabilidade dos servi&#231;os cardiovasculares entre as regi&#245;es do Brasil.</p>
    </sec>
    <sec sec-type="methods">
      <title>M&#233;todos</title>
      <p>Este &#233; um estudo descritivo ecol&#243;gico, que teve como fonte de dados o painel de indicadores <italic>iCardio,</italic> plataforma online desenvolvida por pesquisadores do Instituto de Avalia&#231;&#227;o de Tecnologia em Sa&#250;de (IATS) sediado no Hospital de Cl&#237;nicas de Porto Alegre (https://ror.org/010we4y38), como parte de projeto aprovado em edital do Conselho Nacional de Desenvolvimento Cient&#237;fico e Tecnol&#243;gico (CNPq &#8211; 407282/2022-0) a partir de bases de dados administrativas do Sistema &#218;nico de Sa&#250;de (SUS). A concep&#231;&#227;o, arquitetura, fluxos de processamento e valida&#231;&#227;o dos dados do painel <italic>iCardio</italic> est&#227;o descritos em publica&#231;&#227;o pr&#233;via.<sup><xref ref-type="bibr" rid="B10">10</xref></sup></p>
      <p>O painel consolida 17 indicadores quantitativos principais relativos a aspectos cl&#237;nicos, de produ&#231;&#227;o assistencial, de equidade e sustentabilidade financeira, ao n&#237;vel hospitalar, estadual e nacional. Esses indicadores est&#227;o relacionados a procedimentos de cirurgias cardiovasculares e cardiologia intervencionista realizadas no SUS, nos anos de 2019 e 2020, registrados nos sistemas de dados SIH e SIM. Um total de 165 foram inclu&#237;dos (<xref ref-type="supplementary-material" rid="suppl01">Arquivo Suplementar 1</xref>) com base nos c&#243;digos que come&#231;am com os formul&#225;rios organizacionais 04.06.01 (cirurgia cardiovascular) e 04.06.03 (cardiologia intervencionista) da Tabela de Procedimentos, Medicamentos, &#211;rteses, Pr&#243;teses e Materiais Especiais do SUS (SIGTAP). Inclu&#237;ram-se dados de todos os centros que executaram um ou mais procedimento listados acima, o que os classifica como centros de Alta Complexidade Cardiovascular (ACC), que corresponde a unidades e centros de refer&#234;ncia qualificados para realizar procedimentos classificados como de alta complexidade, alta tecnologia e alto custo, de acordo com as normas vigentes do SUS.<sup><xref ref-type="bibr" rid="B11">11</xref></sup></p>
      <p>A base de dados do <italic>iCardio</italic> permite estratifica&#231;&#227;o de dados por sexo, faixa et&#225;ria, ra&#231;a/cor, regi&#227;o de resid&#234;ncia, tipo de atendimento (urg&#234;ncia/eletivo), diagn&#243;stico (CID-10) e procedimento realizado. Todos os dados s&#227;o apresentados de forma agregada, sem possibilidade de identifica&#231;&#227;o individual. Para este estudo, foram selecionados os indicadores apresentados na <xref ref-type="table" rid="t1">Tabela 1</xref>, analisados para cada uma das regi&#245;es brasileiras, considerando a an&#225;lise por localidade dispon&#237;vel no painel. A taxa m&#233;dia padronizada dos procedimentos realizados por idade e sexo expressa por milh&#227;o de habitantes foi utilizada para a compara&#231;&#227;o entre regi&#245;es que apresentam popula&#231;&#245;es com diferentes pir&#226;mides et&#225;rias e distribui&#231;&#227;o de sexo. Conforme o material descritivo do desenvolvimento do painel<sup><xref ref-type="bibr" rid="B10">10</xref>,<xref ref-type="bibr" rid="B12">12</xref></sup> o m&#233;todo de padroniza&#231;&#227;o direta foi aplicado, estimando as taxas de procedimento esperadas por regi&#227;o e estado de resid&#234;ncia dos pacientes, sexo e faixa et&#225;ria as quais foram projetadas em fun&#231;&#227;o da popula&#231;&#227;o de refer&#234;ncia definida pelo n&#250;mero de habitantes nessas classes estimado para 2020 pelo Instituto Brasileiro de Geografia e Estat&#237;stica.</p>
      <p>
        <table-wrap id="t1">
          <label>Tabela 1</label>
          <caption>
            <title>&#8211; Indicadores de atendimento, desfechos cl&#237;nicos e custos das interna&#231;&#245;es hospitalares selecionados por regi&#227;o do Brasil, 2019</title>
          </caption>
          <table frame="hsides" rules="groups">
            <colgroup width="33%">
              <col/>
              <col/>
              <col/>
            </colgroup>
            <thead>
              <tr>
                <th align="left">Indicador</th>
                <th>Medida/Unidade</th>
                <th align="left">Finalidade da an&#225;lise</th>
              </tr>
            </thead>
            <tbody>
              <tr>
                <td>Quantidade de pacientes (interven&#231;&#227;o e cirurgia)</td>
                <td align="center">N&#250;mero absoluto</td>
                <td>Dimensionar a popula&#231;&#227;o atendida</td>
              </tr>
              <tr>
                <td>Quantidade de pacientes interven&#231;&#227;o</td>
                <td align="center">N&#250;mero absoluto</td>
                <td>Caracterizar volume espec&#237;fico de procedimentos intervencionistas</td>
              </tr>
              <tr>
                <td>Quantidade de pacientes cirurgia</td>
                <td align="center">N&#250;mero absoluto</td>
                <td>Caracterizar volume espec&#237;fico de procedimentos cir&#250;rgicos</td>
              </tr>
              <tr>
                <td>Taxa padronizada por idade e sexo por milh&#227;o de habitantes</td>
                <td align="center">Taxa por milh&#227;o de habitantes</td>
                <td>Comparar regi&#245;es independentemente da estrutura et&#225;ria e distribui&#231;&#227;o de sexo</td>
              </tr>
              <tr>
                <td>Homens</td>
                <td align="center">N&#250;mero absoluto</td>
                <td>Descrever perfil demogr&#225;fico dos pacientes</td>
              </tr>
              <tr>
                <td>Mulheres</td>
                <td align="center">N&#250;mero absoluto</td>
                <td>Descrever perfil demogr&#225;fico dos pacientes</td>
              </tr>
              <tr>
                <td>Principal faixa et&#225;ria</td>
                <td align="center">Faixa et&#225;ria (%)</td>
                <td>Identificar grupo et&#225;rio mais acometido</td>
              </tr>
              <tr>
                <td>Interna&#231;&#227;o em UTI</td>
                <td align="center">Percentual (%)</td>
                <td>Avaliar gravidade e complexidade do atendimento</td>
              </tr>
              <tr>
                <td>Estabelecimentos</td>
                <td align="center">N&#250;mero absoluto</td>
                <td>Dimensionar a rede de atendimento dispon&#237;vel</td>
              </tr>
              <tr>
                <td>Habitantes/Estabelecimento</td>
                <td align="center">Raz&#227;o (habitantes/unidade)</td>
                <td>Estimar acesso e distribui&#231;&#227;o dos servi&#231;os</td>
              </tr>
              <tr>
                <td>Deslocamento m&#233;dio para atendimento</td>
                <td align="center">Quil&#244;metros (km)</td>
                <td>Avaliar acessibilidade geogr&#225;fica</td>
              </tr>
              <tr>
                <td>Quantidade de pacientes &#8211; Regi&#227;o distinta</td>
                <td align="center">N&#250;mero absoluto</td>
                <td>Quantificar deslocamentos inter-regionais, considerando a divis&#227;o regional do SUS</td>
              </tr>
              <tr>
                <td>Pacientes atendidos em uma regi&#227;o distinta</td>
                <td align="center">Percentual (%)</td>
                <td>Propor&#231;&#227;o de pacientes que precisaram atendimento fora da regi&#227;o de resid&#234;ncia, considerando a divis&#227;o regional do SUS</td>
              </tr>
              <tr>
                <td>Quantidade de pacientes &#8211; Macrorregi&#227;o distinta</td>
                <td align="center">N&#250;mero absoluto</td>
                <td>Quantificar deslocamentos entre macrorregionais, considerando a divis&#227;o regional do SUS</td>
              </tr>
              <tr>
                <td>Pacientes atendidos em uma macrorregi&#227;o distinta</td>
                <td align="center">Percentual (%)</td>
                <td>Propor&#231;&#227;o de pacientes que precisaram atendimento fora da macrorregi&#227;o de resid&#234;ncia, considerando a divis&#227;o regional do SUS</td>
              </tr>
              <tr>
                <td>Reinterna&#231;&#227;o em at&#233; 30 dias do procedimento</td>
                <td align="center">Percentual (%)</td>
                <td>Avaliar desfecho precoce ap&#243;s interven&#231;&#227;o</td>
              </tr>
              <tr>
                <td>Reinterna&#231;&#227;o em at&#233; 30 dias da alta</td>
                <td align="center">Percentual (%)</td>
                <td>Avaliar qualidade do cuidado e acompanhamento p&#243;s-alta</td>
              </tr>
              <tr>
                <td>Mortalidade hospitalar</td>
                <td align="center">Percentual (%)</td>
                <td>Mensurar letalidade durante a interna&#231;&#227;o</td>
              </tr>
              <tr>
                <td>Mortalidade em at&#233; 30 dias do procedimento</td>
                <td align="center">Percentual (%)</td>
                <td>Avaliar risco de &#243;bito precoce ap&#243;s procedimento</td>
              </tr>
              <tr>
                <td>Mortalidade em at&#233; 30 dias da alta</td>
                <td align="center">Percentual (%)</td>
                <td>Avaliar risco de &#243;bito precoce ap&#243;s alta hospitalar</td>
              </tr>
              <tr>
                <td>Custo m&#233;dio por interna&#231;&#227;o</td>
                <td align="center">Reais (R$)</td>
                <td>Estimar custo m&#233;dio por paciente atendido</td>
              </tr>
              <tr>
                <td>Valor total</td>
                <td align="center">Reais (R$)</td>
                <td>Dimensionar impacto financeiro agregado</td>
              </tr>
            </tbody>
          </table>
          <table-wrap-foot>
            <attrib>UTI: unidade de terapia intensiva. Fonte: iCardio.</attrib>
          </table-wrap-foot>
        </table-wrap>
      </p>
      <p>Adicionalmente, se analisou a distribui&#231;&#227;o das taxas de mortalidade, segundo sexo e faixa et&#225;ria, com estratifica&#231;&#227;o entre pacientes pedi&#225;tricos e adultos. E tamb&#233;m os desfechos assist&#234;ncias foram analisados de forma individualizada para cada procedimento. Foram analisados dados apenas das interna&#231;&#245;es do ano de 2019.</p>
      <p>Os dados foram extra&#237;dos do painel <italic>iCardio</italic> e organizados em planilhas eletr&#244;nicas para an&#225;lise descritiva utilizando o Microsoft Excel (Microsoft Corporation, Redmond, WA, EUA). Foram calculadas medidas de frequ&#234;ncia (valores absolutos e percentuais) para sumarizar os indicadores selecionados. A compara&#231;&#227;o entre as diferentes regi&#245;es geogr&#225;ficas brasileiras e os perfis de pacientes foi realizada por meio de an&#225;lise descritiva comparativa. Como os dados utilizados se referem ao conjunto populacional total de interna&#231;&#245;es registradas no SUS para os procedimentos analisados, n&#227;o foram aplicados testes de hip&#243;teses ou m&#233;todos estat&#237;sticos inferenciais.</p>
      <p>O estudo foi aprovado pelo Comit&#234; de &#201;tica em Pesquisa Institucional do Hospital de Cl&#237;nicas de Porto Alegre, sob o n&#250;mero do projeto CAAE 6784523.0.0000.5327 e todo o processamento dos dados seguiu a legisla&#231;&#227;o nacional de prote&#231;&#227;o de dados. Este estudo utilizou apenas dados secund&#225;rios e anonimizados, dispon&#237;veis em dom&#237;nio p&#250;blico, n&#227;o envolvendo qualquer interven&#231;&#227;o em seres humanos.</p>
    </sec>
    <sec sec-type="results">
      <title>Resultados</title>
      <sec>
        <title>Distribui&#231;&#227;o territorial da assist&#234;ncia cardiovascular no Brasil</title>
        <p>Para cada uma das cinco regi&#245;es brasileiras, os dados dos indicadores cl&#237;nicos est&#227;o resumidos na <xref ref-type="table" rid="t2">Tabela 2</xref>. Ao comparar o n&#250;mero total de pacientes atendidos com o n&#250;mero de atendimentos registrados nas &#225;reas de cardiologia intervencionista e cirurgia cardiovascular, se observa que a soma dos atendimentos nas duas &#225;reas excede o n&#250;mero total de pacientes atendidos; isso ocorre, pois, alguns pacientes foram submetidos a procedimentos em ambas as especialidades.</p>
        <p>
          <table-wrap id="t2">
            <label>Tabela 2</label>
            <caption>
              <title>&#8211; Perfil cl&#237;nico e demogr&#225;fico das interna&#231;&#245;es por procedimentos cardiovasculares no SUS, Brasil, 2019</title>
            </caption>
            <table frame="hsides" rules="groups">
              <colgroup width="14%">
                <col/>
                <col/>
                <col/>
                <col/>
                <col/>
                <col/>
                <col/>
              </colgroup>
              <thead>
                <tr>
                  <th align="left">Indicadores (2019)</th>
                  <th>Norte</th>
                  <th>Nordeste</th>
                  <th>Centro-Oeste</th>
                  <th>Sudeste</th>
                  <th>Sul</th>
                  <th>Brasil</th>
                </tr>
              </thead>
              <tbody>
                <tr>
                  <td colspan="7">
                    <bold>Dados cl&#237;nicos</bold>
                  </td>
                </tr>
                <tr>
                  <td>Quantidade de pacientes (interven&#231;&#227;o e cirurgia)</td>
                  <td align="center">4.742</td>
                  <td align="center">30.693</td>
                  <td align="center">10.956</td>
                  <td align="center">71.350</td>
                  <td align="center">43.188</td>
                  <td align="center">160.897</td>
                </tr>
                <tr>
                  <td>Quantidade de pacientes interven&#231;&#227;o</td>
                  <td align="center">2.165</td>
                  <td align="center">15.128</td>
                  <td align="center">5.351</td>
                  <td align="center">40.203</td>
                  <td align="center">24.665</td>
                  <td align="center">87.497</td>
                </tr>
                <tr>
                  <td>Quantidade de pacientes cirurgia</td>
                  <td align="center">2.653</td>
                  <td align="center">16.119</td>
                  <td align="center">5.825</td>
                  <td align="center">32.647</td>
                  <td align="center">19.893</td>
                  <td align="center">77.128</td>
                </tr>
                <tr>
                  <td>Homens</td>
                  <td align="center">3.071</td>
                  <td align="center">17.583</td>
                  <td align="center">6609</td>
                  <td align="center">43.931</td>
                  <td align="center">27.167</td>
                  <td align="center">98.340</td>
                </tr>
                <tr>
                  <td>Mulheres</td>
                  <td align="center">1.669</td>
                  <td align="center">13.088</td>
                  <td align="center">4.3364</td>
                  <td align="center">27.389</td>
                  <td align="center">16.009</td>
                  <td align="center">62.480</td>
                </tr>
                <tr>
                  <td>Taxa padronizada por idade e sexo por milh&#227;o de habitantes</td>
                  <td align="center">3,65</td>
                  <td align="center">4,66</td>
                  <td align="center">5,88</td>
                  <td align="center">6,02</td>
                  <td align="center">10,54</td>
                  <td align="center">5,43</td>
                </tr>
                <tr>
                  <td colspan="7">
                    <bold>Distribui&#231;&#227;o et&#225;ria da quantidade de pacientes</bold>
                  </td>
                </tr>
                <tr>
                  <td>&lt;1 ano</td>
                  <td align="center">88</td>
                  <td align="center">451</td>
                  <td align="center">139</td>
                  <td align="center">921</td>
                  <td align="center">462</td>
                  <td align="center">2.061</td>
                </tr>
                <tr>
                  <td>01 a 08 anos</td>
                  <td align="center">181</td>
                  <td align="center">903</td>
                  <td align="center">306</td>
                  <td align="center">1.741</td>
                  <td align="center">780</td>
                  <td align="center">3.910</td>
                </tr>
                <tr>
                  <td>09 a 17 anos</td>
                  <td align="center">82</td>
                  <td align="center">336</td>
                  <td align="center">85</td>
                  <td align="center">576</td>
                  <td align="center">246</td>
                  <td align="center">1.324</td>
                </tr>
                <tr>
                  <td>18 a 26 anos</td>
                  <td align="center">73</td>
                  <td align="center">370</td>
                  <td align="center">92</td>
                  <td align="center">529</td>
                  <td align="center">239</td>
                  <td align="center">1.302</td>
                </tr>
                <tr>
                  <td>27 a 35 anos</td>
                  <td align="center">114</td>
                  <td align="center">635</td>
                  <td align="center">160</td>
                  <td align="center">844</td>
                  <td align="center">423</td>
                  <td align="center">2.175</td>
                </tr>
                <tr>
                  <td>36 a 44 anos</td>
                  <td align="center">209</td>
                  <td align="center">1.506</td>
                  <td align="center">448</td>
                  <td align="center">2.608</td>
                  <td align="center">1.460</td>
                  <td align="center">6.229</td>
                </tr>
                <tr>
                  <td>45 a 53 anos</td>
                  <td align="center">543</td>
                  <td align="center">3.696</td>
                  <td align="center">1.320</td>
                  <td align="center">8.166</td>
                  <td align="center">4.801</td>
                  <td align="center">18.520</td>
                </tr>
                <tr>
                  <td>54 a 62 anos</td>
                  <td align="center">1.072</td>
                  <td align="center">6.796</td>
                  <td align="center">2.564</td>
                  <td align="center">17.466</td>
                  <td align="center">10,395</td>
                  <td align="center">38.284</td>
                </tr>
                <tr>
                  <td>63 a 71 anos</td>
                  <td align="center">1.208</td>
                  <td align="center">7.737</td>
                  <td align="center">2.870</td>
                  <td align="center">19.904</td>
                  <td align="center">12,397</td>
                  <td align="center">44.109</td>
                </tr>
                <tr>
                  <td>72 a 80 anos</td>
                  <td align="center">813</td>
                  <td align="center">5.485</td>
                  <td align="center">2.037</td>
                  <td align="center">12.875</td>
                  <td align="center">8.398</td>
                  <td align="center">29.606</td>
                </tr>
                <tr>
                  <td colspan="7">
                    <bold>Distribui&#231;&#227;o racial dos pacientes</bold>
                  </td>
                </tr>
                <tr>
                  <td>Amarela</td>
                  <td align="center">105</td>
                  <td align="center">344</td>
                  <td align="center">97</td>
                  <td align="center">628</td>
                  <td align="center">400</td>
                  <td align="center">1.574</td>
                </tr>
                <tr>
                  <td>Branca</td>
                  <td align="center">408</td>
                  <td align="center">2.484</td>
                  <td align="center">1.559</td>
                  <td align="center">38.412</td>
                  <td align="center">36.553</td>
                  <td align="center">79.406</td>
                </tr>
                <tr>
                  <td>Ind&#237;gena</td>
                  <td align="center">7</td>
                  <td align="center">6</td>
                  <td align="center">9</td>
                  <td align="center">5</td>
                  <td align="center">16</td>
                  <td align="center">43</td>
                </tr>
                <tr>
                  <td>Parda</td>
                  <td align="center">3.048</td>
                  <td align="center">14.770</td>
                  <td align="center">5.140</td>
                  <td align="center">21.385</td>
                  <td align="center">2.724</td>
                  <td align="center">47.057</td>
                </tr>
                <tr>
                  <td>Preta</td>
                  <td align="center">116</td>
                  <td align="center">595</td>
                  <td align="center">118</td>
                  <td align="center">3.527</td>
                  <td align="center">1.031</td>
                  <td align="center">5.387</td>
                </tr>
                <tr>
                  <td>Conflito</td>
                  <td align="center">39</td>
                  <td align="center">65</td>
                  <td align="center">43</td>
                  <td align="center">436</td>
                  <td align="center">135</td>
                  <td align="center">707</td>
                </tr>
                <tr>
                  <td>Sem informa&#231;&#227;o</td>
                  <td align="center">1.019</td>
                  <td align="center">12.429</td>
                  <td align="center">3.990</td>
                  <td align="center">6.957</td>
                  <td align="center">2.329</td>
                  <td align="center">26.723</td>
                </tr>
              </tbody>
            </table>
            <table-wrap-foot>
              <attrib>Fonte: iCardio. Nota: A soma dos pacientes por faixa et&#225;ria n&#227;o corresponde ao total de pacientes atendidos reportado, discrep&#226;ncia explicada pelo processamento espec&#237;fico adotado para ajuste de inconsist&#234;ncias nas datas de nascimento registradas nas Autoriza&#231;&#245;es de Interna&#231;&#227;o Hospitalar (AIHs), garantindo maior confiabilidade na classifica&#231;&#227;o et&#225;ria.<sup>12</sup></attrib>
            </table-wrap-foot>
          </table-wrap>
        </p>
        <p>Considerando todos os atendimentos realizados no pa&#237;s, se observou predomin&#226;ncia de atendimento de pacientes brancos do sexo masculino, com idade entre 63 e 71 anos. Entretanto, a predomin&#226;ncia de pacientes brancos na an&#225;lise agregada reflete, em grande medida, a maior concentra&#231;&#227;o de atendimentos nas regi&#245;es Sudeste e Sul, onde h&#225;, conforme evidenciado no painel, tanto maior volume de procedimentos quanto maior propor&#231;&#227;o de popula&#231;&#227;o autodeclarada branca. Em contraste, nas regi&#245;es Norte, Nordeste e Centro-Oeste, se observa predomin&#226;ncia de atendimentos a pacientes que se identificam como pardos, evidenciando diferen&#231;as regionais no perfil racial dos pacientes atendidos, ainda que n&#227;o tenham sido observadas diferen&#231;as expressivas quanto ao sexo e &#224; distribui&#231;&#227;o et&#225;ria entre as regi&#245;es.</p>
        <p>Na an&#225;lise estratificada por idade e sexo (<xref ref-type="supplementary-material" rid="suppl04">Tabela Suplementar 2</xref>), a mortalidade hospitalar foi consistentemente mais elevada na popula&#231;&#227;o pedi&#225;trica em compara&#231;&#227;o aos adultos, com destaque para a regi&#227;o Norte, onde, entre os homens, a taxa nos pacientes pedi&#225;tricos &#233; mais que o dobro da observada nos adultos. Entre as mulheres, essa diferen&#231;a et&#225;ria &#233; menos pronunciada. Em contraste, ao considerar apenas a popula&#231;&#227;o adulta, se observa um padr&#227;o inverso: em todas as regi&#245;es do pa&#237;s, as mulheres apresentam taxas de mortalidade superiores &#224;s dos homens.</p>
        <p>Observa-se que a regi&#227;o Sudeste concentra o maior n&#250;mero bruto de atendimentos e procedimentos realizados, seguida pela regi&#227;o Sul. No entanto, ao considerar a taxa padronizada por idade e sexo, se verifica que a regi&#227;o Sul apresenta o maior valor. Embora Sudeste e Sul se destaquem em volume de atendimentos, a maior propor&#231;&#227;o de interna&#231;&#245;es em UTI (unidade de terapia intensiva) &#233; observada na regi&#227;o Nordeste, conforme apresentado na <xref ref-type="table" rid="t3">Tabela 3</xref>, que re&#250;ne os indicadores assistenciais e econ&#244;micos. No total, o pa&#237;s possui 558 estabelecimentos de ACC, com a raz&#227;o entre o n&#250;mero de habitantes e estabelecimentos variando entre 130.149 e 229.644, a depender da regi&#227;o. As dist&#226;ncias m&#233;dias percorridas pelos pacientes para receber atendimento tamb&#233;m variam conforme a localidade, com a regi&#227;o Sudeste apresentando um deslocamento m&#233;dio significativamente menor que o das outras regi&#245;es.</p>
        <p>
          <table-wrap id="t3">
            <label>Tabela 3</label>
            <caption>
              <title>&#8211; Distribui&#231;&#227;o regional dos indicadores assistenciais e econ&#244;micos das interna&#231;&#245;es hospitalares por procedimentos cardiovasculares, Brasil, 2019</title>
            </caption>
            <table frame="hsides" rules="groups">
              <colgroup width="14%">
                <col/>
                <col/>
                <col/>
                <col/>
                <col/>
                <col/>
                <col/>
              </colgroup>
              <thead>
                <tr>
                  <th align="left">Indicadores (2019)</th>
                  <th>Norte</th>
                  <th>Nordeste</th>
                  <th>Centro-Oeste</th>
                  <th>Sudeste</th>
                  <th>Sul</th>
                  <th>Brasil</th>
                </tr>
              </thead>
              <tbody>
                <tr>
                  <td colspan="7">
                    <bold>Indicadores assistenciais</bold>
                  </td>
                </tr>
                <tr>
                  <td>Interna&#231;&#227;o em UTI (%)</td>
                  <td align="center">45,79</td>
                  <td align="center">58,21</td>
                  <td align="center">49,49</td>
                  <td align="center">56,04</td>
                  <td align="center">53,39</td>
                  <td align="center">54,69</td>
                </tr>
                <tr>
                  <td>Estabelecimentos</td>
                  <td align="center">35</td>
                  <td align="center">93</td>
                  <td align="center">41</td>
                  <td align="center">276</td>
                  <td align="center">113</td>
                  <td align="center">588</td>
                </tr>
                <tr>
                  <td>Habitantes/Estabelecimento</td>
                  <td align="center">229.644</td>
                  <td align="center">194.685</td>
                  <td align="center">201.276</td>
                  <td align="center">204.558</td>
                  <td align="center">130.149</td>
                  <td align="center">189.176</td>
                </tr>
                <tr>
                  <td>Deslocamento m&#233;dio para atendimento (Km)</td>
                  <td align="center">90,77</td>
                  <td align="center">94,59</td>
                  <td align="center">92,23</td>
                  <td align="center">45,86</td>
                  <td align="center">68,91</td>
                  <td align="center">65,77</td>
                </tr>
                <tr>
                  <td>Quantidade de pacientes &#8211; Regi&#227;o distinta</td>
                  <td align="center">1.726</td>
                  <td align="center">14.824</td>
                  <td align="center">3.544</td>
                  <td align="center">24.249</td>
                  <td align="center">14.990</td>
                  <td align="center">59.327</td>
                </tr>
                <tr>
                  <td>Pacientes atendidos em uma regi&#227;o distinta (%)</td>
                  <td align="center">36,38</td>
                  <td align="center">48,23</td>
                  <td align="center">32,18</td>
                  <td align="center">33,88</td>
                  <td align="center">34,60</td>
                  <td align="center">36,76</td>
                </tr>
                <tr>
                  <td>Quantidade de pacientes &#8211;Macrorregi&#227;o distinta</td>
                  <td align="center">969</td>
                  <td align="center">7.158</td>
                  <td align="center">3.112</td>
                  <td align="center">10.360</td>
                  <td align="center">4.984</td>
                  <td align="center">26.579</td>
                </tr>
                <tr>
                  <td>Pacientes atendidos em uma macrorregi&#227;o distinta (%)</td>
                  <td align="center">20,43</td>
                  <td align="center">23,29</td>
                  <td align="center">28,25</td>
                  <td align="center">14,49</td>
                  <td align="center">11,52</td>
                  <td align="center">16,49</td>
                </tr>
                <tr>
                  <td>Reinterna&#231;&#227;o em at&#233; 30 dias do procedimento (%)</td>
                  <td align="center">6,05</td>
                  <td align="center">5,64</td>
                  <td align="center">10,30</td>
                  <td align="center">7,08</td>
                  <td align="center">9,79</td>
                  <td align="center">7,75</td>
                </tr>
                <tr>
                  <td>Reinterna&#231;&#227;o em at&#233; 30 dias da alta (%)</td>
                  <td align="center">7,56</td>
                  <td align="center">7,04</td>
                  <td align="center">11,97</td>
                  <td align="center">8,71</td>
                  <td align="center">11,70</td>
                  <td align="center">9,41</td>
                </tr>
                <tr>
                  <td>Mortalidade hospitalar (%)</td>
                  <td align="center">7,56</td>
                  <td align="center">6,28</td>
                  <td align="center">6,34</td>
                  <td align="center">5,65</td>
                  <td align="center">6,58</td>
                  <td align="center">6,12</td>
                </tr>
                <tr>
                  <td>Mortalidade em at&#233; 30 dias do procedimento (%)</td>
                  <td align="center">7,70</td>
                  <td align="center">6,49</td>
                  <td align="center">6,91</td>
                  <td align="center">5,73</td>
                  <td align="center">6,88</td>
                  <td align="center">6,32</td>
                </tr>
                <tr>
                  <td>Mortalidade em at&#233; 30 dias da alta (%)</td>
                  <td align="center">8,70</td>
                  <td align="center">7,52</td>
                  <td align="center">7,72</td>
                  <td align="center">6,77</td>
                  <td align="center">7,81</td>
                  <td align="center">7,32</td>
                </tr>
                <tr>
                  <td colspan="7">
                    <bold>Indicadores econ&#244;micos</bold>
                  </td>
                </tr>
                <tr>
                  <td>Custo m&#233;dio por interna&#231;&#227;o (R$)</td>
                  <td align="center">11.153,55</td>
                  <td align="center">11.518,30</td>
                  <td align="center">11.059,58</td>
                  <td align="center">10.765,24</td>
                  <td align="center">11.411,49</td>
                  <td align="center">11.114,04</td>
                </tr>
                <tr>
                  <td>Valor total (R$)</td>
                  <td align="center">54.73 M</td>
                  <td align="center">369.54 M</td>
                  <td align="center">133.77 M</td>
                  <td align="center">818.89 M</td>
                  <td align="center">538.15 M</td>
                  <td align="center">1.92 B</td>
                </tr>
              </tbody>
            </table>
            <table-wrap-foot>
              <attrib>UTI: unidade de terapia intensiva. Fonte: iCardio.</attrib>
            </table-wrap-foot>
          </table-wrap>
        </p>
        <p>Com base na divis&#227;o em regi&#245;es e macrorregi&#245;es de sa&#250;de, se observa que os percentuais atendidos fora da regi&#227;o e da macrorregi&#227;o variam entre os tipos de atendimentos. Entre os pacientes submetidos &#224; cirurgia cardiovascular, 41,44% foram atendidos fora da regi&#227;o de resid&#234;ncia e 21,16% fora da macrorregi&#227;o. J&#225; na cardiologia intervencionista, esses valores foram menores, com 32,46% dos atendimentos ocorrendo fora da regi&#227;o e 12,23% fora da macrorregi&#227;o.</p>
        <p>As regi&#245;es Centro-Oeste e Sul apresentam elevadas taxas de reinterna&#231;&#227;o, tanto no per&#237;odo de at&#233; 30 dias ap&#243;s o procedimento quanto no intervalo de at&#233; 30 dias ap&#243;s a alta. Em rela&#231;&#227;o &#224; mortalidade, se observam os maiores valores brutos na regi&#227;o Norte nos tr&#234;s desfechos analisados - mortalidade hospitalar, em at&#233; 30 dias ap&#243;s o procedimento e em at&#233; 30 dias ap&#243;s a alta - al&#233;m do maior tempo m&#233;dio de interna&#231;&#227;o. J&#225; a regi&#227;o Sudeste apresenta os menores percentuais brutos para os tr&#234;s tipos de mortalidade.</p>
        <p>Em uma an&#225;lise por grupo de procedimentos (<xref ref-type="supplementary-material" rid="suppl02">Arquivo Suplementar 2</xref>), os procedimentos com maiores taxas de mortalidade hospitalar no Brasil foram cateterismo (31,87%), corre&#231;&#227;o de anomalias card&#237;acas (21,55%) e cardiorressec&#231;&#227;o (17,01%). Entre regi&#245;es, a septectomia ventricular alcan&#231;a 57,14% no Centro-Oeste, corre&#231;&#227;o de anomalias card&#237;acas atinge 31,75% no Norte, e anastomose registra 32% no Nordeste. Cardiorressec&#231;&#227;o apresenta 22,73% no Norte e pl&#225;stica card&#237;aca 18,76% no Sul. O Nordeste se destaca em mortalidade 30 dias da alta para anastomose (48%). Entretanto, &#233; importante salientar que a an&#225;lise considerou a ocorr&#234;ncia de &#243;bito durante interna&#231;&#245;es associadas aos procedimentos, e n&#227;o a causa b&#225;sica do &#243;bito, de modo que os resultados refletem mortalidade hospitalar associada &#224; interna&#231;&#227;o e n&#227;o mortalidade atribu&#237;vel ao procedimento.</p>
        <p>Quanto aos recursos financeiros, a taxa de reembolso para os procedimentos realizados foi de cerca de R$1,9 bilh&#227;o no per&#237;odo analisado. Embora o custo m&#233;dio por interna&#231;&#227;o apresente varia&#231;&#245;es modestas entre as regi&#245;es, os gastos regionais variam amplamente, com valores que v&#227;o de R$54,73 milh&#245;es a R$818,89 milh&#245;es, acompanhando o volume de atendimentos prestados nas regi&#245;es.</p>
      </sec>
    </sec>
    <sec sec-type="discussion">
      <title>Discuss&#227;o</title>
      <p>Com base nos dados do painel <italic>iCardio</italic> para o ano de 2019, foi realizada uma an&#225;lise das varia&#231;&#245;es regionais nos atendimentos cardiovasculares, visando aprofundar a compreens&#227;o das desigualdades na assist&#234;ncia cardiovascular no Brasil, estando os resultados resumidos na <xref ref-type="fig" rid="f01">Figura Central</xref>, visto que o pa&#237;s apresenta grande extens&#227;o territorial, com alta heterogeneidade entre suas regi&#245;es em termos de aspectos naturais, econ&#244;micos e sociais.<sup><xref ref-type="bibr" rid="B13">13</xref></sup> O SUS vem buscando reduzir as desigualdades na sa&#250;de entre as regi&#245;es e melhorar a cobertura e o acesso aos cuidados de sa&#250;de em todo o pa&#237;s, embora persistam grandes varia&#231;&#245;es em termos de infraestrutura, recursos humanos, capacidade de gest&#227;o e acesso a servi&#231;os de sa&#250;de eficazes.<sup><xref ref-type="bibr" rid="B14">14</xref></sup> Assim, &#233; fundamental considerar como essas particularidades influenciam os indicadores de sa&#250;de.</p>
      <p>Considerando o perfil cl&#237;nico geral do pa&#237;s, estudos comparativos entre pacientes pedi&#225;tricos e adultos refor&#231;am os dados apresentados, a literatura indica que a taxa de mortalidade em procedimentos e cirurgias cardiovasculares de alta complexidade &#233; significativamente maior em crian&#231;as,<sup><xref ref-type="bibr" rid="B15">15</xref></sup> quando comparada a adultos submetidos a procedimentos similares. Al&#233;m disso, a an&#225;lise de bancos de dados internacionais indica que a mortalidade pedi&#225;trica est&#225; fortemente associada &#224; complexidade do procedimento, idade e peso ao nascer,<sup><xref ref-type="bibr" rid="B16">16</xref></sup> enquanto em adultos o risco &#233; mais influenciado por comorbidades adquiridas e idade avan&#231;ada.<sup><xref ref-type="bibr" rid="B17">17</xref></sup> Ao passo que a diferen&#231;a entre sexos &#233; principalmente explicada diferentes perfis de risco pr&#233;-operat&#243;rio,<sup><xref ref-type="bibr" rid="B18">18</xref></sup> estando a mortalidade em mulheres mais associada a doen&#231;as sistem&#225;ticas e nos homens a dano card&#237;aco direto. Al&#233;m disso, h&#225; relato de que alguns dos sintomas indicativos de DCV tendem a ser diferentes entre homens e mulheres, sendo um fator contribuinte para atrasos no diagn&#243;stico, menor acesso a procedimentos invasivos e pior progn&#243;stico em cirurgias cardiovasculares de alta complexidade.<sup><xref ref-type="bibr" rid="B19">19</xref>-<xref ref-type="bibr" rid="B21">21</xref></sup>Por&#233;m, maiores investiga&#231;&#245;es seriam necess&#225;rias para identificar mais precisamente as causas das diferen&#231;as observadas no presente estudo.</p>
      <p>No geral, no estudo, se observou que a regi&#227;o Norte apresenta indicadores de desfechos duros inferiores aos observados em outras regi&#245;es. Uma an&#225;lise pr&#233;via de indicadores na &#225;rea de cardiologia indicou uma tend&#234;ncia da regi&#227;o Norte de apresentar piores rendimentos, o que pode ser explicado por fatores socioecon&#244;micos.<sup><xref ref-type="bibr" rid="B1">1</xref></sup> Essa regi&#227;o apresenta ainda algumas limita&#231;&#245;es com rela&#231;&#227;o &#224; disponibilidade de dados precisos nos sistemas de sa&#250;de.<sup><xref ref-type="bibr" rid="B22">22</xref></sup> Assim como o Norte, o Nordeste tende apresentar dados preocupantes com rela&#231;&#227;o ao acesso a cuidados m&#233;dicos e a sua qualidade.<sup><xref ref-type="bibr" rid="B1">1</xref>,<xref ref-type="bibr" rid="B22">22</xref></sup> Na an&#225;lise do painel, se observou que essa regi&#227;o apresentou a maior porcentagem de interna&#231;&#245;es em UTI e maior deslocamento m&#233;dio. Ao passo que, a regi&#227;o Sudeste se destaca por concentrar o maior n&#250;mero de centros de ACC e minimizar o deslocamento dos pacientes, apresentando os melhores indicadores de sa&#250;de e o menor custo por interna&#231;&#227;o. Isso pode ser explicado pelo fato de que hospitais maiores ou com alto fluxo diluem custos fixos em mais casos, aproveitam melhor a capacidade instalada e ganham efici&#234;ncia no uso de recursos.<sup><xref ref-type="bibr" rid="B23">23</xref></sup></p>
      <p>Sul e Centro-Oeste se destacam por apresentarem as maiores taxas de reinterna&#231;&#227;o, fatores que influenciam esse desfecho incluem qualidade da alta hospitalar, continuidade do cuidado ambulatorial e ades&#227;o ao tratamento. Al&#233;m disso, o perfil dos pacientes &#233; um fator cr&#237;tico no que se refere a interna&#231;&#245;es, comorbidades, idade avan&#231;ada e maior preval&#234;ncia de tabagismo na popula&#231;&#227;o tamb&#233;m contribuem para esse cen&#225;rio, aumentando custos e press&#227;o sobre leitos hospitalares.<sup><xref ref-type="bibr" rid="B24">24</xref></sup> Estudos pr&#233;vios apontam que a regi&#227;o Sul do Brasil apresenta o maior percentual de fumantes em compara&#231;&#227;o com outras regi&#245;es<sup><xref ref-type="bibr" rid="B25">25</xref></sup> e a segunda popula&#231;&#227;o mais idosa do pa&#237;s, com 12,1% de seus moradores com 65 anos ou mais.<sup><xref ref-type="bibr" rid="B26">26</xref></sup>Mas essas regi&#245;es divergem no que diz respeito ao n&#250;mero de pacientes atendidos em uma macrorregi&#227;o distinta. O centro-Oeste apresenta a maior porcentagem de pacientes atendidos em uma macrorregi&#227;o distinta, ao passo que o Sul apresenta a menor porcentagem. Essa divis&#227;o territorial em regi&#245;es e macrorregi&#245;es de sa&#250;de proposta pelo SUS visa otimizar o planejamento e a integra&#231;&#227;o das a&#231;&#245;es e servi&#231;os de sa&#250;de, assegurando que os recursos e necessidades da popula&#231;&#227;o sejam considerados de forma regionalizada.<sup><xref ref-type="bibr" rid="B27">27</xref>,<xref ref-type="bibr" rid="B28">28</xref></sup></p>
      <p>Al&#233;m disso, a an&#225;lise nacional dos dados do painel <italic>iCardio</italic> evidenciou varia&#231;&#245;es no deslocamento de pacientes entre as regi&#245;es e macrorregi&#245;es conforme o tipo de procedimento. Observou-se que os procedimentos de cardiologia intervencionista, em geral, s&#227;o realizados dentro da pr&#243;pria regi&#227;o ou macrorregi&#227;o de resid&#234;ncia, sugerindo uma oferta mais distribu&#237;da desse tipo de servi&#231;o. Em contrapartida, as cirurgias cardiovasculares demandam, com maior frequ&#234;ncia, o deslocamento dos pacientes para outras regi&#245;es ou macrorregi&#245;es, indicando que a macrorregi&#227;o de origem muitas vezes n&#227;o possui capacidade instalada suficiente para atender a demandas de maior complexidade.</p>
      <p>A an&#225;lise dos indicadores econ&#244;micos evidenciou diferen&#231;as expressivas no gasto total entre as regi&#245;es, as quais se relacionam primordialmente ao volume de interna&#231;&#245;es realizadas, dado que n&#227;o se observaram varia&#231;&#245;es relevantes no custo m&#233;dio por interna&#231;&#227;o entre elas. Esse padr&#227;o &#233; consistente com a l&#243;gica de financiamento do SUS, em que a maior parte da produ&#231;&#227;o hospitalar &#233; remunerada por procedimentos com valores previamente definidos em tabela, o que tende a equalizar o custo m&#233;dio por interna&#231;&#227;o regionalmente, ainda que existam diferen&#231;as no perfil de casos e na complexidade assistencial. Dessa forma, o gasto total passa a refletir, sobretudo, o tamanho populacional<sup><xref ref-type="bibr" rid="B29">29</xref></sup> e a carga de doen&#231;a.<sup><xref ref-type="bibr" rid="B30">30</xref></sup> Al&#233;m disso, outro fator que influencia &#233; a capacidade instalada, incluindo a oferta de leitos e servi&#231;os de alta complexidade que atuam como polos de refer&#234;ncia e concentram interna&#231;&#245;es oriundas de outras regi&#245;es.<sup><xref ref-type="bibr" rid="B5">5</xref></sup> Sendo que o alto deslocamento m&#233;dio para atendimento da regi&#227;o Nordeste pode justificar por que, apesar de essa ser a segunda regi&#227;o mais populosa, n&#227;o &#233; a segunda com maior gasto total.</p>
      <p>Os dados de mundo real analisados foram disponibilizados por uma plataforma de <italic>Business Intelligence</italic> (BI) que visa facilitar a an&#225;lise e a tomada de decis&#245;es em sa&#250;de p&#250;blica com informa&#231;&#245;es mais acess&#237;veis e precisas. O uso desses dados como evid&#234;ncias ganhou destaque desde 2007, com o relat&#243;rio da <italic>International Society for Pharmacoeconomics and Outcomes Research</italic> (ISPOR) incentivando o uso de dados e evid&#234;ncias do mundo real na avalia&#231;&#227;o de tecnologias em sa&#250;de e no apoio &#224; tomada de decis&#245;es cl&#237;nicas e pol&#237;ticas,<sup><xref ref-type="bibr" rid="B31">31</xref></sup> e hoje &#233; incentivado por ag&#234;ncias regulat&#243;rias como FDA (<italic>Food and Drug Administration</italic>) e ANVISA (Ag&#234;ncia Nacional de Vigil&#226;ncia Sanit&#225;ria).<sup><xref ref-type="bibr" rid="B32">32</xref>,<xref ref-type="bibr" rid="B33">33</xref></sup> No Brasil, se conta com o Departamento de Inform&#225;tica do Sistema &#218;nico de Sa&#250;de (DATASUS) que gerencia os sistemas nacionais SIH e SIM, utilizados neste estudo. Al&#233;m do SIGTAP que serve de apoio gerencial log&#237;stico de qualifica&#231;&#227;o das informa&#231;&#245;es.<sup><xref ref-type="bibr" rid="B34">34</xref></sup></p>
      <p>Diversos estudos j&#225; demonstraram o potencial de ferramentas de BI aplicadas &#224; sa&#250;de, com exemplos nas &#225;reas de pneumologia, cardiologia e neurologia, onde pain&#233;is automatizados foram utilizados para an&#225;lise da efetividade de terapias, taxas de mortalidade e impacto econ&#244;mico.<sup><xref ref-type="bibr" rid="B35">35</xref>-<xref ref-type="bibr" rid="B38">38</xref></sup> Exemplos relevantes incluem o painel interativo da Universidade Johns Hopkins<sup><xref ref-type="bibr" rid="B39">39</xref></sup> e o painel do Minist&#233;rio da Sa&#250;de para monitorar casos e &#243;bitos de COVID-19.<sup><xref ref-type="bibr" rid="B40">40</xref></sup> Esses exemplos refor&#231;am o valor das plataformas baseadas em BI, tanto para a resposta a emerg&#234;ncias sanit&#225;rias quanto para a gest&#227;o cont&#237;nua dos servi&#231;os de sa&#250;de, evidenciando a import&#226;ncia do uso sistem&#225;tico de dados do mundo real para gerar evid&#234;ncias que qualifiquem o planejamento e a tomada de decis&#245;es em sa&#250;de.</p>
      <p>Os sistemas de informa&#231;&#227;o do SUS foram criados com prop&#243;sito administrativo e se caracterizam pela fragmenta&#231;&#227;o das informa&#231;&#245;es. O DATASUS e outras iniciativas de transpar&#234;ncia disponibilizam acesso p&#250;blico ao conte&#250;do de cada sistema separadamente, n&#227;o havendo identificador que possibilite a conex&#227;o de eventos de sa&#250;de e mortalidade geral ao n&#237;vel do paciente. Nesse aspecto, o <italic>iCardio</italic>, utilizando os dados pareados entre sistemas<sup><xref ref-type="bibr" rid="B41">41</xref></sup> consegue disponibilizar, em um &#250;nico ambiente, indicadores cl&#237;nico-epidemiol&#243;gicos, assistenciais, geogr&#225;ficos e econ&#244;micos, j&#225; calculados e estratific&#225;veis. Assim, o uso do <italic>iCardio</italic> apresenta potencial relevante para subsidiar a tomada de decis&#227;o cl&#237;nicas e em pol&#237;ticas p&#250;blicas. Embora neste estudo a an&#225;lise tenha sido conduzida em n&#237;vel regional, o painel permite tamb&#233;m outras avalia&#231;&#245;es, possibilitando o monitoramento de desempenho de servi&#231;os, a identifica&#231;&#227;o de variabilidade injustificada e o apoio a iniciativas de melhoria da qualidade. Dessa forma, a ferramenta pode orientar a organiza&#231;&#227;o de redes de aten&#231;&#227;o, a defini&#231;&#227;o de centros de refer&#234;ncia e a aloca&#231;&#227;o de recursos, contribuindo para decis&#245;es mais informadas, eficientes e equ&#226;nimes no SUS.</p>
      <p>Embora o painel <italic>iCardio</italic> disponibilize dados de 2019 e 2020, se analisou apenas 2019, j&#225; que a pandemia de COVID-19 alterou profundamente o perfil de interna&#231;&#245;es e o uso de recursos, podendo distorcer a an&#225;lise e comprometer a comparabilidade regional. Ressalta-se que este estudo apresenta algumas limita&#231;&#245;es que devem ser consideradas na interpreta&#231;&#227;o dos resultados. Primeiramente, a plataforma <italic>iCardio</italic> ainda n&#227;o integra ferramentas oficiais e est&#225; dispon&#237;vel apenas em portugu&#234;s. Al&#233;m disso, a an&#225;lise se baseia em dados secund&#225;rios do SUS (SIH e SIM), sujeitos a subnotifica&#231;&#227;o e inconsist&#234;ncias; e, na an&#225;lise principal, os dados foram avaliados de forma agregada, sem estratifica&#231;&#227;o por tipo espec&#237;fico de procedimento (por exemplo, angioplastia, cirurgia de revasculariza&#231;&#227;o do mioc&#225;rdio ou cirurgias valvares), e os desfechos de reinterna&#231;&#227;o e mortalidade n&#227;o foram ajustados para a complexidade dos procedimentos. No entanto, an&#225;lises adicionais estratificadas por tipo de procedimento, sexo e faixa et&#225;ria foram realizadas e est&#227;o dispon&#237;veis no <xref ref-type="sec" rid="suppl">material suplementar</xref>. Por fim, o uso exclusivo de dados de 2019, embora evite distor&#231;&#245;es da pandemia, restringe as conclus&#245;es a um per&#237;odo pr&#233;-COVID-19 e configura uma limita&#231;&#227;o temporal relevante, uma vez que a plataforma n&#227;o tem perspectivas de atualiza&#231;&#227;o, impossibilitando a an&#225;lise de tend&#234;ncias mais recentes.</p>
    </sec>
    <sec sec-type="conclusions">
      <title>Conclus&#245;es</title>
      <p>Este artigo demonstra uma aplica&#231;&#227;o pr&#225;tica da automatiza&#231;&#227;o na avalia&#231;&#227;o da presta&#231;&#227;o de cuidados cardiovasculares com base em dados do mundo real, ampliando a maturidade digital para uso dessa informa&#231;&#227;o. O uso de pain&#233;is interativos, como o <italic>iCardio</italic>, permite tornar a an&#225;lise de grandes volumes de dados mais acess&#237;vel, r&#225;pida e intuitiva, o que potencializa seu uso por gestores, pesquisadores e formuladores de pol&#237;ticas p&#250;blicas. Essa abordagem tem o potencial de transformar a gest&#227;o em sa&#250;de, ao apoiar decis&#245;es mais assertivas, baseadas em evid&#234;ncias, e contribuir para a identifica&#231;&#227;o de desigualdades regionais, otimiza&#231;&#227;o de recursos e melhoria da qualidade da assist&#234;ncia cardiovascular no pa&#237;s. Esses achados refor&#231;am a exist&#234;ncia de disparidades na oferta e utiliza&#231;&#227;o de servi&#231;os de alta complexidade, apontadas previamente na literatura, e destacam a import&#226;ncia de estrat&#233;gias que promovam maior equidade no acesso e na qualidade do cuidado cardiovascular no pa&#237;s.</p>
    </sec>
    <sec id="suppl" sec-type="supplementary-material">
      <title>Material suplementar</title>
      <supplementary-material id="suppl01">
        <label>Material Suplementar 1</label>
        <media mime-subtype="pdf" mimetype="application" xlink:href="https://minio.scielo.br/documentstore/1678-4170/VhK7qhwZpfKHXT4bN8NgB3x/eb16f5da6b95296a66029397e184eaa808998ea8.pdf"/>
      </supplementary-material>
      <supplementary-material id="suppl02">
        <label>Material Suplementar 2</label>
        <media mime-subtype="pdf" mimetype="application" xlink:href="https://minio.scielo.br/documentstore/1678-4170/VhK7qhwZpfKHXT4bN8NgB3x/efe97757c1453d5e7540a6c93afa05925ca33a8c.pdf"/>
      </supplementary-material>
      <supplementary-material id="suppl03">
        <label>Material Suplementar 3</label>
        <media mime-subtype="pdf" mimetype="application" xlink:href="https://minio.scielo.br/documentstore/1678-4170/VhK7qhwZpfKHXT4bN8NgB3x/ecbeecf4d3659321b7049eab9a0bb57373ed7a17.pdf"/>
      </supplementary-material>
      <supplementary-material id="suppl04">
        <label>Tabela Suplementar</label>
        <media mime-subtype="pdf" mimetype="application" xlink:href="https://minio.scielo.br/documentstore/1678-4170/VhK7qhwZpfKHXT4bN8NgB3x/a4b4ec88da10ccba4e4438d9920c4fa53ff6f8f7.pdf"/>
      </supplementary-material>
    </sec>
  </body>
  <back>
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    <fn-group>
      <fn fn-type="other">
        <label>Vincula&#231;&#227;o Acad&#234;mica:</label>
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      <fn fn-type="other">
        <label>Aprova&#231;&#227;o &#201;tica e Consentimento Informado:</label>
        <p> Este artigo n&#227;o cont&#233;m estudos com humanos ou animais realizados por nenhum dos autores.</p>
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      <fn fn-type="other">
        <label>Uso de Intelig&#234;ncia Artificial:</label>
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        <p>Para Material Suplementar 2, por favor, <ext-link ext-link-type="uri" xlink:href="http://abccardiol.org/supplementary-material/2026/12304/2025-0765_AO_Arquivo_suplementar_2.pdf">clique aqui</ext-link>.</p>
        <p>Para Material Suplementar 3, por favor, <ext-link ext-link-type="uri" xlink:href="http://abccardiol.org/supplementary-material/2026/12304/2025-0765_AO_Arquivo_suplementar_3.pdf">clique aqui</ext-link>.</p>
        <p>Para Tabela Suplementar, por favor, <ext-link ext-link-type="uri" xlink:href="http://abccardiol.org/supplementary-material/2026/12304/2025-0765_AO_Tabela_suplementar_1.pdf">clique aqui</ext-link>.</p>
      </fn>
      <fn fn-type="financial-disclosure">
        <label>Fontes de Financiamento:</label>
        <p> O presente estudo foi financiado pelo Conselho Nacional de Desenvolvimento Cient&#237;fico e Tecnol&#243;gico (CNPq) e Minist&#233;rio da S&#225;ude por meio do edital CNPq/Decit/SCTIE/MS/DESID/SE/MS Economia as Sa&#250;de &#8211; Pesquisas para a Melhoria do Gasto P&#250;blico em Sa&#250;de (No.44/2022) e recebeu apoio financeiro do Hospital de Cl&#237;nicas de Porto Alegre (HCPA), Porto Alegre, Brasil (ROR: <ext-link ext-link-type="uri" xlink:href="https://ror.org/010we4y38">https://ror.org/010we4y38</ext-link>).</p>
      </fn>
    </fn-group>
  </back>
  <sub-article article-type="translation" id="TRen" xml:lang="en">
    <front-stub>
      <article-id pub-id-type="doi">10.36660/abc.20250765i</article-id>
      <article-categories>
        <subj-group subj-group-type="heading">
          <subject>Original Article</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>iCardio: Applying Business Intelligence to Assess Regional Disparity in Cardiovascular Care with Real-World Data</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <contrib-id contrib-id-type="orcid">0009-0005-8879-1306</contrib-id>
          <name>
            <surname>Souza</surname>
            <given-names>Let&#237;cia Cunha Pereira de</given-names>
          </name>
          <role>Analysis and interpretation of the data and Writing of the manuscript</role>
          <xref ref-type="aff" rid="aff1001">
            <sup>1</sup>
          </xref>
          <xref ref-type="aff" rid="aff2001">
            <sup>2</sup>
          </xref>
        </contrib>
        <contrib contrib-type="author">
          <contrib-id contrib-id-type="orcid">0000-0001-5333-3173</contrib-id>
          <name>
            <surname>Marcolino</surname>
            <given-names>Miriam Allein Zago</given-names>
          </name>
          <role>Conception and design of the research</role>
          <role>Acquisition of data</role>
          <role>Critical revision of the manuscript for intellectual content</role>
          <xref ref-type="aff" rid="aff1001">
            <sup>1</sup>
          </xref>
          <xref ref-type="aff" rid="aff3001">
            <sup>3</sup>
          </xref>
        </contrib>
        <contrib contrib-type="author">
          <contrib-id contrib-id-type="orcid">0009-0002-0659-8878</contrib-id>
          <name>
            <surname>Engster</surname>
            <given-names>Pedro Henrique</given-names>
          </name>
          <role>Analysis and interpretation of the data and Writing of the manuscript</role>
          <xref ref-type="aff" rid="aff1001">
            <sup>1</sup>
          </xref>
          <xref ref-type="aff" rid="aff4001">
            <sup>4</sup>
          </xref>
        </contrib>
        <contrib contrib-type="author">
          <contrib-id contrib-id-type="orcid">0000-0003-4762-4569</contrib-id>
          <name>
            <surname>Casiraghi</surname>
            <given-names>Yohan</given-names>
          </name>
          <role>Analysis and interpretation of the data and Writing of the manuscript</role>
          <xref ref-type="aff" rid="aff1001">
            <sup>1</sup>
          </xref>
          <xref ref-type="aff" rid="aff5001">
            <sup>5</sup>
          </xref>
        </contrib>
        <contrib contrib-type="author">
          <contrib-id contrib-id-type="orcid">0009-0003-4558-4097</contrib-id>
          <name>
            <surname>Linn</surname>
            <given-names>Thiago Brusca da Costa</given-names>
          </name>
          <role>Analysis and interpretation of the data and Writing of the manuscript</role>
          <xref ref-type="aff" rid="aff1001">
            <sup>1</sup>
          </xref>
          <xref ref-type="aff" rid="aff5001">
            <sup>5</sup>
          </xref>
        </contrib>
        <contrib contrib-type="author">
          <contrib-id contrib-id-type="orcid">0009-0009-4909-0741</contrib-id>
          <name>
            <surname>Bairos</surname>
            <given-names>Jerry Eduardo de Almeida de</given-names>
          </name>
          <role>Analysis and interpretation of the data and Writing of the manuscript</role>
          <xref ref-type="aff" rid="aff1001">
            <sup>1</sup>
          </xref>
          <xref ref-type="aff" rid="aff5001">
            <sup>5</sup>
          </xref>
        </contrib>
        <contrib contrib-type="author">
          <contrib-id contrib-id-type="orcid">0000-0002-1934-1003</contrib-id>
          <name>
            <surname>Lara</surname>
            <given-names>Luciana Rodrigues de</given-names>
          </name>
          <role>Acquisition of data</role>
          <xref ref-type="aff" rid="aff1001">
            <sup>1</sup>
          </xref>
          <xref ref-type="aff" rid="aff3001">
            <sup>3</sup>
          </xref>
          <xref ref-type="aff" rid="aff6001">
            <sup>6</sup>
          </xref>
        </contrib>
        <contrib contrib-type="author">
          <contrib-id contrib-id-type="orcid">0000-0003-0985-6510</contrib-id>
          <name>
            <surname>Schneider</surname>
            <given-names>Nay&#234; Balzan</given-names>
          </name>
          <role>Critical revision of the manuscript for intellectual content</role>
          <xref ref-type="aff" rid="aff1001">
            <sup>1</sup>
          </xref>
          <xref ref-type="aff" rid="aff3001">
            <sup>3</sup>
          </xref>
          <xref ref-type="aff" rid="aff6001">
            <sup>6</sup>
          </xref>
        </contrib>
        <contrib contrib-type="author">
          <contrib-id contrib-id-type="orcid">0000-0002-6411-3480</contrib-id>
          <name>
            <surname>Etges</surname>
            <given-names>Ana Paula Beck da Silva</given-names>
          </name>
          <role>Conception and design of the research</role>
          <role>Critical revision of the manuscript for intellectual content</role>
          <xref ref-type="aff" rid="aff1001">
            <sup>1</sup>
          </xref>
          <xref ref-type="aff" rid="aff3001">
            <sup>3</sup>
          </xref>
          <xref ref-type="aff" rid="aff6001">
            <sup>6</sup>
          </xref>
        </contrib>
        <contrib contrib-type="author">
          <contrib-id contrib-id-type="orcid">0000-0002-2447-2577</contrib-id>
          <name>
            <surname>Polanczyk</surname>
            <given-names>Carisi Anne</given-names>
          </name>
          <role>Conception and design of the research</role>
          <role>Obtaining financing</role>
          <role>Critical revision of the manuscript for intellectual content</role>
          <xref ref-type="aff" rid="aff1001">
            <sup>1</sup>
          </xref>
          <xref ref-type="aff" rid="aff3001">
            <sup>3</sup>
          </xref>
          <xref ref-type="aff" rid="aff5001">
            <sup>5</sup>
          </xref>
          <xref ref-type="aff" rid="aff6001">
            <sup>6</sup>
          </xref>
          <xref ref-type="aff" rid="aff7001">
            <sup>7</sup>
          </xref>
        </contrib>
      </contrib-group>
      <aff id="aff1001">
        <label>1</label>
        <country country="BR">Brasil</country>
        <institution content-type="original">Hospital de Cl&#237;nicas de Porto Alegre (HCPA), Porto Alegre, RS &#8211; Brasil</institution>
      </aff>
      <aff id="aff2001">
        <label>2</label>
        <country country="BR">Brasil</country>
        <institution content-type="original">Programa de P&#243;s-gradua&#231;&#227;o em Ci&#234;ncias Biol&#243;gicas: Bioqu&#237;mica, Departamento de Bioqu&#237;mica, Instituto de Ci&#234;ncias B&#225;sicas da Sa&#250;de, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS &#8211; Brasil</institution>
      </aff>
      <aff id="aff3001">
        <label>3</label>
        <country country="BR">Brasil</country>
        <institution content-type="original">Instituto Nacional de Avalia&#231;&#227;o de Tecnologia em Sa&#250;de INCT/IATS, Porto Alegre, RS &#8211; Brasil</institution>
      </aff>
      <aff id="aff4001">
        <label>4</label>
        <country country="BR">Brasil</country>
        <institution content-type="original">Faculdade de Medicina, Universidade Federal de Ci&#234;ncias da Sa&#250;de de Porto Alegre, Porto Alegre, RS &#8211; Brasil</institution>
      </aff>
      <aff id="aff5001">
        <label>5</label>
        <country country="BR">Brasil</country>
        <institution content-type="original">Faculdade de Medicina, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS &#8211; Brasil</institution>
      </aff>
      <aff id="aff6001">
        <label>6</label>
        <country country="BR">Brasil</country>
        <institution content-type="original">Programa de P&#243;s-Gradua&#231;&#227;o em Epidemiologia, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS &#8211; Brasil</institution>
      </aff>
      <aff id="aff7001">
        <label>7</label>
        <country country="BR">Brasil</country>
        <institution content-type="original">Programa de P&#243;s-Gradua&#231;&#227;o em Cardiologia e Ci&#234;ncias Cardiovasculares, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS &#8211; Brasil</institution>
      </aff>
      <author-notes>
        <corresp id="c01001"><label>Mailing Address</label>: Carisi Anne Polanczyk &#8226; Hospital de Clinicas de Porto Alegre &#8211; Rua Ramiro Barcelos, 2350, Sala 21507, Centro de Pesquisa Cl&#237;nica. Postal Code 90035-900, Porto Alegre, RS &#8211; Brazil E-mail: carisi.anne@gmail.com </corresp>
        <fn fn-type="coi-statement">
          <label>Potential Conflict of Interest:</label>
          <p> No potential conflict of interest relevant to this article was reported.</p>
        </fn>
        <fn fn-type="edited-by">
          <label>Editor responsible for the review:</label>
          <p> Gl&#225;ucia Maria Moraes de Oliveira</p>
        </fn>
      </author-notes>
      <abstract>
        <title>Abstract</title>
        <sec>
          <title>Background</title>
          <p> The assessment of cardiovascular care in Brazil is challenged by the country&#8217;s vast territory and the large volume of data. Business Intelligence (BI) tools may enhance the visualization and utilization of such information to support public health decision-making.</p>
        </sec>
        <sec>
          <title>Objectives</title>
          <p> To demonstrate the practical application of the interactive dashboard iCardio in analyzing regional inequalities in high-complexity cardiovascular care in Brazil.</p>
        </sec>
        <sec>
          <title>Methods</title>
          <p> Descriptive study based on secondary data made available by the iCardio dashboard, which integrates healthcare information from the Brazilian Unified Health System (SUS). Variables from 2019 were analyzed by region, considering service volume, mortality rates, hospital readmissions, and patient travel distance.</p>
        </sec>
        <sec>
          <title>Results</title>
          <p> Analyses of cardiovascular surgery and interventional cardiology procedures indicated that the Southeast region exhibited the lowest mortality rates (in-hospital: 5.65%, 30 days after procedure: 5.73%, and 30 days after discharge: 6.77%). The North had the highest mortality rates (in-hospital: 7.56%, within 30 days after the procedure: 7.7%, and within 30 days after discharge: 8.7%). The South and Central-West regions registered the highest readmission rates within 30 days after the procedure (9.79% and 10.3%, respectively) and within 30 days after discharge (11.7% and 11.97%, respectively). The South also presented the highest standardized procedure rate (10.54), nearly double that of the Southeast (6.02). The Northeast showed the longest mean patient travel distance (94.59 km).</p>
        </sec>
        <sec>
          <title>Conclusions</title>
          <p> The use of BI through the iCardio dashboard enables rapid and accessible analysis of healthcare data, with the potential to inform evidence-based decision-making, optimize resource allocation, and identify regional disparities in cardiovascular care.</p>
        </sec>
      </abstract>
      <abstract abstract-type="graphical">
        <p>
          <fig id="f01001">
            <label>Central Illustration</label>
            <caption>
              <title>: iCardio: Applying Business Intelligence to Assess Regional Disparity in Cardiovascular Care with Real-World Data</title>
              <p>The regions with the highest and the lowest values for each aspect were considered.</p>
            </caption>
            <graphic xlink:href="1678-4170-abc-123-04-e20250765-50765-gf01-en.tif"/></fig>
        </p>
      </abstract>
      <kwd-group xml:lang="en">
        <title>Keywords:</title>
        <kwd>Cardiology</kwd>
        <kwd>Health Care Quality Indicators</kwd>
        <kwd>Routinely Collected Health Data</kwd>
      </kwd-group>
    </front-stub>
    <body>
      <sec sec-type="intro">
        <title>Introduction</title>
        <p>Cardiovascular diseases (CVDs) currently represent a major burden on healthcare systems worldwide. In Brazil, CVDs stand out both in terms of mortality and financial costs, with more than R$1 billion spent annually on cardiovascular procedures by the Unified Health System (SUS).<sup><xref ref-type="bibr" rid="B1">1</xref></sup> Furthermore, the cardiology literature highlights regional disparities in the provision of high-complexity services,<sup><xref ref-type="bibr" rid="B2">2</xref></sup>the volume of care in specialized centers,<sup><xref ref-type="bibr" rid="B3">3</xref></sup> supplementary health coverage,<sup><xref ref-type="bibr" rid="B4">4</xref></sup> and the distribution of healthcare professionals across the Brazilian territory.<sup><xref ref-type="bibr" rid="B5">5</xref></sup>Given the magnitude of these factors, improving the efficiency of health investments is of broad public interest. In this context, the use of indicator dashboards has emerged as a promising tool, simplifying access to and interpretation of information and potentially enhancing resource management and decision-making.<sup><xref ref-type="bibr" rid="B6">6</xref>-<xref ref-type="bibr" rid="B8">8</xref></sup></p>
        <p>Despite this potential, there is a scarcity of studies evaluating the application of dashboards with CVD-specific data in Brazil, which limits the systematic exploration of already available information to support decisions in public health. In this context, this study aims to contribute to this field by analyzing real-world data available in the interactive iCardio dashboard,<sup><xref ref-type="bibr" rid="B9">9</xref></sup>an initiative for organizing and visualizing data of interventional cardiology and cardiovascular surgery procedures performed in 2019 and 2020 in the Unified Health System (SUS). It was developed based on the Hospital Information System (SIH) and Mortality Information System (SIM) linked data.</p>
        <p>iCardio provides detailed epidemiological parameters, including patient profiles, care locations, length of hospital stay, mortality, and financial resources allocated to procedures. Thus, the objective of this study was to explore the iCardio platform as a tool to assess variability in cardiovascular services across Brazilian regions.</p>
      </sec>
      <sec sec-type="methods">
        <title>Methods</title>
        <p>This is a descriptive ecological study that used the iCardio indicators dashboard as its data source. This online platform was developed by researchers from the Instituto de Avalia&#231;&#227;o de Tecnologia em Sa&#250;de (IATS), based at the Hospital de Cl&#237;nicas de Porto Alegre (<ext-link ext-link-type="uri" xlink:href="https://ror.org/010we4y38">https://ror.org/010we4y38</ext-link>) as part of a project approved in a call for proposals from the National Council for Scientific and Technological Development (CNPq &#8211; grant 407282/2022-0), based on the administrative SUS&#8217;s databases. The conception, architecture, processing flows, and validation of the iCardio panel data were previously described.<sup><xref ref-type="bibr" rid="B10">10</xref></sup></p>
        <p>The panel consolidates 17 key quantitative indicators related to clinical aspects, healthcare service provision, equity, and financial sustainability, at the hospital, state, and national levels. These indicators refer to cardiovascular surgery procedures and interventional cardiology performed within the SUS, in the years 2019 and 2020, recorded on SIH and SIM linked databases. A total of 165 procedures were included (<xref ref-type="supplementary-material" rid="suppl01001">Supplementary file 1</xref>), based on the codes beginning with the organizational forms 04.06.01 (cardiovascular surgery) and 04.06.03 (interventional cardiology) from the Table of Procedures, Medications, Orthoses, Prostheses, and Special Materials of the SUS (SIGTAP). Data included all centers that performed one or more of the procedures listed above, which classifies them as High Complexity Cardiovascular Center (ACC), corresponding to units and reference centers qualified to perform procedures classified as high complexity, high technology, and high cost, according to current SUS regulations.<sup><xref ref-type="bibr" rid="B11">11</xref></sup></p>
        <p>The iCardio database allows for data stratification by gender, age group, race/skin color, region of residence, type of admission (emergency/elective), diagnosis (ICD-10), and procedure performed. Only aggregated data is presented, with no possibility of individual identification.<sup><xref ref-type="bibr" rid="B10">10</xref></sup> For this study, the indicators listed in <xref ref-type="table" rid="t1001">Table 1</xref> were selected for each Brazilian region, using the locality-based analysis available on the dashboard. The age- and gender-standardized procedure rate expressed per million inhabitants was used for comparison across regions with populations exhibiting different demographic structures. As described in the descriptive material for the panel&#8217;s development,<sup><xref ref-type="bibr" rid="B10">10</xref>,<xref ref-type="bibr" rid="B12">12</xref></sup> the direct standardization method was applied, estimating the expected procedure rates by region and state of residence of the patients, sex and age group to which they were projected based on the reference population defined by the number of inhabitants in these classes estimated for 2020 by the Brazilian Institute of Geography and Statistics.</p>
        <p>
          <table-wrap id="t1001">
            <label>Table 1</label>
            <caption>
              <title>&#8211; Selected indicators of care delivery, clinical outcomes, and hospitalization costs by region of Brazil, 2019</title>
            </caption>
            <table frame="hsides" rules="groups">
              <colgroup width="33%">
                <col/>
                <col/>
                <col/>
              </colgroup>
              <thead>
                <tr>
                  <th align="left">Indicator</th>
                  <th>Measure/Unit</th>
                  <th align="left">Purpose of Analysis</th>
                </tr>
              </thead>
              <tbody>
                <tr>
                  <td>Number of patients (intervention and surgery)</td>
                  <td align="center">Absolute number</td>
                  <td>Estimate the total treated population</td>
                </tr>
                <tr>
                  <td>Number of intervention patients</td>
                  <td align="center">Absolute number</td>
                  <td>Characterize the specific volume of interventional procedures</td>
                </tr>
                <tr>
                  <td>Number of surgery patients</td>
                  <td align="center">Absolute number</td>
                  <td>Characterize the specific volume of surgical procedures</td>
                </tr>
                <tr>
                  <td>Age- and sex-standardized rate per million inhabitants</td>
                  <td align="center">Rate per million inhabitants</td>
                  <td>Compare regions independently of age structure and sex distribution</td>
                </tr>
                <tr>
                  <td>Male</td>
                  <td align="center">Absolute number</td>
                  <td>Describe the patients&#8217; demographic profile</td>
                </tr>
                <tr>
                  <td>Female</td>
                  <td align="center">Absolute number</td>
                  <td>Describe the patients&#8217; demographic profile</td>
                </tr>
                <tr>
                  <td>Main age group</td>
                  <td align="center">Age group (%)</td>
                  <td>Identify the most affected age group</td>
                </tr>
                <tr>
                  <td>ICU admission</td>
                  <td align="center">Percentage (%)</td>
                  <td>Assess the severity and complexity of care</td>
                </tr>
                <tr>
                  <td>Facilities</td>
                  <td align="center">Absolute number</td>
                  <td>Estimate the available healthcare network</td>
                </tr>
                <tr>
                  <td>Inhabitants per facility</td>
                  <td align="center">Ratio (inhabitants/unit)</td>
                  <td>Estimate service access and distribution</td>
                </tr>
                <tr>
                  <td>Mean travel distance for care</td>
                  <td align="center">Kilometers (km)</td>
                  <td>Assess geographical accessibility</td>
                </tr>
                <tr>
                  <td>Number of patients &#8211; Different region</td>
                  <td align="center">Absolute number</td>
                  <td>Quantify interregional displacements, considering the SUS regional division.</td>
                </tr>
                <tr>
                  <td>Patients are treated in a different region.</td>
                  <td align="center">Percentage (%)</td>
                  <td>Proportion of patients treated outside their region of residence, according to the SUS regional division</td>
                </tr>
                <tr>
                  <td>Number of patients &#8211; Different macroregion</td>
                  <td align="center">Absolute number</td>
                  <td>Quantify displacements between macroregions, considering the SUS regional division.</td>
                </tr>
                <tr>
                  <td>Patients treated in a different macroregion.</td>
                  <td align="center">Percentage (%)</td>
                  <td>Proportion of patients treated outside their macroregion of residence, according to SUS regional division</td>
                </tr>
                <tr>
                  <td>Readmission within 30 days of the procedure</td>
                  <td align="center">Percentage (%)</td>
                  <td>Evaluate the early post-procedure outcome</td>
                </tr>
                <tr>
                  <td>Readmission within 30 days of discharge</td>
                  <td align="center">Percentage (%)</td>
                  <td>Assess the quality of care and post-discharge follow-up</td>
                </tr>
                <tr>
                  <td>In-hospital mortality</td>
                  <td align="center">Percentage (%)</td>
                  <td>Measure lethality during hospitalization</td>
                </tr>
                <tr>
                  <td>Mortality within 30 days of the procedure</td>
                  <td align="center">Percentage (%)</td>
                  <td>Evaluate the risk of early death after the procedure</td>
                </tr>
                <tr>
                  <td>Mortality within 30 days of discharge</td>
                  <td align="center">Percentage (%)</td>
                  <td>Evaluate the risk of early death after hospital discharge</td>
                </tr>
                <tr>
                  <td>Mean cost per hospitalization</td>
                  <td align="center">Brazilian reais (R$)</td>
                  <td>Estimate the average cost per treated patient</td>
                </tr>
                <tr>
                  <td>Total expenditure</td>
                  <td align="center">Brazilian reais (R$)</td>
                  <td>Estimate aggregate financial impact</td>
                </tr>
              </tbody>
            </table>
            <table-wrap-foot>
              <attrib>UTI: unidade de terapia intensiva. Fonte: iCardio.</attrib>
            </table-wrap-foot>
          </table-wrap>
        </p>
        <p>Additionally, the distribution of mortality rates according to sex and age group was analyzed, with stratification between pediatric and adult patients. Furthermore, care outcomes were analyzed individually for each procedure. Only hospitalization data from 2019 were included in the analyses.</p>
        <p>Data were extracted from the iCardio dashboard and organized into spreadsheets for descriptive analysis using Microsoft Excel (Microsoft Corporation, Redmond, WA, USA). Frequency measures (absolute values and percentages) were calculated to summarize the selected indicators. Comparisons between Brazilian geographic regions and patient profiles were conducted through comparative descriptive analysis. Since the data represent the total population of hospitalizations registered in SUS for the procedures under study, no hypothesis testing or inferential statistical methods were applied.</p>
        <p>The study was approved by the Institutional Research Ethics Committee of The study was approved by the Institutional Research Ethics Committee of Hospital de Cl&#237;nicas de Porto Alegre (CAAE 66784523.0.0000.5327). All data processing followed national data protection regulations. Only secondary, anonymized, and publicly available data were used, with no human subjects directly involved.</p>
      </sec>
      <sec sec-type="results">
        <title>Results</title>
        <sec>
          <title>Geographic distribution of cardiovascular care in Brazil</title>
          <p>For each of the five Brazilian regions, the clinical indicators data are summarized in <xref ref-type="table" rid="t2001">Table 2</xref>. When comparing the total number of patients with the number of hospitalizations recorded for interventional cardiology and cardiovascular surgery, the sum of procedures in both specialties exceeds the total number of patients treated. This discrepancy reflects the fact that some patients underwent procedures in both specialties.</p>
          <p>
            <table-wrap id="t2001">
              <label>Table 2</label>
              <caption>
                <title>&#8211; Clinical and demographic profile of hospitalizations for cardiovascular procedures in the Brazilian public health system (SUS), Brazil, 2019</title>
              </caption>
              <table frame="hsides" rules="groups">
                <colgroup width="14%">
                  <col/>
                  <col/>
                  <col/>
                  <col/>
                  <col/>
                  <col/>
                  <col/>
                </colgroup>
                <thead>
                  <tr>
                    <th align="left">Indicators (2019)</th>
                    <th>North</th>
                    <th>Northeast</th>
                    <th>Central-West</th>
                    <th>Southeast</th>
                    <th>South</th>
                    <th>Brazil</th>
                  </tr>
                </thead>
                <tbody>
                  <tr>
                    <td colspan="7">
                      <bold>Clinical data</bold>
                    </td>
                  </tr>
                  <tr>
                    <td>Number of patients (intervention and surgery)</td>
                    <td align="center">4,742</td>
                    <td align="center">30,693</td>
                    <td align="center">10,956</td>
                    <td align="center">71,35</td>
                    <td align="center">43,188</td>
                    <td align="center">160,897</td>
                  </tr>
                  <tr>
                    <td>Number of intervention patients</td>
                    <td align="center">2,165</td>
                    <td align="center">15,128</td>
                    <td align="center">5,351</td>
                    <td align="center">40,203</td>
                    <td align="center">24,665</td>
                    <td align="center">87,497</td>
                  </tr>
                  <tr>
                    <td>Number of surgery patients</td>
                    <td align="center">2,653</td>
                    <td align="center">16,119</td>
                    <td align="center">5,825</td>
                    <td align="center">32,647</td>
                    <td align="center">19,893</td>
                    <td align="center">77,128</td>
                  </tr>
                  <tr>
                    <td>Male</td>
                    <td align="center">3,071</td>
                    <td align="center">17,583</td>
                    <td align="center">6,609</td>
                    <td align="center">43,931</td>
                    <td align="center">27,167</td>
                    <td align="center">98,340</td>
                  </tr>
                  <tr>
                    <td>Female</td>
                    <td align="center">1,669</td>
                    <td align="center">13,088</td>
                    <td align="center">4,3364</td>
                    <td align="center">27,389</td>
                    <td align="center">16,009</td>
                    <td align="center">62,480</td>
                  </tr>
                  <tr>
                    <td>Age- and sex-standardized rate per million inhabitants</td>
                    <td align="center">3.65</td>
                    <td align="center">4.66</td>
                    <td align="center">5.88</td>
                    <td align="center">6.02</td>
                    <td align="center">10.54</td>
                    <td align="center">5.43</td>
                  </tr>
                  <tr>
                    <td colspan="7">
                      <bold>Age Distribution of the Patients</bold>
                    </td>
                  </tr>
                  <tr>
                    <td>&lt; 1 year</td>
                    <td align="center">88</td>
                    <td align="center">451</td>
                    <td align="center">139</td>
                    <td align="center">921</td>
                    <td align="center">462</td>
                    <td align="center">2,061</td>
                  </tr>
                  <tr>
                    <td>1 to 8 years</td>
                    <td align="center">181</td>
                    <td align="center">903</td>
                    <td align="center">306</td>
                    <td align="center">1,741</td>
                    <td align="center">780</td>
                    <td align="center">3,910</td>
                  </tr>
                  <tr>
                    <td>9 to 17 years</td>
                    <td align="center">82</td>
                    <td align="center">336</td>
                    <td align="center">85</td>
                    <td align="center">576</td>
                    <td align="center">246</td>
                    <td align="center">1,324</td>
                  </tr>
                  <tr>
                    <td>9 to 17 years</td>
                    <td align="center">73</td>
                    <td align="center">370</td>
                    <td align="center">92</td>
                    <td align="center">529</td>
                    <td align="center">239</td>
                    <td align="center">1,302</td>
                  </tr>
                  <tr>
                    <td>27 to 35 years</td>
                    <td align="center">114</td>
                    <td align="center">365</td>
                    <td align="center">160</td>
                    <td align="center">844</td>
                    <td align="center">423</td>
                    <td align="center">2,175</td>
                  </tr>
                  <tr>
                    <td>36 to 44 years</td>
                    <td align="center">209</td>
                    <td align="center">1,506</td>
                    <td align="center">448</td>
                    <td align="center">2,608</td>
                    <td align="center">1,460</td>
                    <td align="center">6,229</td>
                  </tr>
                  <tr>
                    <td>45 to 53 years</td>
                    <td align="center">543</td>
                    <td align="center">3,696</td>
                    <td align="center">1,320</td>
                    <td align="center">8,166</td>
                    <td align="center">4,801</td>
                    <td align="center">18,520</td>
                  </tr>
                  <tr>
                    <td>54 to 62 years</td>
                    <td align="center">1,072</td>
                    <td align="center">6,796</td>
                    <td align="center">2,564</td>
                    <td align="center">17,466</td>
                    <td align="center">10,395</td>
                    <td align="center">38,284</td>
                  </tr>
                  <tr>
                    <td>63 to 71 years</td>
                    <td align="center">1,208</td>
                    <td align="center">7,737</td>
                    <td align="center">2,870</td>
                    <td align="center">19,904</td>
                    <td align="center">12,397</td>
                    <td align="center">44,109</td>
                  </tr>
                  <tr>
                    <td>72 to 80 years</td>
                    <td align="center">813</td>
                    <td align="center">5,485</td>
                    <td align="center">2,037</td>
                    <td align="center">12,875</td>
                    <td align="center">8,398</td>
                    <td align="center">29,606</td>
                  </tr>
                  <tr>
                    <td colspan="7">
                      <bold>Racial distribution of patients</bold>
                    </td>
                  </tr>
                  <tr>
                    <td>Yellow</td>
                    <td align="center">105</td>
                    <td align="center">344</td>
                    <td align="center">97</td>
                    <td align="center">628</td>
                    <td align="center">400</td>
                    <td align="center">1,574</td>
                  </tr>
                  <tr>
                    <td>White</td>
                    <td align="center">408</td>
                    <td align="center">2,484</td>
                    <td align="center">1,559</td>
                    <td align="center">38.412</td>
                    <td align="center">36,553</td>
                    <td align="center">79,406</td>
                  </tr>
                  <tr>
                    <td>Indigenous</td>
                    <td align="center">7</td>
                    <td align="center">6</td>
                    <td align="center">9</td>
                    <td align="center">5</td>
                    <td align="center">16</td>
                    <td align="center">43</td>
                  </tr>
                  <tr>
                    <td>Brown</td>
                    <td align="center">3,048</td>
                    <td align="center">14,770</td>
                    <td align="center">5,140</td>
                    <td align="center">21.385</td>
                    <td align="center">2,724</td>
                    <td align="center">47,057</td>
                  </tr>
                  <tr>
                    <td>Black</td>
                    <td align="center">116</td>
                    <td align="center">595</td>
                    <td align="center">118</td>
                    <td align="center">3,527</td>
                    <td align="center">1,031</td>
                    <td align="center">5,387</td>
                  </tr>
                  <tr>
                    <td>Conflict</td>
                    <td align="center">39</td>
                    <td align="center">65</td>
                    <td align="center">43</td>
                    <td align="center">436</td>
                    <td align="center">135</td>
                    <td align="center">707</td>
                  </tr>
                  <tr>
                    <td>No information</td>
                    <td align="center">1,019</td>
                    <td align="center">12,429</td>
                    <td align="center">3,990</td>
                    <td align="center">6,957</td>
                    <td align="center">2,329</td>
                    <td align="center">26,723</td>
                  </tr>
                </tbody>
              </table>
              <table-wrap-foot>
                <attrib>Source: iCardio. Note: The sum of patients by age group does not correspond to the total number of patients treated reported, a discrepancy explained by the specific processing adopted to adjust for inconsistencies in the birth dates recorded in the Hospital Admission Authorizations (AIHs), ensuring greater reliability in the age classification.<sup>12</sup></attrib>
              </table-wrap-foot>
            </table-wrap>
          </p>
          <p>Considering all hospitalizations nationwide, the majority of patients were white males, with a mean age ranging from 63 to 71 years. However, the predominance of white patients in the aggregate analysis largely reflects the higher concentration of care in the Southeast and South regions, where, as evidenced in the panel, there is both a greater volume of procedures and a higher proportion of the population self-identifying as white. In contrast, in the North, Northeast, and Central-West regions, there is a predominance of care provided to patients who identify as mixed-race, highlighting regional differences in the racial profile of patients served, even though no significant differences were observed regarding sex and age distribution among the regions.</p>
          <p>In the age- and sex-stratified analysis (<xref ref-type="supplementary-material" rid="suppl04001">Supplementary Table 1</xref>), hospital mortality was consistently higher in the pediatric population compared to adults, particularly in the North region, where, among men, the rate in pediatric patients is more than double that observed in adults. Among women, this age difference is less pronounced. In contrast, when considering only the adult population, an inverse pattern is observed: in all regions of the country, women have higher mortality rates than men.</p>
          <p>The Southeast had the highest absolute number of hospitalizations and procedures, followed by the South. However, when age- and sex-standardized rates were considered, the South exhibited the highest values. Although the Southeast and South stood out in terms of service volume, the highest proportion of intensive care unit (ICU) admissions was observed in the Northeast, as shown in <xref ref-type="table" rid="t3001">Table 3</xref>, which brings together the healthcare and economic indicators.</p>
          <p>
            <table-wrap id="t3001">
              <label>Table 3</label>
              <caption>
                <title>&#8211; Regional distribution of healthcare and economic indicators for hospitalizations, Brazil, 2019</title>
              </caption>
              <table frame="hsides" rules="groups">
                <colgroup width="14%">
                  <col/>
                  <col/>
                  <col/>
                  <col/>
                  <col/>
                  <col/>
                  <col/>
                </colgroup>
                <thead>
                  <tr>
                    <th align="left">Indicators (2019)</th>
                    <th>North</th>
                    <th>Northeast</th>
                    <th>Central-West</th>
                    <th>Southeast</th>
                    <th>South</th>
                    <th>Brazil</th>
                  </tr>
                </thead>
                <tbody>
                  <tr>
                    <td colspan="7">
                      <bold>Healthcare indicators</bold>
                    </td>
                  </tr>
                  <tr>
                    <td>ICU admission (%)</td>
                    <td align="center">45.79</td>
                    <td align="center">58.21</td>
                    <td align="center">49.49</td>
                    <td align="center">56.04</td>
                    <td align="center">53.39</td>
                    <td align="center">54.69</td>
                  </tr>
                  <tr>
                    <td>Facilities</td>
                    <td align="center">35</td>
                    <td align="center">93</td>
                    <td align="center">41</td>
                    <td align="center">276</td>
                    <td align="center">113</td>
                    <td align="center">588</td>
                  </tr>
                  <tr>
                    <td>Inhabitants per facility</td>
                    <td align="center">229,644</td>
                    <td align="center">194,685</td>
                    <td align="center">201,276</td>
                    <td align="center">204,558</td>
                    <td align="center">130,149</td>
                    <td align="center">189,176</td>
                  </tr>
                  <tr>
                    <td>Mean travel distance for care (km)</td>
                    <td align="center">90.77</td>
                    <td align="center">94.59</td>
                    <td align="center">92.23</td>
                    <td align="center">45.86</td>
                    <td align="center">68.91</td>
                    <td align="center">65.77</td>
                  </tr>
                  <tr>
                    <td>Number of patients &#8211; Different region</td>
                    <td align="center">1,726</td>
                    <td align="center">14,824</td>
                    <td align="center">3,544</td>
                    <td align="center">24,249</td>
                    <td align="center">14,99</td>
                    <td align="center">59,327</td>
                  </tr>
                  <tr>
                    <td>Patients treated in a different region (%)</td>
                    <td align="center">36.38</td>
                    <td align="center">48.23</td>
                    <td align="center">32.18</td>
                    <td align="center">33.88</td>
                    <td align="center">34.60</td>
                    <td align="center">36.76</td>
                  </tr>
                  <tr>
                    <td>Number of patients &#8211; Different macroregion</td>
                    <td align="center">969</td>
                    <td align="center">7,158</td>
                    <td align="center">3,112</td>
                    <td align="center">10,36</td>
                    <td align="center">4,984</td>
                    <td align="center">26,579</td>
                  </tr>
                  <tr>
                    <td>Patients treated in a different macroregion (%)</td>
                    <td align="center">20.43</td>
                    <td align="center">23.30</td>
                    <td align="center">28.25</td>
                    <td align="center">14.49</td>
                    <td align="center">11.52</td>
                    <td align="center">16.49</td>
                  </tr>
                  <tr>
                    <td>Readmission within 30 days of the procedure (%)</td>
                    <td align="center">6.05</td>
                    <td align="center">5.64</td>
                    <td align="center">10.30</td>
                    <td align="center">7.08</td>
                    <td align="center">9.79</td>
                    <td align="center">7.75</td>
                  </tr>
                  <tr>
                    <td>Readmission within 30 days of discharge (%)</td>
                    <td align="center">7.56</td>
                    <td align="center">7.04</td>
                    <td align="center">11.97</td>
                    <td align="center">8.71</td>
                    <td align="center">11.70</td>
                    <td align="center">9.41</td>
                  </tr>
                  <tr>
                    <td>In-hospital mortality (%)</td>
                    <td align="center">7.56</td>
                    <td align="center">6.28</td>
                    <td align="center">6.34</td>
                    <td align="center">5.65</td>
                    <td align="center">6.58</td>
                    <td align="center">6.12</td>
                  </tr>
                  <tr>
                    <td>Mortality within 30 days of the procedure (%)</td>
                    <td align="center">7.70</td>
                    <td align="center">6.49</td>
                    <td align="center">6.91</td>
                    <td align="center">5.73</td>
                    <td align="center">6.88</td>
                    <td align="center">6.32</td>
                  </tr>
                  <tr>
                    <td>Mortality within 30 days of discharge (%)</td>
                    <td align="center">8.70</td>
                    <td align="center">7.52</td>
                    <td align="center">7.72</td>
                    <td align="center">6.77</td>
                    <td align="center">7.81</td>
                    <td align="center">7.32</td>
                  </tr>
                  <tr>
                    <td colspan="7">
                      <bold>Mean cost per hospitalization (R$)</bold>
                    </td>
                  </tr>
                  <tr>
                    <td>Mean cost per hospitalization (R$)</td>
                    <td align="center">11,153.55</td>
                    <td align="center">11,518.30</td>
                    <td align="center">11,059.58</td>
                    <td align="center">10,765.24</td>
                    <td align="center">11,411.49</td>
                    <td align="center">11,114.04</td>
                  </tr>
                  <tr>
                    <td>Total expenditure (R$)</td>
                    <td align="center">54.73 M</td>
                    <td align="center">369.54 M</td>
                    <td align="center">133.77 M</td>
                    <td align="center">818.89 M</td>
                    <td align="center">538.15 M</td>
                    <td align="center">1.92 B</td>
                  </tr>
                </tbody>
              </table>
              <table-wrap-foot>
                <attrib>ICU: intensive care unit. Source: iCardio.</attrib>
              </table-wrap-foot>
            </table-wrap>
          </p>
          <p>In total, Brazil had 558 High-Complexity Cardiovascular (HCC) facilities, with the ratio of inhabitants per facility ranging from 130,149 to 229,644 depending on the region. The average distance traveled by patients to access care also varied geographically, with the Southeast presenting a significantly shorter mean travel distance compared with the other regions.</p>
          <p>When stratified by region and health macro-region, the percentage of patients receiving care outside their region of residence varied by specialty. Among those undergoing cardiovascular surgery, 41.44% were treated outside their home region, and 21.16% outside their health macro-region. For interventional cardiology, these proportions were lower, with 32.46% of procedures performed outside the home region and 12.23% outside the macro-region.</p>
          <p>The Center-West and South regions showed the highest readmission rates, both within 30 days of the procedure and within 30 days of hospital discharge. Regarding mortality, the North exhibited the highest crude rates across all three outcomes analyzed - in-hospital mortality, mortality within 30 days post-procedure, and mortality within 30 days post-discharge - as well as the longest mean hospital stay. In contrast, the Southeast presented the lowest crude mortality rates across the three outcomes.</p>
          <p>In an analysis by procedure group (<xref ref-type="supplementary-material" rid="suppl02001">Supplementary File 2</xref>), the procedures with the highest hospital mortality rates in Brazil were catheterization (31.87%), correction of cardiac anomalies (21.55%), and cardio resection (17.01%). By region, ventricular septectomy reaches 57.14% in the Central-West, correction of cardiac anomalies reaches 31.75% in the North, and anastomosis registers 32% in the Northeast. Cardio resection presents 22.73% in the North and cardiac plastic surgery 18.76% in the South. The Northeast stands out in mortality 30 days after discharge for anastomosis (48%). However, it is important to emphasize that the analysis considered deaths occurring during hospitalizations associated with the procedures, rather than the underlying cause of death. Therefore, the results reflect in-hospital mortality associated with the hospitalization itself, and not mortality directly attributable to the procedure.</p>
          <p>With respect to financial resources, reimbursement for the procedures performed totaled approximately R$1.9 billion during the study period. While the mean cost per hospitalization showed only modest variation across regions, regional expenditure varied widely, ranging from R$54.73 million to R$818.89 million, reflecting differences in service volume among regions.</p>
        </sec>
      </sec>
      <sec sec-type="discussion">
        <title>Discussion</title>
        <p>Based on data from the iCardio dashboard for 2019, we conducted an analysis of regional variations in cardiovascular care, aiming to deepen the understanding of inequalities in cardiovascular assistance in Brazil; the results are summarized in the <xref ref-type="fig" rid="f01001">Central Illustration</xref>. Given the country&#8217;s vast territory and high heterogeneity in natural, economic, and social conditions across its regions,<sup><xref ref-type="bibr" rid="B13">13</xref></sup> this type of evaluation is particularly relevant. The Unified Health System (SUS) has sought to reduce health disparities between regions and to improve coverage and access nationwide. Nevertheless, substantial variations persist in infrastructure, human resources, management capacity, and access to effective healthcare services.<sup><xref ref-type="bibr" rid="B14">14</xref></sup> Thus, it is essential to consider how these regional specificities influence health indicators.</p>
        <p>Considering the country&#8217;s overall clinical profile, comparative studies between pediatric and adult patients reinforce the data presented. The literature indicates that the mortality rate in HCC procedures and surgeries is significantly higher in children<sup><xref ref-type="bibr" rid="B15">15</xref></sup> when compared to adults undergoing similar procedures. Furthermore, analysis of international databases indicates that pediatric mortality is strongly associated with the complexity of the procedure, age, and birth weight,<sup><xref ref-type="bibr" rid="B16">16</xref></sup> while in adults, the risk is more influenced by acquired comorbidities and advanced age.<sup><xref ref-type="bibr" rid="B17">17</xref></sup> The difference between sexes is mainly explained by different preoperative risk profiles,<sup><xref ref-type="bibr" rid="B18">18</xref></sup> with mortality in women being more associated with systemic diseases and in men with direct cardiac damage. In addition, there are reports that some of the symptoms indicative of CVDs tend to be different between men and women, contributing to delays in diagnosis, less access to invasive procedures, and a worse prognosis in HCC surgeries.<sup><xref ref-type="bibr" rid="B19">19</xref>-<xref ref-type="bibr" rid="B21">21</xref></sup> However, further investigations would be necessary to more precisely identify the causes of the differences observed in the present study.</p>
        <p>Overall, the study showed that the North region exhibited less favorable outcomes compared to other regions. Previous analyses of cardiology indicators had already suggested a trend of poorer performance in the North, likely explained by socioeconomic factors.<sup><xref ref-type="bibr" rid="B1">1</xref></sup> Moreover, this region faces limitations regarding the availability and accuracy of health system data.<sup><xref ref-type="bibr" rid="B22">22</xref></sup> Similar to the North, the Northeast also tends to present critical issues in both access to and quality of medical care.<sup><xref ref-type="bibr" rid="B1">1</xref>,<xref ref-type="bibr" rid="B22">22</xref></sup> In the present analysis, the Northeast recorded the highest proportion of ICU (intensive care unit) admissions and the greatest mean patient travel distance. Conversely, the Southeast concentrated the largest number of HCC centers, minimized patient travel, and presented the most favorable health indicators along with the lowest mean hospitalization costs. This may be explained by the efficiency of larger or high-volume hospitals, which are able to dilute fixed costs over a greater number of cases, optimize installed capacity, and achieve more efficient resource utilization.<sup><xref ref-type="bibr" rid="B23">23</xref></sup></p>
        <p>The South and the Center-West stood out for their elevated readmission rates, both within 30 days post-procedure and within 30 days post-discharge. Factors influencing this outcome include the quality of hospital discharge practices, continuity of outpatient care, and treatment adherence. Patient profiles are indeed a critical factor, as comorbidities, advanced age, and a higher prevalence of smoking also contribute to higher readmission rates, greater costs, and increased demand for hospital beds.<sup><xref ref-type="bibr" rid="B24">24</xref></sup> Previous studies have shown that the South has the highest prevalence of smokers compared with other Brazilian regions,<sup><xref ref-type="bibr" rid="B25">25</xref></sup> and it also has the second-oldest population in the country, with 12.1% of residents aged 65 years or older.<sup><xref ref-type="bibr" rid="B26">26</xref></sup> However, these two regions diverge in terms of the proportion of patients treated outside their macro-region: the Center-West showed the highest percentage, whereas the South had the lowest. This territorial division into regions and health macro-regions, as established by SUS, is designed to optimize the planning and integration of healthcare actions and services, ensuring that resources and population needs are considered in a regionalized manner.<sup><xref ref-type="bibr" rid="B27">27</xref>,<xref ref-type="bibr" rid="B28">28</xref></sup></p>
        <p>The national analysis of iCardio data also revealed variations in patient travel between regions and macro-regions according to procedure type. Interventional cardiology procedures were generally performed within the patient&#8217;s own region or macro-region, suggesting a broader distribution of this type of service. In contrast, cardiovascular surgeries more frequently required patients to travel outside their region or macro-region, indicating that the locally installed capacity was often insufficient to meet the demand for more complex care.</p>
        <p>The analysis of economic indicators revealed significant differences in total expenditure between regions, primarily related to the volume of hospitalizations, given that no relevant variations were observed in the average cost per hospitalization between them. This pattern is consistent with the SUS (Brazilian Unified Health System) financing logic, in which most hospital production is reimbursed for procedures with values previously defined in a table, which tends to equalize the average cost per hospitalization regionally, even though there are differences in the profile of cases and the complexity of care. Thus, total expenditure reflects, above all, population size<sup><xref ref-type="bibr" rid="B29">29</xref></sup> and disease burden.<sup><xref ref-type="bibr" rid="B30">30</xref></sup> In addition, another influencing factor is installed capacity, including the supply of beds and high-complexity services that act as referral centers and concentrate hospitalizations from other regions.<sup><xref ref-type="bibr" rid="B5">5</xref></sup> The high average travel distance for care in the Northeast region may explain why, despite being the second most populous region, it is not the second with the highest total expenditure.</p>
        <p>The real-world data analyzed in this study were made available through a Business Intelligence (BI) platform designed to facilitate data analysis and evidence-based decision-making in public health by providing more accessible and reliable information. The use of real-world evidence has gained prominence since 2007, following the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) report, which encouraged its application in health technology assessment and in supporting both clinical and policy decisions.<sup><xref ref-type="bibr" rid="B31">31</xref></sup> Regulatory agencies such as the U.S. Food and Drug Administration (FDA) and the Brazilian Health Regulatory Agency (ANVISA) have since endorsed the use of real-world data.<sup><xref ref-type="bibr" rid="B32">32</xref>,<xref ref-type="bibr" rid="B33">33</xref></sup> In Brazil, DATASUS manages national information systems such as SIH and SIM, both used in this study, while SIGTAP supports logistical and managerial qualification of information.<sup><xref ref-type="bibr" rid="B34">34</xref></sup></p>
        <p>Several studies have already demonstrated the potential of BI tools in healthcare, with applications in pulmonology, cardiology, and neurology, where automated dashboards have been used to analyze therapy effectiveness, mortality rates, and economic impact.<sup><xref ref-type="bibr" rid="B35">35</xref>-<xref ref-type="bibr" rid="B38">38</xref></sup> Notable examples include the interactive dashboard developed by Johns Hopkins University<sup><xref ref-type="bibr" rid="B39">39</xref></sup> and the Brazilian Ministry of Health&#8217;s COVID-19 monitoring dashboard.<sup><xref ref-type="bibr" rid="B40">40</xref></sup> These initiatives reinforce the value of BI-based platforms, not only for responding to public health emergencies but also for the continuous management of healthcare services, highlighting the importance of systematically using real-world data to generate evidence for planning and decision-making.</p>
        <p>The SUS information systems were created for administrative purposes and are characterized by the fragmentation of information. DATASUS and other transparency initiatives provide public access to the content of each system separately, without an identifier that allows linkage between health events and overall mortality at the patient level. In this context, iCardio, using data linked across systems,<sup><xref ref-type="bibr" rid="B41">41</xref></sup> is able to provide, within a single environment, pre-calculated and stratifiable clinical-epidemiological, care-related, geographic, and economic indicators.</p>
        <p>Thus, the use of iCardio has significant potential to support clinical decision-making and public policies. Although in this study the analysis was conducted at the regional level, the panel also allows evaluations at the institutional level, enabling the monitoring of service performance, the identification of unjustified variability, and support for quality improvement initiatives. In this way, the tool can guide the organization of care networks, the definition of referral centers, and the allocation of resources, contributing to more informed, efficient, and equitable decisions in the SUS.</p>
        <p>Although the iCardio dashboard covers data from both 2019 and 2020, only the 2019 dataset was analyzed here, since the COVID-19 pandemic profoundly altered hospitalization patterns and resource utilization, which could distort the analysis and compromise regional comparability. Finally, some limitations of this study must be acknowledged. First, the iCardio platform has not yet been integrated into official systems and is available only in Portuguese. Furthermore, the analysis relies on secondary SUS data (SIH and SIM), which are subject to underreporting and inconsistencies; in the main analysis, the data were evaluated in aggregate, without stratification by specific procedure type (e.g., angioplasty, coronary artery bypass grafting, or valve surgery), and readmission and mortality outcomes were not adjusted for procedure complexity. However, additional analyses stratified by procedure type, sex, and age group were performed and are available in the <xref ref-type="sec" rid="supplen">supplementary material</xref>. Finally, the exclusive use of 2019 data, while avoiding pandemic-related distortions, restricts the conclusions to the pre-COVID-19 period and presents a significant time limitation, as the platform lacks any prospect of being updated, making it impossible to analyze more recent trends.</p>
      </sec>
      <sec sec-type="conclusions">
        <title>Conclusions</title>
        <p>This article illustrates a practical application of automation in the evaluation of cardiovascular care delivery using real-world data, thereby advancing the digital maturity required for leveraging such information. The use of interactive dashboards, such as iCardio, enables the analysis of large datasets to become more accessible, faster, and more intuitive, thus enhancing their applicability for healthcare managers, researchers, and policymakers. This approach holds the potential to transform healthcare management by supporting more evidence-based and assertive decision-making, contributing to the identification of regional disparities, optimization of resources, and improvement of the quality of cardiovascular care nationwide. These findings reinforce the existence of inequities in the provision and utilization of high-complexity services, as previously reported in the literature, and highlight the importance of strategies aimed at promoting greater equity in access to and quality of cardiovascular care across the country.</p>
      </sec>
      <sec id="supplen" sec-type="supplementary-material">
        <title>Supplemental Materials</title>
        <supplementary-material id="suppl01001">
          <label>Supplementary Figure 1</label>
          <media mime-subtype="pdf" mimetype="application" xlink:href="https://minio.scielo.br/documentstore/1678-4170/VhK7qhwZpfKHXT4bN8NgB3x/eb16f5da6b95296a66029397e184eaa808998ea8.pdf"/>
        </supplementary-material>
        <supplementary-material id="suppl02001">
          <label>Supplementary Figure 2</label>
          <media mime-subtype="pdf" mimetype="application" xlink:href="https://minio.scielo.br/documentstore/1678-4170/VhK7qhwZpfKHXT4bN8NgB3x/efe97757c1453d5e7540a6c93afa05925ca33a8c.pdf"/>
        </supplementary-material>
        <supplementary-material id="suppl03001">
          <label>Supplementary Figure 3</label>
          <media mime-subtype="pdf" mimetype="application" xlink:href="https://minio.scielo.br/documentstore/1678-4170/VhK7qhwZpfKHXT4bN8NgB3x/ecbeecf4d3659321b7049eab9a0bb57373ed7a17.pdf"/>
        </supplementary-material>
        <supplementary-material id="suppl04001">
          <label>Supplementary Table</label>
          <media mime-subtype="pdf" mimetype="application" xlink:href="https://minio.scielo.br/documentstore/1678-4170/VhK7qhwZpfKHXT4bN8NgB3x/a4b4ec88da10ccba4e4438d9920c4fa53ff6f8f7.pdf"/>
        </supplementary-material>
      </sec>
    </body>
    <back>
      <fn-group>
        <fn fn-type="other">
          <label>Study Association:</label>
          <p> This study is not associated with any thesis or dissertation work.</p>
        </fn>
        <fn fn-type="other">
          <label>Ethics Approval and Consent to Participate:</label>
          <p> This article does not contain any studies with human participants or animals performed by any of the authors.</p>
        </fn>
        <fn fn-type="other">
          <label>Use of Artificial Intelligence:</label>
          <p> During the preparation of this work, the author(s) used ChatGPT for text translation and spelling correction. After using this tool/service, the author(s) reviewed and edited the content as needed and take full responsibility for the content of the published article.</p>
        </fn>
        <fn fn-type="data-availability" specific-use="data-available">
          <label>Availability of Research Data:</label>
          <p> The content is available at the link <ext-link ext-link-type="uri" xlink:href="https://www.iats.com.br/projects/icardio/">https://www.iats.com.br/projects/icardio/</ext-link> </p>
        </fn>
        <fn fn-type="supplementary-material" id="s2">
          <label>*Supplemental Materials</label>
          <p>For Supplementary Figure 1, please <ext-link ext-link-type="uri" xlink:href="http://abccardiol.org/supplementary-material/2026/12304/2025-0765_AO_Arquivo_suplementar_1.pdf">click here</ext-link>.</p>
          <p>For Supplementary Figure 2, please <ext-link ext-link-type="uri" xlink:href="http://abccardiol.org/supplementary-material/2026/12304/2025-0765_AO_Arquivo_suplementar_2.pdf">click here</ext-link>.</p>
          <p>For Supplementary Figure 3, please <ext-link ext-link-type="uri" xlink:href="http://abccardiol.org/supplementary-material/2026/12304/2025-0765_AO_Arquivo_suplementar_3.pdf">click here</ext-link>.</p>
          <p>For Supplementary Table, <ext-link ext-link-type="uri" xlink:href="http://abccardiol.org/supplementary-material/2026/12304/2025-0765_AO_Tabela_suplementar_1.pdf">please click here</ext-link>.</p>
        </fn>
        <fn fn-type="financial-disclosure">
          <label>Sources of Funding:</label>
          <p> This study was funded by Conselho Nacional de Desenvolvimento Cient&#237;fico e Tecnol&#243;gico (CNPq) and Minist&#233;rio da Sa&#250;de through the CNPq/Decit/SCTIE/MS/DESID/SE/MS &#8220;Economia da Sa&#250;de &#8211; Pesquisas para a Melhoria do Gasto P&#250;blico em Sa&#250;de&#8221; call for proposals (No. 44/2022) and received financial support from the Hospital de Cl&#237;nicas de Porto Alegre (HCPA), Porto Alegre, Brazil (ROR: <ext-link ext-link-type="uri" xlink:href="https://ror.org/010we4y38">https://ror.org/010we4y38</ext-link>).</p>
        </fn>
      </fn-group>
    </back>
  </sub-article>
</article>
