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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">cWDhYLtbcgbtc6nCGNvF54d</article-id>
      <article-id specific-use="scielo-v2" pub-id-type="publisher-id">S0066-782X2024000900209</article-id>
      <article-id pub-id-type="doi">10.36660/abc.20240662</article-id>
      <article-id pub-id-type="other">00209</article-id>
      <article-categories>
        <subj-group subj-group-type="heading">
          <subject>Minieditorial</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>M&#233;todos de Intelig&#234;ncia Artificial para Detec&#231;&#227;o de Fibrila&#231;&#227;o Atrial Assintom&#225;tica. Uma Oportunidade para Novas Abordagens de Preven&#231;&#227;o e o Papel do Olho do M&#233;dico</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="editor">
          <contrib-id contrib-id-type="orcid">0000-0001-5818-5958</contrib-id>
          <name>
            <surname>Darrieux</surname>
            <given-names>Francisco</given-names>
          </name>
          <xref ref-type="aff" rid="aff1">
            <sup>1</sup>
          </xref>
          <xref ref-type="corresp" rid="c1"/>
        </contrib>
        <contrib contrib-type="editor">
          <name>
            <surname>Wu</surname>
            <given-names>Tan Chen</given-names>
          </name>
          <xref ref-type="aff" rid="aff1">
            <sup>1</sup>
          </xref>
        </contrib>
        <aff id="aff1">
          <label>1</label>
          <institution content-type="orgname">Hospital das Cl&#237;nicas da Faculdade de Medicina da Universidade de S&#227;o Paulo</institution>
          <institution content-type="orgdiv1">Instituto do Cora&#231;&#227;o</institution>
          <addr-line>
            <named-content content-type="city">S&#227;o Paulo</named-content>
            <named-content content-type="state">SP</named-content>
          </addr-line>
          <country country="BR">Brasil</country>
          <institution content-type="original">Instituto do Cora&#231;&#227;o do Hospital das Cl&#237;nicas da Faculdade de Medicina da Universidade de S&#227;o Paulo, S&#227;o Paulo, SP &#8211; Brasil</institution>
        </aff>
      </contrib-group>
      <author-notes>
        <corresp id="c1"><label>Correspond&#234;ncia:</label><bold>Francisco Darrieux</bold> &#8226; Instituto do Cora&#231;&#227;o do Hospital das Cl&#237;nicas da Faculdade de Medicina da Universidade de S&#227;o Paulo &#8211; Av. Dr. Eneas de Carvalho Aguiar, 44. CEP <postal-code>05403-000</postal-code>, S&#227;o Paulo, SP &#8211; Brasil E-mail: <email>francisco.darrieux@incor.usp.br</email></corresp>
      </author-notes>
      <pub-date publication-format="electronic" date-type="pub">
        <day>02</day>
        <month>12</month>
        <year>2024</year>
      </pub-date>
      <pub-date publication-format="electronic" date-type="collection">
        <year>2024</year>
      </pub-date>
      <volume>121</volume>
      <issue>9</issue>
      <elocation-id>e20240662</elocation-id>
      <history>
        <date date-type="received">
          <day>08</day>
          <month>10</month>
          <year>2024</year>
        </date>
        <date date-type="rev-recd">
          <day>16</day>
          <month>10</month>
          <year>2024</year>
        </date>
        <date date-type="accepted">
          <day>16</day>
          <month>10</month>
          <year>2024</year>
        </date>
      </history>
      <permissions>
        <license license-type="open-access" xml:lang="en" xlink:href="https://creativecommons.org/licenses/by/4.0/">
          <license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License</license-p>
        </license>
      </permissions>
      <kwd-group xml:lang="pt">
        <title>Palavras-chave</title>
        <kwd>Intelig&#234;ncia Artificial</kwd>
        <kwd>Fibrila&#231;&#227;o Atrial</kwd>
        <kwd>Programa de Triagem</kwd>
        <kwd>An&#225;lise de Risco de Acidente Vascular Cerebral</kwd>
      </kwd-group>
      <counts>
        <fig-count count="0"/>
        <table-count count="0"/>
        <equation-count count="0"/>
        <ref-count count="14"/>
      </counts>
    </article-meta>
  </front>
  <body>
    <p>A fibrila&#231;&#227;o atrial (FA) continua sendo a arritmia card&#237;aca mais comum, associada a um risco cinco vezes maior de acidente vascular cerebral, bem como a um risco maior de insufici&#234;ncia card&#237;aca. O tratamento da FA abrange triagem e diagn&#243;stico, melhora da qualidade de vida por meio do controle da frequ&#234;ncia ou ritmo, redu&#231;&#227;o da morbidade e mortalidade por meio da preven&#231;&#227;o de acidente vascular cerebral e tromboembolismo sist&#234;mico, bem como tratamento de condi&#231;&#245;es associadas. A detec&#231;&#227;o precoce pode mitigar complica&#231;&#245;es por meio de interven&#231;&#227;o imediata. Infelizmente, a FA geralmente n&#227;o &#233; reconhecida e tratada porque &#233; frequentemente assintom&#225;tica ou minimamente sintom&#225;tica e frequentemente parox&#237;stica.</p>
    <p>As diretrizes do CCS/CHRS e da ESC recomendam o rastreamento oportunista para FA em pessoas com idade &#8805; 65 anos no momento de um encontro m&#233;dico n&#227;o relacionado. A justificativa para essas recomenda&#231;&#245;es &#233; baseada em uma combina&#231;&#227;o de efic&#225;cia (a taxa de detec&#231;&#227;o de FA para pessoas &#8805; 65 anos foi relatada como 1,44% (intervalo de confian&#231;a [IC] de 95%, 1,13%-1,82%] vs. 0,41% [IC de 95%, 0,31%-0,53%] em pessoas &lt; 65 anos de idade) e os resultados acion&#225;veis (ou seja, pacientes &#8805; 65 anos de idade com FA t&#234;m indica&#231;&#227;o de terapia de anticoagula&#231;&#227;o oral [ACO] para preven&#231;&#227;o de AVC).<sup><xref ref-type="bibr" rid="B1">1</xref></sup> Al&#233;m disso, as diretrizes da ESC recomendam triagem sistem&#225;tica para pacientes com maior risco de AVC (escores CHA2DS2-VA &#8805; 2), amplamente baseadas no aumento de 4 vezes na detec&#231;&#227;o de FA no estudo <italic>Systematic ECG Screening for Atrial Fibrillation Among 75- Year-Old Subjects in the Region of Stockholm and Halland,</italic> Su&#233;cia (STROKESTOP), no qual nova FA foi detectada em 3,0% (IC de 95%, 2,7%-3,5%) dos pacientes que participaram de um programa de triagem de 2 semanas em compara&#231;&#227;o com um eletrocardiograma (ECG) de &#237;ndice sozinho.<sup><xref ref-type="bibr" rid="B2">2</xref></sup></p>
    <p>Embora a estrat&#233;gia de triagem mais comum seja a palpa&#231;&#227;o oportunista do pulso, <italic>smartwatches</italic> habilitados por algoritmos de intelig&#234;ncia artificial (IA) surgiram como ferramentas promissoras para a detec&#231;&#227;o precoce de FA devido ao seu uso generalizado, facilidade de uso e potencial custo-efetividade. Estudos pragm&#225;ticos em larga escala que foram conduzidos para grandes empresas de <italic>smartwatches</italic> como Apple e Fitbit avaliaram a capacidade de triagem de FA na popula&#231;&#227;o em geral com base na fotopletismografia. O Apple Heart Study avaliou mais de 400.000 indiv&#237;duos (idade m&#233;dia de 41 &#177; 13 anos, 42% mulheres) para notifica&#231;&#227;o de pulso irregular (NPI), com o Apple Watch indicando poss&#237;vel FA mais um patch de ECG de deriva&#231;&#227;o &#250;nica para diagnosticar FA.<sup><xref ref-type="bibr" rid="B3">3</xref></sup> No geral, apenas 0,52% da popula&#231;&#227;o do estudo recebeu uma NPI usando o algoritmo da Apple, com a porcentagem correlacionada com o aumento da idade (&#8805;65 anos: NPI 3,2%). Da mesma forma, o <italic>Fitbit Heart Study</italic> avaliou o desempenho do Fitbit Watch entre mais de 400.000 participantes (idade m&#233;dia de 47 anos, intervalo interquartil de 35 a 58 anos, 71% mulheres), dos quais 1% recebeu uma NPI (&#8805;65 anos: NPI 3,6%).<sup><xref ref-type="bibr" rid="B4">4</xref></sup> Daqueles que receberam uma NPI nos estudos Fitbit e Apple Heart, apenas at&#233; 25% de todos os participantes notificados nesses estudos retornaram o patch de ECG, e 32,2% e 34,0% dos casos, respectivamente, foram confirmados como tendo FA com dura&#231;&#227;o de pelo menos 30 s no patch de ECG de refer&#234;ncia. O valor preditivo positivo (VPP) para FA, confirmado simultaneamente no patch de ECG, do algoritmo da Apple foi de 84,0% (IC de 95%, 76,0%&#8211;92,0%). O VPP foi menor entre aqueles com mais de 65 anos, ou seja, 78% (IC de 95%, 64,0%&#8211;92,0%). O algoritmo Fitbit produziu uma sensibilidade de 67,6%, especificidade de 98,4% e PPV de 98,2% (IC de 95%, 95,5%&#8211;99,5%), com uma ligeira redu&#231;&#227;o entre aqueles com idade &#8805;65 anos em 97,0% (IC de 95%, 91,4%&#8211;99,4%). Entre outros estudos conduzidos em ambientes de pesquisa e entre popula&#231;&#245;es de alto risco para FA, o desempenho dos sensores PPG variou com sensibilidade variando de 87,8% (42) a 94,2% (22) e especificidade de at&#233; 99,1%.</p>
    <p>Identificar FA entre pacientes assintom&#225;ticos de alto risco pode levar ao in&#237;cio de ACO, potencialmente reduzindo o risco de AVC. O estudo eBRAVE-AF mostrou que a triagem baseada em PPG mais que dobrou a taxa de detec&#231;&#227;o de FA assintom&#225;tica, levando ao subsequente in&#237;cio de ACO.<sup><xref ref-type="bibr" rid="B5">5</xref></sup> Uma metan&#225;lise recente demonstrou uma redu&#231;&#227;o significativa no risco de AVC ap&#243;s o in&#237;cio de ACO em pacientes com FA assintom&#225;tica detectada por dispositivos card&#237;acos implant&#225;veis.<sup><xref ref-type="bibr" rid="B6">6</xref></sup></p>
    <p>Outros estudos examinaram o desempenho do ECG inteligente (iECG), um ECG de deriva&#231;&#227;o &#250;nica baseado em smartwatch com uma fun&#231;&#227;o de detec&#231;&#227;o autom&#225;tica de FA. Mannhart et al. avaliaram a precis&#227;o de 5 dispositivos inteligentes na identifica&#231;&#227;o de FA em compara&#231;&#227;o com um ECG de 12 deriva&#231;&#245;es interpretado por um m&#233;dico como padr&#227;o de refer&#234;ncia em uma coorte de pacientes do mundo real. Eles encontraram diferen&#231;as no n&#250;mero de tra&#231;ados inconclusivos e conclu&#237;ram que, em um ambiente cl&#237;nico, uma revis&#227;o manual dos tra&#231;ados &#233; necess&#225;ria em cerca de um quarto dos casos.<sup><xref ref-type="bibr" rid="B7">7</xref></sup></p>
    <p>Estudos recentes com um modelo de redes de aprendizado profundo (DNN) identificaram pacientes com alto risco de FA de in&#237;cio recente. Attia et al. demonstraram a capacidade de uma DNN de identificar a assinatura eletrocardiogr&#225;fica de FA parox&#237;stica (PxAF) a partir de ECGs de 12 deriva&#231;&#245;es mostrando ritmo sinusal em uma janela de tempo curta.<sup><xref ref-type="bibr" rid="B8">8</xref></sup> Raghunath et al. usaram tra&#231;ados de ECG digitais de 12 deriva&#231;&#245;es de 430.000 pacientes para treinar DNN para prever FA de in&#237;cio recente (dentro de 1 ano). Em pacientes sem hist&#243;rico de FA que tiveram um AVC relacionado &#224; FA, quase dois ter&#231;os teriam sido previstos como de alto risco para FA antes do AVC pelo modelo de aprendizado profundo.<sup><xref ref-type="bibr" rid="B9">9</xref></sup></p>
    <p>Nos &#250;ltimos anos, uma tecnologia de telemedicina chamada <italic>Stroke Risk Analysis</italic> (SRA) tem analisado um ECG cont&#237;nuo de 1 hora para identificar pacientes com alto risco de PxAF, mesmo que a an&#225;lise do ECG n&#227;o inclua manifesta&#231;&#245;es de FA. O algoritmo foi desenvolvido com base em centenas de conjuntos de dados de pacientes com PxAF e indiv&#237;duos sem hist&#243;rico de FA. Revis&#245;es sistem&#225;ticas concluem que os resultados obtidos ainda s&#227;o insuficientes para a tomada de decis&#227;o cl&#237;nica quanto &#224; validade da SRA para preven&#231;&#227;o secund&#225;ria de AVC, mas n&#227;o excluem a possibilidade de que a t&#233;cnica tenha potencial para detectar FA e, consequentemente, prevenir novos eventos isqu&#234;micos.<sup><xref ref-type="bibr" rid="B10">10</xref>,<xref ref-type="bibr" rid="B11">11</xref></sup> Gomes et al., em uma revis&#227;o sistem&#225;tica, mostraram que h&#225; evid&#234;ncias de que a SRA tem um valor preditivo negativo significativo (entre 96 e 99,1%) para a detec&#231;&#227;o de PxAF, apesar da qualidade dos estudos ser classificada como moderada de acordo com o GRADE. Assim, a SRA pode ser uma ferramenta de pr&#233;-sele&#231;&#227;o com implica&#231;&#245;es para identificar pacientes com maior risco de PxAF como poss&#237;vel causa de AVC que podem se beneficiar do monitoramento card&#237;aco implant&#225;vel.<sup><xref ref-type="bibr" rid="B12">12</xref></sup></p>
    <p>Nesta quest&#227;o, Andrade et al. avaliaram 120 pacientes &#8805; 65 anos com hipertens&#227;o ou insufici&#234;ncia card&#237;aca com maior risco de FA por SRA que continuaram usando iECG (Kardia) por 7 dias para detectar FA. Em um total de 408 pacientes, 13,7% apresentaram epis&#243;dios de FA (3,2% por SRA e 10,5% por Kardia). Eles conclu&#237;ram que a estrat&#233;gia adotada no estudo RITMO foi eficaz e pode ser &#250;til na identifica&#231;&#227;o de epis&#243;dios de FA em pacientes assintom&#225;ticos com fatores de risco para FA, como hipertens&#227;o e insufici&#234;ncia card&#237;aca.<sup><xref ref-type="bibr" rid="B13">13</xref></sup> No entanto, a aus&#234;ncia de um grupo controle e o diagn&#243;stico baseado apenas em IA, n&#227;o confirmado pelos cl&#237;nicos, pode ser preocupante como um farol de uso &#250;nico na pr&#225;tica cl&#237;nica. Considerando que o uso do AliveCor Kardia n&#227;o permite o registro cont&#237;nuo do ECG, mas sim sob demanda (o que foi bem desenhado no estudo), certamente haver&#225; a possibilidade de n&#227;o detec&#231;&#227;o de FA em per&#237;odos fora do registro. Outra quest&#227;o importante no estudo &#233; que ele s&#243; foi capaz de detectar FA nos 7 dias de monitoramento "n&#227;o cont&#237;nuo", n&#227;o excluindo a possibilidade de ocorr&#234;ncia de FA em per&#237;odos posteriores a uma semana.</p>
    <p>O diagn&#243;stico se torna mais desafiador no contexto de epis&#243;dios assintom&#225;ticos, ou FA detectada em dispositivos de monitoramento de longo prazo, particularmente aqueles que n&#227;o fornecem um ECG. Para se proteger contra o diagn&#243;stico inadequado de FA, a diretriz ESC de 2024 publicada recentemente para FA continua a recomendar que a documenta&#231;&#227;o de ECG por ECG padr&#227;o de 12 deriva&#231;&#245;es ou dispositivos de deriva&#231;&#227;o &#250;nica e m&#250;ltipla, confirmada por m&#233;dicos, seja necess&#225;ria para iniciar a estratifica&#231;&#227;o de risco e o gerenciamento de FA.<sup><xref ref-type="bibr" rid="B14">14</xref></sup></p>
  </body>
  <back>
    <fn-group>
      <fn fn-type="other" id="fn1">
        <p>Minieditorial referente ao artigo: Programa para Otimizar a Detec&#231;&#227;o da Fibrila&#231;&#227;o Atrial Parox&#237;stica: Estudo Ritmo</p>
      </fn>
    </fn-group>
    <ref-list>
      <title>Refer&#234;ncias</title>
      <ref id="B1">
        <label>1</label>
        <element-citation publication-type="journal">
          <person-group person-group-type="author">
            <name>
              <surname>Cheung</surname>
              <given-names>CC</given-names>
            </name>
            <name>
              <surname>Nattel</surname>
              <given-names>S</given-names>
            </name>
            <name>
              <surname>Macle</surname>
              <given-names>L</given-names>
            </name>
            <name>
              <surname>Andrade</surname>
              <given-names>JG</given-names>
            </name>
          </person-group>
          <article-title>Management of Atrial Fibrillation in 2021: An Updated Comparison of the Current CCS/CHRS, ESC, and AHA/ACC/HRS Guidelines</article-title>
          <source>Can J Cardiol</source>
          <year>2021</year>
          <volume>37</volume>
          <issue>10</issue>
          <fpage>1607</fpage>
          <lpage>1618</lpage>
          <pub-id pub-id-type="doi">10.1016/j.cjca.2021.06.011</pub-id>
        </element-citation>
        <mixed-citation>1 Cheung CC, Nattel S, Macle L, Andrade JG. Management of Atrial Fibrillation in 2021: An Updated Comparison of the Current CCS/CHRS, ESC, and AHA/ACC/HRS Guidelines. Can J Cardiol. 2021;37(10):1607-18. doi: 10.1016/j.cjca.2021.06.011.</mixed-citation>
      </ref>
      <ref id="B2">
        <label>2</label>
        <element-citation publication-type="journal">
          <person-group person-group-type="author">
            <name>
              <surname>Svennberg</surname>
              <given-names>E</given-names>
            </name>
            <name>
              <surname>Friberg</surname>
              <given-names>L</given-names>
            </name>
            <name>
              <surname>Frykman</surname>
              <given-names>V</given-names>
            </name>
            <name>
              <surname>Al-Khalili</surname>
              <given-names>F</given-names>
            </name>
            <name>
              <surname>Engdahl</surname>
              <given-names>J</given-names>
            </name>
            <name>
              <surname>Rosenqvist</surname>
              <given-names>M</given-names>
            </name>
          </person-group>
          <article-title>Clinical Outcomes in Systematic Screening for Atrial Fibrillation (STROKESTOP): a Multicentre, Parallel Group, Unmasked, Randomised Controlled Trial</article-title>
          <source>Lancet</source>
          <year>2021</year>
          <volume>398</volume>
          <issue>10310</issue>
          <fpage>1498</fpage>
          <lpage>1506</lpage>
          <pub-id pub-id-type="doi">10.1016/S0140-6736(21)01637-8</pub-id>
        </element-citation>
        <mixed-citation>2 Svennberg E, Friberg L, Frykman V, Al-Khalili F, Engdahl J, Rosenqvist M. Clinical Outcomes in Systematic Screening for Atrial Fibrillation (STROKESTOP): a Multicentre, Parallel Group, Unmasked, Randomised Controlled Trial. Lancet. 2021;398(10310):1498-506. doi: 10.1016/S0140-6736(21)01637-8.</mixed-citation>
      </ref>
      <ref id="B3">
        <label>3</label>
        <element-citation publication-type="journal">
          <person-group person-group-type="author">
            <name>
              <surname>Lubitz</surname>
              <given-names>SA</given-names>
            </name>
            <name>
              <surname>Faranesh</surname>
              <given-names>AZ</given-names>
            </name>
            <name>
              <surname>Selvaggi</surname>
              <given-names>C</given-names>
            </name>
            <name>
              <surname>Atlas</surname>
              <given-names>SJ</given-names>
            </name>
            <name>
              <surname>McManus</surname>
              <given-names>DD</given-names>
            </name>
            <name>
              <surname>Singer</surname>
              <given-names>DE</given-names>
            </name>
            <etal/>
          </person-group>
          <article-title>Detection of Atrial Fibrillation in a Large Population Using Wearable Devices: The Fitbit Heart Study</article-title>
          <source>Circulation</source>
          <year>2022</year>
          <volume>146</volume>
          <issue>19</issue>
          <fpage>1415</fpage>
          <lpage>1424</lpage>
          <pub-id pub-id-type="doi">10.1161/CIRCULATIONAHA.122.060291</pub-id>
        </element-citation>
        <mixed-citation>3 Lubitz SA, Faranesh AZ, Selvaggi C, Atlas SJ, McManus DD, Singer DE, et al. Detection of Atrial Fibrillation in a Large Population Using Wearable Devices: The Fitbit Heart Study. Circulation. 2022;146(19):1415-24. doi: 10.1161/CIRCULATIONAHA.122.060291.</mixed-citation>
      </ref>
      <ref id="B4">
        <label>4</label>
        <element-citation publication-type="journal">
          <person-group person-group-type="author">
            <name>
              <surname>Avram</surname>
              <given-names>R</given-names>
            </name>
            <name>
              <surname>Ramsis</surname>
              <given-names>M</given-names>
            </name>
            <name>
              <surname>Cristal</surname>
              <given-names>AD</given-names>
            </name>
            <name>
              <surname>Nathan</surname>
              <given-names>V</given-names>
            </name>
            <name>
              <surname>Zhu</surname>
              <given-names>L</given-names>
            </name>
            <name>
              <surname>Kim</surname>
              <given-names>J</given-names>
            </name>
            <etal/>
          </person-group>
          <article-title>Validation of an Algorithm for Continuous Monitoring of Atrial Fibrillation Using a Consumer Smartwatch</article-title>
          <source>Heart Rhythm</source>
          <year>2021</year>
          <volume>18</volume>
          <issue>9</issue>
          <fpage>1482</fpage>
          <lpage>1490</lpage>
          <pub-id pub-id-type="doi">10.1016/j.hrthm.2021.03.044</pub-id>
        </element-citation>
        <mixed-citation>4 Avram R, Ramsis M, Cristal AD, Nathan V, Zhu L, Kim J, et al. Validation of an Algorithm for Continuous Monitoring of Atrial Fibrillation Using a Consumer Smartwatch. Heart Rhythm. 2021;18(9):1482-90. doi: 10.1016/j.hrthm.2021.03.044.</mixed-citation>
      </ref>
      <ref id="B5">
        <label>5</label>
        <element-citation publication-type="journal">
          <person-group person-group-type="author">
            <name>
              <surname>Rizas</surname>
              <given-names>KD</given-names>
            </name>
            <name>
              <surname>Freyer</surname>
              <given-names>L</given-names>
            </name>
            <name>
              <surname>Sappler</surname>
              <given-names>N</given-names>
            </name>
            <name>
              <surname>von St&#252;lpnagel</surname>
              <given-names>L</given-names>
            </name>
            <name>
              <surname>Spielbichler</surname>
              <given-names>P</given-names>
            </name>
            <name>
              <surname>Krasniqi</surname>
              <given-names>A</given-names>
            </name>
            <etal/>
          </person-group>
          <article-title>Smartphone-based Screening for Atrial Fibrillation: A Pragmatic Randomized Clinical Trial</article-title>
          <source>Nat Med</source>
          <year>2022</year>
          <volume>28</volume>
          <issue>9</issue>
          <fpage>1823</fpage>
          <lpage>1830</lpage>
          <pub-id pub-id-type="doi">10.1038/s41591-022-01979-w</pub-id>
        </element-citation>
        <mixed-citation>5 Rizas KD, Freyer L, Sappler N, von St&#252;lpnagel L, Spielbichler P, Krasniqi A, et al. Smartphone-based Screening for Atrial Fibrillation: A Pragmatic Randomized Clinical Trial. Nat Med. 2022;28(9):1823-30. doi: 10.1038/s41591-022-01979-w.</mixed-citation>
      </ref>
      <ref id="B6">
        <label>6</label>
        <element-citation publication-type="journal">
          <person-group person-group-type="author">
            <name>
              <surname>McIntyre</surname>
              <given-names>WF</given-names>
            </name>
            <name>
              <surname>Benz</surname>
              <given-names>AP</given-names>
            </name>
            <name>
              <surname>Becher</surname>
              <given-names>N</given-names>
            </name>
            <name>
              <surname>Healey</surname>
              <given-names>JS</given-names>
            </name>
            <name>
              <surname>Granger</surname>
              <given-names>CB</given-names>
            </name>
            <name>
              <surname>Rivard</surname>
              <given-names>L</given-names>
            </name>
            <etal/>
          </person-group>
          <article-title>Direct Oral Anticoagulants for Stroke Prevention in Patients with Device-Detected Atrial Fibrillation: A Study-Level Meta-analysis of the NOAH-AFNET 6 and ARTESiA Trials</article-title>
          <source>Circulation</source>
          <year>2024</year>
          <volume>149</volume>
          <issue>13</issue>
          <fpage>981</fpage>
          <lpage>988</lpage>
          <pub-id pub-id-type="doi">10.1161/CIRCULATIONAHA.123.067512</pub-id>
        </element-citation>
        <mixed-citation>6 McIntyre WF, Benz AP, Becher N, Healey JS, Granger CB, Rivard L, et al. Direct Oral Anticoagulants for Stroke Prevention in Patients with Device-Detected Atrial Fibrillation: A Study-Level Meta-analysis of the NOAH-AFNET 6 and ARTESiA Trials. Circulation. 2024;149(13):981-88. doi: 10.1161/CIRCULATIONAHA.123.067512.</mixed-citation>
      </ref>
      <ref id="B7">
        <label>7</label>
        <element-citation publication-type="journal">
          <person-group person-group-type="author">
            <name>
              <surname>Mannhart</surname>
              <given-names>D</given-names>
            </name>
            <name>
              <surname>Lischer</surname>
              <given-names>M</given-names>
            </name>
            <name>
              <surname>Knecht</surname>
              <given-names>S</given-names>
            </name>
            <name>
              <surname>Lavallaz</surname>
              <given-names>JF</given-names>
            </name>
            <name>
              <surname>Strebel</surname>
              <given-names>I</given-names>
            </name>
            <name>
              <surname>Serban</surname>
              <given-names>T</given-names>
            </name>
            <etal/>
          </person-group>
          <article-title>Clinical Validation of 5 Direct-to-consumer Wearable Smart Devices to Detect Atrial Fibrillation: BASEL Wearable Study</article-title>
          <source>JACC Clin Electrophysiol</source>
          <year>2023</year>
          <volume>9</volume>
          <issue>2</issue>
          <fpage>232</fpage>
          <lpage>242</lpage>
          <pub-id pub-id-type="doi">10.1016/j.jacep.2022.09.011</pub-id>
        </element-citation>
        <mixed-citation>7 Mannhart D, Lischer M, Knecht S, Lavallaz JF, Strebel I, Serban T, et al. Clinical Validation of 5 Direct-to-consumer Wearable Smart Devices to Detect Atrial Fibrillation: BASEL Wearable Study. JACC Clin Electrophysiol. 2023;9(2):232-42. doi: 10.1016/j.jacep.2022.09.011.</mixed-citation>
      </ref>
      <ref id="B8">
        <label>8</label>
        <element-citation publication-type="journal">
          <person-group person-group-type="author">
            <name>
              <surname>Attia</surname>
              <given-names>ZI</given-names>
            </name>
            <name>
              <surname>Noseworthy</surname>
              <given-names>PA</given-names>
            </name>
            <name>
              <surname>Lopez-Jimenez</surname>
              <given-names>F</given-names>
            </name>
            <name>
              <surname>Asirvatham</surname>
              <given-names>SJ</given-names>
            </name>
            <name>
              <surname>Deshmukh</surname>
              <given-names>AJ</given-names>
            </name>
            <name>
              <surname>Gersh</surname>
              <given-names>BJ</given-names>
            </name>
            <etal/>
          </person-group>
          <article-title>An Artificial Intelligence-enabled ECG Algorithm for the Identification of Patients with Atrial Fibrillation During Sinus Rhythm: A Retrospective Analysis of Outcome Prediction</article-title>
          <source>Lancet</source>
          <year>2019</year>
          <volume>394</volume>
          <issue>10201</issue>
          <fpage>861</fpage>
          <lpage>867</lpage>
          <pub-id pub-id-type="doi">10.1016/S0140-6736(19)31721-0</pub-id>
        </element-citation>
        <mixed-citation>8 Attia ZI, Noseworthy PA, Lopez-Jimenez F, Asirvatham SJ, Deshmukh AJ, Gersh BJ, et al. An Artificial Intelligence-enabled ECG Algorithm for the Identification of Patients with Atrial Fibrillation During Sinus Rhythm: A Retrospective Analysis of Outcome Prediction. Lancet. 2019;394(10201):861-7. doi: 10.1016/S0140-6736(19)31721-0.</mixed-citation>
      </ref>
      <ref id="B9">
        <label>9</label>
        <element-citation publication-type="journal">
          <person-group person-group-type="author">
            <name>
              <surname>Raghunath</surname>
              <given-names>S</given-names>
            </name>
            <name>
              <surname>Pfeifer</surname>
              <given-names>JM</given-names>
            </name>
            <name>
              <surname>Ulloa-Cerna</surname>
              <given-names>AE</given-names>
            </name>
            <name>
              <surname>Nemani</surname>
              <given-names>A</given-names>
            </name>
            <name>
              <surname>Carbonati</surname>
              <given-names>T</given-names>
            </name>
            <name>
              <surname>Jing</surname>
              <given-names>L</given-names>
            </name>
            <etal/>
          </person-group>
          <article-title>Deep Neural Networks Can Predict New-onset Atrial Fibrillation from the 12-Lead ECG and Help Identify Those at Risk of Atrial Fibrillation-Related Stroke</article-title>
          <source>Circulation</source>
          <year>2021</year>
          <volume>143</volume>
          <issue>13</issue>
          <fpage>1287</fpage>
          <lpage>1298</lpage>
          <pub-id pub-id-type="doi">10.1161/CIRCULATIONAHA.120.047829</pub-id>
        </element-citation>
        <mixed-citation>9 Raghunath S, Pfeifer JM, Ulloa-Cerna AE, Nemani A, Carbonati T, Jing L, et al. Deep Neural Networks Can Predict New-onset Atrial Fibrillation from the 12-Lead ECG and Help Identify Those at Risk of Atrial Fibrillation-Related Stroke. Circulation. 2021;143(13):1287-98. doi: 10.1161/CIRCULATIONAHA.120.047829.</mixed-citation>
      </ref>
      <ref id="B10">
        <label>10</label>
        <element-citation publication-type="journal">
          <person-group person-group-type="author">
            <name>
              <surname>Kishore</surname>
              <given-names>A</given-names>
            </name>
            <name>
              <surname>Vail</surname>
              <given-names>A</given-names>
            </name>
            <name>
              <surname>Majid</surname>
              <given-names>A</given-names>
            </name>
            <name>
              <surname>Dawson</surname>
              <given-names>J</given-names>
            </name>
            <name>
              <surname>Lees</surname>
              <given-names>KR</given-names>
            </name>
            <name>
              <surname>Tyrrell</surname>
              <given-names>PJ</given-names>
            </name>
            <etal/>
          </person-group>
          <article-title>Detection of Atrial Fibrillation after Ischemic Stroke or Transient Ischemic Attack: A Systematic Review and Meta-analysis</article-title>
          <source>Stroke</source>
          <year>2014</year>
          <volume>45</volume>
          <issue>2</issue>
          <fpage>520</fpage>
          <lpage>526</lpage>
          <pub-id pub-id-type="doi">10.1161/STROKEAHA.113.003433</pub-id>
        </element-citation>
        <mixed-citation>10 Kishore A, Vail A, Majid A, Dawson J, Lees KR, Tyrrell PJ, et al. Detection of Atrial Fibrillation after Ischemic Stroke or Transient Ischemic Attack: A Systematic Review and Meta-analysis. Stroke. 2014;45(2):520-6. doi: 10.1161/STROKEAHA.113.003433.</mixed-citation>
      </ref>
      <ref id="B11">
        <label>11</label>
        <element-citation publication-type="journal">
          <person-group person-group-type="author">
            <name>
              <surname>Korompoki</surname>
              <given-names>E</given-names>
            </name>
            <name>
              <surname>Del Giudice</surname>
              <given-names>A</given-names>
            </name>
            <name>
              <surname>Hillmann</surname>
              <given-names>S</given-names>
            </name>
            <name>
              <surname>Malzahn</surname>
              <given-names>U</given-names>
            </name>
            <name>
              <surname>Gladstone</surname>
              <given-names>DJ</given-names>
            </name>
            <name>
              <surname>Heuschmann</surname>
              <given-names>P</given-names>
            </name>
            <etal/>
          </person-group>
          <article-title>Cardiac Monitoring for Detection of Atrial Fibrillation after TIA: A Systematic Review and Meta-analysis</article-title>
          <source>Int J Stroke</source>
          <year>2017</year>
          <volume>12</volume>
          <issue>1</issue>
          <fpage>33</fpage>
          <lpage>45</lpage>
          <pub-id pub-id-type="doi">10.1177/1747493016669885</pub-id>
        </element-citation>
        <mixed-citation>11 Korompoki E, Del Giudice A, Hillmann S, Malzahn U, Gladstone DJ, Heuschmann P, et al. Cardiac Monitoring for Detection of Atrial Fibrillation after TIA: A Systematic Review and Meta-analysis. Int J Stroke. 2017;12(1):33-45. doi: 10.1177/1747493016669885.</mixed-citation>
      </ref>
      <ref id="B12">
        <label>12</label>
        <element-citation publication-type="journal">
          <person-group person-group-type="author">
            <name>
              <surname>Gomes</surname>
              <given-names>RAF</given-names>
            </name>
            <name>
              <surname>S&#225;</surname>
              <given-names>MPBO</given-names>
            </name>
            <name>
              <surname>Montenegro</surname>
              <given-names>MV</given-names>
            </name>
            <name>
              <surname>Furtado</surname>
              <given-names>LCC</given-names>
            </name>
            <name>
              <surname>Costa</surname>
              <given-names>JHCFD</given-names>
            </name>
            <name>
              <surname>Coutinho</surname>
              <given-names>DB</given-names>
            </name>
            <etal/>
          </person-group>
          <article-title>Is Stroke Risk Analysis (SRA) a Reliable Method for Predicting Atrial Fibrillation? A Systematic Review</article-title>
          <source>PLoS One</source>
          <year>2024</year>
          <volume>19</volume>
          <issue>6</issue>
          <elocation-id>e0305339</elocation-id>
          <pub-id pub-id-type="doi">10.1371/journal.pone.0305339</pub-id>
        </element-citation>
        <mixed-citation>12 Gomes RAF, S&#225; MPBO, Montenegro MV, Furtado LCC, Costa JHCFD, Coutinho DB, et al. Is Stroke Risk Analysis (SRA) a Reliable Method for Predicting Atrial Fibrillation? A Systematic Review. PLoS One. 2024;19(6):e0305339. doi: 10.1371/journal.pone.0305339.</mixed-citation>
      </ref>
      <ref id="B13">
        <label>13</label>
        <element-citation publication-type="journal">
          <person-group person-group-type="author">
            <name>
              <surname>Andrade</surname>
              <given-names>RP</given-names>
            </name>
            <name>
              <surname>Vitorino</surname>
              <given-names>PVO</given-names>
            </name>
            <name>
              <surname>Sousa</surname>
              <given-names>ALL</given-names>
            </name>
            <name>
              <surname>Miranda</surname>
              <given-names>RD</given-names>
            </name>
            <name>
              <surname>Nogueira</surname>
              <given-names>BAA</given-names>
            </name>
            <name>
              <surname>Cest&#225;rio</surname>
              <given-names>EES</given-names>
            </name>
            <etal/>
          </person-group>
          <article-title>A Program to Optimize the Detection of Paroxysmal Atrial Fibrillation: The RITMO Study</article-title>
          <source>Arq Bras Cardiol</source>
          <year>2024</year>
          <volume>121</volume>
          <issue>9</issue>
          <elocation-id>e20240235</elocation-id>
          <comment>
            <ext-link ext-link-type="uri" xlink:href="https://doi.org/10.36660/abc.20240235">https://doi.org/10.36660/abc.20240235</ext-link>
          </comment>
        </element-citation>
        <mixed-citation>13 Andrade RP, Vitorino PVO, Sousa ALL, Miranda RD, Nogueira BAA, Cest&#225;rio EES, et al. A Program to Optimize the Detection of Paroxysmal Atrial Fibrillation: The RITMO Study. Arq Bras Cardiol. 2024; 121(9):e20240235. doi: <ext-link ext-link-type="uri" xlink:href="https://doi.org/10.36660/abc.20240235">https://doi.org/10.36660/abc.20240235</ext-link>.</mixed-citation>
      </ref>
      <ref id="B14">
        <label>14</label>
        <element-citation publication-type="journal">
          <person-group person-group-type="author">
            <name>
              <surname>van Gelder</surname>
              <given-names>IC</given-names>
            </name>
            <name>
              <surname>Rienstra</surname>
              <given-names>M</given-names>
            </name>
            <name>
              <surname>Bunting</surname>
              <given-names>KV</given-names>
            </name>
            <name>
              <surname>Casado-Arroyo</surname>
              <given-names>R</given-names>
            </name>
            <name>
              <surname>Caso</surname>
              <given-names>V</given-names>
            </name>
            <name>
              <surname>Crijns</surname>
              <given-names>HJGM</given-names>
            </name>
            <etal/>
          </person-group>
          <article-title>2024 ESC Guidelines for the Management of Atrial Fibrillation Developed in Collaboration with the European Association for Cardio-thoracic Surgery (EACTS)</article-title>
          <source>Eur Heart J</source>
          <year>2024</year>
          <volume>45</volume>
          <issue>36</issue>
          <fpage>3314</fpage>
          <lpage>3414</lpage>
          <pub-id pub-id-type="doi">10.1093/eurheartj/ehae176</pub-id>
        </element-citation>
        <mixed-citation>14 van Gelder IC, Rienstra M, Bunting KV, Casado-Arroyo R, Caso V, Crijns HJGM, et al. 2024 ESC Guidelines for the Management of Atrial Fibrillation Developed in Collaboration with the European Association for Cardio-thoracic Surgery (EACTS). Eur Heart J. 2024;45(36):3314-414. doi: 10.1093/eurheartj/ehae176.</mixed-citation>
      </ref>
    </ref-list>
  </back>
  <sub-article article-type="translation" id="S1" xml:lang="en">
    <front-stub>
      <article-id pub-id-type="doi">10.36660/abc.20240662i</article-id>
      <article-categories>
        <subj-group subj-group-type="heading">
          <subject>Short Editorial</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>Artificial Intelligence Methods for Detecting Asymptomatic Atrial Fibrillation. An Opportunity for New Prevention Approaches and the Role of the Doctor's Eye</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="editor">
          <contrib-id contrib-id-type="orcid">0000-0001-5818-5958</contrib-id>
          <name>
            <surname>Darrieux</surname>
            <given-names>Francisco</given-names>
          </name>
          <xref ref-type="aff" rid="aff2">
            <sup>1</sup>
          </xref>
          <xref ref-type="corresp" rid="c2"/>
        </contrib>
        <contrib contrib-type="editor">
          <name>
            <surname>Wu</surname>
            <given-names>Tan Chen</given-names>
          </name>
          <xref ref-type="aff" rid="aff2">
            <sup>1</sup>
          </xref>
        </contrib>
        <aff id="aff2">
          <label>1</label>
          <addr-line>
            <named-content content-type="city">S&#227;o Paulo</named-content>
            <named-content content-type="state">SP</named-content>
          </addr-line>
          <country country="BR">Brazil</country>
          <institution content-type="original">Instituto do Cora&#231;&#227;o do Hospital das Cl&#237;nicas da Faculdade de Medicina da Universidade de S&#227;o Paulo, S&#227;o Paulo, SP &#8211; Brazil</institution>
        </aff>
      </contrib-group>
      <author-notes>
        <corresp id="c2"><label>Mailing Address:</label><bold>Francisco Darrieux</bold> &#8226; Instituto do Cora&#231;&#227;o do Hospital das Cl&#237;nicas da Faculdade de Medicina da Universidade de S&#227;o Paulo &#8211; Av. Dr. Eneas de Carvalho Aguiar, 44. Postal Code <postal-code>05403-000</postal-code>, S&#227;o Paulo, SP &#8211; Brazil E-mail: <email>francisco.darrieux@incor.usp.br</email></corresp>
      </author-notes>
      <kwd-group xml:lang="en">
        <title>Keywords</title>
        <kwd>Artificial Intelligence</kwd>
        <kwd>Atrial Fibrillation</kwd>
        <kwd>Screening Program</kwd>
        <kwd>Sroke Risk Analysis</kwd>
      </kwd-group>
    </front-stub>
    <body>
      <p>Atrial fibrillation (AF) remains the most common cardiac arrhythmia, associated with a fivefold increased risk of stroke, as well as an increased risk for heart failure. The management of AF encompasses screening and diagnosis, improvement of quality of life through rate or rhythm control, reduction of morbidity and mortality through prevention of stroke and systemic thromboembolism, as well as treatment of associated conditions. Early detection may mitigate complications through prompt intervention. Unfortunately, AF is often unrecognized and untreated because it is frequently asymptomatic or minimally symptomatic and often paroxysmal.</p>
      <p>Both the CCS/CHRS and ESC guidelines advocate opportunistic screening for AF in people &#8805; 65 years of age at the time of an unrelated medical encounter. The rationale for these recommendations is based on a combination of effectiveness (the AF detection rate for persons &#8805; 65 years has been reported as 1.44% (95% confidence interval [CI], 1.13%-1.82%] vs 0.41% [95% CI, 0.31%-0.53%] in persons &lt; 65 years of age), and the actionable outcomes (ie, patients &#8805; 65 years of age with AF indicate oral anticoagulation [OAC] therapy for stroke prevention)<sup><xref ref-type="bibr" rid="B1">1</xref></sup>. In addition, the ESC guidelines recommend systematic screening for patients with higher risk of stroke (CHA2DS2-VA scores &#8805; 2), largely based on the 4-fold increase in detection of AF in the Systematic ECG Screening for Atrial Fibrillation Among 75- Year-Old Subjects in the Region of Stockholm and Halland, Sweden (STROKESTOP) study, in which new AF was detected in 3.0% (95% CI, 2.7%-3.5%) of patients participating in a 2-week screening program compared with an index electrocardiogram (ECG) alone.<sup><xref ref-type="bibr" rid="B2">2</xref></sup></p>
      <p>Although the most common screening strategy is opportunistic pulse palpation, smartwatches empowered by artificial intelligence (AI) algorithms have emerged as promising tools for early detection of AF due to their widespread use, easiness of use, and potential cost-effectiveness. Large-scale, pragmatic studies that were conducted for major smartwatch companies like Apple and Fitbit assessed the ability to screen for AF in the general population relying on photoplethysmography. The Apple Heart Study evaluated over 400,000 individuals (mean age 41 &#177; 13 years, 42% women) for irregular pulse notification (IPN), with the Apple Watch indicating possible AF plus a single-lead ECG patch to diagnose AF.<sup><xref ref-type="bibr" rid="B3">3</xref></sup> Overall, only 0.52% of the study population received an IPN using the Apple algorithm, with the percentage correlating with increased age (&#8805;65 years: IPN 3.2%). Similarly, the Fitbit Heart Study assessed the performance of the Fitbit Watch among over 400,000 participants (median age 47 years, interquartile range 35&#8211;58 years, 71% women), of whom 1% received an IPN (&#8805;65 years: IPN 3.6%).<sup><xref ref-type="bibr" rid="B4">4</xref></sup> Of those who received an IPN in the Fitbit and Apple Heart studies, only up to 25% of all notified participants in these studies returned the ECG patch, and 32.2% and 34.0% of the cases, respectively, were confirmed to have AF lasting at least 30 s on the reference ECG patch. The positive predictive value (PPV) for AF, confirmed concurrently on the ECG patch, of the Apple algorithm was 84.0% (95% CI, 76.0%&#8211;92.0%). The PPV was lower among those over 65, i.e., 78% (95% CI, 64.0%&#8211;92.0%). The Fitbit algorithm yielded a sensitivity of 67.6%, specificity of 98.4%, and PPV of 98.2% (95% CI, 95.5%&#8211;99.5%), with a slight reduction among those aged &#8805;65 years at 97.0% (95% CI, 91.4%&#8211;99.4%). Among other studies conducted in research settings and among populations at high risk for AF, the performance of PPG sensors varied with sensitivity ranging from 87.8% (42) to 94.2% (22) and specificity up to 99.1%.</p>
      <p>Identifying AF among high-risk, asymptomatic patients could lead to the initiation of OAC, potentially reducing the risk of stroke. The eBRAVE-AF trial showed that PPG-based screening more than doubled the detection rate of asymptomatic AF, leading to subsequent OAC initiation.<sup><xref ref-type="bibr" rid="B5">5</xref></sup> A recent meta-analysis demonstrated a significant reduction in stroke risk after OAC initiation in patients with asymptomatic AF detected by implantable cardiac devices.<sup><xref ref-type="bibr" rid="B6">6</xref></sup></p>
      <p>Other studies examined the performance of intelligent ECG (iECG), a smartwatch-based single-lead ECG with an automatic AF detection function. Mannhart et al. assess the accuracy of 5 smart devices in identifying AF compared with a physician-interpreted 12-lead ECG as the reference standard in a real-world cohort of patients. They found differences in the number of inconclusive tracings and concluded that in a clinical setting, a manual review of tracings is required in about one-fourth of cases.<sup><xref ref-type="bibr" rid="B7">7</xref></sup></p>
      <p>Recent studies with a deep learning networks (DNN) model identified patients at high risk for new-onset AF. Attia et al. demonstrated the ability of a DNN to identify the electrocardiographic signature of paroxysmal AF (PxAF) from 12-lead ECGs showing sinus rhythm in a short time window.<sup><xref ref-type="bibr" rid="B8">8</xref></sup> Raghunath et al. used 12-lead digital ECG traces from 430000 patients to train DNN to predict new-onset AF (within 1 year). In patients with no history of AF who have an AF-related stroke, nearly two-thirds would have been predicted to be high-risk for AF before the stroke by the deep learning model.<sup><xref ref-type="bibr" rid="B9">9</xref></sup></p>
      <p>In recent years, a telemedicine technology called Stroke Risk Analysis (SRA) has analyzed a 1-hour continuous ECG to identify patients at high risk of PxAF, even if the ECG analysis does not include manifestations of AF. The algorithm was developed based on hundreds of datasets of patients with PxAF and individuals with no history of AF. Systematic reviews conclude that the results achieved are still insufficient for clinical decision-making regarding the validity of SRA for secondary stroke prevention but do not exclude the possibility that the technique has the potential to detect AF and, consequently, prevent new ischemic events.<sup><xref ref-type="bibr" rid="B10">10</xref>,<xref ref-type="bibr" rid="B11">11</xref></sup> Gomes et al., in a systematic review, showed that there is evidence that the SRA has a significant negative predictive value (between 96 and 99.1%) for the detection of PxAF despite the quality of the studies being classified as moderate according to GRADE. Thus, SRA can be a pre-selection tool with implications for identifying patients at higher risk of PxAF as a possible cause of stroke who may benefit from implantable cardiac monitoring.<sup><xref ref-type="bibr" rid="B12">12</xref></sup></p>
      <p>In this issue, Andrade et al. evaluated 120 patients &#8805; 65 years with hypertension or heart failure at higher risk of AF by SRA who continued using iECG (Kardia) for 7 days to detect AF. In a total of 408 patients, 13.7% presented episodes of AF (3.2% by SRA and 10.5% by Kardia). They concluded that the strategy adopted in the RITMO study was effective and may be useful in identifying episodes of AF in asymptomatic patients with risk factors for AF, like hypertension and heart failure.<sup><xref ref-type="bibr" rid="B13">13</xref></sup> However, the lack of a control group and the diagnosis based only on AI, not confirmed by clinicians may be of concern as a single-use beacon in clinical practice. Considering that the use of AliveCor Kardia does not allow continuous recording of the ECG but rather on demand (which was well designed in the study), there will certainly be the possibility of non-detection of AF in periods outside of recording. Another important issue in the study is that it was only able to detect AF in the 7 days of "non-continuous" monitoring, not excluding the possibility of AF occurring in periods subsequent to one week.</p>
      <p>Diagnosis becomes more challenging in the context of asymptomatic episodes, or AF detected on longer-term monitoring devices, particularly those that do not provide an ECG. To guard against the inappropriate diagnosis of AF, the recently published 2024 ESC guideline for AF continues to recommend that ECG documentation by standard 12-lead ECG or single- and multiple-lead devices, confirmed by physicians, is required to initiate risk stratification and AF management.<sup><xref ref-type="bibr" rid="B14">14</xref></sup></p>
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          <p>Short Editorial related to the article: A Program to Optimize the Detection of Paroxysmal Atrial Fibrillation: The RITMO Study</p>
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