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<article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" article-type="research-article" dtd-version="1.0" specific-use="sps-1.6" xml:lang="en">
  <front>
    <journal-meta>
      <journal-id journal-id-type="publisher-id">ijcs</journal-id>
      <journal-title-group>
        <journal-title>International Journal of Cardiovascular Sciences</journal-title>
        <abbrev-journal-title abbrev-type="publisher">Int. J. Cardiovasc. Sci.</abbrev-journal-title>
      </journal-title-group>
      <issn pub-type="ppub">2359-4802</issn>
      <issn pub-type="epub">2359-5647</issn>
      <publisher>
        <publisher-name>Sociedade Brasileira de Cardiologia</publisher-name>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="publisher-id" specific-use="scielo-v3">ZNf6p93WQfLZghDVGtHbGhc</article-id>
      <article-id pub-id-type="publisher-id" specific-use="scielo-v2">S2359-56472017000100032</article-id>
      <article-id pub-id-type="doi">10.5935/2359-4802.20170017</article-id>
      <article-categories>
        <subj-group subj-group-type="heading">
          <subject>Original Articles</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>The Relationship Between Gensini Score and In-Hospital Mortality in Patients with ST-Segment Elevation Myocardial Infarction</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <name>
            <surname>Yildirim</surname>
            <given-names>Erkan</given-names>
          </name>
          <xref ref-type="aff" rid="aff1">1</xref>
          <xref ref-type="corresp" rid="c1"/>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Iyisoy</surname>
            <given-names>Atila</given-names>
          </name>
          <xref ref-type="aff" rid="aff1">1</xref>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Celik</surname>
            <given-names>Murat</given-names>
          </name>
          <xref ref-type="aff" rid="aff1">1</xref>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Yuksel</surname>
            <given-names>Uygar Cagdas</given-names>
          </name>
          <xref ref-type="aff" rid="aff1">1</xref>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Acikel</surname>
            <given-names>Cengizhan</given-names>
          </name>
          <xref ref-type="aff" rid="aff1">1</xref>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Bugan</surname>
            <given-names>Baris</given-names>
          </name>
          <xref ref-type="aff" rid="aff2">2</xref>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Gokoglan</surname>
            <given-names>Yalcin</given-names>
          </name>
          <xref ref-type="aff" rid="aff1">1</xref>
        </contrib>
      </contrib-group>
      <aff id="aff1">
        <label>1</label>
        <institution content-type="orgname">Academia M&#233;dica Militar Gulhane</institution>
        <institution content-type="orgdiv1">Departamento de Cardiologia</institution>
        <addr-line>
          <named-content content-type="city">Ankara</named-content>
        </addr-line>
        <country country="TR">Turkey</country>
        <institution content-type="original">Academia M&#233;dica Militar Gulhane, Departamento de Cardiologia, Ankara - Turquia</institution>
        <institution content-type="normalized">G&#252;lhane Military Medical Academy</institution>
      </aff>
      <aff id="aff2">
        <label>2</label>
        <institution content-type="orgname">Hospital Militar Girne</institution>
        <country country="TR">Turkey</country>
        <institution content-type="original">Hospital Militar Girne - Turquia</institution>
        <institution content-type="normalized">Military Academy</institution>
      </aff>
      <author-notes>
        <corresp id="c1"><bold>Mailing Address: Erkan Yildirim</bold>. Gata Kardiyoloji Ad. Postal Code: 06010, Etlik, Ankara - Turkey. E-mail: <email>dr_erkanyildirim@yahoo.com.tr</email>; <email>erkan_2157@yahoo.com</email></corresp>
        <fn fn-type="con">
          <p>
            <bold>Author contributions</bold>
          </p>
          <p>Conception and design of the research: Yildirim E, Iyisoy A, Yuksel UC. Acquisition of data: Yildirim E, Celik M, Yuksel UC, Bugan B, Gokoglan Y. Analysis and interpretation of the data: Yildirim E, Iyisoy A, Celik M, Yuksel UC, Bugan B, Gokoglan Y. Statistical analysis: Yildirim E, Celik M, Acikel C. Writing of the manuscript: Celik M, Bugan B. Critical revision of the manuscript for intellectual content: Yildirim E, Iyisoy A, Yuksel UC, Acikel C, Gokoglan Y. Supervision: Iyisoy A.</p>
        </fn>
        <fn fn-type="conflict">
          <p>
            <bold>Potential Conflict of Interest</bold>
          </p>
          <p>No potential conflict of interest relevant to this article was reported.</p>
        </fn>
      </author-notes>
      <pub-date pub-type="epub-ppub">
        <season>Jan-Feb</season>
        <year>2017</year>
      </pub-date>
      <volume>30</volume>
      <issue>1</issue>
      <fpage>32</fpage>
      <lpage>41</lpage>
      <history>
        <date date-type="received">
          <day>17</day>
          <month>05</month>
          <year>2016</year>
        </date>
        <date date-type="rev-recd">
          <day>08</day>
          <month>08</month>
          <year>2016</year>
        </date>
        <date date-type="accepted">
          <day>25</day>
          <month>01</month>
          <year>2017</year>
        </date>
      </history>
      <permissions>
        <license license-type="open-access" xlink:href="http://creativecommons.org/licenses/by/4.0/" xml:lang="en">
          <license-p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p>
        </license>
      </permissions>
      <abstract>
        <title>Abstract</title>
        <sec>
          <title>Background:</title>
          <p>To date, several validated patient-based risk scores have been established to predict mortality and morbidity in patients presenting with ST-segment elevation myocardial infarction (STEMI). The Gensini score was originally developed to quantify the severity of coronary artery disease (CAD).</p>
        </sec>
        <sec>
          <title>Objectives:</title>
          <p>We intend to assess the association between severity of CAD assessed by Gensini score and in-hospital mortality in patients with STEMI undergoing primary percutaneous coronary intervention (P-PCI).</p>
        </sec>
        <sec>
          <title>Methods:</title>
          <p>A total of 539 patients presenting with acute STEMI, who underwent P-PCI within the first 12 hours from the onset of symptoms, were included. The severity of CAD was expressed as the sum of the Gensini scores for each lesion. Patients' demographic variables, medical histories and clinical features, as well as in hospital major adverse events were obtained from the medical reports.</p>
        </sec>
        <sec>
          <title>Results:</title>
          <p>Of these 539 patients, 416 (77.2%) were male and mean age was 59.14 &#177; 12.68 years. In-hospital mortality rate was 5.4% (29 patients; 16 men). Mortality rate was 10.5% in female patients and 3.8% in males (P = 0.004). Mean Gensini scores were significantly different in the comparison between patients who survived (54.54 &#177; 26.34) and those who died (80.17 &#177; 26.51) (P = 0.001). The multivariable Cox proportional hazards regression analysis model revealed that the Gensini score (P = 0.037), female gender (P = 0.039), serum urea levels (P = 0.041), uric acid levels (P = 0.008) and LVEF (P = 0.001) were independently associated with in-hospital mortality in patients with STEMI undergoing P-PCI.</p>
        </sec>
        <sec>
          <title>Conclusion:</title>
          <p>The Gensini score is independently associated with in-hospital mortality in STEMI patients treated with P-PCI. Therefore, it might play an important role in risk stratification of STEMI patients.</p>
        </sec>
      </abstract>
      <kwd-group xml:lang="en">
        <title>Keywords:</title>
        <kwd>Myocardial Infarction</kwd>
        <kwd>Hospital Mortality</kwd>
        <kwd>Data Interpretation, Statistical</kwd>
        <kwd>Percutaneous Coronary Intervention</kwd>
      </kwd-group>
    </article-meta>
  </front>
  <body>
    <sec sec-type="intro">
      <title>Introduction</title>
      <p>Although primary percutaneous coronary intervention (P-PCI) has been contributing to a decrease in mortality in recent years, ST-segment elevation myocardial infarction (STEMI) is still one of the leading cause of mortality and morbidity all over the world.<xref ref-type="bibr" rid="B1">1</xref> In-hospital mortality rates were reported as 7-10% in some registries.<xref ref-type="bibr" rid="B2">2</xref> Currently, several validated risk factors and scoring systems are used to predict mortality in STEMI patients. In the fibrinolytic era, thrombolysis in myocardial infarction (TIMI) risk score was developed as a clinical risk score to predict 30-day mortality in patients with STEMI.<xref ref-type="bibr" rid="B3">3</xref> TIMI score was derived and validated among fibrinolytic-eligible patients enrolled in clinical trials, so it can not be easily applied in STEMI patients undergoing P-PCI. Recently, the Global Registry of Acute Coronary Events (GRACE) score has been defined for predicting mortality at 6 months in patients with acute coronary syndromes (ACS), but critically ill patients such as those who died early may be underrepresented.<xref ref-type="bibr" rid="B4">4</xref> In addition to these clinical scores, some coronary angiography based scoring systems such as Gensini, SYNTAX (s<italic>ynergy between percutaneous coronary intervention with Taxus and cardiac surgery</italic>) and ACC/AHA have been established to assess the severity of lesions and provide some prognostic information for patients with coronary artery disease (CAD). Although these scoring systems provide quantitative evaluation, the valuable detailed information derived from angiography is not sufficiently used. In clinical practice, there is a need for an initial stratification of STEMI patients, which aims to identify those at higher risk and decrease the incidence of major adverse cardiovascular events through more appropriate targeting of preventive measures.</p>
      <p>The Gensini scoring system is an objective method to determine the severity of CAD according to angiographic findings.<xref ref-type="bibr" rid="B5">5</xref> It was originally developed to quantify the severity of CAD; however, subsequent studies have demonstrated its ability to identify patients who are at high risk of adverse events who are treated with PCI.<xref ref-type="bibr" rid="B6">6</xref> However, little is known about the association between the severity of CAD assesed by the Gensini score and in-hospital mortality in patients with STEMI undergoing P-PCI.</p>
    </sec>
    <sec>
      <title>Objective</title>
      <p>The aim of the present study was to evaluate the predictive role of Gensini score to detect in-hospital mortality in STEMI patients treated with P-PCI.</p>
    </sec>
    <sec sec-type="methods">
      <title>Methods</title>
      <p>Our single-center study was retrospective and non-randomized (prospective researching the information, retrospectively). A total of 539 consecutive patients presenting with first acute STEMI who underwent P-PCI within the 12 hours from the onset of symptoms, between 2004 and 2013, were included in our study. Patients` demographic variables, medical histories and clinical features, as well as in hospital major adverse events were obtained from medical reports. Since our institute is a tertiary center, P-PCI was conducted as the preferred reperfusion strategy for most STEMI cases. Briefly, the diagnosis of STEMI was made through the criteria of classical symptoms of coronary ischemia (chest pain lasting &gt; 30 minutes), detection of &gt; 1 mm ST-segment elevation in at least two contiguous leads and elevation in cardiac biomarkers, and defined by the guidelines of the American College of Cardiology and the European Society of Cardiology.<xref ref-type="bibr" rid="B7">7</xref> All STEMI patients were admitted and followed up in intensive coronary care unit and hospital stay was defined as this first admission period of STEMI patients. Previous CABG (coronary artery bypass graft) or PCI, previous STEMI history, reperfusion with thrombolytic drug, known malignancy and severe liver diseases, insufficient data from the clinical recordings and angiographic recordings were all defined as exclusion criteria.</p>
      <p>The severity of CAD was calculated using the Gensini score, in which the calculation is based on the evaluation of the number of stenotic segments along with their respective degrees of luminal narrowing and localization within the coronary tree.<xref ref-type="bibr" rid="B5">5</xref> The severity of the disease was expressed as the sum of the scores for each lesion. Two investigators blinded to the patient's clinical data analyzed the results of the coronary angiography. The study protocol was approved by the local ethics committee (2013-157).</p>
      <sec>
        <title>Statistical analysis</title>
        <p>Data was processed through SPSS 16.0 statistical package (SPSS Inc, Chicago, IL, USA). All data are expressed as numbers and percentages and continuous variables were tested for normal distribution using the Kolmogorov-Smirnov test. Continuous data with normal distribution are presented as mean and standard deviation and compared by the unpaired T-test, and non-normally distributed data are presented as median and interquartile range and compared through the Mann-Whitney U test. Categorical data are presented as counts and percentages and compared using the chi-square test. Logistic regression analysis was performed to identify independent predictors of in-hospital mortality. For the multivariate analysis, a forward stepwise logistic regression was performed using the variables which were significant at the univariate analysis, together with risk factors. P values &lt; 0.05 were considered statistically significant.</p>
      </sec>
    </sec>
    <sec sec-type="results">
      <title>Results</title>
      <p>The study population consisted of 539 patients with first STEMI that underwent P-PCI. Of these 539 patients, 416 (77.2%) were male and 123 (22.8%) were female. Mean age of the study population was 59.14 &#177; 12.68 years. The baseline characteristics are presented in <xref ref-type="table" rid="t1">Table 1</xref> and <xref ref-type="table" rid="t2">Table 2</xref>. In-hospital mortality rate was 5.4% (29 patients; 16 men). Twenty-nine patients (5.3%) died during the hospital stay. Causes of death for these 29 patients were: malignant cardiac arrhythmias such as VT/VF in 8 patients (27.5%), cardiogenic shock in 8 patients (27.5%), acute pulmonary edema in 4 patients (13.7%), right heart failure in 3 patients (10.3%), bleeding complications in 2 patients (6.8%), septic shock in 1 patient (3%), acute mitral regurgitation in 1 patient (3%), ventricular septal rupture in 1 patient (3%), and ventricular free wall rupture in 1 patient (3%). Mortality rate was 10.5% for female patients and 3.8% for males (P = 0.004). Of these 539 patients who underwent P-PCI, 454 (84%) were treated successfully with BMS. From the remaining 85 patients, 18 (4%) were treated with DES, 26 (5%) with plain PTCA, in 18 (3%) patients the procedure failed, and 23 (5%) patients underwent CABG. In-hospital mortality did not differ between the groups on different therapies during P-PCI (P &gt; 0.05).</p>
      <table-wrap id="t1">
        <label>Table 1</label>
        <caption>
          <title>Baseline demographical and clinical features of the groups</title>
        </caption>
        <table frame="hsides" rules="groups">
          <colgroup>
            <col width="25%"/>
            <col width="25%"/>
            <col width="25%"/>
            <col width="25%"/>
          </colgroup>
          <thead>
            <tr>
              <th align="left" style="background-color:#B2B2B2">
							</th>
              <th align="center" style="background-color:#B2B2B2">Survival (n = 510)</th>
              <th align="center" style="background-color:#B2B2B2">Death (n = 29)</th>
              <th align="center" style="background-color:#B2B2B2">p value</th>
            </tr>
          </thead>
          <tbody>
            <tr>
              <td align="left">Age (years)</td>
              <td align="center">58.58 &#177; 12.52</td>
              <td align="center">68.96 &#177; 11.52</td>
              <td align="center">0.001<xref ref-type="table-fn" rid="TFN1">*</xref>
							</td>
            </tr>
            <tr>
              <td align="left">Sex (men), n (%)</td>
              <td align="center">400 (%78.4)</td>
              <td align="center">16 (%55.2)</td>
              <td align="center">0.004</td>
            </tr>
            <tr>
              <td align="left">Smoking, n (%)</td>
              <td align="center">233 (%45.7)</td>
              <td align="center">13 (%44.8)</td>
              <td align="center">0.928</td>
            </tr>
            <tr>
              <td align="left">Heart rate (beats/minute)</td>
              <td align="center">79.90 &#177; 16.99</td>
              <td align="center">86.51 &#177; 24.64</td>
              <td align="center">0.004</td>
            </tr>
            <tr>
              <td align="left">Systolic blood pressure, (mmHg)</td>
              <td align="center">132.23 &#177; 24.96</td>
              <td align="center">116.96 &#177; 24.67</td>
              <td align="center">0.756</td>
            </tr>
            <tr>
              <td align="left">Diastolic blood pressure, (mmHg)</td>
              <td align="center">77.73 &#177; 14.60</td>
              <td align="center">69.29 &#177; 17.43</td>
              <td align="center">0.119</td>
            </tr>
            <tr>
              <td align="left">Diabetes mellitus n (%)</td>
              <td align="center">116 (%22.7)</td>
              <td align="center">10 (%34.5)</td>
              <td align="center">0.146</td>
            </tr>
            <tr>
              <td align="left">Hypertension, n (%)</td>
              <td align="center">218 (%42.8)</td>
              <td align="center">19 (%65.5)</td>
              <td align="center">0.017</td>
            </tr>
            <tr>
              <td align="left">Hyperlipidemia, n (%)</td>
              <td align="center">96 (%18.8)</td>
              <td align="center">5 (%17.29)</td>
              <td align="center">0.832</td>
            </tr>
            <tr>
              <td align="left">Chronic renal disease, n (%)</td>
              <td align="center">18 (%3.5)</td>
              <td align="center">10 (%34.5)</td>
              <td align="center">0.001</td>
            </tr>
            <tr>
              <td align="left">Coronary artery disease, n (%)</td>
              <td align="center">84 (%%16.5)</td>
              <td align="center">6 (%20.7)</td>
              <td align="center">0.553</td>
            </tr>
            <tr>
              <td align="left">Left ventricular ejection fraction, (%)</td>
              <td align="center">50.98 &#177; 8.50</td>
              <td align="center">34.72 &#177; 5.66</td>
              <td align="center">0.028</td>
            </tr>
            <tr>
              <td align="left">Left ventricular end diastolic diameter, (mm)</td>
              <td align="center">48.52 &#177; 5.58</td>
              <td align="center">29.38 &#177; 4.17</td>
              <td align="center">0.436<xref ref-type="table-fn" rid="TFN1">*</xref>
							</td>
            </tr>
          </tbody>
        </table>
        <table-wrap-foot>
          <fn id="TFN1">
            <label>*</label>
            <p>Data without normal distribution compared through Mann-Whitney U test.</p>
          </fn>
        </table-wrap-foot>
      </table-wrap>
      <table-wrap id="t2">
        <label>Table 2</label>
        <caption>
          <title>Laboratory findings of the groups</title>
        </caption>
        <table frame="hsides" rules="groups">
          <colgroup>
            <col width="25%"/>
            <col width="25%"/>
            <col width="25%"/>
            <col width="25%"/>
          </colgroup>
          <thead>
            <tr>
              <th align="left" style="background-color:#B2B2B2">
							</th>
              <th align="center" style="background-color:#B2B2B2">Survival (n = 510)</th>
              <th align="center" style="background-color:#B2B2B2">Death (n = 29)</th>
              <th align="center" style="background-color:#B2B2B2">p value</th>
            </tr>
          </thead>
          <tbody>
            <tr>
              <td align="left">Glucose (mg/dL)</td>
              <td align="center">149.37 &#177; 68.17</td>
              <td align="center">199.42 &#177; 103.91</td>
              <td align="center">0.048<xref ref-type="table-fn" rid="TFN2">*</xref>
							</td>
            </tr>
            <tr>
              <td align="left">Urea (mg/dL)</td>
              <td align="center">38.09 &#177; 17.82</td>
              <td align="center">59.03 &#177; 35.34</td>
              <td align="center">0.001<xref ref-type="table-fn" rid="TFN2">*</xref>
							</td>
            </tr>
            <tr>
              <td align="left">Creatinine (mg/dL)</td>
              <td align="center">1.05 &#177; 0.49</td>
              <td align="center">1.41 &#177; 0.66</td>
              <td align="center">0.001<xref ref-type="table-fn" rid="TFN2">*</xref>
							</td>
            </tr>
            <tr>
              <td align="left">Uric acid (mg/dL)</td>
              <td align="center">5.52 &#177; 2.01</td>
              <td align="center">7.59 &#177; 2.97</td>
              <td align="center">0.002<xref ref-type="table-fn" rid="TFN2">*</xref>
							</td>
            </tr>
            <tr>
              <td align="left">High density lipoprotein, (mg/dL)</td>
              <td align="center">42.13 &#177; 23.01</td>
              <td align="center">40.00 &#177; 7.82</td>
              <td align="center">0.670</td>
            </tr>
            <tr>
              <td align="left">Low density lipoprotein, (mg/dL)</td>
              <td align="center">113.74 &#177; 36.70</td>
              <td align="center">112.95 &#177; 46.49</td>
              <td align="center">0.881<xref ref-type="table-fn" rid="TFN2">*</xref>
							</td>
            </tr>
            <tr>
              <td align="left">Triglycerides (mg/dL)</td>
              <td align="center">140.06 &#177; 100.43</td>
              <td align="center">128.20 &#177; 76.43</td>
              <td align="center">0.569<xref ref-type="table-fn" rid="TFN2">*</xref>
							</td>
            </tr>
            <tr>
              <td align="left">Albumin (g/dL)</td>
              <td align="center">3.40 &#177; 0.54</td>
              <td align="center">3.15 &#177; 0.57</td>
              <td align="center">0.085<xref ref-type="table-fn" rid="TFN2">*</xref>
							</td>
            </tr>
            <tr>
              <td align="left">Leukocytes (n/&#181;L)</td>
              <td align="center">11.695.39 &#177; 4.031.71</td>
              <td align="center">14.646.42 &#177; 7.062.39</td>
              <td align="center">0.017<xref ref-type="table-fn" rid="TFN2">*</xref>
							</td>
            </tr>
            <tr>
              <td align="left">Hemoglobin (g/dL)</td>
              <td align="center">13.77 &#177; 1.97</td>
              <td align="center">12.55 &#177; 2.07</td>
              <td align="center">0.006<xref ref-type="table-fn" rid="TFN2">*</xref>
							</td>
            </tr>
            <tr>
              <td align="left">Hematocrit (%)</td>
              <td align="center">41.19 &#177; 5.18</td>
              <td align="center">37.68 &#177; 5.60</td>
              <td align="center">0.202</td>
            </tr>
            <tr>
              <td align="left">Platelet (x10<sup>3</sup>/&#181;L)</td>
              <td align="center">248.37 &#177; 70.35</td>
              <td align="center">253.25 &#177; 100.67</td>
              <td align="center">0.010</td>
            </tr>
            <tr>
              <td align="left">Neutrophil (n/&#181;L)</td>
              <td align="center">7.953.01 &#177; 3.384.94</td>
              <td align="center">10.045.93 &#177; 6.286.28</td>
              <td align="center">0.032<xref ref-type="table-fn" rid="TFN2">*</xref>
							</td>
            </tr>
            <tr>
              <td align="left">Lymphocyte (n/&#181;L)</td>
              <td align="center">2531.72 &#177; 2362.84</td>
              <td align="center">2407.87 &#177; 2173.31</td>
              <td align="center">0.279<xref ref-type="table-fn" rid="TFN2">*</xref>
							</td>
            </tr>
          </tbody>
        </table>
        <table-wrap-foot>
          <fn id="TFN2">
            <label>*</label>
            <p>Data without normal distribution is compared by Mann-Whitney U test.</p>
          </fn>
        </table-wrap-foot>
      </table-wrap>
      <p>In the initial statistical analysis, we found that some variables significantly differed between patients who survived and those who died. Female gender, history of hypertension, chronic renal disease, age, admission blood glucose, urea, creatinine and uric acid levels, heart rate, leukocytes, hemoglobin, platelet and neutrophil count, and LVEF were found to be statistically significantly different between the two groups. (P &lt; 0.05 for all). Angiographic data is shown in <xref ref-type="table" rid="t3">Table 3</xref>. Mean Gensini scores were significantly different in the comparison between patients who survived (54.54 &#177; 26.34) and those who died (80.17 &#177; 26.51) (P = 0.001) (<xref ref-type="fig" rid="f1">Figure 1</xref>). The infarction-related vessel was the left anterior descending coronary artery (LAD) in 224 patients (41.5%), the circumflex coronary artery (CFX) in 104 patients (19.2%), and the right coronary artery (RCA) in 196 patients (36.3%). In-hospital mortality did not differ between the groups of different infarction-related vessels (P &gt; 0.05).</p>
      <table-wrap id="t3">
        <label>Table 3</label>
        <caption>
          <title>Angiographic data</title>
        </caption>
        <table frame="hsides" rules="groups">
          <colgroup>
            <col width="25%"/>
            <col width="25%"/>
            <col width="25%"/>
            <col width="25%"/>
          </colgroup>
          <thead>
            <tr>
              <th align="left" style="background-color:#B2B2B2">
							</th>
              <th align="center" style="background-color:#B2B2B2">Survival (n = 510)</th>
              <th align="center" style="background-color:#B2B2B2">Death (n = 29)</th>
              <th align="center" style="background-color:#B2B2B2">p value</th>
            </tr>
          </thead>
          <tbody>
            <tr style="border-bottom-width:thin;border-bottom-style:solid">
              <td align="left" style="background-color:#B2B2B2">
                <bold>Coronary artery involvement</bold>
              </td>
              <td align="center" style="background-color:#B2B2B2">
							</td>
              <td align="center" style="background-color:#B2B2B2">
							</td>
              <td align="center" style="background-color:#B2B2B2">
							</td>
            </tr>
            <tr>
              <td align="left">LAD, n (%)</td>
              <td align="center">210 (41.1)</td>
              <td align="center">14 (48.2)</td>
              <td align="center" rowspan="3">0.148</td>
            </tr>
            <tr>
              <td align="left">CFX, n (%)</td>
              <td align="center">104 (20.3)</td>
              <td align="center">5 (17.2)</td>
            </tr>
            <tr>
              <td align="left">RCA, n (%)</td>
              <td align="center">196 (38.4)</td>
              <td align="center">10 (34.4)</td>
            </tr>
            <tr>
              <td align="left">Gensini score</td>
              <td align="center">54.54 &#177; 26.34</td>
              <td align="center">80.17 &#177; 26.51</td>
              <td align="center">0.001</td>
            </tr>
          </tbody>
        </table>
        <table-wrap-foot>
          <fn id="TFN3">
            <p>LAD: left anterior descending coronary artery; CFX: circumflex coronary artery; RCA: right coronary artery</p>
          </fn>
        </table-wrap-foot>
      </table-wrap>
      <p>
        <fig id="f1">
          <label>Figure 1</label>
          <caption>
            <title>Gensini score</title>
          </caption>
          <graphic xlink:href="2359-5647-ijcs-30-01-0032-70017-gf01.jpg"/></fig>
      </p>
      <p>We applied a logistic regression analysis to identify independent risk factors for in-hospital mortality in patients with STEMI undergoing P-PCI. The results of the univariable logistic regression analysis are shown in <xref ref-type="table" rid="t4">Table 4</xref>. For the multivariate analysis, a forward stepwise logistic regression was performed using the variables which were significant at the univariate analysis, together with risk factors. The multivariable Cox proportional hazards regression analysis model revealed that the Gensini score (P = 0.037), female gender (P = 0.039), urea levels (P = 0.041), uric acid levels (P = 0.008) and LVEF (P = 0.001) were independently associated to in-hospital mortality in patients with STEMI undergoing P-PCI (<xref ref-type="table" rid="t5">Table 5</xref>).</p>
      <table-wrap id="t4">
        <label>Table 4</label>
        <caption>
          <title>Results of univariable logistic regression analysis</title>
        </caption>
        <table frame="hsides" rules="groups">
          <colgroup>
            <col width="33%"/>
            <col width="33%"/>
            <col width="33%"/>
          </colgroup>
          <thead>
            <tr>
              <th align="left" style="background-color:#B2B2B2">Variable</th>
              <th align="center" style="background-color:#B2B2B2">OR</th>
              <th align="center" style="background-color:#B2B2B2">p value</th>
            </tr>
          </thead>
          <tbody>
            <tr>
              <td align="left">Female gender</td>
              <td align="center">2.955</td>
              <td align="center">0.005</td>
            </tr>
            <tr>
              <td align="left">Gensini score</td>
              <td align="center">1.029</td>
              <td align="center">0.001</td>
            </tr>
            <tr>
              <td align="left">Urea</td>
              <td align="center">1.026</td>
              <td align="center">0.001</td>
            </tr>
            <tr>
              <td align="left">Uric acid</td>
              <td align="center">1.363</td>
              <td align="center">0.001</td>
            </tr>
            <tr>
              <td align="left">LVEF</td>
              <td align="center">0.797</td>
              <td align="center">0.001</td>
            </tr>
            <tr>
              <td align="left">History of hypertension</td>
              <td align="center">1.007</td>
              <td align="center">0.020</td>
            </tr>
            <tr>
              <td align="left">History of chronic renal disease</td>
              <td align="center">14.36</td>
              <td align="center">0.001</td>
            </tr>
            <tr>
              <td align="left">Age</td>
              <td align="center">1.076</td>
              <td align="center">0.001</td>
            </tr>
            <tr>
              <td align="left">Admission blood glucose</td>
              <td align="center">1.007</td>
              <td align="center">0.001</td>
            </tr>
            <tr>
              <td align="left">Creatinine</td>
              <td align="center">1.811</td>
              <td align="center">0.001</td>
            </tr>
            <tr>
              <td align="left">Uric acid</td>
              <td align="center">1.363</td>
              <td align="center">0.001</td>
            </tr>
            <tr>
              <td align="left">Heart rate</td>
              <td align="center">1.022</td>
              <td align="center">0.048</td>
            </tr>
            <tr>
              <td align="left">Systolic blood pressure</td>
              <td align="center">0.974</td>
              <td align="center">0.002</td>
            </tr>
            <tr>
              <td align="left">Diastolic blood pressure</td>
              <td align="center">0.962</td>
              <td align="center">0.004</td>
            </tr>
            <tr>
              <td align="left">Leucocytes count</td>
              <td align="center">1.000</td>
              <td align="center">0.004</td>
            </tr>
            <tr>
              <td align="left">Neutrophil count</td>
              <td align="center">1.156</td>
              <td align="center">0.001</td>
            </tr>
            <tr>
              <td align="left">Hemoglobin</td>
              <td align="center">0.733</td>
              <td align="center">0.002</td>
            </tr>
            <tr>
              <td align="left">Hematocrit</td>
              <td align="center">0.879</td>
              <td align="center">0.001</td>
            </tr>
            <tr>
              <td align="left">Albumin</td>
              <td align="center">0.424</td>
              <td align="center">0.044</td>
            </tr>
          </tbody>
        </table>
      </table-wrap>
      <table-wrap id="t5">
        <label>Table 5</label>
        <caption>
          <title>Effects of several variables on in-hospital mortality by the multivariable regression analysis</title>
        </caption>
        <table frame="hsides" rules="groups">
          <colgroup>
            <col width="25%"/>
            <col width="25%"/>
            <col width="25%"/>
            <col width="25%"/>
          </colgroup>
          <thead>
            <tr>
              <th align="left" style="background-color:#B2B2B2">Variable</th>
              <th align="center" style="background-color:#B2B2B2">Adjusted OR</th>
              <th align="center" style="background-color:#B2B2B2">95% CI</th>
              <th align="center" style="background-color:#B2B2B2">p value</th>
            </tr>
          </thead>
          <tbody>
            <tr>
              <td align="left">Female gender</td>
              <td align="center">7.780</td>
              <td align="center">1.10-54.66</td>
              <td align="center">0.039</td>
            </tr>
            <tr>
              <td align="left">Gensini score</td>
              <td align="center">1.033</td>
              <td align="center">1.00-1.06</td>
              <td align="center">0.037</td>
            </tr>
            <tr>
              <td align="left">Urea</td>
              <td align="center">1.030</td>
              <td align="center">1.00-1.06</td>
              <td align="center">0.041</td>
            </tr>
            <tr>
              <td align="left">Uric acid</td>
              <td align="center">1.544</td>
              <td align="center">1.11-2.13</td>
              <td align="center">0.008</td>
            </tr>
            <tr>
              <td align="left">LVEF</td>
              <td align="center">0.761</td>
              <td align="center">0.65-0.88</td>
              <td align="center">0.001</td>
            </tr>
          </tbody>
        </table>
        <table-wrap-foot>
          <fn id="TFN4">
            <p>* By entering gender, age, history of hypertension, chronic renal disease, admission blood glucose, urea, creatinine, uric acid, heart rate, SBP, DBP, leukocytes count, neutrophil count, hemoglobin, hematocrit, albumin, LVEF and the Gensini score</p>
          </fn>
        </table-wrap-foot>
      </table-wrap>
      <p>We independently examined 25 randomly chosen angiograms, visually estimated lesion scores, and calculated Gensini scores. The intra-observer and inter-observer correlation coefficient was 0.96 and 0.95 for the Gensini score, indicating good reproducibility and reliability.</p>
    </sec>
    <sec sec-type="discussion">
      <title>Discussion</title>
      <p>The main finding of this study was that the Gensini score is independently associated to in-hospital mortality in STEMI patients treated with P-PCI. Nonetheless, the female gender, higher serum urea and uric acid levels, and lower LVEF were also associated to in-hospital mortality in this group of patients.</p>
      <p>STEMI is the most important part of ACS. Consistently with our data, in-hospital mortality rates ranged between 6% and 14%.<xref ref-type="bibr" rid="B8">8</xref> Many factors such as previous MI, door to balloon time, presence of diabetes, anemia, chronic renal failure, and ejection fraction on admission are known to be predictors of mortality in this era.<xref ref-type="bibr" rid="B9">9</xref> To date, several validated patient-based risk scores such as TIMI, GRACE, ZWOLLE, CADILLAC and PAMI have been established for predicting mortality and morbidity in patients presenting with STEMI.<xref ref-type="bibr" rid="B10">10</xref><sup>-</sup><xref ref-type="bibr" rid="B13">13</xref> Most of these scores were based on clinical parameters such as age, gender, Killip class, serum creatinine levels, and degree of ST-segment change. This is not surprising, as these scores were developed before the widespread adoption of P-PCI for STEMI. TIMI and GRACE scores are valuable tools for initial risk stratification of STEMI patients. TIMI is widely accepted due to its ease of use. Despite its good predictive power, the GRACE score has limited bedside use because of the necessity to use specialized computer software and graphs. These scores were not specifically optimized for patients with STEMI undergoing P-PCI. In the P-PCI era, some of the scoring systems such as CADILLAC (Controlled Abciximab and Device Investigation to Lower Late Angioplasty Complications),<xref ref-type="bibr" rid="B12">12</xref> Stent-PAMI (<italic>Stent-Primary Angioplasty in Myocardial Infarction</italic>),<xref ref-type="bibr" rid="B10">10</xref> and MCRS (<italic>The Mayo Clinic Risk Score)</italic><xref ref-type="bibr" rid="B14">14</xref> included angiographic variables in the scoring algorithm. The CADILLAC risk score was able to accurately predict 30-day and 1-year mortalities after P-PCI in STEMI patients. Baptista et al.<xref ref-type="bibr" rid="B15">15</xref> applied the PAMI risk score to a small population of STEMI patients and revealed that this method was able to predict in-hospital, 30-day and 6-month mortality. The MCRS is also a validated risk model for pre-procedure risk stratification and has been showing a good capacity for the prediction of in-hospital mortality.<xref ref-type="bibr" rid="B11">11</xref> In addition to those scores, which were established from combination of both clinical and angiographic variables, some scoring systems based solely on angiographic data such as SYNTAX are associated to short and long-term follow-up in patients with STEMI undergoing P-PCI.<xref ref-type="bibr" rid="B16">16</xref><sup>-</sup><xref ref-type="bibr" rid="B18">18</xref>
			</p>
      <p>The purpose of coronary scoring systems is to quantify the severity of coronary stenosis. Different coronary arteries carry different volumes of blood to the heart, and coronary scores take this into account. The degree of stenosis was also considered in these scoring systems. Overall, the individual ability of these angiography based scores to predict mortality is uncertain, and an important limitation is that these scores have been largely limited to elective patients.<xref ref-type="bibr" rid="B19">19</xref> Although the SYNTAX score is frequently used for predicting mortality in patients with STEMI undergoing P-PCI, the percent diameter stenosis is not considered in scoring and a distinction is made only between occlusive (100%) and non-occlusive (50-99% stenosis) disease. Furthermore, stenosis is considered severe when it causes &#8805; 50% reduction in the luminal diameter by visual assessment in vessels &#8805; 1.5 mm. However, in the Gensini score, lesions causing &lt; 50% reduction in the luminal diameter in vessels &lt; 1.5 mm diameter are considered in the scoring algorithm. The Gensini score was originally developed to quantify the severity of CAD. It has been widely used in clinical trials to assess the extent and severity of CAD. However, subsequent studies have demonstrated its ability to identify patients treated by PCI who are at high risk of adverse events. Nevertheless, little is known about the correlation between the Gensini score and short-term mortality in STEMI patients.<xref ref-type="bibr" rid="B20">20</xref><sup>-</sup><xref ref-type="bibr" rid="B23">23</xref> There are few recently published studies that have evaluated the Gensini score in the context of P-PCI. In a recent report, the Gensini score was found to be associated with lower MACE during hospital stay and at 6 months after PCI in acute STEMI patients.<xref ref-type="bibr" rid="B24">24</xref> Thus, the authors concluded that it could be used to predict short-term MACE in STEMI patients during the post PCI period. Acet et al.<xref ref-type="bibr" rid="B25">25</xref> found that the TIMI risk index is significantly related to the Gensini score in predicting the extent and severity of CAD in patients with STEMI.</p>
      <p>This study represents the analysis of the relationship between the Gensini score and in-hospital mortality in patients with STEMI undergoing P-PCI. The current study has demonstrated that STEMI patients with higher Gensini scores, regardless of other clinical variables, are at increased risk of in-hospital mortality. According to our study, a 1 point increase in the Gensini score is related to a 3% increase of in-hospital mortality risk in patients with STEMI undergoing P-PCI. Several factors could account for this finding: the Gensini score concentrates on the LMCA, proximal LAD, and mid-LAD arteries, which are assigned relatively higher weighting factors. Occlusion of these coronary arteries causes large areas of infarction which may be related to higher mortality rates. One of the limitations of using the Gensini score for risk stratification is the absence of any clinical variables. This deficiency can be successfully addressed through its combination with clinical-based risk models. In our study, we also found that female gender, higher urea and uric acid levels and lower LVEF were associated to in-hospital mortality in STEMI patients undergoing P-PCI. These findings were consistent with previous studies in literature.<xref ref-type="bibr" rid="B20">20</xref><sup>-</sup><xref ref-type="bibr" rid="B23">23</xref> In the univariate regression analysis, differently from other variables, hemoglobin, hematocrit, albumin and LVEF were found to be negatively correlated to in-hospital mortality, meaning that lower values in these variables were associated to a higher mortality. However, this association was no longer significant after the multivariate analysis, except for LVEF. Mortality rate was 10.5% (13/123) in women and 3.8% (16/416) in men. Higher in-hospital mortality in women is often attributed to a delayed hospital admission, a higher clustering of comorbidities, older age and more bleeding complications after interventions.<xref ref-type="bibr" rid="B26">26</xref><sup>-</sup><xref ref-type="bibr" rid="B28">28</xref> Our study highlights the importance of adjusting mortality to these variables. We did not find a difference in the presence of diabetes, even though others have reported that this tends to be more common.</p>
      <p>The result of our study was an improvement of existing conventional risk factors and scoring systems for STEMI patients. A particular strength of our study is that all patients admitted to hospital with a diagnosis of STEMI were included, unlike reports from some registries, in which only data from selected patients were analysed. Improving the prediction of mortality is a major challenge and selecting the proper therapy is difficult, but we think that the combination of established risk factors such as TIMI and GRACE with angiographic score assessment provides the best information to predict in-hospital mortality in STEMI patients undergoing P-PCI. Readily available risk factors such as hypertension, smoking, diabetes, LDL, family history of MI and clinical variables such as age, gender, door to balloon time and biomarkers should also be considered to improve the prediction of mortality.</p>
      <sec>
        <title>Limitations</title>
        <p>Our study has some important limitations. First and foremost, this study has a retrospective and single-center design. The results of this study may not be extrapolated to all patients such as those who present with other form of ACS or those who were not treated with PCI. The relatively small sample size of the current study indicates the need to validate the findings with a larger patient cohort. Door to balloon time and onset of the infarction are important parameters which may also be associated to in hospital mortality. However, due to our retrospective design and insufficient medical reports, a considerable amount of patient data about the onset of the infarction or door to balloon time were lacking or unreliable. Therefore, these parameters could not be studied. In clinical practice, angiographic scores are calculated by visual lesion assessment (rather than laboratory determination), which would likely lead to greater interobserver variability. Also, we did not compare the Gensini score to other angiographic score systems such as the SYNTAX, which is frequently used to predict mortality in STEMI patients undergoing P-PCI. Further prospective randomized studies with a large sample and attendance of multicentre are required to confirm this hypothesis more effectively.</p>
      </sec>
    </sec>
    <sec sec-type="conclusions">
      <title>Conclusions</title>
      <p>It is clear that there is a clinical necessity for an accurate and useful scoring system to identify patients at a higher risk for in-hospital mortality, who require more intensive care. The Gensini score may have a valuable role in the risk stratification of STEMI patients undergoing P-PCI. However, the Gensini score should be improved through a combination that includes clinical, procedural and laboratory variables.</p>
    </sec>
  </body>
  <back>
    <fn-group>
      <fn fn-type="supported-by">
        <p>
          <bold>Sources of Funding</bold>
        </p>
        <p>There were no external funding sources for this study.</p>
      </fn>
      <fn fn-type="other">
        <p>
          <bold>Study Association</bold>
        </p>
        <p>This article is part of the thesis of Doctoral submitted by Erkan Yildirim, from G&#252;lhane Military Medical Academy.</p>
      </fn>
    </fn-group>
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  <sub-article article-type="translation" id="s1" xml:lang="pt">
    <front-stub>
      <article-categories>
        <subj-group subj-group-type="heading">
          <subject>Artigos Originais</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>Rela&#231;&#227;o entre o Escore de Gensini e Mortalidade Intra-Hospitalar em Pacientes com Infarto do Mioc&#225;rdio com Supradesnivelamento do Segmento ST</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <name>
            <surname>Yildirim</surname>
            <given-names>Erkan</given-names>
          </name>
          <xref ref-type="aff" rid="aff3">1</xref>
          <xref ref-type="corresp" rid="c2"/>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Iyisoy</surname>
            <given-names>Atila</given-names>
          </name>
          <xref ref-type="aff" rid="aff3">1</xref>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Celik</surname>
            <given-names>Murat</given-names>
          </name>
          <xref ref-type="aff" rid="aff3">1</xref>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Yuksel</surname>
            <given-names>Uygar Cagdas</given-names>
          </name>
          <xref ref-type="aff" rid="aff3">1</xref>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Acikel</surname>
            <given-names>Cengizhan</given-names>
          </name>
          <xref ref-type="aff" rid="aff3">1</xref>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Bugan</surname>
            <given-names>Baris</given-names>
          </name>
          <xref ref-type="aff" rid="aff4">2</xref>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Gokoglan</surname>
            <given-names>Yalcin</given-names>
          </name>
          <xref ref-type="aff" rid="aff3">1</xref>
        </contrib>
      </contrib-group>
      <aff id="aff3">
        <label>1</label>
        <institution content-type="original">Academia M&#233;dica Militar Gulhane, Departamento de Cardiologia, Ankara - Turquia</institution>
      </aff>
      <aff id="aff4">
        <label>2</label>
        <institution content-type="original">Hospital Militar Girne - Turquia</institution>
      </aff>
      <author-notes>
        <corresp id="c2"><bold>Correspond&#234;ncia: Erkan Yildirim</bold>. Gata Kardiyoloji Ad. CEP: 06010, Etlik, Ankara - Turquia. E-mail:<email>dr_erkanyildirim@yahoo.com.tr</email>; <email>erkan_2157@yahoo.com</email></corresp>
        <fn fn-type="con">
          <p>
            <bold>Contribui&#231;&#227;o dos autores</bold>
          </p>
          <p>Concep&#231;&#227;o e desenho da pesquisa: Yildirim E, Iyisoy A, Yuksel UC. Obten&#231;&#227;o de dados: Yildirim E, Celik M, Yuksel UC, Bugan B, Gokoglan Y. An&#225;lise e interpreta&#231;&#227;o dos dados: Yildirim E, Iyisoy A, Celik M, Yuksel UC, Bugan B, Gokoglan Y. An&#225;lise estat&#237;stica: Yildirim E, Celik M, Acikel C. Reda&#231;&#227;o do manuscrito: Celik M, Bugan B. Revis&#227;o cr&#237;tica do manuscrito quanto ao conte&#250;do intelectual importante: Yildirim E, Iyisoy A, Yuksel UC, Acikel C, Gokoglan Y. Supervis&#227;o: Iyisoy A.</p>
        </fn>
        <fn fn-type="conflict">
          <p>
            <bold>Potencial Conflito de Interesse</bold>
          </p>
          <p>Declaro n&#227;o haver conflito de interesses pertinentes.</p>
        </fn>
      </author-notes>
      <abstract>
        <title>Resumo</title>
        <sec>
          <title>Fundamento:</title>
          <p>At&#233; o momento, diversos escores de risco baseados em pacientes foram estabelecidos para a predi&#231;&#227;o de mortalidade em pacientes com infarto do mioc&#225;rdio com supradesnivelamento do segmento ST (IAMCSST). O escore de Gensini foi originalmente desenvolvido para quantificar a gravidade de doen&#231;a arterial coronariana (DAC).</p>
        </sec>
        <sec>
          <title>Objetivos:</title>
          <p>Avaliar a associa&#231;&#227;o entre a gravidade de DAC avaliada pelo escore de Gensini e mortalidade intra-hospitalar em pacientes com IAMCSST submetidos &#224; interven&#231;&#227;o coron&#225;ria percut&#226;nea prim&#225;ria (ICP-P).</p>
        </sec>
        <sec>
          <title>M&#233;todos:</title>
          <p>Foram inclu&#237;dos 539 pacientes com IAMCSST, submetidos &#224; ICP-P dentro das primeiras 12 horas do in&#237;cio dos sintomas. A gravidade da DAC foi expressa como a soma do escore de Gensini para cada les&#227;o. Vari&#225;veis demogr&#225;ficas, anamnese e caracter&#237;sticas cl&#237;nicas dos pacientes, bem como eventos hospitalares significativos, foram obtidos de relat&#243;rios m&#233;dicos.</p>
        </sec>
        <sec>
          <title>Resultados:</title>
          <p>Dos 539 pacientes, 416 (77,2%) eram do sexo masculino, e a idade m&#233;dia era 59,14 &#177; 12,68 anos. A taxa de mortalidade intra-hospitalar foi de 5,4% (29 pacientes, 16 homens). A taxa de mortalidade foi de 10,5% em pacientes do sexo feminino e 3,8% em pacientes do sexo masculino (P = 0,004). Os escores de Gensini m&#233;dios mostraram-se significativamente diferentes na compara&#231;&#227;o entre pacientes que sobreviveram (54,54 &#177; 26,34) e aqueles que foram a &#243;bito (80,17 &#177; 26,51) (P = 0,001). O modelo de an&#225;lise multivariada de regress&#227;o de Cox de risco proporcional revelou que o escore de Gensini (P = 0,037), o sexo feminino (P = 0,039), n&#237;veis s&#233;ricos de ureia (P = 0,041), n&#237;veis de &#225;cido &#250;rico (P = 0,008) e FEVE (P = 0,001) estavam independentemente associados &#224; mortalidade intra-hospitalar em pacientes com IAMCSST submetidos &#224; ICP-P.</p>
        </sec>
        <sec>
          <title>Conclus&#245;es:</title>
          <p>O escore de Gensini est&#225; independentemente associado &#224; mortalidade intra-hospitalar em pacientes com IAMCSST tratados com ICP-P. Portanto, o escore pode ter um papel importante na estratifica&#231;&#227;o de risco de pacientes com IAMCSST.</p>
        </sec>
      </abstract>
      <kwd-group xml:lang="pt">
        <title>Palavras-chave:</title>
        <kwd>Infarto do Mioc&#225;rdio</kwd>
        <kwd>Mortalidade Hospitalar</kwd>
        <kwd>Interpreta&#231;&#227;o Estat&#237;stica de Dados</kwd>
        <kwd>Interven&#231;&#227;o Coron&#225;ria Percut&#226;nea</kwd>
      </kwd-group>
    </front-stub>
    <body>
      <sec sec-type="intro">
        <title>Introdu&#231;&#227;o</title>
        <p>Embora interven&#231;&#245;es coron&#225;rias percut&#226;neas prim&#225;rias (ICP-P) venham recentemente contribuindo com a redu&#231;&#227;o da mortalidade, o infarto do mioc&#225;rdio com supradesnivelamento do segmento ST (IAMCSST) ainda &#233; uma das principais causas de mortalidade no mundo.<xref ref-type="bibr" rid="B1">1</xref> Taxas de mortalidade foram relatadas em 7-10% em alguns registros.<xref ref-type="bibr" rid="B2">2</xref> Atualmente, s&#227;o usados diversos fatores de risco e sistemas de escores validados na predi&#231;&#227;o de mortalidade em pacientes com IAMCSST. Na era fibrinol&#237;tica, o escore TIMI (<italic>thrombolysis in myocardial infarction -</italic> risco de tromb&#243;lise no infarto do mioc&#225;rdio) foi desenvolvido como um escore de risco cl&#237;nico para prever uma mortalidade de 30 dias em pacientes com IAMCSST.<xref ref-type="bibr" rid="B3">3</xref> O escore TIMI foi derivado e validado com pacientes eleg&#237;veis para tratamento fibrinol&#237;tico inclu&#237;dos em ensaios cl&#237;nicos; portanto, n&#227;o &#233; facilmente aplicado em pacientes com IAMCSST submetidos a ICP-P. Recentemente, o escore GRACE (<italic>Global Registry of Acute Coronary Events</italic> - Registro Global de Eventos Coron&#225;rios Agudos) foi definido para prever mortalidade de 6 meses em pacientes com s&#237;ndrome coronariana aguda (SCA), mas pacientes em estado cr&#237;tico, como aqueles que foram a &#243;bito precocemente, podem estar sub-representados.<xref ref-type="bibr" rid="B4">4</xref> Al&#233;m desses escores cl&#237;nicos, alguns sistemas de escore baseados em angiografia, como o de Gensini, o SYNTAX (<italic>synergy between percutaneous coronary intervention with Taxus and cardiac surgery -</italic> sinergia entre interven&#231;&#227;o coron&#225;ria percut&#226;nea com Taxus e cirurgia card&#237;aca) e o ACC/AHA (<italic>American College of Cardiology/American Heart Association</italic>) foram estabelecidos para avaliar a gravidade de les&#245;es e proporcionar informa&#231;&#245;es progn&#243;sticas para pacientes com doen&#231;a arterial coronariana (DAC). Embora esses sistemas de escore proporcionem uma avalia&#231;&#227;o quantitativa, as informa&#231;&#245;es detalhadas provenientes de angiografia n&#227;o s&#227;o suficientemente utilizadas. Na pr&#225;tica cl&#237;nica, existe a necessidade de uma estratifica&#231;&#227;o de pacientes com IAMCSST, com o objetivo de identificar aqueles com maior risco e diminuir a incid&#234;ncia de eventos cardiovasculares adversos maiores por meio de medidas preventivas apropriadas.</p>
        <p>O sistema de escore de Gensini &#233; um m&#233;todo objetivo para determinar a gravidade da DAC de acordo com achados angiogr&#225;ficos.<xref ref-type="bibr" rid="B5">5</xref> Foi originalmente desenvolvido para quantificar a gravidade da DAC. Por&#233;m, estudos subsequentes demonstraram sua habilidade para identificar pacientes em alto risco de eventos adversos, tratados com ICP-P.<xref ref-type="bibr" rid="B6">6</xref> Contudo, ainda sabe-se muito pouco sobre a associa&#231;&#227;o entre a gravidade da DAC avaliada pelo escore de Gensini e mortalidade intra-hospitalar em pacientes com IAMCSST submetidos a ICP-P.</p>
      </sec>
      <sec>
        <title>Objetivo</title>
        <p>O objetivo do presente estudo foi avaliar o papel preditivo do escore de Gensini na detec&#231;&#227;o de mortalidade intra-hospitalar em pacientes com IAMCSST tratados com ICP-P.</p>
      </sec>
      <sec sec-type="methods">
        <title>M&#233;todos</title>
        <p>Nosso estudo de centro &#250;nico foi retrospectivo e n&#227;o-randomizado (pesquisa prospectiva de informa&#231;&#245;es, retrospectivamente). Foram inclu&#237;dos em nosso estudo 539 pacientes consecutivos com primeiro IAMCSST submetidos a ICP-P dentro das primeiras 12 horas do in&#237;cio dos sintomas, entre 2004 e 2013. Vari&#225;veis demogr&#225;ficas, anamnese e caracter&#237;sticas cl&#237;nicas dos pacientes, bem como eventos adversos maiores intra-hospitalares foram obtidos de relat&#243;rios m&#233;dicos. J&#225; que nosso instituto &#233; um centro terci&#225;rio, a ICP-P foi a estrat&#233;gia de reperfus&#227;o escolhida para a maioria dos casos de IAMCSST. De forma breve, o diagn&#243;stico de IAMCSST foi feito pelo crit&#233;rio de sintomas cl&#225;ssicos de isquemia coron&#225;ria (dores no peito &gt; 30 minutos), detec&#231;&#227;o de &gt; 1 mm de supradesnivelamento do segmento ST em pelo menos duas deriva&#231;&#245;es cont&#237;nuas e eleva&#231;&#227;o de biomarcadores card&#237;acos, e definido pelas diretrizes da <italic>American College of Cardiology</italic> e da <italic>European Society of Cardiology</italic>.<xref ref-type="bibr" rid="B7">7</xref> Todos os pacientes com IAMCSST foram admitidos e monitorados na unidade coronariana intensiva e o tempo de interna&#231;&#227;o foi definido como o primeiro per&#237;odo de hospitaliza&#231;&#227;o de pacientes com IAMCSST. Crit&#233;rios de exclus&#227;o inclu&#237;ram CRM (cirurgia de revasculariza&#231;&#227;o mioc&#225;rdica) ou ICP pr&#233;via, hist&#243;ria pr&#233;via de IAMCSST, reperfus&#227;o com droga trombol&#237;tica, malignidade conhecida e doen&#231;a hep&#225;tica grave, dados insuficientes de registros cl&#237;nicos e angiogr&#225;ficos.</p>
        <p>A gravidade da DAC foi calculada pelo escore de Gensini, em que o c&#225;lculo &#233; baseado na avalia&#231;&#227;o do n&#250;mero de segmentos esten&#243;ticos e seus respectivos graus de estreitamento luminal e localiza&#231;&#227;o na &#225;rvore coron&#225;ria.<xref ref-type="bibr" rid="B5">5</xref> A gravidade da doen&#231;a foi expressa como a soma dos escores para cada les&#227;o. Dois investigadores, cegos aos dados dos pacientes, analisaram os resultados da angiografia coron&#225;ria. O protocolo do estudo foi aprovado pelo comit&#234; de &#233;tica local (2013-157).</p>
        <sec>
          <title>An&#225;lise estat&#237;stica</title>
          <p>Os dados foram processados pelo SPSS 16.0 (SPSS Inc, Chicago, IL, EUA). Todos os dados est&#227;o expressos em n&#250;meros e porcentagens, e vari&#225;veis cont&#237;nuas foram testadas para distribui&#231;&#227;o normal com o teste de Kolmogorov-Smirnov. Dados cont&#237;nuos com distribui&#231;&#227;o normal est&#227;o apresentados como m&#233;dia e desvio padr&#227;o e comparados pelo teste t n&#227;o pareado, e dados n&#227;o normalmente distribu&#237;dos est&#227;o apresentados como mediana e intervalo interquartil e comparados pelo teste U de Mann-Whitney. Dados categ&#243;ricos est&#227;o apresentados como contagens e porcentagens e comparados com o teste do qui-quadrado. A an&#225;lise de regress&#227;o log&#237;stica foi feita para identificar preditores independentes de mortalidade intra-hospitalar. Para a an&#225;lise multivariada, foi feita uma regress&#227;o log&#237;stica passo a passo (forward stepwise) usando-se as vari&#225;veis que se mostraram significativas na an&#225;lise univariada, juntamente com fatores de risco. Valores de P &lt; 0,05 foram considerados estatisticamente significativos.</p>
        </sec>
      </sec>
      <sec sec-type="results">
        <title>Resultados</title>
        <p>A popula&#231;&#227;o do estudo foi composta por 539 pacientes com primeiro IAMCSST submetidos a ICP-P. Dos 539, 416 (77,2%) eram do sexo masculino e 123 (22,8%) do sexo feminino. A idade m&#233;dia da popula&#231;&#227;o do estudo era de 59,14 &#177; 12,68 anos. As caracter&#237;sticas base est&#227;o apresentadas nas Tabelas<xref ref-type="table" rid="t6">1</xref> e <xref ref-type="table" rid="t7">2</xref>. A taxa de mortalidade intra-hospitalar foi de 5,4% (29 pacientes, 16 homens). Vinte e nove pacientes (5,3%) foram a &#243;bito durante o per&#237;odo de interna&#231;&#227;o. As causas do &#243;bito para esses 29 pacientes foram: arritmias card&#237;acas malignas como taquicardia ventricular e fibrila&#231;&#227;o ventricular em 8 pacientes (27,5%), choque cardiog&#234;nico em 8 pacientes (27,5%), edema pulmonar agudo em 4 pacientes (13,7%), insufici&#234;ncia card&#237;aca direita em 3 pacientes (10,3%), complica&#231;&#245;es hemorr&#225;gicas em 2 pacientes (6,8%), choque s&#233;ptico em 1 paciente (3%), regurgita&#231;&#227;o mitral aguda em 1 paciente (3%), ruptura do septo ventricular em 1 paciente (3%) e ruptura da parede livre ventricular em 1 paciente (3%). A taxa de mortalidade foi de 10,5% em pacientes do sexo feminino e 3,8% em pacientes do sexo masculino (P = 0,004). Dos 539 pacientes submetidos &#224; ICP-P, 454 (84%) foram tratados com &#234;xito com BMS (stent n&#227;o revestido). Dos outros 85 pacientes, 18 (4%) foram tratados com DES (stents eluidores de drogas), 26 (5%) com angioplastia coron&#225;ria transluminal percut&#226;nea (PTCA); em 18 (3%) dos pacientes o procedimento falhou e 23 (5%) pacientes foram submetidos &#224; CRM. A mortalidade intra-hospitalar n&#227;o diferiu entre os grupos tratados com tratamentos diferentes durante a ICP-P (P &gt; 0,05).</p>
        <table-wrap id="t6">
          <label>Tabela 1</label>
          <caption>
            <title>Caracter&#237;sticas cl&#237;nicas e demogr&#225;ficas base dos grupos</title>
          </caption>
          <table frame="hsides" rules="groups">
            <colgroup>
              <col width="25%"/>
              <col width="25%"/>
              <col width="25%"/>
              <col width="25%"/>
            </colgroup>
            <thead>
              <tr>
                <th align="left" style="background-color:#B2B2B2">
								</th>
                <th align="center" style="background-color:#B2B2B2">Sobreviv&#234;ncia (n = 510)</th>
                <th align="center" style="background-color:#B2B2B2">&#211;bito (n = 29)</th>
                <th align="center" style="background-color:#B2B2B2">Valor de p</th>
              </tr>
            </thead>
            <tbody>
              <tr>
                <td align="left">Idade (anos)</td>
                <td align="center">58,58 &#177; 12,52</td>
                <td align="center">68,96 &#177; 11,52</td>
                <td align="center">0,001<xref ref-type="table-fn" rid="TFN5">*</xref>
								</td>
              </tr>
              <tr>
                <td align="left">G&#234;nero (masc.), n (%)</td>
                <td align="center">400 (%78,4)</td>
                <td align="center">16 (%55,2)</td>
                <td align="center">0,004</td>
              </tr>
              <tr>
                <td align="left">Tabagismo, n (%)</td>
                <td align="center">233 (%45,7)</td>
                <td align="center">13 (%44,8)</td>
                <td align="center">0,928</td>
              </tr>
              <tr>
                <td align="left">Frequ&#234;ncia card&#237;aca (bat/minuto)</td>
                <td align="center">79,90 &#177; 16,99</td>
                <td align="center">86,51 &#177; 24,64</td>
                <td align="center">0,004</td>
              </tr>
              <tr>
                <td align="left">Press&#227;o arterial sist&#243;lica, (mmHg)</td>
                <td align="center">132,23 &#177; 24,96</td>
                <td align="center">116,96 &#177; 24,67</td>
                <td align="center">0,756</td>
              </tr>
              <tr>
                <td align="left">Press&#227;o arterial diast&#243;lica, (mmHg)</td>
                <td align="center">77,73 &#177; 14,60</td>
                <td align="center">69,29 &#177; 17,43</td>
                <td align="center">0,119</td>
              </tr>
              <tr>
                <td align="left">Diabetes mellitus n (%)</td>
                <td align="center">116 (%22,7)</td>
                <td align="center">10 (%34,5)</td>
                <td align="center">0,146</td>
              </tr>
              <tr>
                <td align="left">Hipertens&#227;o, n (%)</td>
                <td align="center">218 (%42,8)</td>
                <td align="center">19 (%65,5)</td>
                <td align="center">0,017</td>
              </tr>
              <tr>
                <td align="left">Hiperlipidemia, n (%)</td>
                <td align="center">96 (%18,8)</td>
                <td align="center">5 (%17,29)</td>
                <td align="center">0,832</td>
              </tr>
              <tr>
                <td align="left">Doen&#231;a renal cr&#244;nica, n (%)</td>
                <td align="center">18 (%3,5)</td>
                <td align="center">10 (%34,5)</td>
                <td align="center">0,001</td>
              </tr>
              <tr>
                <td align="left">Doen&#231;a arterial coronariana, n (%)</td>
                <td align="center">84 (%%16,5)</td>
                <td align="center">6 (%20,7)</td>
                <td align="center">0,553</td>
              </tr>
              <tr>
                <td align="left">Fra&#231;&#227;o de eje&#231;&#227;o do ventr&#237;culo esquerdo, (%)</td>
                <td align="center">50,98 &#177; 8,50</td>
                <td align="center">34,72 &#177; 5,66</td>
                <td align="center">0,028</td>
              </tr>
              <tr>
                <td align="left">Di&#226;metro diast&#243;lico final do ventr&#237;culo esquerdo, (mm)</td>
                <td align="center">48,52 &#177; 5,58</td>
                <td align="center">29,38 &#177; 4,17</td>
                <td align="center">0,436<xref ref-type="table-fn" rid="TFN5">*</xref>
								</td>
              </tr>
            </tbody>
          </table>
          <table-wrap-foot>
            <fn id="TFN5">
              <label>*</label>
              <p>Dados sem distribui&#231;&#227;o normal comparados pelo teste U de Mann-Whitney.</p>
            </fn>
          </table-wrap-foot>
        </table-wrap>
        <table-wrap id="t7">
          <label>Tabela 2</label>
          <caption>
            <title>Achados laboratoriais dos grupos</title>
          </caption>
          <table frame="hsides" rules="groups">
            <colgroup>
              <col width="25%"/>
              <col width="25%"/>
              <col width="25%"/>
              <col width="25%"/>
            </colgroup>
            <thead>
              <tr>
                <th align="left" style="background-color:#B2B2B2">
								</th>
                <th align="center" style="background-color:#B2B2B2">Sobreviv&#234;ncia (n = 510)</th>
                <th align="center" style="background-color:#B2B2B2">&#211;bito (n = 29)</th>
                <th align="center" style="background-color:#B2B2B2">Valor de p</th>
              </tr>
            </thead>
            <tbody>
              <tr>
                <td align="left">Glicose (mg/dL)</td>
                <td align="center">149,37 &#177; 68,17</td>
                <td align="center">199,42 &#177; 103,91</td>
                <td align="center">0,048<xref ref-type="table-fn" rid="TFN6">*</xref>
								</td>
              </tr>
              <tr>
                <td align="left">Ureia (mg/dL)</td>
                <td align="center">38,09 &#177; 17,82</td>
                <td align="center">59,03 &#177; 35,34</td>
                <td align="center">0,001<xref ref-type="table-fn" rid="TFN6">*</xref>
								</td>
              </tr>
              <tr>
                <td align="left">Creatinina (mg/dL)</td>
                <td align="center">1,05 &#177; 0,49</td>
                <td align="center">1,41 &#177; 0,66</td>
                <td align="center">0,001<xref ref-type="table-fn" rid="TFN6">*</xref>
								</td>
              </tr>
              <tr>
                <td align="left">&#193;cido &#250;rico (mg/dL)</td>
                <td align="center">5,52 &#177; 2,01</td>
                <td align="center">7,59 &#177; 2,97</td>
                <td align="center">0,002<xref ref-type="table-fn" rid="TFN6">*</xref>
								</td>
              </tr>
              <tr>
                <td align="left">Lipoprote&#237;na de alta densidade, (mg/dL)</td>
                <td align="center">42,13 &#177; 23,01</td>
                <td align="center">40,00 &#177; 7,82</td>
                <td align="center">0,670</td>
              </tr>
              <tr>
                <td align="left">Lipoprote&#237;na de baixa densidade, (mg/dL)</td>
                <td align="center">113,74 &#177; 36,70</td>
                <td align="center">112,95 &#177; 46,49</td>
                <td align="center">0,881<xref ref-type="table-fn" rid="TFN6">*</xref>
								</td>
              </tr>
              <tr>
                <td align="left">Triglic&#233;rides (mg/dL)</td>
                <td align="center">140,06 &#177; 100,43</td>
                <td align="center">128,20 &#177; 76,43</td>
                <td align="center">0,569<xref ref-type="table-fn" rid="TFN6">*</xref>
								</td>
              </tr>
              <tr>
                <td align="left">Albumina (g/dL)</td>
                <td align="center">3,40 &#177; 0,54</td>
                <td align="center">3,15 &#177; 0,57</td>
                <td align="center">0,085<xref ref-type="table-fn" rid="TFN6">*</xref>
								</td>
              </tr>
              <tr>
                <td align="left">Leuc&#243;citos (n/&#181;L)</td>
                <td align="center">11,695,39 &#177; 4,031,71</td>
                <td align="center">14,646,42 &#177; 7,062,39</td>
                <td align="center">0,017<xref ref-type="table-fn" rid="TFN6">*</xref>
								</td>
              </tr>
              <tr>
                <td align="left">Hemoglobina (g/dL)</td>
                <td align="center">13,77 &#177; 1,97</td>
                <td align="center">12,55 &#177; 2,07</td>
                <td align="center">0,006<xref ref-type="table-fn" rid="TFN6">*</xref>
								</td>
              </tr>
              <tr>
                <td align="left">Hemat&#243;critos (%)</td>
                <td align="center">41,19 &#177; 5,18</td>
                <td align="center">37,68 &#177; 5,60</td>
                <td align="center">0,202</td>
              </tr>
              <tr>
                <td align="left">Plaquetas (x10<sup>3</sup>/&#181;L)</td>
                <td align="center">248,37 &#177; 70,35</td>
                <td align="center">253,25 &#177; 100,67</td>
                <td align="center">0,010</td>
              </tr>
              <tr>
                <td align="left">Neutr&#243;filos (n/&#181;L)</td>
                <td align="center">7,953,01 &#177; 3,384,94</td>
                <td align="center">10,045,93 &#177; 6,286,28</td>
                <td align="center">0,032<xref ref-type="table-fn" rid="TFN6">*</xref>
								</td>
              </tr>
              <tr>
                <td align="left">Linf&#243;citos (n/&#181;L)</td>
                <td align="center">2531,72 &#177; 2362,84</td>
                <td align="center">2407,87 &#177; 2173,31</td>
                <td align="center">0,279<xref ref-type="table-fn" rid="TFN6">*</xref>
								</td>
              </tr>
            </tbody>
          </table>
          <table-wrap-foot>
            <fn id="TFN6">
              <label>*</label>
              <p>Dados sem distribui&#231;&#227;o normal comparados pelo teste U de Mann-Whitney.</p>
            </fn>
          </table-wrap-foot>
        </table-wrap>
        <p>Na an&#225;lise estat&#237;stica inicial, vimos que algumas vari&#225;veis diferiram significativamente entre pacientes que sobreviveram e aqueles que foram a &#243;bito. O sexo feminino, hist&#243;ria de hipertens&#227;o, doen&#231;a renal cr&#244;nica, idade, n&#237;veis de glicose sangu&#237;nea na interna&#231;&#227;o, ureia, creatinina e &#225;cido &#250;rico, frequ&#234;ncia card&#237;aca, contagem de leuc&#243;citos, hemoglobina, plaquetas e neutr&#243;filos e FEVE mostraram-se significativamente, do ponto de vista estat&#237;stico, diferentes entre os dois grupos (P &lt; 0,05 para todos). Os dados angiogr&#225;ficos est&#227;o demonstrados na <xref ref-type="table" rid="t8">Tabela 3</xref>. Os escores m&#233;dios de Gensini foram significativamente diferentes na compara&#231;&#227;o entre os pacientes que sobreviveram (54,54 &#177; 26,34) e aqueles que foram a &#243;bito (80,17 &#177; 26,51) (P = 0,001) (<xref ref-type="fig" rid="f2">Figura 1</xref>). O vaso relacionado ao infarto foi a art&#233;ria coron&#225;ria descendente anterior esquerda (DAE) em 224 pacientes (41,5%), a art&#233;ria coron&#225;ria circunflexa (CX) em 104 pacientes (19,2%) e a art&#233;ria coron&#225;ria direita (ACD) em 196 pacientes (36,3%). A mortalidade intra-hospitalar n&#227;o diferiu entre os grupos de diferentes vasos relacionados ao infarto (P &gt; 0,05).</p>
        <table-wrap id="t8">
          <label>Tabela 3</label>
          <caption>
            <title>Dados angiogr&#225;ficos</title>
          </caption>
          <table frame="hsides" rules="groups">
            <colgroup>
              <col width="25%"/>
              <col width="25%"/>
              <col width="25%"/>
              <col width="25%"/>
            </colgroup>
            <thead>
              <tr>
                <th align="left" style="background-color:#B2B2B2">
								</th>
                <th align="center" style="background-color:#B2B2B2">Sobreviv&#234;ncia (n = 510)</th>
                <th align="center" style="background-color:#B2B2B2">&#211;bito (n = 29)</th>
                <th align="center" style="background-color:#B2B2B2">Valor de p</th>
              </tr>
            </thead>
            <tbody>
              <tr style="border-bottom-width:thin;border-bottom-style:solid">
                <td align="left" style="background-color:#B2B2B2">
                  <bold>Envolvimento da art&#233;ria coron&#225;ria</bold>
                </td>
                <td align="center" style="background-color:#B2B2B2">
								</td>
                <td align="center" style="background-color:#B2B2B2">
								</td>
                <td align="center" style="background-color:#B2B2B2">
								</td>
              </tr>
              <tr>
                <td align="left">DAE, n (%)</td>
                <td align="center">210 (41,1)</td>
                <td align="center">14 (48,2)</td>
                <td align="center" rowspan="3">0,148</td>
              </tr>
              <tr>
                <td align="left">CX, n (%)</td>
                <td align="center">104 (20,3)</td>
                <td align="center">5 (17,2)</td>
              </tr>
              <tr>
                <td align="left">ACD, n (%)</td>
                <td align="center">196 (38,4)</td>
                <td align="center">10 (34,4)</td>
              </tr>
              <tr>
                <td align="left">Escore de Gensini</td>
                <td align="center">54,54 &#177; 26,34</td>
                <td align="center">80,17 &#177; 26,51</td>
                <td align="center">0,001</td>
              </tr>
            </tbody>
          </table>
          <table-wrap-foot>
            <fn id="TFN7">
              <p>DAE: art&#233;ria coron&#225;ria descendente anterior esquerda; CX: art&#233;ria coron&#225;ria circunflexa; ACD: art&#233;ria coron&#225;ria direita</p>
            </fn>
          </table-wrap-foot>
        </table-wrap>
        <p>
          <fig id="f2">
            <label>Figura 1</label>
            <caption>
              <title>Gensini score</title>
            </caption>
            <graphic xlink:href="2359-5647-ijcs-30-01-0032-70017-gf01-pt.jpg"/></fig>
        </p>
        <p>Foi aplicada uma an&#225;lise de regress&#227;o log&#237;stica para identificar fatores de risco independentes para mortalidade intra-hospitalar em pacientes com IAMCSST submetidos a ICP-P. Os resultados da an&#225;lise de regress&#227;o log&#237;stica univari&#225;vel est&#227;o demonstrados na <xref ref-type="table" rid="t9">Tabela 4</xref>. Para a an&#225;lise multivariada, foi feita uma regress&#227;o log&#237;stica passo a passo (forward stepwise), utilizando-se as vari&#225;veis que mostraram-se significativas na an&#225;lise univari&#225;vel, juntamente com os fatores de risco. O modelo de an&#225;lise multivariada de regress&#227;o de Cox de risco proporcional revelou que o escore de Gensini (P = 0,037), sexo feminino (P = 0,039), n&#237;veis de ureia (P = 0,041), n&#237;veis de &#225;cido &#250;rico (P = 0,008) e FEVE (P = 0,001) mostraram-se independentemente associados &#224; mortalidade intra-hospitalar em pacientes com IAMCSST submetidos a ICP-P (<xref ref-type="table" rid="t10">Tabela 5</xref>).</p>
        <table-wrap id="t9">
          <label>Tabela 4</label>
          <caption>
            <title>Resultados da an&#225;lise de regress&#227;o log&#237;stica univari&#225;vel</title>
          </caption>
          <table frame="hsides" rules="groups">
            <colgroup>
              <col width="33%"/>
              <col width="33%"/>
              <col width="33%"/>
            </colgroup>
            <thead>
              <tr>
                <th align="left" style="background-color:#B2B2B2">Vari&#225;vel</th>
                <th align="center" style="background-color:#B2B2B2">OR</th>
                <th align="center" style="background-color:#B2B2B2">Valor de p</th>
              </tr>
            </thead>
            <tbody>
              <tr>
                <td align="left">Sexo feminino</td>
                <td align="center">2,955</td>
                <td align="center">0,005</td>
              </tr>
              <tr>
                <td align="left">Escore de Gensini</td>
                <td align="center">1,029</td>
                <td align="center">0,001</td>
              </tr>
              <tr>
                <td align="left">Ureia</td>
                <td align="center">1,026</td>
                <td align="center">0,001</td>
              </tr>
              <tr>
                <td align="left">&#193;cido &#250;rico</td>
                <td align="center">1,363</td>
                <td align="center">0,001</td>
              </tr>
              <tr>
                <td align="left">FEVE</td>
                <td align="center">0,797</td>
                <td align="center">0,001</td>
              </tr>
              <tr>
                <td align="left">Hist&#243;rico de hipertens&#227;o</td>
                <td align="center">1,007</td>
                <td align="center">0,020</td>
              </tr>
              <tr>
                <td align="left">Hist&#243;rico de doen&#231;a renal cr&#244;nica</td>
                <td align="center">14,36</td>
                <td align="center">0,001</td>
              </tr>
              <tr>
                <td align="left">Idade</td>
                <td align="center">1,076</td>
                <td align="center">0,001</td>
              </tr>
              <tr>
                <td align="left">Glicose sangu&#237;nea no momento da interna&#231;&#227;o</td>
                <td align="center">1,007</td>
                <td align="center">0,001</td>
              </tr>
              <tr>
                <td align="left">Creatinina</td>
                <td align="center">1,811</td>
                <td align="center">0,001</td>
              </tr>
              <tr>
                <td align="left">&#193;cido &#250;rico</td>
                <td align="center">1,363</td>
                <td align="center">0,001</td>
              </tr>
              <tr>
                <td align="left">Frequ&#234;ncia card&#237;aca</td>
                <td align="center">1,022</td>
                <td align="center">0,048</td>
              </tr>
              <tr>
                <td align="left">Press&#227;o arterial sist&#243;lica</td>
                <td align="center">0,974</td>
                <td align="center">0,002</td>
              </tr>
              <tr>
                <td align="left">Press&#227;o arterial diast&#243;lica</td>
                <td align="center">0,962</td>
                <td align="center">0,004</td>
              </tr>
              <tr>
                <td align="left">Contagem de leuc&#243;citos</td>
                <td align="center">1,000</td>
                <td align="center">0,004</td>
              </tr>
              <tr>
                <td align="left">Contagem de neutr&#243;filos</td>
                <td align="center">1,156</td>
                <td align="center">0,001</td>
              </tr>
              <tr>
                <td align="left">Hemoglobina</td>
                <td align="center">0,733</td>
                <td align="center">0,002</td>
              </tr>
              <tr>
                <td align="left">Hemat&#243;critos</td>
                <td align="center">0,879</td>
                <td align="center">0,001</td>
              </tr>
              <tr>
                <td align="left">Albumina</td>
                <td align="center">0,424</td>
                <td align="center">0,044</td>
              </tr>
            </tbody>
          </table>
        </table-wrap>
        <table-wrap id="t10">
          <label>Tabela 5</label>
          <caption>
            <title>Efeitos de diversas vari&#225;veis sobre mortalidade intra-hospitalar pela an&#225;lise de regress&#227;o multivari&#225;vel</title>
          </caption>
          <table frame="hsides" rules="groups">
            <colgroup>
              <col width="25%"/>
              <col width="25%"/>
              <col width="25%"/>
              <col width="25%"/>
            </colgroup>
            <thead>
              <tr>
                <th align="left" style="background-color:#B2B2B2">Vari&#225;vel</th>
                <th align="center" style="background-color:#B2B2B2">OR ajustado</th>
                <th align="center" style="background-color:#B2B2B2">95% IC</th>
                <th align="center" style="background-color:#B2B2B2">Valor de p</th>
              </tr>
            </thead>
            <tbody>
              <tr>
                <td align="left">Sexo feminino</td>
                <td align="center">7,780</td>
                <td align="center">1,10-54,66</td>
                <td align="center">0,039</td>
              </tr>
              <tr>
                <td align="left">Escore de Gensini</td>
                <td align="center">1,033</td>
                <td align="center">1,00-1,06</td>
                <td align="center">0,037</td>
              </tr>
              <tr>
                <td align="left">Ureia</td>
                <td align="center">1,030</td>
                <td align="center">1,00-1,06</td>
                <td align="center">0,041</td>
              </tr>
              <tr>
                <td align="left">&#193;cido &#250;rico</td>
                <td align="center">1,544</td>
                <td align="center">1,11-2,13</td>
                <td align="center">0,008</td>
              </tr>
              <tr>
                <td align="left">FEVE</td>
                <td align="center">0,761</td>
                <td align="center">0,65-0,88</td>
                <td align="center">0,001</td>
              </tr>
            </tbody>
          </table>
          <table-wrap-foot>
            <fn id="TFN8">
              <p>* Registrando g&#234;nero, idade, hist&#243;rico de hipertens&#227;o, doen&#231;a renal cr&#244;nica, glicose sangu&#237;nea no momento da interna&#231;&#227;o, ureia, creatinina, &#225;cido &#250;rico, frequ&#234;ncia card&#237;aca, PAS, PAD, contagem de leuc&#243;citos, neutr&#243;filos, hemoglobina, hemat&#243;critos, albumina, FEVE e escore de Gensini.</p>
            </fn>
          </table-wrap-foot>
        </table-wrap>
        <p>O estudo, independentemente, examinou 25 angiogramas selecionados aleatoriamente, estimou visualmente escores das les&#245;es e calculou os escores de Gensini. O coeficiente da correla&#231;&#227;o intra- e inter-observador foi de 0,96 e 0,95 para o escore de Gensini, indicando boa reprodutibilidade e confiabilidade.</p>
      </sec>
      <sec sec-type="discussion">
        <title>Discuss&#227;o</title>
        <p>O principal achado deste estudo foi que o escore de Gensini est&#225; independentemente associado &#224; mortalidade intra-hospitalar em pacientes com IAMCSST submetidos &#224; ICP-P. Por&#233;m, o sexo feminino, n&#237;veis mais altos de ureia e &#225;cido &#250;rico s&#233;ricos e FEVE mais baixa tamb&#233;m mostraram-se associados &#224; mortalidade intra-hospitalar neste grupo de pacientes.</p>
        <p>O IAMCSST &#233; a parte mais importante de SCA. Consistentemente com nossos dados, taxas de mortalidade intra-hospitalar variaram entre 6% e 14%.<xref ref-type="bibr" rid="B8">8</xref> Sabe-se que muitos fatores, como IM pr&#233;vio, tempo porta-bal&#227;o, presen&#231;a de diabetes, anemia, insufici&#234;ncia renal cr&#244;nica e fra&#231;&#227;o de eje&#231;&#227;o no momento da interna&#231;&#227;o s&#227;o preditores de mortalidade nessa era.<xref ref-type="bibr" rid="B9">9</xref> At&#233; hoje, diversos escores de risco validados baseados em pacientes, como TIMI, GRACE, ZWOLLE, CADILLAC e PAMI foram estabelecidos para prever mortalidade em pacientes com IAMCSST.<xref ref-type="bibr" rid="B10">10</xref><sup>-</sup><xref ref-type="bibr" rid="B13">13</xref> A maioria desses escores foram baseados em par&#226;metros cl&#237;nicos como idade, g&#234;nero, classe de Killip, n&#237;veis s&#233;ricos de creatinina e grau de altera&#231;&#227;o do segmento ST. Este n&#227;o &#233; um fato surpreendente, j&#225; que esses escores foram desenvolvidos antes do uso difundido de ICP-P para IAMCSST. Os escores TIMI e GRACE s&#227;o ferramentas valiosas para a estratifica&#231;&#227;o de risco inicial de pacientes com IAMCSST. O escore TIMI &#233; amplamente aceito devido &#224; sua facilidade de uso. Apesar de ter boa pot&#234;ncia preditiva, o escore GRACE &#233; limitado em seu uso &#224; beira do leito, pois requer gr&#225;ficos e software computadorizados especializados. Esses escores n&#227;o foram especificamente otimizados para pacientes com IAMCSST submetidos &#224; ICP-P. Na era do ICP-P, alguns sistemas de escore como o CADILLAC (<italic>Controlled Abciximab and Device Investigation to Lower Late Angioplasty Complications</italic>),<xref ref-type="bibr" rid="B12">12</xref> Stent-PAMI (<italic>Stent-Primary Angioplasty in Myocardial Infarction</italic>)<xref ref-type="bibr" rid="B10">10</xref> e o MCRS (<italic>The Mayo Clinic Risk Score)</italic><xref ref-type="bibr" rid="B14">14</xref> inclu&#237;ram vari&#225;veis angiogr&#225;ficas no algoritmo do escore. O escore de risco CADILLAC conseguiu prever com precis&#227;o mortalidade de 30 dias e 1 ano ap&#243;s ICP-P em pacientes com IAMCSST. Baptista et al.<xref ref-type="bibr" rid="B15">15</xref> aplicaram o escore de risco PAMI a uma pequena popula&#231;&#227;o de pacientes com IAMCSST e revelaram que este m&#233;todo conseguiu prever mortalidade intra-hospitalar de 30 e 60 dias. O escore MCRS tamb&#233;m &#233; um modelo de risco validade para a estratifica&#231;&#227;o de risco pr&#233;-procedimento e tem mostrado boa capacidade na predi&#231;&#227;o de mortalidade intra-hospitalar.<xref ref-type="bibr" rid="B11">11</xref> Al&#233;m desses escores, estabelecidos de uma combina&#231;&#227;o de vari&#225;veis cl&#237;nicas e angiogr&#225;ficas, alguns sistemas de escore baseados apenas em dados angiogr&#225;ficos, como o SYNTAX, est&#227;o associados a monitoramentos a curto e longo prazo em pacientes com IAMCSST submetidos a ICP-P.<xref ref-type="bibr" rid="B16">16</xref><sup>-</sup><xref ref-type="bibr" rid="B18">18</xref>
				</p>
        <p>O objetivo de sistemas de escore coron&#225;rio &#233; quantificar a gravidade de estenose coron&#225;ria. Diferentes art&#233;rias coron&#225;rias transportam diferentes volumes de sangue para o cora&#231;&#227;o, e os escores coron&#225;rios levam isso em considera&#231;&#227;o. O grau de estenose tamb&#233;m foi considerado nesses sistemas de escore. De modo geral, a habilidade individual desses escores baseados em dados angiogr&#225;ficos de prever a mortalidade &#233; incerta, e uma importante limita&#231;&#227;o &#233; que esses escores s&#227;o muito limitados a pacientes eletivos.<xref ref-type="bibr" rid="B19">19</xref> Embora o escore SYNTAX seja frequentemente usado para prever mortalidade em pacientes com IAMCSST submetidos &#224; ICP-P, a porcentagem do di&#226;metro da estenose n&#227;o &#233; considerado no escore, e &#233; feita uma distin&#231;&#227;o somente entre doen&#231;as oclusivas (100%) e n&#227;o oclusivas (50-99% de estenose). Al&#233;m disso, a estenose &#233; considerada grave quando causa &#8805; 50% de redu&#231;&#227;o no di&#226;metro luminal na avalia&#231;&#227;o visual nos vasos &#8805; 1,5 mm. Contudo, no escore de Gensini, les&#245;es que causam &lt; 50% de redu&#231;&#227;o no di&#226;metro luminal em vasos &lt; 1,5 mm de di&#226;metro s&#227;o consideradas no algoritmo do escore. O escore de Gensini foi originalmente desenvolvido para quantificar a gravidade da DAC, e &#233; amplamente usado em ensaios cl&#237;nicos para avaliar a extens&#227;o e a gravidade da DAC. Todavia, estudos subsequentes demonstraram sua habilidade de identificar pacientes tratados com ICP que correm alto risco de eventos adversos. Por&#233;m, pouco se sabe sobre a correla&#231;&#227;o entre o escore de Gensini e mortalidade a curto prazo em pacientes com IAMCSST.<xref ref-type="bibr" rid="B20">20</xref><sup>-</sup><xref ref-type="bibr" rid="B23">23</xref> H&#225; poucos estudos publicados recentemente que avaliaram o escore de Gensini no contexto de ICP-P. Um relato recente demonstrou que o escore de Gensini est&#225; associado a eventos card&#237;acos adversos maiores (ECAM) durante a interna&#231;&#227;o e 6 meses ap&#243;s ICP em pacientes com IAMCSST.<xref ref-type="bibr" rid="B24">24</xref> Portanto, os autores conclu&#237;ram que o escore poderia ser usado para prever ECAM a curto prazo em pacientes com IAMCSST no per&#237;odo p&#243;s ICP. Acet et al.<xref ref-type="bibr" rid="B25">25</xref> viram que o &#237;ndice do escore TIMI est&#225; significativamente associado ao escore de Gensini na predi&#231;&#227;o da extens&#227;o e gravidade da DAC em pacientes com IAMCSST.</p>
        <p>Este estudo representa a an&#225;lise da rela&#231;&#227;o entre o escore de Gensini e mortalidade intra-hospitalar em pacientes com IAMCSST submetidos a ICP-P. O presente estudo demonstrou que pacientes com IAMCSST que apresentam escores de Gensini mais altos, independentemente de outras vari&#225;veis cl&#237;nicas, corre maior risco de mortalidade intra-hospitalar. De acordo com nosso estudo, um aumento de 1 ponto no escore de Gensini est&#225; relacionado a um aumento de 3% no risco de mortalidade intra-hospitalar em pacientes com IAMCSST submetidos a ICP-P. S&#227;o diversos os fatores respons&#225;veis por esse achado: O escore de Gensini se concentra na art&#233;ria coron&#225;ria principal esquerda (LMCA), DAE proximal e segmento m&#233;dio da DAE, &#224;s quais foram atribu&#237;dos fatores de pondera&#231;&#227;o relativamente maiores. A oclus&#227;o dessas art&#233;rias coron&#225;rias causa grandes &#225;reas de infarto que podem estar relacionadas a taxas mais altas de mortalidade. Uma das limita&#231;&#245;es da aplica&#231;&#227;o do escore de Gensini na estratifica&#231;&#227;o de risco &#233; a aus&#234;ncia de qualquer vari&#225;vel cl&#237;nica. Esta defici&#234;ncia pode ser resolvida por meio de sua combina&#231;&#227;o com modelos de risco baseados em fatores cl&#237;nicos. Em nosso estudo, tamb&#233;m vimos que o sexo feminino, n&#237;veis mais altos de ureia e &#225;cido &#250;rico e FEVE mais baixa mostraram-se associados &#224; mortalidade intra-hospitalar de pacientes com IAMCSST submetidos a ICP-P. Esses achados foram consistentes com estudos pr&#233;vios da literatura.<xref ref-type="bibr" rid="B20">20</xref><sup>-</sup><xref ref-type="bibr" rid="B23">23</xref> Na an&#225;lise de regress&#227;o univariada, diferentemente de outras vari&#225;veis, a hemoglobina, hemat&#243;crito, albumina e FEVE demonstraram-se negativamente correlacionados &#224; mortalidade intra-hospitalar, o que significa que valores mais baixos dessas vari&#225;veis estavam associados a uma maior mortalidade. Contudo, essa associa&#231;&#227;o deixou de ser significativa ap&#243;s a an&#225;lise multivariada, com exce&#231;&#227;o da FEVE. A taxa de mortalidade foi de 10,5% (13/123) em mulheres e 3,8% (16/416) em homens. A taxa mais alta de mortalidade intra-hospitalar de pacientes do sexo feminino &#233; geralmente atribu&#237;da a interna&#231;&#245;es tardias, maior n&#250;mero de comorbidades, idade mais avan&#231;ada e mais complica&#231;&#245;es hemorr&#225;gicas ap&#243;s interven&#231;&#245;es.<xref ref-type="bibr" rid="B26">26</xref><sup>-</sup><xref ref-type="bibr" rid="B28">28</xref> Nosso estudo destaca a import&#226;ncia de ajuste de mortalidade a essas vari&#225;veis. N&#227;o foi encontrada diferen&#231;a na presen&#231;a de diabetes, embora outros estudos tenham relatado que isso tende a ser mais comum.</p>
        <p>O resultado de nosso estudo apresenta um aperfei&#231;oamento de fatores de risco convencionais existentes e sistemas de escore para pacientes com IAMCSST. Um dos pontos fortes do estudo &#233; que todos os pacientes internados com diagn&#243;stico de IAMCSST foram inclu&#237;dos, ao contr&#225;rio de relatos de alguns registros em que apenas os dados de alguns pacientes selecionados foram analisados. Melhorar a predi&#231;&#227;o de mortalidade &#233; um grande desafio, e selecionar o tratamento adequado &#233; dif&#237;cil, mas acreditamos que a combina&#231;&#227;o de fatores de risco estabelecidos como o TIMI e GRACE com avalia&#231;&#245;es de escores angiogr&#225;ficos proporciona a melhor quantidade de informa&#231;&#245;es para a predi&#231;&#227;o de mortalidade intra-hospitalar em pacientes com IAMCSST submetidos a ICP-P. Fatores de risco dispon&#237;veis como hipertens&#227;o, tabagismo, diabetes, LDL, hist&#243;rico familiar de IM e vari&#225;veis cl&#237;nicas como idade, sexo, tempo porta-bal&#227;o e biomarcadores tamb&#233;m devem ser considerados para o aperfei&#231;oamento da predi&#231;&#227;o de mortalidade.</p>
        <sec>
          <title>Limita&#231;&#245;es</title>
          <p>Nosso estudo tem algumas limita&#231;&#245;es importantes. Primeiramente, trata-se de um estudo retrospectivo de centro &#250;nico. Os resultados desse estudo n&#227;o podem ser extrapolados a todos os pacientes, como aqueles que apresentam outros tipos de SCA ou aqueles que n&#227;o foram tratados com ICP. O tamanho relativamente pequeno da amostra indica a necessidade de valida&#231;&#227;o dos achados com uma coorte de pacientes maior. O tempo porta-bal&#227;o e in&#237;cio do infarto s&#227;o par&#226;metros importante que tamb&#233;m podem estar associados &#224; mortalidade intra-hospitalar. Por&#233;m, devido ao design retrospectivo e n&#250;mero insuficiente de relat&#243;rios m&#233;dicos, uma quantidade consider&#225;vel de dados de pacientes sobre o in&#237;cio do infarto ou tempo porta-bal&#227;o estava faltando ou n&#227;o se mostrou confi&#225;vel. Dessa forma, esses par&#226;metros n&#227;o puderam ser estudados. Na pr&#225;tica cl&#237;nica, escores angiogr&#225;ficos s&#227;o calculados por avalia&#231;&#227;o visual das les&#245;es (em vez de via determina&#231;&#227;o do laborat&#243;rio), o que provavelmente leva a uma maior variabilidade interobservador. Al&#233;m disso, o escore de Gensini n&#227;o foi comparado a outros sistemas de escore angiogr&#225;ficos como o SYTAX, frequentemente usado para prever mortalidade em pacientes com IAMCSST submetidos &#224; ICP-P. Estudos randomizados prospectivos multic&#234;ntricos com amostras maiores ser&#227;o necess&#225;rios para confirmar mais efetivamente nossa hip&#243;tese.</p>
        </sec>
      </sec>
      <sec sec-type="conclusions">
        <title>Conclus&#245;es</title>
        <p>Est&#225; claro que h&#225; uma necessidade cl&#237;nica de um sistema de escore preciso para identificar pacientes com maior risco de mortalidade intra-hospitalar que requerem mais cuidados intensivos. O escore de Gensini pode ter um papel valioso na estratifica&#231;&#227;o de risco de pacientes com IAMCSST submetidos a ICP-P. Contudo, o escore de Gensini deve ser aperfei&#231;oado por meio de uma combina&#231;&#227;o que inclua vari&#225;veis cl&#237;nicas, procedurais e laboratoriais.</p>
      </sec>
    </body>
    <back>
      <fn-group>
        <fn fn-type="supported-by">
          <p>
            <bold>Fontes de Financiamento</bold>
          </p>
          <p>O presente estudo n&#227;o teve fontes de financiamento externas.</p>
        </fn>
        <fn fn-type="other">
          <p>
            <bold>Vincula&#231;&#227;o Acad&#234;mica</bold>
          </p>
          <p>Este artigo &#233; parte de tese de Doutorado de Erkan Yildirim pela G&#252;lhane Military Medical Academy.</p>
        </fn>
      </fn-group>
    </back>
  </sub-article>
</article>
