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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Cardiovasc. Med.</journal-id>
<journal-title>Frontiers in Cardiovascular Medicine</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Cardiovasc. Med.</abbrev-journal-title>
<issn pub-type="epub">2297-055X</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/fcvm.2021.626724</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Cardiovascular Medicine</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>The Predictive Value of N-Terminal Probrain Natriuretic Peptide for Infection in Patients With Acute Myocardial Infarction</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name><surname>Dai</surname> <given-names>YiNing</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="author-notes" rid="fn002"><sup>&#x02020;</sup></xref>
</contrib>
<contrib contrib-type="author">
<name><surname>Wan</surname> <given-names>XiaoLiang</given-names></name>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
<xref ref-type="author-notes" rid="fn002"><sup>&#x02020;</sup></xref>
</contrib>
<contrib contrib-type="author">
<name><surname>Liu</surname> <given-names>Can</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="author-notes" rid="fn002"><sup>&#x02020;</sup></xref>
</contrib>
<contrib contrib-type="author">
<name><surname>Duan</surname> <given-names>ChongYang</given-names></name>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
<uri xlink:href="http://loop.frontiersin.org/people/1067813/overview"/>
</contrib>
<contrib contrib-type="author">
<name><surname>Shao</surname> <given-names>Shuai</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
</contrib>
<contrib contrib-type="author">
<name><surname>Chen</surname> <given-names>HongHuan</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
</contrib>
<contrib contrib-type="author">
<name><surname>Wang</surname> <given-names>Litao</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
</contrib>
<contrib contrib-type="author">
<name><surname>Lin</surname> <given-names>JiJin</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
</contrib>
<contrib contrib-type="author">
<name><surname>Xue</surname> <given-names>Ling</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
</contrib>
<contrib contrib-type="author">
<name><surname>Chen</surname> <given-names>JiYan</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<uri xlink:href="http://loop.frontiersin.org/people/1055025/overview"/>
</contrib>
<contrib contrib-type="author">
<name><surname>He</surname> <given-names>PengCheng</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<uri xlink:href="http://loop.frontiersin.org/people/1079228/overview"/>
</contrib>
<contrib contrib-type="author" corresp="yes">
<name><surname>Liu</surname> <given-names>YuanHui</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="corresp" rid="c002"><sup>&#x0002A;</sup></xref>
<uri xlink:href="http://loop.frontiersin.org/people/1104967/overview"/>
</contrib>
<contrib contrib-type="author" corresp="yes">
<name><surname>Tan</surname> <given-names>Ning</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="corresp" rid="c001"><sup>&#x0002A;</sup></xref>
<uri xlink:href="http://loop.frontiersin.org/people/1067177/overview"/>
</contrib>
</contrib-group>
<aff id="aff1"><sup>1</sup><institution>Department of Cardiology, Guangdong Cardiovascular Institute, Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Provincial People&#x00027;s Hospital, Guangdong Academy of Medical Sciences</institution>, <addr-line>Guangzhou</addr-line>, <country>China</country></aff>
<aff id="aff2"><sup>2</sup><institution>Department of Cardiology, Foshan Second People&#x00027;s Hospital, Foshan</institution>, <addr-line>Guangdong</addr-line>, <country>China</country></aff>
<aff id="aff3"><sup>3</sup><institution>Department of Biostatistics, School of Public Health, Southern Medical University</institution>, <addr-line>Guangzhou</addr-line>, <country>China</country></aff>
<author-notes>
<fn fn-type="edited-by"><p>Edited by: Leonardo Roever, Federal University of Uberlandia, Brazil</p></fn>
<fn fn-type="edited-by"><p>Reviewed by: Kye Hun Kim, Chonnam National University Medical School, South Korea; Xuejuan Jin, Zhongshan Hospital, Fudan University, China</p></fn>
<corresp id="c001">&#x0002A;Correspondence: Ning Tan <email>gdtanning&#x00040;126.com</email></corresp>
<corresp id="c002">YuanHui Liu <email>liuyuanhui&#x00040;gdph.org.cn</email></corresp>
<fn fn-type="other" id="fn001"><p>This article was submitted to General Cardiovascular Medicine, a section of the journal Frontiers in Cardiovascular Medicine</p></fn>
<fn fn-type="other" id="fn002"><p>&#x02020;These authors contributed equally to the work</p></fn></author-notes>
<pub-date pub-type="epub">
<day>25</day>
<month>08</month>
<year>2021</year>
</pub-date>
<pub-date pub-type="collection">
<year>2021</year>
</pub-date>
<volume>8</volume>
<elocation-id>626724</elocation-id>
<history>
<date date-type="received">
<day>10</day>
<month>11</month>
<year>2020</year>
</date>
<date date-type="accepted">
<day>04</day>
<month>08</month>
<year>2021</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#x000A9; 2021 Dai, Wan, Liu, Duan, Shao, Chen, Wang, Lin, Xue, Chen, He, Liu and Tan.</copyright-statement>
<copyright-year>2021</copyright-year>
<copyright-holder>Dai, Wan, Liu, Duan, Shao, Chen, Wang, Lin, Xue, Chen, He, Liu and Tan</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/"><p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p></license> </permissions>
<abstract><p><bold>Background:</bold> Infections increase the risk of poor outcomes in patients with ST-elevation myocardial infarction (STEMI) undergoing percutaneous coronary intervention (PCI). However, predicting patients at a high risk of developing infection remains unclear. Moreover, the value of N-terminal probrain natriuretic peptide (NT-proBNP) for predicting infection is still unknown. Thus, we aimed to assess the relationship between NT-proBNP and the following development of infection, and clinical adverse outcomes in patients with STEMI undergoing PCI.</p>
<p><bold>Methods:</bold> STEMI patients undergoing PCI were consecutively enrolled from January 2010 to July 2016 and divided into groups according to baseline NT-proBNP levels: tertiles T1 (&#x0003C;988 pg/mL), T2 (988&#x02013;3520 pg/mL), and T3 (&#x02265;3520 pg/mL). The primary endpoint was infection during hospitalization.</p>
<p><bold>Results:</bold> A total of 182 (27%) patients developed in-hospital infection. The incidence of infection increased from T1 to T3 (10.5, 17.7, and 54.5%, <italic>P</italic> &#x0003C; 0.001). NT-proBNP was an independent risk factor (adjusted odds ratio = 1.39, 95% confidence interval (CI) = 1.12&#x02013;1.73, <italic>P</italic> = 0.003) and presented accurately predicting infection (area under curve = 0.774). Multivariate cox analysis showed that NT-proBNP was a significant risk factor for major adverse clinical events (MACE) at follow-up (adjusted HR = 1.92, 95% CI = 1.61&#x02013;2.29, <italic>P</italic> &#x0003C; 0.001).</p>
<p><bold>Conclusion:</bold> The baseline NT-proBNP level has a good predictive value for infection and MACE in STEMI patients undergoing PCI.</p></abstract>
<kwd-group>
<kwd>acute myocardial infarction</kwd>
<kwd>percutaneous coronary intervention</kwd>
<kwd>N-terminal probrain natriuretic peptide</kwd>
<kwd>infection</kwd>
<kwd>predictor</kwd>
</kwd-group>
<counts>
<fig-count count="3"/>
<table-count count="3"/>
<equation-count count="0"/>
<ref-count count="31"/>
<page-count count="7"/>
<word-count count="4725"/>
</counts>
</article-meta>
</front>
<body>
<sec sec-type="intro" id="s1">
<title>Introduction</title>
<p>Infection is an important complication in patients with ST-elevation myocardial infarction (STEMI) undergoing percutaneous coronary intervention (PCI). It increases the mortality even by 10-fold in 30 days (<xref ref-type="bibr" rid="B1">1</xref>&#x02013;<xref ref-type="bibr" rid="B3">3</xref>). Early identification high-risk patients for post-acute myocardial infarction (P-AMI) infections, helps to determine a better treatment strategy, avoided aggressive invasive operations, and potentially improve outcomes.</p>
<p>The N-terminal probrain natriuretic peptide (NT-proBNP) is released predominantly from the ventricular myocardium in response to increased ventricular wall stress (<xref ref-type="bibr" rid="B4">4</xref>). It is a well-recognized prognostic marker of multiple disease states, including heart failure (<xref ref-type="bibr" rid="B5">5</xref>) and chronic obstructive pulmonary disease (COPD) (<xref ref-type="bibr" rid="B6">6</xref>&#x02013;<xref ref-type="bibr" rid="B8">8</xref>), which are potentially associated with a high risk of infection. Previous researches focused on the predictive value of NT-proBNP for clinical outcomes in existing sepsis or septic shock that expanding the clinically awareness and utility of NT-proBNP in these infectious stage (<xref ref-type="bibr" rid="B9">9</xref>&#x02013;<xref ref-type="bibr" rid="B11">11</xref>). However, the value of NT-proBNP in predicting infection is still lack of knowledge, especially in patients with AMI. Thus, we aim to assess the relationship between the NT-proBNP and infection, and clinical adverse outcomes in patients with STEMI undergoing PCI.</p></sec>
<sec sec-type="materials and methods" id="s2">
<title>Materials and Methods</title>
<sec>
<title>Study Population</title>
<p>Consecutive patients with STEMI undergoing PCI were prospectively enrolled at Guangdong Provincial People&#x00027;s Hospital between January 2010 and July 2016. The diagnosis of STEMI was according to the international guidelines (<xref ref-type="bibr" rid="B12">12</xref>). Patients with NT-proBNP measurements within 24 h after hospital admission were enrolled. We excluded the patients who considered as infection, chronic or tumors inflammatory diseases and chronic renal failure required hemodialysis therapy at admission. In addition, we excluded patients who died within 24 h after admission and those undergoing cardiac surgery. The study flow was showed in <xref ref-type="fig" rid="F1">Figure 1</xref>. The study was approved by the research ethics committee of Guangdong Provincial People&#x00027;s Hospital and followed the guidelines stipulated in the Declaration of Helsinki and the ethical standards of the responsible committee on human experimentation. Informed consent was obtained from all included patients.</p>
<fig id="F1" position="float">
<label>Figure 1</label>
<caption><p>Flow diagram of the study.</p></caption>
<graphic xlink:href="fcvm-08-626724-g0001.tif"/>
</fig></sec>
<sec>
<title>Clinical Procedure</title>
<p>NT-proBNP was measured using an electrochemiluminescence immunoassay (Roche Diagnostics, Germany). Furthermore, laboratory tests, such electrolytes, blood lipid, creatinine, and other clinical routine parameter examinations, were also performed within 24 h after admission. Coronary angiography and then PCI were performed according to the guidelines for catheter techniques (<xref ref-type="bibr" rid="B13">13</xref>). The choice of stent and need for intra-aortic balloon pump (IABP) were at the discretion of the interventional cardiologist. Antiplatelet therapy as well as other medications were prescribed at the physicians&#x00027; discretion. Clinical parameters were first collected by one researcher and later confirmed by another one.</p></sec>
<sec>
<title>Endpoints and Definitions</title>
<p>The primary endpoint was infection, which was defined as infection requiring antibiotics (<xref ref-type="bibr" rid="B14">14</xref>). In addition, infections were categorized as pulmonary, urinary infections, or others (including abdominal sepsis, primary bacteremia, and unidentified primary infection site), by the cardiologist. Secondary endpoints were regarded as in-hospital and follow-up major adverse clinical events (MACE), which were defined as all-cause mortality, stroke, and any bleeding. Our in-hospital adverse clinical events were accumulated by two independent researchers according to the electronic records. After discharge, all of the patients were follow-up by researchers or nurses, through telephone interviews or clinic visits for at least three years.</p></sec>
<sec>
<title>Statistical Analyses</title>
<p>Baseline NT-proBNP level was skewed distribution, thus, they were logarithmically transformed (lgNT-proBNP) and sort the orders, then the patients were divided into the following tertiles: T1 (<italic>n</italic> = 219, &#x0003C;988 pg/mL), T2 (<italic>n</italic> = 220, 988&#x02013;3520 pg/mL), and T3 (<italic>n</italic> = 220, &#x02265;3520 pg/mL). Continuous data were presented as mean &#x000B1; standard deviation, otherwise data were presented as median and interquartile range and compared using Kruskal-Wallis test. Categorical data were presented as percentages, and &#x003C7;<sup>2</sup> test or Fisher&#x00027;s exact test was selected for comparison. Clinical events were compared between patients with and without infection. Receiver operating characteristic (ROC) curves were conducted and the area under the curve (AUC) was then calculated to evaluate the predictive value of NT-proBNP for infection. The optimal statistical cutoff point was selected by using the Youden index (Youden = sensitivity &#x0002B; specificity &#x02013; 1). Univariate and multivariate logistic regression were used to determine the risk factors for in-hospital infection. Clinically important or significant potential confounders in the univariate analysis were included for multivariable analysis. We performed two multivariate analysis models to adjust the potential risk factor. In model one, we included variables of lgNT-proBNP, sex, smoke, chronic obstructive pulmonary disease, diabetes, stroke, hypertension, prior myocardial infarction, femoral access, serum albumin and white blood cell. In other model, we included the variables of lgNT-proBNP, age, aspirin, contrast volume, clopidogrel, hemoglobin A1c, low-density lipoprotein cholesterol, total cholesterol and statins. The adjusted odds ratio (OR) or adjusted hazard ratio (HR) and 95% confidence interval (CI) were also presented. Univariate analyses of follow-up mortality and MACE were performed using the Kaplan-Meier survival method for patients categorized by NT-proBNP. Multivariate cox regression analysis was also conducted to recognize NT-proBNP for follow-up MACE. SAS version 9.4 (SAS Institute, Cary, NC) software were used to perform the statistical analyses. <italic>P</italic> values were two-sided, and <italic>P</italic> &#x0003C; 0.05 was regarded as significant.</p></sec></sec>
<sec sec-type="results" id="s3">
<title>Results</title>
<p>A total of 659 patients were included (mean age: 64 &#x000B1; 12 years; female, 23.2%). The baseline characteristics are shown in <xref ref-type="table" rid="T1">Table 1</xref>. Patients in T3 were older, more likely to be a smoker, had higher Killip grade and longer hospital stay, and tend to have a history of hypertension, stroke, diabetes and COPD. A positive association in heart rate, anemia and creatinine levels were noted with increasing NT-proBNP levels, but a negative association in LVEF, estimated glomerular filtration rate, and transradial access was found.</p>
<table-wrap position="float" id="T1">
<label>Table 1</label>
<caption><p>Baseline characteristics according to NT-proBNP tertiles.</p></caption>
<table frame="hsides" rules="groups">
<thead><tr>
<th/>
<th valign="top" align="center" colspan="3"><bold>NT-proBNP</bold></th>
<th/>
</tr>
<tr>
<th valign="top" align="left"><bold>Variables</bold></th>
<th valign="top" align="center"><bold>T1 (<italic>n</italic> &#x0003D; 219)</bold></th>
<th valign="top" align="center"><bold>T2 (<italic>n</italic> &#x0003D; 220)</bold></th>
<th valign="top" align="center"><bold>T3 (<italic>n</italic> &#x0003D; 220)</bold></th>
<th valign="top" align="center"><bold><italic>P</italic>-value</bold></th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">Age</td>
<td/>
<td/>
<td/>
<td/>
</tr>
<tr>
<td valign="top" align="left">&#x000A0;Age&#x0003E;75 year, n (%)</td>
<td valign="top" align="center">65 (29.7%)</td>
<td valign="top" align="center">102 (46.4%)</td>
<td valign="top" align="center">161 (73.2%)</td>
<td valign="top" align="center">&#x0003C;0.001</td>
</tr>
<tr>
<td valign="top" align="left">&#x000A0;Mean (SD), year</td>
<td valign="top" align="center">58.76 &#x000B1; 11.90</td>
<td valign="top" align="center">62.15 &#x000B1; 12.29</td>
<td valign="top" align="center">70.73 &#x000B1; 9.86</td>
<td valign="top" align="center">&#x0003C;0.001</td>
</tr>
<tr>
<td valign="top" align="left">Female, n (%)</td>
<td valign="top" align="center">26 (11.9%)</td>
<td valign="top" align="center">44 (20.0%)</td>
<td valign="top" align="center">83 (37.7%)</td>
<td valign="top" align="center">&#x0003C;0.001</td>
</tr>
<tr>
<td valign="top" align="left">Hypertension, n (%)</td>
<td valign="top" align="center">92 (42.0%)</td>
<td valign="top" align="center">114 (51.8%)</td>
<td valign="top" align="center">152 (69.1%)</td>
<td valign="top" align="center">&#x0003C;0.001</td>
</tr>
<tr>
<td valign="top" align="left">Diabetes, n (%)</td>
<td valign="top" align="center">48 (21.9%)</td>
<td valign="top" align="center">57 (25.9%)</td>
<td valign="top" align="center">92 (41.8%)</td>
<td valign="top" align="center">&#x0003C;0.001</td>
</tr>
<tr>
<td valign="top" align="left">Hyperlipaemia, n (%)</td>
<td valign="top" align="center">26 (11.9%)</td>
<td valign="top" align="center">13 (5.9%)</td>
<td valign="top" align="center">10 (4.5%)</td>
<td valign="top" align="center">0.008</td>
</tr>
<tr>
<td valign="top" align="left">Smoking, n (%)</td>
<td valign="top" align="center">109 (49.8%)</td>
<td valign="top" align="center">104 (47.3%)</td>
<td valign="top" align="center">46 (20.9%)</td>
<td valign="top" align="center">&#x0003C;0.001</td>
</tr>
<tr>
<td valign="top" align="left">COPD, n (%)</td>
<td valign="top" align="center">1 (0.5%)</td>
<td valign="top" align="center">3 (1.4%)</td>
<td valign="top" align="center">18 (8.2%)</td>
<td valign="top" align="center">&#x0003C;0.001</td>
</tr>
<tr>
<td valign="top" align="left">Prior myocardial infarction, n (%)</td>
<td valign="top" align="center">6 (2.7%)</td>
<td valign="top" align="center">11 (5.0%)</td>
<td valign="top" align="center">17 (7.7%)</td>
<td valign="top" align="center">0.061</td>
</tr>
<tr>
<td valign="top" align="left">Prior stroke, n (%)</td>
<td valign="top" align="center">7 (3.2%)</td>
<td valign="top" align="center">19 (8.6%)</td>
<td valign="top" align="center">35 (15.9%)</td>
<td valign="top" align="center">&#x0003C;0.001</td>
</tr>
<tr>
<td valign="top" align="left">Atrial fibrillation, n (%)</td>
<td valign="top" align="center">5 (2.3%)</td>
<td valign="top" align="center">7 (3.2%)</td>
<td valign="top" align="center">14 (6.4%)</td>
<td valign="top" align="center">0.070</td>
</tr>
<tr>
<td valign="top" align="left">Systolic blood pressure (mmHg)</td>
<td valign="top" align="center">122.45 &#x000B1; 22.01</td>
<td valign="top" align="center">121.93 &#x000B1; 21.68</td>
<td valign="top" align="center">121.50 &#x000B1; 27.77</td>
<td valign="top" align="center">0.918</td>
</tr>
<tr>
<td valign="top" align="left">Diastolic blood pressure (mmHg)</td>
<td valign="top" align="center">74.16 &#x000B1; 12.80</td>
<td valign="top" align="center">72.36 &#x000B1; 12.68</td>
<td valign="top" align="center">71.71 &#x000B1; 15.28</td>
<td valign="top" align="center">0.153</td>
</tr>
<tr>
<td valign="top" align="left">Heart rate, beat per minutes</td>
<td valign="top" align="center">77.06 &#x000B1; 14.76</td>
<td valign="top" align="center">82.31 &#x000B1; 16.19</td>
<td valign="top" align="center">89.53 &#x000B1; 21.39</td>
<td valign="top" align="center">&#x0003C;0.001</td>
</tr>
<tr>
<td valign="top" align="left">Killip class &#x02265; II, n (%)</td>
<td valign="top" align="center">44 (20.1%)</td>
<td valign="top" align="center">70 (31.8%)</td>
<td valign="top" align="center">151 (68.7%)</td>
<td valign="top" align="center">&#x0003C;0.001</td>
</tr>
<tr>
<td valign="top" align="left">White blood cell</td>
<td valign="top" align="center">11.52 &#x000B1; 3.62</td>
<td valign="top" align="center">11.45 &#x000B1; 3.62</td>
<td valign="top" align="center">12.85 &#x000B1; 4.71</td>
<td valign="top" align="center">&#x0003C;0.001</td>
</tr>
<tr>
<td valign="top" align="left">Total cholesterol (mmol/L)</td>
<td valign="top" align="center">5.13 &#x000B1; 1.32</td>
<td valign="top" align="center">4.62 &#x000B1; 1.15</td>
<td valign="top" align="center">4.52 &#x000B1; 1.31</td>
<td valign="top" align="center">&#x0003C;0.001</td>
</tr>
<tr>
<td valign="top" align="left">LDL-C (mmol/L)</td>
<td valign="top" align="center">3.30 &#x000B1; 1.02</td>
<td valign="top" align="center">2.92 &#x000B1; 0.98</td>
<td valign="top" align="center">2.78 &#x000B1; 1.06</td>
<td valign="top" align="center">&#x0003C;0.001</td>
</tr>
<tr>
<td valign="top" align="left">HDL-C (mmol/L)</td>
<td valign="top" align="center">1.00 &#x000B1; 0.23</td>
<td valign="top" align="center">0.98 &#x000B1; 0.26</td>
<td valign="top" align="center">0.97 &#x000B1; 0.30</td>
<td valign="top" align="center">0.515</td>
</tr>
<tr>
<td valign="top" align="left">Anemia, n (%)</td>
<td valign="top" align="center">53 (24.2%)</td>
<td valign="top" align="center">84 (38.2%)</td>
<td valign="top" align="center">137 (62.3%)</td>
<td valign="top" align="center">&#x0003C;0.001</td>
</tr>
<tr>
<td valign="top" align="left">HbA1c (%)</td>
<td valign="top" align="center">6.49 &#x000B1; 1.49</td>
<td valign="top" align="center">6.49 &#x000B1; 1.46</td>
<td valign="top" align="center">6.93 &#x000B1; 1.68</td>
<td valign="top" align="center">0.010</td>
</tr>
<tr>
<td valign="top" align="left">Serum albumin (g/L)</td>
<td valign="top" align="center">34.99 &#x000B1; 4.00</td>
<td valign="top" align="center">32.66 &#x000B1; 4.19</td>
<td valign="top" align="center">30.48 &#x000B1; 4.91</td>
<td valign="top" align="center">&#x0003C;0.001</td>
</tr>
<tr>
<td valign="top" align="left">Hemoglobin (g/L)</td>
<td valign="top" align="center">136.87 &#x000B1; 20.41</td>
<td valign="top" align="center">128.33 &#x000B1; 28.88</td>
<td valign="top" align="center">119.31 &#x000B1; 21.18</td>
<td valign="top" align="center">&#x0003C;0.001</td>
</tr>
<tr>
<td valign="top" align="left">eGFR (mL/min/1.73 m<sup>2</sup>)</td>
<td valign="top" align="center">97.92 &#x000B1; 28.86</td>
<td valign="top" align="center">82.75 &#x000B1; 27.00</td>
<td valign="top" align="center">51.84 &#x000B1; 28.57</td>
<td valign="top" align="center">&#x0003C;0.001</td>
</tr>
<tr>
<td valign="top" align="left">Serum creatinine (mg/dL)</td>
<td valign="top" align="center">82.10 &#x000B1; 21.08</td>
<td valign="top" align="center">96.93 &#x000B1; 48.30</td>
<td valign="top" align="center">174.73 &#x000B1; 170.87</td>
<td valign="top" align="center">&#x0003C;0.001</td>
</tr>
<tr>
<td valign="top" align="left">LVEF (%)</td>
<td valign="top" align="center">55.38 &#x000B1; 10.37</td>
<td valign="top" align="center">51.14 &#x000B1; 10.04</td>
<td valign="top" align="center">44.18 &#x000B1; 12.00</td>
<td valign="top" align="center">&#x0003C;0.001</td>
</tr>
<tr>
<td valign="top" align="left">Aspirin, n (%)</td>
<td valign="top" align="center">210 (95.9%)</td>
<td valign="top" align="center">211 (95.9%)</td>
<td valign="top" align="center">203 (92.3%)</td>
<td valign="top" align="center">0.147</td>
</tr>
<tr>
<td valign="top" align="left">Clopidogrel, n (%)</td>
<td valign="top" align="center">209 (95.4%)</td>
<td valign="top" align="center">216 (98.2%)</td>
<td valign="top" align="center">207 (94.1%)</td>
<td valign="top" align="center">0.088</td>
</tr>
<tr>
<td valign="top" align="left">Statins, n (%)</td>
<td valign="top" align="center">212 (96.8%)</td>
<td valign="top" align="center">218 (99.1%)</td>
<td valign="top" align="center">207 (94.1%)</td>
<td valign="top" align="center">0.014</td>
</tr>
<tr>
<td valign="top" align="left">ACEI, n (%)</td>
<td valign="top" align="center">173 (79.0%)</td>
<td valign="top" align="center">162 (73.6%)</td>
<td valign="top" align="center">144 (65.5%)</td>
<td valign="top" align="center">0.006</td>
</tr>
<tr>
<td valign="top" align="left">ARB, n (%)</td>
<td valign="top" align="center">28 (12.8%)</td>
<td valign="top" align="center">35 (15.9%)</td>
<td valign="top" align="center">43 (19.5%)</td>
<td valign="top" align="center">0.155</td>
</tr>
<tr>
<td valign="top" align="left">CCB, n (%)</td>
<td valign="top" align="center">16 (7.3%)</td>
<td valign="top" align="center">26 (11.8%)</td>
<td valign="top" align="center">35 (15.9%)</td>
<td valign="top" align="center">0.019</td>
</tr>
<tr>
<td valign="top" align="left">Transradial access, n (%)</td>
<td valign="top" align="center">193 (90.6%)</td>
<td valign="top" align="center">167 (84.3%)</td>
<td valign="top" align="center">126 (69.2%)</td>
<td valign="top" align="center">&#x0003C;0.001</td>
</tr>
<tr>
<td valign="top" align="left">Stents, median (Q25&#x0007E;Q75)</td>
<td valign="top" align="center">1 (1&#x0007E;2)</td>
<td valign="top" align="center">1 (1&#x0007E;2)</td>
<td valign="top" align="center">1 (1&#x0007E;2)</td>
<td valign="top" align="center">0.371</td>
</tr>
<tr>
<td valign="top" align="left">Contrast volume &#x02265; 100 ml, n (%)</td>
<td valign="top" align="center">145 (70.0%)</td>
<td valign="top" align="center">149 (78.0%)</td>
<td valign="top" align="center">122 (69.7%)</td>
<td valign="top" align="center">0.121</td>
</tr>
<tr>
<td valign="top" align="left">Multi-lesion, n (%)</td>
<td valign="top" align="center">157 (73.4%)</td>
<td valign="top" align="center">153 (77.7%)</td>
<td valign="top" align="center">152 (83.5%)</td>
<td valign="top" align="center">0.052</td>
</tr>
<tr>
<td valign="top" align="left">Length of hospitalization, median (Q25&#x0007E;Q75)</td>
<td valign="top" align="center">6 (5&#x0007E;8)</td>
<td valign="top" align="center">7 (6&#x0007E;10)</td>
<td valign="top" align="center">10 (6&#x0007E;17)</td>
<td valign="top" align="center">&#x0003C;0.001</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p><italic>COPD, chronic obstructive pulmonary disease; LDL-C, low-density lipoprotein cholesterol; HDL-C, high density lipoprotein cholesterol; HbA1c, hemoglobin A1c; eGFR, estimated glomerular filtration rate; LVEF, left ventricular ejection fraction; ACEI/ARB, Angiotensin-Converting Enzyme Inhibitors/Angiotensin receptor blocker; CCB, calcium channel blockers</italic>.</p>
</table-wrap-foot>
</table-wrap>
<p>The incidence of infection (10.5, 17.7, 54.5%) and in hospital all cause death (3.7, 4.1, 16.4%) was higher when NT-proBNP increased from T1 to T3 (<xref ref-type="fig" rid="F2">Figure 2</xref>). T1 and T2 have same rates of MACE (15.5% for each) and both lower than that in T3 (43.6%). Patients with infection were more likely to have invasive procedures such as IABP, and adverse clinical events including death, and MACE. In addition, the baseline characteristics of patients with or without infection were compared (<xref ref-type="supplementary-material" rid="SM1">Supplementary Table 1</xref>). The summary of subtypes of infection were summarized (<xref ref-type="supplementary-material" rid="SM1">Supplementary Table 2</xref>).</p>
<fig id="F2" position="float">
<label>Figure 2</label>
<caption><p>The incidence of infection, death, and major adverse clinical events during hospitalization.</p></caption>
<graphic xlink:href="fcvm-08-626724-g0002.tif"/>
</fig>
<p>Univariate analysis showed that lgNT-proBNP (OR = 2.16, 95% CI = 1.85&#x02013;2.53, <italic>P</italic> &#x0003C; 0.001) was associated with infection. After adjusting for other significant factors, the multiple logistic analysis verified that lgNT-proBNP was still a significant risk factor of infection (adjusted OR = 1.39, 95% CI = 1.12&#x02013;1.73, <italic>P</italic> = 0.003, or adjusted OR = 2.16, 95% CI = 1.73&#x02013;2.70, <italic>P</italic> &#x0003C; 0.001) (<xref ref-type="table" rid="T2">Table 2</xref>).</p>
<table-wrap position="float" id="T2">
<label>Table 2</label>
<caption><p>Independent risk factor of infection based on multivariate analysis.</p></caption>
<table frame="hsides" rules="groups">
<thead><tr>
<th valign="top" align="left"><bold>Variables</bold></th>
<th valign="top" align="center"><bold>OR</bold></th>
<th valign="top" align="center"><bold>95% CI</bold></th>
<th valign="top" align="center"><bold><italic>P</italic>-Value</bold></th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">lgNT-proBNP</td>
<td valign="top" align="center">1.39</td>
<td valign="top" align="center">1.12&#x02013;1.73</td>
<td valign="top" align="center">0.003</td>
</tr>
<tr>
<td valign="top" align="left">Sex</td>
<td valign="top" align="center">0.54</td>
<td valign="top" align="center">0.28&#x02013;1.01</td>
<td valign="top" align="center">0.054</td>
</tr>
<tr>
<td valign="top" align="left">Smoke</td>
<td valign="top" align="center">0.61</td>
<td valign="top" align="center">0.36&#x02013;1.04</td>
<td valign="top" align="center">0.069</td>
</tr>
<tr>
<td valign="top" align="left">COPD</td>
<td valign="top" align="center">2.07</td>
<td valign="top" align="center">0.56&#x02013;7.67</td>
<td valign="top" align="center">0.276</td>
</tr>
<tr>
<td valign="top" align="left">Diabetes</td>
<td valign="top" align="center">1.20</td>
<td valign="top" align="center">0.71&#x02013;2.04</td>
<td valign="top" align="center">0.494</td>
</tr>
<tr>
<td valign="top" align="left">Stroke</td>
<td valign="top" align="center">1.82</td>
<td valign="top" align="center">0.84&#x02013;3.97</td>
<td valign="top" align="center">0.131</td>
</tr>
<tr>
<td valign="top" align="left">Hypertension</td>
<td valign="top" align="center">1.00</td>
<td valign="top" align="center">0.60&#x02013;1.67</td>
<td valign="top" align="center">0.991</td>
</tr>
<tr>
<td valign="top" align="left">Prior myocardial infarction</td>
<td valign="top" align="center">2.27</td>
<td valign="top" align="center">0.88&#x02013;5.88</td>
<td valign="top" align="center">0.091</td>
</tr>
<tr>
<td valign="top" align="left">Femoral access</td>
<td valign="top" align="center">2.41</td>
<td valign="top" align="center">1.35&#x02013;4.32</td>
<td valign="top" align="center">0.003</td>
</tr>
<tr>
<td valign="top" align="left">Serum albumin</td>
<td valign="top" align="center">0.93</td>
<td valign="top" align="center">0.88&#x02013;0.98</td>
<td valign="top" align="center">0.008</td>
</tr>
<tr>
<td valign="top" align="left">White blood cell</td>
<td valign="top" align="center">1.19</td>
<td valign="top" align="center">1.12&#x02013;1.27</td>
<td valign="top" align="center">0.000</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p><italic>COPD, chronic obstructive pulmonary disease. lgNT-proBNP (adjusted OR = 2.16, 95% CI = 1.73&#x02013;2.70, P &#x0003C; 0.001), adjusted for age, aspirin, contrast volume, clopidogrel, hemoglobin A1c, low-density lipoprotein cholesterol, total cholesterol, statins</italic>.</p>
</table-wrap-foot>
</table-wrap>
<p>ROC curve analysis confirmed that NT-proBNP could predict in-hospital infection accurately (AUC = 0.774, 95% CI = 0.73&#x02013;0.82, <italic>P</italic> &#x0003C; 0.001) (<xref ref-type="fig" rid="F3">Figure 3</xref>). The optimal statistical cutoff point was 3864 pg/mL by using the Youden index. The predictive value of NT-proBNP were evaluated based on the types of infection. The result showed that NT-proBNP had good predictive value for pulmonary infections (AUC = 0.729, 95% CI = 0.63&#x02013;0.83, <italic>P</italic> &#x0003C; 0.001) and urinary infections (AUC = 0.741, 95% CI = 0.58&#x02013;0.90, <italic>P</italic> = 0.004) (<xref ref-type="table" rid="T3">Table 3</xref>).</p>
<fig id="F3" position="float">
<label>Figure 3</label>
<caption><p>The receiver operating characteristic curve of N-terminal probrain natriuretic peptide (NT-proBNP) for infection.</p></caption>
<graphic xlink:href="fcvm-08-626724-g0003.tif"/>
</fig>
<table-wrap position="float" id="T3">
<label>Table 3</label>
<caption><p>NT-proBNP for predicting infections.</p></caption>
<table frame="hsides" rules="groups">
<thead><tr>
<th valign="top" align="left"><bold>Cutoff &#x0003D; 3864 pg/mL</bold></th>
<th valign="top" align="center"><bold>Infection</bold></th>
<th valign="top" align="center"><bold>Pulmonary infection</bold></th>
<th valign="top" align="center"><bold>Urinary infection</bold></th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">Sensitivity</td>
<td valign="top" align="center">0.648</td>
<td valign="top" align="center">0.669</td>
<td valign="top" align="center">0.600</td>
</tr>
<tr>
<td valign="top" align="left">Specificity</td>
<td valign="top" align="center">0.811</td>
<td valign="top" align="center">0.781</td>
<td valign="top" align="center">0.696</td>
</tr>
<tr>
<td valign="top" align="left">Positive predictive value</td>
<td valign="top" align="center">0.567</td>
<td valign="top" align="center">0.457</td>
<td valign="top" align="center">0.072</td>
</tr>
<tr>
<td valign="top" align="left">Negative predictive value</td>
<td valign="top" align="center">0.858</td>
<td valign="top" align="center">0.896</td>
<td valign="top" align="center">0.978</td>
</tr>
<tr>
<td valign="top" align="left">Positive likelihood ratios</td>
<td valign="top" align="center">3.436</td>
<td valign="top" align="center">3.061</td>
<td valign="top" align="center">1.971</td>
</tr>
<tr>
<td valign="top" align="left">Negative likelihood ratios</td>
<td valign="top" align="center">0.433</td>
<td valign="top" align="center">0.424</td>
<td valign="top" align="center">0.575</td>
</tr>
<tr>
<td valign="top" align="left">Youden index</td>
<td valign="top" align="center">0.460</td>
<td valign="top" align="center">0.450</td>
<td valign="top" align="center">0.296</td>
</tr>
<tr>
<td valign="top" align="left">Area under curve</td>
<td valign="top" align="center">0.774</td>
<td valign="top" align="center">0.729</td>
<td valign="top" align="center">0.741</td>
</tr>
</tbody>
</table>
</table-wrap>
<p>Multiple logistic analysis showed that lgNT-proBNP was a risk factor for MACE during hospitalization (OR = 1.61, 95% CI = 1.35&#x02013;1.92, <italic>P</italic> &#x0003C; 0.001). Kaplan-Meier survival curves showed that patients in higher tertiles had worse outcomes (log-rank test, <italic>P</italic> &#x0003C; 0.001). At follow-up, multivariate cox analysis found that lgNT-proBNP was a risk factor for MACE (adjusted hazard ratio = 1.92, 95% CI = 1.61&#x02013;2.29, <italic>P</italic> &#x0003C; 0.001).</p></sec>
<sec sec-type="discussion" id="s4">
<title>Discussion</title>
<p>To the best of our knowledge, our study was the first to show that NT-proBNP was a good predictor for P-AMI infection in patients with STEMI undergoing PCI, and provided additional information on the association between NT-proBNP and infection. In addition, the current study also further confirmed that NT-proBNP was a hospital and follow-up prognostic factor for these patients.</p>
<p>Although the definition of infection in our study was similar to that of a previous study (<xref ref-type="bibr" rid="B1">1</xref>), the incidence of infection was higher in our patients than previous studies (<xref ref-type="bibr" rid="B1">1</xref>, <xref ref-type="bibr" rid="B2">2</xref>, <xref ref-type="bibr" rid="B15">15</xref>). The difference in results may be due to the following reasons. First, the risk of infection was proved to increase along with aging (<xref ref-type="bibr" rid="B15">15</xref>). Given the different methods of data presentation, it was difficult for us to compare previous studies, but our data showed that the higher number of elderly patients in our study might have caused the high incidence of infection. Second, apart from aging, other risk factors of infection, including accompanying diseases, might later increase the risk of invasive procedures. Patients in our study might have worse baseline conditions, as 16.5% of our patients had Killip grades of III-IV, which was higher than those in other studies (<xref ref-type="bibr" rid="B1">1</xref>, <xref ref-type="bibr" rid="B2">2</xref>). Moreover, previous studies have reported that the risk of infection increases with the increasing duration of intensive care unit (ICU) stay (<xref ref-type="bibr" rid="B16">16</xref>, <xref ref-type="bibr" rid="B17">17</xref>). Another study showed that the median ICU stay was 1 day in stable patients with STEMI treated in the ICU after primary PCI. Thus, the higher incidence of infection in our study may be due to the prolonged median ICU stay, compared to that of a previous study (4 days vs. 1 day) (<xref ref-type="bibr" rid="B18">18</xref>).</p>
<p>Whether NT-proBNP or BNP could predict an infection remained unclear. Two previous studies including patients who underwent lung cancer surgery demonstrated that preoperative BNP levels could predict postoperative complications (<xref ref-type="bibr" rid="B19">19</xref>, <xref ref-type="bibr" rid="B20">20</xref>), including pneumonia, and a trend of BNP for predicting postoperative pneumonia was observed. The following other two researches also demonstrated that NT-proBNP or BNP could predict later infections. One study by Bobik et al. (<xref ref-type="bibr" rid="B21">21</xref>) showed that preoperative NT-proBNP levels could predict postoperative infection in 31 patients with atrioventricular septal defect. The other study showed that BNP could predict urosepsis after ureteroscopy in patients with unilateral ureteral obstruction (<xref ref-type="bibr" rid="B22">22</xref>). However, Attaran and colleagues who investigated 141 patients undergoing cardiac surgery showed that the preoperative BNP level was not significantly higher in patients with existing postoperative infection (<xref ref-type="bibr" rid="B23">23</xref>). Despite the differences in the design and the great heterogeneity among these studies, most of them were limited by the sample size; thus, a definite conclusion on whether NT-proBNP could predict infection during hospitalization cannot be made in their studies. Besides, an observational study found that NT-proBNP were able to predict (AUC = 0.72) the concurrent infection at the time of admission in patients with AMI, but in our study, such cohort were excluded (<xref ref-type="bibr" rid="B3">3</xref>). This study combined with our findings suggested that NT-proBNP might well predict infection in patients with AMI.</p>
<p>It is well known that NT-proBNP measurements are commonly performed in patients with AMI, and our results strengthen the role of NT-proBNP for these patients and call for an earlier test. The current study highlighted that patients with elevated NT-proBNP levels are at a high risk of developing infection. However, the pathophysiologic mechanisms behind their association remained unclear. First, NT-proBNP was proved to be associated with COPD severity and could predict its exacerbation in both patients with and without cardiovascular diseases (<xref ref-type="bibr" rid="B6">6</xref>). Strong connections have also been found between COPD and pneumonia, because they share similar risk factors and present overlap in terms of epidemiology (<xref ref-type="bibr" rid="B24">24</xref>). Therefore, these connections supported the predictive role of NT-proBNP for pneumonia. However, we should also notice that NT-proBNP remains similar for predicting infection, after adjusting for COPD. There are other unknown mechanisms beside COPD. Second, patients with high NT-proBNP levels were more likely to suffer from cardiovascular events and death (<xref ref-type="bibr" rid="B25">25</xref>, <xref ref-type="bibr" rid="B26">26</xref>), which may increase the use of invasive procedures, such as IABP implantation or mechanical ventilation, subsequently increasing the risk of infection (<xref ref-type="bibr" rid="B27">27</xref>, <xref ref-type="bibr" rid="B28">28</xref>). Third, the increasing NT-proBNP levels was a marker that reflecting cardiopulmonary stress, including systolic dysfunction, diastolic dysfunction, pulmonary hypertension and right heart strain (<xref ref-type="bibr" rid="B29">29</xref>, <xref ref-type="bibr" rid="B30">30</xref>), might cause a large infarction size or serious damage, which would then later increase the risk of developing pneumonia (<xref ref-type="bibr" rid="B31">31</xref>).</p>
<p>There were several limitations in our study. First, it was prospective observational study with small sample size, and some of the baseline characteristics was difference between the NT-proBNP groups. Although multivariate analysis was used to reduce the inherent bias, potential confounders due to unevaluated variables were still possible. The inherent limitations of observational study warrant a well-designed, prospective, multi-centers researches to evaluate the relationship between the level of NT-proBNP and clinical outcomes. Second, we did not monitor the dynamic change of NT-proBNP levels; hence, its dynamic effect on P-AMI infection is unknown. Therefore, further exploration is necessary to identify the clinical impact of risk stratification using the dynamic changes of NT-proBNP for infection. Third, given that procalcitonin measurement is expensive and not routinely used in patients with AMI, we were not sure about its predictive value for infection in our study cohort. Fourth, the reverse causality was still possible. Finally, we only included the patients with STEMI; thus, the predictive value of NT-proBNP for infection in other populations should be demonstrated in future investigations.</p>
<p>In conclusion, The NT-proBNP level is an independent predictor for infection in STEMI patients undergoing PCI. Patients with a high level of NT-proBNP should be considered to undergo effective prophylactic strategies for infection.</p></sec>
<sec sec-type="data-availability-statement" id="s5">
<title>Data Availability Statement</title>
<p>The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.</p></sec>
<sec id="s6">
<title>Ethics Statement</title>
<p>The studies involving human participants were reviewed and approved by the research ethics committee of Guangdong Provincial People&#x00027;s Hospital. The patients/participants provided their written informed consent to participate in this study.</p></sec>
<sec id="s7">
<title>Author Contributions</title>
<p>NT and YL: supervisors of the study and guarantee the study data and accuracy. NT and YL: study concept and design. YD and XW: drafting of the manuscript. CD and YL: statistical analysis. All authors acquisition, analysis, interpretation of data, critical revision, and final approval of the manuscript. All authors agreed to submit the manuscript for publication.</p></sec>
<sec sec-type="COI-statement" id="conf1">
<title>Conflict of Interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec>
<sec sec-type="disclaimer" id="s8">
<title>Publisher&#x00027;s Note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p></sec>
</body>
<back>
<sec sec-type="supplementary-material" id="s9">
<title>Supplementary Material</title>
<p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type="uri" xlink:href="https://www.frontiersin.org/articles/10.3389/fcvm.2021.626724/full#supplementary-material">https://www.frontiersin.org/articles/10.3389/fcvm.2021.626724/full#supplementary-material</ext-link></p>
<supplementary-material xlink:href="Table_1.docx" id="SM1" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document" xmlns:xlink="http://www.w3.org/1999/xlink"/></sec>
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<fn fn-type="financial-disclosure"><p><bold>Funding.</bold> This study was supported by a grant from the National Science Foundation for Young Scientists of China (grant number: 81800325), the Key Project of Natural Science Foundation of Guangdong Province (grant number: 2017B030311010), Outstanding Young Talent Program of Guangdong Provincial People&#x00027;s Hospital (grant numbers: KJ012019095 and KJ012019084), and the High-level Hospital Construction Project (grant number: DFJH2020021). The funders did not participate in the study design, data collection, data analysis, preparation of the manuscript, or decision to submit it for publication. The study was not funded by any industry sponsors.</p>
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