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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Cell. Infect. Microbiol.</journal-id>
<journal-title>Frontiers in Cellular and Infection Microbiology</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Cell. Infect. Microbiol.</abbrev-journal-title>
<issn pub-type="epub">2235-2988</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/fcimb.2022.1087701</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Cellular and Infection Microbiology</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Protective efficacy of statins in patients with <italic>Klebsiella pneumoniae</italic> bloodstream infection</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname>Xu</surname>
<given-names>Qian</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/2077524"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Zheng</surname>
<given-names>Beiwen</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/392523"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Shen</surname>
<given-names>Ping</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<xref ref-type="aff" rid="aff3">
<sup>3</sup>
</xref>
</contrib>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Xiao</surname>
<given-names>Yonghong</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<xref ref-type="author-notes" rid="fn001">
<sup>*</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/1469804"/>
</contrib>
</contrib-group>
<aff id="aff1">
<sup>1</sup>
<institution>Laboratory Medicine Center, Department of Transfusion Medicine, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College</institution>, <addr-line>Hangzhou, Zhejiang Province</addr-line>, <country>China</country>
</aff>
<aff id="aff2">
<sup>2</sup>
<institution>State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University, School of Medicine</institution>, <addr-line>Hangzhou, Zhejiang Province</addr-line>, <country>China</country>
</aff>
<aff id="aff3">
<sup>3</sup>
<institution>Jinan Microecological Biomedicine Shandong Laboratory</institution>, <addr-line>Jinan, Shandong Province</addr-line>, <country>China</country>
</aff>
<author-notes>
<fn fn-type="edited-by">
<p>Edited by: Majdi Al-Hasan, University of South Carolina, United States</p>
</fn>
<fn fn-type="edited-by">
<p>Reviewed by: Nazan Tuna, Namik Kemal University, Turkey; Fatemeh Ahangarkani, Mazandaran University of Medical Sciences, Iran</p>
</fn>
<fn fn-type="corresp" id="fn001">
<p>*Correspondence: Yonghong Xiao, <email xlink:href="mailto:xiaoyonghong@zju.edu.cn">xiaoyonghong@zju.edu.cn</email>
</p>
</fn>
<fn fn-type="other" id="fn002">
<p>This article was submitted to Clinical Microbiology, a section of the journal Frontiers in Cellular and Infection Microbiology</p>
</fn>
</author-notes>
<pub-date pub-type="epub">
<day>05</day>
<month>01</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="collection">
<year>2022</year>
</pub-date>
<volume>12</volume>
<elocation-id>1087701</elocation-id>
<history>
<date date-type="received">
<day>02</day>
<month>11</month>
<year>2022</year>
</date>
<date date-type="accepted">
<day>08</day>
<month>12</month>
<year>2022</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2023 Xu, Zheng, Shen and Xiao</copyright-statement>
<copyright-year>2023</copyright-year>
<copyright-holder>Xu, Zheng, Shen and Xiao</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>
<sec>
<title>Background</title>
<p>Patients with bloodstream infection of <italic>Klebsiella pneumoniae</italic> (BSI-KP) have a high risk of death and septic shock. This study aims to identify the risk factors for mortality and severity in patients of BSI-KP.</p>
</sec>
<sec>
<title>Methods</title>
<p>Data of BSI-KP patients were extracted from the MIMIC IV (Medical Information Mart for Intensive Care IV) database, and patients infected with only <italic>K. pneumoniae</italic> in blood were included in this study. The risk factors of 28-day mortality and septic shock in BSI-KP patients were analyzed, respectively.</p>
</sec>
<sec>
<title>Results</title>
<p>A total of 279 patients enrolled and the all-cause 28-day mortality rate was 11.8%. The use of statins (OR 0.220, 95% CI 0.060-0.801, p = 0.022) and quinolones (OR 0.356, 95% CI 0.143-0.887, p = 0.027) were both independent protective factors for death within 28 days, while the use of vasoactive drugs (OR 7.377, 95% CI 1.775-30.651, p = 0.006) was a risk factor. Besides, pulmonary disease (OR 2.348, 95% CI 1.126-4.897, p = 0.023), bleeding and coagulation disorders (OR 3.626, 95% CI 1.783-7.372, p &lt; 0.001), respiratory failure (OR 2.823, 95% CI 0.178-6.767, p = 0.020) and kidney dysfunction (OR 2.450, 95% CI 1.189-5.047, p = 0.015) were independent risk factors for patients suffered from septic shock while hypertension was a protective one. The receiver operating characteristic (ROC) curves could well predict the risk of death within 28-day (area under ROC = 0.855, 95% CI = 0.796&#x2013;0.914, p &lt; 0.001) and septic shock (AUROC = 0.815, 95% CI = 0.755&#x2013;0.874, p &lt; 0.001) in patients with BSI-KP.</p>
</sec>
<sec>
<title>Conclusion</title>
<p>The use of statins could decrease the risk of 28-day mortality in patients of BSI-KP. The risk factor-based prediction model provided evidence for drug treatment in BSI-KP patients. Paying more attention to the strategy of drug treatment will be an optimal way to improve patient&#x2019;s outcome in clinical practice.</p>
</sec>
</abstract>
<kwd-group>
<kwd>
<italic>Klebsiella pneumoniae</italic>
</kwd>
<kwd>bloodstream infection</kwd>
<kwd>28-day mortality</kwd>
<kwd>septic shock</kwd>
<kwd>statins</kwd>
</kwd-group>
<contract-sponsor id="cn001">National Key Research and Development Program of China<named-content content-type="fundref-id">10.13039/501100012166</named-content>
</contract-sponsor>
<counts>
<fig-count count="4"/>
<table-count count="4"/>
<equation-count count="0"/>
<ref-count count="26"/>
<page-count count="13"/>
<word-count count="5366"/>
</counts>
</article-meta>
</front>
<body>
<sec id="s1" sec-type="intro">
<title>Introduction</title>
<p>
<italic>Klebsiella pneumoniae</italic> is a Gram-negative, encapsulated, facultatively anaerobic bacteria which is one of the most important opportunistic pathogens in hospitals all over the world (<xref ref-type="bibr" rid="B24">Wyres et&#xa0;al., 2020</xref>; <xref ref-type="bibr" rid="B2">Chang et&#xa0;al., 2021</xref>). With the widespread around the world, the infection of <italic>K. pneumoniae</italic> has caused huge health and economic burden to mankind in the past decade. In 2017, the World Health Organization included it in the list of dominant pathogens, emphasizing its extremely important position in public health issues (<xref ref-type="bibr" rid="B23">World Health Organization, 2017</xref>).</p>
<p>In humans, <italic>K. pneumoniae</italic> often colonizes the nasal and digestive tract without causing any symptomatic disease. However, the colonization can turn into a bloodstream infection of <italic>K. pneumoniae</italic> (BSI-KP) when the host immunity fails to control the pathogen growth. As a large-scale multicenter epidemiological study and in-depth genomic analysis in China demonstrated, BSI-KP has a considerable prevalence and high mortality worldwide (<xref ref-type="bibr" rid="B9">Jia et&#xa0;al., 2021</xref>). It is reported that the case fatality rate of BSI is between 21% and 69% (<xref ref-type="bibr" rid="B26">Zhang et&#xa0;al., 2020</xref>). Clinicians give great attention to BSI-KP because it can easily cause sepsis/septic shock in patients that often lead to multiple organ dysfunction syndromes (MODS) and death, especially encountered with hyper-virulent strains. Furthermore, BSI-KP can affect the patient's prognosis by increasing the length of hospital stay, treatment cost, and complications after discharge. Therefore, it is an urgent and valuable task for us to explore the risk predictors in patients with BSI-KP.</p>
<p>Statin is a kind of commonly used drug in clinical practice and have both lipid-lowering and anti-inflammatory properties. Nearly twenty years ago, Almog et al. (<xref ref-type="bibr" rid="B1">Almog, 2003</xref>) hypothesized that statins have a strong protective effect against sepsis by virtue of diverse anti-inflammatory effects that are independent of their lipid-lowering ability. Five years ago, Lee et al. (<xref ref-type="bibr" rid="B11">Lee et&#xa0;al., 2018</xref>) found that statins potentially decreased the 30-day mortality in sepsis patients from a large population.Recently, it was found that prior statin treatment take the positive effect in patients with COVID-19 and type 2 diabetes infection (<xref ref-type="bibr" rid="B3">Cheng et&#xa0;al., 2019</xref>; <xref ref-type="bibr" rid="B4">Davoudi et&#xa0;al., 2021</xref>). During the past two decades, a few researches was focused on the evidence of the statins use improving the outcome of patients with different infections, but are still need to be strengthened.</p>
<p>Patients with BSI-KP have a relatively high risk of poor outcomes, and the situation of CRKP infection often emerged after treating with antibiotics (<xref ref-type="bibr" rid="B6">Fair and Tor, 2014</xref>). There were a series of studies on the risk factors of mortality in patients infected with carbapenem-resistant <italic>K. pneumoniae</italic> (CRKP) but few was focused on the susceptible strains which accounted for majority episodes in clinical settings (<xref ref-type="bibr" rid="B9">Jia et&#xa0;al., 2021</xref>). The database of MIMIC IV (Medical Information Mart for Intensive Care IV) includes clinical data of patients with BSI-KP during an eleven-year period, and most of the <italic>K. pneumoniae</italic> strains were susceptible to carbapenems. In this study, based on the MIMIC IV database, we aimed to find effective factors especially the medication factors such as antibiotics and statins, to predict the risk of death and severity, which would provide the evidence for clinical practice in reducing the mortality and severity in BSI-KP patients.</p>
</sec>
<sec id="s2">
<title>Methods</title>
<sec id="s2_1">
<title>Data source</title>
<p>The data in this retrospective study were extracted from the MIMIC IV (<xref ref-type="bibr" rid="B10">Johnson et&#xa0;al., 2021</xref>), which is an extensive, freely-available database comprising de-identified health-related data from patients who were admitted to a tertiary academic medical center in Boston, MA, USA. It contains information between 2008-2019 for each patient while they were in the hospital: laboratory measurements, medications administered, vital signs documented, and so on. The patients&#x2019; information was anonymized, and thus the need for patients&#x2019; informed consent was waived for this study. All data in this study were extracted by the first author (certification number: 37986401), who passed the Collaborative Institutional Training Initiative examination and achieved access to the database for data extraction. The use of the database was approved by the institutional review boards of the Massachusetts Institute of Technology. All data analysis and reporting has been performed in accordance with institutional guidelines and regulations.</p>
</sec>
<sec id="s2_2">
<title>Study population</title>
<p>A total of 6,189 records of positive blood culture with <italic>K. pneumoniae</italic> in the MIMIC IV database from 2008 to 2019 were included in this work. For a collection of patients infected, the duplicated hospitalization and culture record of each patient and culture time three days before admission time or after discharge time were both excluded. For patients who had admission more than once, only data of the first admission was included. For patients who had positive culture results more than once, only data of the first culture was included. Then patients infected with only <italic>K. pneumoniae</italic> in blood were screened out by exclusion criteria: (1). patients with missing key data; (2). non-adult patients (Age&lt;18); (3). patients with other positive pathogens in blood.</p>
</sec>
<sec id="s2_3">
<title>Variable extraction</title>
<p>Data from the MIMIC-IV database that were considered baseline characteristics during the hospitalization listed as follows: age, gender, comorbidities (cardiovascular disease, pulmonary disease, cerebrovascular disease, encephalopathy, hypertension, diabetes mellitus, solid tumor, hematological malignancy, venous thromboembolism, bleeding, and coagulation disorders, hyperlipidemia, etc.), personal history (drinking, smoking, etc.), Charlson comorbidity index (CCI) and days of hospital stay. Besides, the occurrence of invasive operation, vasoactive agent use, and information of antimicrobial strategy were also explored. Data extraction was accomplished using BigQuery, an online database management system that associated with the MIMIC IV data (<uri xlink:href="http://console.cloud.google.com/bigquery">http://console.cloud.google.com/bigquery</uri>).</p>
</sec>
<sec id="s2_4">
<title>Definitions of variables</title>
<p>In an appropriate antimicrobial strategy, empirical therapy was defined as the antimicrobials administered before a susceptibility report was available, and combination therapy was defined as the administration of more than one antibiotic. Community-acquired here meant the source of the bacteria and was defined as the culture time fallen between 3 days before admission and two days after admission. Among all patients, the three most common and significant categories in antibiotics varied from &#x3b2;-lactamase/&#x3b2;-lactamase inhibitors, carbapenems, and quinolones. Emergency admission represented the patients admitted from the emergency department. The invasive line included all blood-related pipelines and ventilation comprised of invasive and non-invasive mechanical types. The outcome was determined as all-cause death at 28 days after admission.</p>
</sec>
<sec id="s2_5">
<title>Statistical analysis</title>
<p>Continuous variables were expressed as mean &#xb1; standard deviation (SD) or median [interquartile range (IQR)] as appropriate. Categorical variables were expressed as percentages (%). The Chi-square test or Fisher's exact test was used to test comparisons between groups for categorical variables. In contrast, the t-test or Mann&#x2013;Whitney U test was used to compare continuous variables, as appropriate. The risk factors were analyzed using binary logistic regression, and age, sex, and the univariable with p &lt; 0.05 were included in the multivariate logistic regression models. Odds ratios (ORs) and 95% confidential intervals (CIs) were calculated. All p values were two-tailed, and p &lt; 0.05 was considered statistically significant. The linear relationship between the logit conversion values of continuous independent variables and dependent variables was confirmed, and no multicollinearity was found between independent variables before the analysis of logistic regression. A risk prediction model for mortality was established based on the binary logistic regression equation. The model's prediction performance and fitting degree were evaluated by the receiver operating characteristic (ROC) curve and Hosmer-Lemeshow test, respectively. All statistical analysis was performed using the SPSS version 24.0, and <italic>p</italic>&lt;0.05 was considered statistically significant. Graphs in this study were created by GraphPad Prism version 7.04.</p>
</sec>
</sec>
<sec id="s3" sec-type="results">
<title>Results</title>
<sec id="s3_1">
<title>Patients&#x2019; characteristics</title>
<p>There were 279 patients with BSIs caused by only <italic>K. pneumoniae</italic> in the MIMIC IV database (<xref ref-type="fig" rid="f1">
<bold>Figure&#xa0;1</bold>
</xref>). The demographic and clinical characteristics of all patients are listed in <xref ref-type="table" rid="T1">
<bold>Table&#xa0;1</bold>
</xref>. A total of 156 (55.9%) patients are males, and the median age was 67 (IQR: 56&#x2013;77) years. Hypertension (57.0%) was the most prevalent comorbidity, followed by kidney dysfunction (51.3%), cardiovascular disease (45.5%), solid tumor (45.2%) and pulmonary disease (43.7%). The median CCI was 6 (IQR: 4&#x2013;8). Besides, the number of community-acquired was 204 (73.1%), and the emergency type of admission was 222 (79.6%). In addition, the history of smoking (32.6%), drinking (7.9%), surgery and trauma (60.6%), and hospitalization (64.2%) of each patient were also summarized. The patients' 28-day all-cause mortality and septic shock incidence were 11.8% and 21.5%, respectively.</p>
<fig id="f1" position="float">
<label>Figure&#xa0;1</label>
<caption>
<p>Flowchart of patients&#x2019; selection.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fcimb-12-1087701-g001.tif"/>
</fig>
<table-wrap id="T1" position="float">
<label>Table&#xa0;1</label>
<caption>
<p>The demographic and clinical characteristics in non-survival and survival groups of 279 patients with BSI-KP.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="bottom" align="left">Variables, n (%)</th>
<th valign="bottom" align="center">Total (n=279)</th>
<th valign="bottom" align="center">Non-survival group (n=33)</th>
<th valign="bottom" align="center">Survival group (n=246)</th>
<th valign="bottom" align="center">P-value</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="bottom" align="left">Age, years (IQR)</td>
<td valign="bottom" align="center">67 (56-77)</td>
<td valign="bottom" align="center">69 (55-80)</td>
<td valign="bottom" align="center">66 (56-77)</td>
<td valign="bottom" align="center">0.429</td>
</tr>
<tr>
<td valign="bottom" align="left">Male</td>
<td valign="bottom" align="center">156 (55.9)</td>
<td valign="bottom" align="center">21 (63.6)</td>
<td valign="bottom" align="center">135 (54.9)</td>
<td valign="bottom" align="center">0.341</td>
</tr>
<tr>
<th valign="bottom" colspan="5" align="left">Comorbidity</th>
</tr>
<tr>
<td valign="bottom" align="left">Cardiovascular disease</td>
<td valign="bottom" align="center">127 (45.5)</td>
<td valign="bottom" align="center">18 (54.5)</td>
<td valign="bottom" align="center">112 (44.3)</td>
<td valign="bottom" align="center">0.268</td>
</tr>
<tr>
<td valign="bottom" align="left">Pulmonary disease</td>
<td valign="bottom" align="center">122 (43.7)</td>
<td valign="bottom" align="center">22 (66.7)</td>
<td valign="bottom" align="center">100 (40.7)</td>
<td valign="bottom" align="center">
<bold>0.005</bold>
</td>
</tr>
<tr>
<td valign="bottom" align="left">Cerebrovascular disease</td>
<td valign="bottom" align="center">37 (13.3)</td>
<td valign="bottom" align="center">2 (6.1)</td>
<td valign="bottom" align="center">35 (14.2)</td>
<td valign="bottom" align="center">0.276</td>
</tr>
<tr>
<td valign="bottom" align="left">Encephalopathy</td>
<td valign="bottom" align="center">70 (25.1)</td>
<td valign="bottom" align="center">9 (27.3)</td>
<td valign="bottom" align="center">61 (24.8)</td>
<td valign="bottom" align="center">0.758</td>
</tr>
<tr>
<td valign="bottom" align="left">Diabetes mellitus</td>
<td valign="bottom" align="center">107 (38.4)</td>
<td valign="bottom" align="center">10 (30.3)</td>
<td valign="bottom" align="center">97 (39.4)</td>
<td valign="bottom" align="center">0.311</td>
</tr>
<tr>
<td valign="bottom" align="left">Hypertension</td>
<td valign="bottom" align="center">159 (57.0)</td>
<td valign="bottom" align="center">12 (36.4)</td>
<td valign="bottom" align="center">147 (59.8)</td>
<td valign="bottom" align="center">
<bold>0.011</bold>
</td>
</tr>
<tr>
<td valign="bottom" align="left">Solid tumor</td>
<td valign="bottom" align="center">126 (45.2)</td>
<td valign="bottom" align="center">11 (33.3)</td>
<td valign="bottom" align="center">115 (46.7)</td>
<td valign="bottom" align="center">0.146</td>
</tr>
<tr>
<td valign="bottom" align="left">Hematological malignancy</td>
<td valign="bottom" align="center">22 (7.9)</td>
<td valign="bottom" align="center">4 (12.1)</td>
<td valign="bottom" align="center">18 (7.3)</td>
<td valign="bottom" align="center">0.309</td>
</tr>
<tr>
<td valign="bottom" align="left">VTE</td>
<td valign="bottom" align="center">43 (15.4)</td>
<td valign="bottom" align="center">4 (12.1)</td>
<td valign="bottom" align="center">39 (15.9)</td>
<td valign="bottom" align="center">0.577</td>
</tr>
<tr>
<td valign="bottom" align="left">Bleeding and coagulation disorders</td>
<td valign="bottom" align="center">73 (26.2)</td>
<td valign="bottom" align="center">16 (48.5)</td>
<td valign="bottom" align="center">57 (23.2)</td>
<td valign="bottom" align="center">
<bold>0.002</bold>
</td>
</tr>
<tr>
<td valign="bottom" align="left">Hyperlipidemia</td>
<td valign="bottom" align="center">109 (39.1)</td>
<td valign="bottom" align="center">6 (18.2)</td>
<td valign="bottom" align="center">103 (41.9)</td>
<td valign="bottom" align="center">
<bold>0.009</bold>
</td>
</tr>
<tr>
<td valign="bottom" align="left">Heart failure</td>
<td valign="bottom" align="center">48 (17.2)</td>
<td valign="bottom" align="center">8 (24.2)</td>
<td valign="bottom" align="center">40 (16.3)</td>
<td valign="bottom" align="center">0.254</td>
</tr>
<tr>
<td valign="bottom" align="left">Respiratory failure</td>
<td valign="bottom" align="center">44 (15.8)</td>
<td valign="bottom" align="center">15 (45.5)</td>
<td valign="bottom" align="center">29 (11,8)</td>
<td valign="bottom" align="center">
<bold>&lt;0.001</bold>
</td>
</tr>
<tr>
<td valign="bottom" align="left">Kidney dysfunction</td>
<td valign="bottom" align="center">143 (51.3)</td>
<td valign="bottom" align="center">22 (66.7)</td>
<td valign="bottom" align="center">121 (49.2)</td>
<td valign="bottom" align="center">0.059</td>
</tr>
<tr>
<td valign="bottom" align="left">CCI, median (IQR)</td>
<td valign="bottom" align="center">6 (4-8)</td>
<td valign="bottom" align="center">6 (5-9)</td>
<td valign="bottom" align="center">6 (4-8)</td>
<td valign="bottom" align="center">0.195</td>
</tr>
<tr>
<th valign="bottom" colspan="5" align="left">Past history</th>
</tr>
<tr>
<td valign="bottom" align="left">Smoking</td>
<td valign="bottom" align="center">91 (32.6)</td>
<td valign="bottom" align="center">9 (27.3)</td>
<td valign="bottom" align="center">82 (33.3)</td>
<td valign="bottom" align="center">0.486</td>
</tr>
<tr>
<td valign="bottom" align="left">Drinking</td>
<td valign="bottom" align="center">22 (7.9)</td>
<td valign="bottom" align="center">5 (15.2)</td>
<td valign="bottom" align="center">17 (6.9)</td>
<td valign="bottom" align="center">0.157</td>
</tr>
<tr>
<td valign="bottom" align="left">Surgery and trauma</td>
<td valign="bottom" align="center">169 (60.6)</td>
<td valign="bottom" align="center">17 (51.5)</td>
<td valign="bottom" align="center">152 (61.8)</td>
<td valign="bottom" align="center">0.257</td>
</tr>
<tr>
<td valign="bottom" align="left">Prior hospitalization</td>
<td valign="bottom" align="center">179 (64.2)</td>
<td valign="bottom" align="center">16 (48.5)</td>
<td valign="bottom" align="center">163 (66.3)</td>
<td valign="bottom" align="center">
<bold>0.046</bold>
</td>
</tr>
<tr>
<th valign="bottom" colspan="5" align="left">Epidemiology</th>
</tr>
<tr>
<td valign="bottom" align="left">Community-acquired</td>
<td valign="bottom" align="center">204 (73.1)</td>
<td valign="bottom" align="center">23 (69.7)</td>
<td valign="bottom" align="center">181 (73.6)</td>
<td valign="bottom" align="center">0.637</td>
</tr>
<tr>
<td valign="bottom" align="left">Emergency admission</td>
<td valign="bottom" align="center">222 (79.6)</td>
<td valign="bottom" align="center">28 (84.8)</td>
<td valign="bottom" align="center">194 (78.9)</td>
<td valign="bottom" align="center">0.423</td>
</tr>
<tr>
<th valign="bottom" colspan="5" align="left">Invasive operation</th>
</tr>
<tr>
<td valign="bottom" align="left">Invasive line</td>
<td valign="bottom" align="center">87 (31.2)</td>
<td valign="bottom" align="center">25 (75.8)</td>
<td valign="bottom" align="center">62 (25.2)</td>
<td valign="bottom" align="center">
<bold>&lt;0.001</bold>
</td>
</tr>
<tr>
<td valign="bottom" align="left">Ventilation</td>
<td valign="bottom" align="center">101 (36.2)</td>
<td valign="bottom" align="center">25 (75.8)</td>
<td valign="bottom" align="center">76 (30.9)</td>
<td valign="bottom" align="center">
<bold>&lt;0.001</bold>
</td>
</tr>
<tr>
<th valign="bottom" colspan="5" align="left">Appropriate antimicrobial strategy</th>
</tr>
<tr>
<td valign="bottom" align="left">Empirical treatment</td>
<td valign="bottom" align="center">45 (16.1)</td>
<td valign="bottom" align="center">6 (18.2)</td>
<td valign="bottom" align="center">39 (15.9)</td>
<td valign="bottom" align="center">0.733</td>
</tr>
<tr>
<td valign="bottom" align="left">Combination therapy</td>
<td valign="bottom" align="center">224 (80.3)</td>
<td valign="bottom" align="center">27 (81.8)</td>
<td valign="bottom" align="center">197 (80.1)</td>
<td valign="bottom" align="center">0.814</td>
</tr>
<tr>
<td valign="bottom" align="left">Treated with BLBLIs</td>
<td valign="bottom" align="center">144 (51.6)</td>
<td valign="bottom" align="center">22 (66.7)</td>
<td valign="bottom" align="center">122 (49.6)</td>
<td valign="bottom" align="center">0.065</td>
</tr>
<tr>
<td valign="bottom" align="left">Treated with Carbapenems</td>
<td valign="bottom" align="center">58 (20.8)</td>
<td valign="bottom" align="center">8 (24.2)</td>
<td valign="bottom" align="center">50 (20.3)</td>
<td valign="bottom" align="center">0.603</td>
</tr>
<tr>
<td valign="bottom" align="left">Treated with Quinolones</td>
<td valign="bottom" align="center">161 (57.7)</td>
<td valign="bottom" align="center">13 (39.4)</td>
<td valign="bottom" align="center">148 (60.2)</td>
<td valign="bottom" align="center">
<bold>0.023</bold>
</td>
</tr>
<tr>
<td valign="bottom" align="left">More than two categories of antibiotics</td>
<td valign="middle" align="center">111 (39.8)</td>
<td valign="middle" align="center">14 (42.4)</td>
<td valign="middle" align="center">97 (39.4)</td>
<td valign="middle" align="center">0.741</td>
</tr>
<tr>
<th valign="bottom" colspan="5" align="left">Related agent use</th>
</tr>
<tr>
<td valign="bottom" align="left">Vasoactive agent use</td>
<td valign="bottom" align="center">74 (26.5)</td>
<td valign="bottom" align="center">25 (75.8)</td>
<td valign="bottom" align="center">49 (19.9)</td>
<td valign="bottom" align="center">
<bold>&lt;0.001</bold>
</td>
</tr>
<tr>
<td valign="bottom" align="left">Statins use</td>
<td valign="bottom" align="center">91 (32.6)</td>
<td valign="bottom" align="center">5 (15.2)</td>
<td valign="bottom" align="center">86 (35.0)</td>
<td valign="bottom" align="center">
<bold>0.023</bold>
</td>
</tr>
<tr>
<th valign="bottom" colspan="5" align="left">Outcome</th>
</tr>
<tr>
<td valign="bottom" align="left">Septic shock</td>
<td valign="bottom" align="center">60 (21.5)</td>
<td valign="bottom" align="center">18 (54.5)</td>
<td valign="bottom" align="center">42 (17.1)</td>
<td valign="bottom" align="center">
<bold>&lt;0.001</bold>
</td>
</tr>
<tr>
<td valign="bottom" align="left">Days of hospital stay (IQR)</td>
<td valign="bottom" align="center">6.3 (4.0-11.3)</td>
<td valign="bottom" align="center">6.0 (1.3-12.2)</td>
<td valign="bottom" align="center">6.3 (4.2-11.3)</td>
<td valign="bottom" align="center">0.059</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>IQR, interquartile range; CCI, Charlson comorbidity index; VTE, Venous thromboembolism; BLBLI, beta-lactam-beta-lactamase inhibitor; OR, odds ratio; CI, confidence interval. The P-values less than 0.05 are in bold.</p>
</fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="s3_2">
<title>Predictors for 28-day mortality</title>
<p>Of the 279 patients, 33 were not survived (non-survival group) and 246 survived (survival group) on day of 28. The univariate analysis results indicated the variables associated with 28-day mortality as followed (<xref ref-type="table" rid="T1">
<bold>Table&#xa0;1</bold>
</xref>): pulmonary disease (p=0.005), hypertension (p = 0.011), bleeding and coagulation disorders (p =0.002), hyperlipidemia (p = 0.009), respiratory failure (p &lt; 0.001), invasive line (p &lt; 0.001), ventilation (p &lt; 0.001), prior hospitalization (p = 0.046), treated with quinolones (p = 0.023), vasoactive agent use (p &lt; 0.001), statins use (p = 0.023), septic shock (p &lt; 0.001).</p>
<p>Co-operated with age and gender in multivariate analysis (<xref ref-type="table" rid="T2">
<bold>Table&#xa0;2</bold>
</xref>), the independent risk factor for 28-day mortality was vasoactive agent use (OR 7.377, 95% CI 1.775-30.651, p = 0.006). By contrast, age (OR 0.965, 95% CI 0.932-0.999, p =0.041), statins use (OR 0.220, 95% CI 0.060-0.801, p =0.022) and treated with quinolones (OR 0.356, 95% CI 0.143-0.887, p = 0.027) were all protective factors for 28-day mortality in patients of BSI-KP.</p>
<table-wrap id="T2" position="float">
<label>Table&#xa0;2</label>
<caption>
<p>Predictors for 28-day mortality in non-survival and survival group by univariate and multivariate analysis.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="bottom" rowspan="2" align="left">Variables</th>
<th valign="bottom" colspan="2" align="center">Univariate</th>
<th valign="bottom" colspan="2" align="center">Multivariate</th>
</tr>
<tr>
<th valign="bottom" align="center">OR (95%CI)</th>
<th valign="bottom" align="center">P-value</th>
<th valign="bottom" align="center">OR (95%CI)</th>
<th valign="bottom" align="center">P-value</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="bottom" align="left">Age, years</td>
<td valign="bottom" align="center">&#x2013;</td>
<td valign="bottom" align="center">0.429</td>
<td valign="top" align="center">0.965 (0.932-0.999)</td>
<td valign="top" align="center">
<bold>0.041</bold>
</td>
</tr>
<tr>
<td valign="bottom" align="left">Male</td>
<td valign="bottom" align="center">1.439 (0.678-3.053)</td>
<td valign="bottom" align="center">0.341</td>
<td valign="top" align="center">1.786 (0.700-4.552)</td>
<td valign="top" align="center">0.225</td>
</tr>
<tr>
<td valign="bottom" align="left">Pulmonary disease</td>
<td valign="bottom" align="center">2.920 (1.356-6.289)</td>
<td valign="bottom" align="center">0.005</td>
<td valign="top" align="center">1.274 (0.463-3.508)</td>
<td valign="top" align="center">0.639</td>
</tr>
<tr>
<td valign="bottom" align="left">Hypertension</td>
<td valign="bottom" align="center">0.385 (0.181-0.818)</td>
<td valign="bottom" align="center">0.011</td>
<td valign="top" align="center">0.719 (0.272-1.901)</td>
<td valign="top" align="center">0.506</td>
</tr>
<tr>
<td valign="bottom" align="left">Bleeding and coagulation disorders</td>
<td valign="bottom" align="center">3.121 (1.483-6.568)</td>
<td valign="bottom" align="center">0.002</td>
<td valign="top" align="center">1.272 (0.464-3.489)</td>
<td valign="top" align="center">0.640</td>
</tr>
<tr>
<td valign="bottom" align="left">Hyperlipidemia</td>
<td valign="bottom" align="center">0.309 (0.123-0.774)</td>
<td valign="bottom" align="center">0.009</td>
<td valign="top" align="center">0.368 (0.112-1.204)</td>
<td valign="top" align="center">0.098</td>
</tr>
<tr>
<td valign="bottom" align="left">Respiratory failure</td>
<td valign="bottom" align="center">6.236 (2.838-13.701)</td>
<td valign="bottom" align="center">&lt;0.001</td>
<td valign="top" align="center">2.431 (0.725-8.151)</td>
<td valign="top" align="center">0.150</td>
</tr>
<tr>
<td valign="bottom" align="left">Invasive line</td>
<td valign="bottom" align="center">9.274 (3.978-21.624)</td>
<td valign="bottom" align="center">&lt;0.001</td>
<td valign="top" align="center">1.703 (0.384-7.554)</td>
<td valign="top" align="center">0.484</td>
</tr>
<tr>
<td valign="bottom" align="left">Ventilation</td>
<td valign="bottom" align="center">6.990 (3.015-16.205)</td>
<td valign="bottom" align="center">&lt;0.001</td>
<td valign="top" align="center">1.347 (0.338-5.367)</td>
<td valign="top" align="center">0.673</td>
</tr>
<tr>
<td valign="bottom" align="left">Hospitalization</td>
<td valign="bottom" align="center">0.479 (0.230-0.997)</td>
<td valign="bottom" align="center">0.046</td>
<td valign="top" align="center">1.378 (0.511-3.717)</td>
<td valign="top" align="center">0.527</td>
</tr>
<tr>
<td valign="bottom" align="left">Treated with Quinolones</td>
<td valign="bottom" align="center">0.430 (0.205-0.905)</td>
<td valign="bottom" align="center">0.023</td>
<td valign="top" align="center">0.356 (0.143-0.887)</td>
<td valign="top" align="center">
<bold>0.027</bold>
</td>
</tr>
<tr>
<td valign="bottom" align="left">Vasoactive agent use</td>
<td valign="bottom" align="center">12.564 (5.341-29.554)</td>
<td valign="bottom" align="center">&lt;0.001</td>
<td valign="top" align="center">7.377 (1.775-30.651)</td>
<td valign="top" align="center">
<bold>0.006</bold>
</td>
</tr>
<tr>
<td valign="bottom" align="left">Statins use</td>
<td valign="bottom" align="center">0.332 (0.124-0.891)</td>
<td valign="bottom" align="center">0.023</td>
<td valign="top" align="center">0.220 (0.060-0.801)</td>
<td valign="top" align="center">
<bold>0.022</bold>
</td>
</tr>
<tr>
<td valign="bottom" align="left">Septic shock</td>
<td valign="bottom" align="center">5.829 (2.722-12.481)</td>
<td valign="bottom" align="center">&lt;0.001</td>
<td valign="top" align="center">0.903 (0.285-2.860)</td>
<td valign="top" align="center">0.863</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>OR, odds ratio; CI, confidence interval. The P-values less than 0.05 are in bold.</p>
</fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="s3_3">
<title>Predictors of septic shock</title>
<p>Among the study population, there were 60 and 219 patients in septic shock group and non- septic shock group, respectively. As demonstrated in <xref ref-type="table" rid="T3">
<bold>Table&#xa0;3</bold>
</xref>, the univariate analysis results indicated the variables associated with sepsis/septic shock as followed: pulmonary disease (p &lt; 0.001), hypertension (p = 0.016), bleeding and coagulation disorders (p &lt; 0.001), respiratory failure (p &lt; 0.001), kidney dysfunction (p = 0.001), prior hospitalization (p = 0.004).</p>
<table-wrap id="T3" position="float">
<label>Table&#xa0;3</label>
<caption>
<p>The demographic and clinical characteristics in septic shock and non-septic shock of 279 patients with BSI-KP.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="bottom" align="left">Variables, n (%)</th>
<th valign="bottom" align="center">Total (n=279)</th>
<th valign="bottom" align="center">Septic shock group (n=60)</th>
<th valign="bottom" align="center">Non-septic shock group (n=219)</th>
<th valign="bottom" align="center">P value</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="bottom" align="left">Age, years median (IQR)</td>
<td valign="bottom" align="center">67 (56-77)</td>
<td valign="bottom" align="center">67 (53-81)</td>
<td valign="bottom" align="center">67 (56-78)</td>
<td valign="bottom" align="center">0.769</td>
</tr>
<tr>
<td valign="bottom" align="left">Male</td>
<td valign="bottom" align="center">156 (55.9)</td>
<td valign="bottom" align="center">28 (46.7)</td>
<td valign="bottom" align="center">128 (58.4)</td>
<td valign="bottom" align="center">0.103</td>
</tr>
<tr>
<th valign="bottom" colspan="5" align="left">Comorbidity</th>
</tr>
<tr>
<td valign="bottom" align="left">Cardiovascular disease</td>
<td valign="bottom" align="center">127 (45.5)</td>
<td valign="bottom" align="center">29 (48.3)</td>
<td valign="bottom" align="center">98 (44.7)</td>
<td valign="bottom" align="center">0.621</td>
</tr>
<tr>
<td valign="bottom" align="left">Pulmonary disease</td>
<td valign="bottom" align="center">122 (43.7)</td>
<td valign="bottom" align="center">39 (65.0)</td>
<td valign="bottom" align="center">83 (37.9)</td>
<td valign="bottom" align="center">
<bold>&lt;0.001</bold>
</td>
</tr>
<tr>
<td valign="bottom" align="left">Cerebrovascular disease</td>
<td valign="bottom" align="center">37 (13.3)</td>
<td valign="bottom" align="center">5 (8.3)</td>
<td valign="bottom" align="center">32 (14.6)</td>
<td valign="bottom" align="center">0.204</td>
</tr>
<tr>
<td valign="bottom" align="left">Encephalopathy</td>
<td valign="bottom" align="center">70 (25.1)</td>
<td valign="bottom" align="center">16 (26.7)</td>
<td valign="bottom" align="center">54 (24.7)</td>
<td valign="bottom" align="center">0.750</td>
</tr>
<tr>
<td valign="bottom" align="left">Diabetes mellitus</td>
<td valign="bottom" align="center">107 (38.4)</td>
<td valign="bottom" align="center">27 (45.0)</td>
<td valign="bottom" align="center">80 (36.5)</td>
<td valign="bottom" align="center">0.232</td>
</tr>
<tr>
<td valign="bottom" align="left">Hypertension</td>
<td valign="bottom" align="center">159 (57.0)</td>
<td valign="bottom" align="center">26 (43.3)</td>
<td valign="bottom" align="center">133 (60.7)</td>
<td valign="bottom" align="center">
<bold>0.016</bold>
</td>
</tr>
<tr>
<td valign="bottom" align="left">Solid tumor</td>
<td valign="bottom" align="center">126 (45.2)</td>
<td valign="bottom" align="center">23 (38.3)</td>
<td valign="bottom" align="center">103 (47.0)</td>
<td valign="bottom" align="center">0.230</td>
</tr>
<tr>
<td valign="bottom" align="left">Hematological malignancy</td>
<td valign="bottom" align="center">22 (7.9)</td>
<td valign="bottom" align="center">5 (8.3)</td>
<td valign="bottom" align="center">17 (7.8)</td>
<td valign="bottom" align="center">1.000</td>
</tr>
<tr>
<td valign="bottom" align="left">VTE</td>
<td valign="bottom" align="center">43 (15.4)</td>
<td valign="bottom" align="center">7 (11.7)</td>
<td valign="bottom" align="center">36 (16.4)</td>
<td valign="bottom" align="center">0.364</td>
</tr>
<tr>
<td valign="bottom" align="left">Bleeding and coagulation disorders</td>
<td valign="bottom" align="center">73 (26.2)</td>
<td valign="bottom" align="center">31 (51.7)</td>
<td valign="bottom" align="center">42 (19.2)</td>
<td valign="bottom" align="center">
<bold>&lt;0.001</bold>
</td>
</tr>
<tr>
<td valign="bottom" align="left">Hyperlipidemia</td>
<td valign="bottom" align="center">109 (39.1)</td>
<td valign="bottom" align="center">19 (31.7)</td>
<td valign="bottom" align="center">90 (41.1)</td>
<td valign="bottom" align="center">0.185</td>
</tr>
<tr>
<td valign="bottom" align="left">Heart failure</td>
<td valign="bottom" align="center">48 (17.2)</td>
<td valign="bottom" align="center">12 (20.0)</td>
<td valign="bottom" align="center">36 (16.4)</td>
<td valign="bottom" align="center">0.517</td>
</tr>
<tr>
<td valign="bottom" align="left">Respiratory failure</td>
<td valign="bottom" align="center">44 (15.8)</td>
<td valign="bottom" align="center">23 (38.3)</td>
<td valign="bottom" align="center">21 (9.6)</td>
<td valign="bottom" align="center">
<bold>&lt;0.001</bold>
</td>
</tr>
<tr>
<td valign="bottom" align="left">Kidney dysfunction</td>
<td valign="bottom" align="center">143 (51.3)</td>
<td valign="bottom" align="center">42 (70.0)</td>
<td valign="bottom" align="center">101 (46.1)</td>
<td valign="bottom" align="center">
<bold>0.001</bold>
</td>
</tr>
<tr>
<td valign="bottom" align="left">CCI, median (IQR)</td>
<td valign="bottom" align="center">6 (4-8)</td>
<td valign="bottom" align="center">6 (4-8)</td>
<td valign="bottom" align="center">6 (4-9)</td>
<td valign="bottom" align="center">0.912</td>
</tr>
<tr>
<th valign="bottom" colspan="5" align="left">Past history</th>
</tr>
<tr>
<td valign="bottom" align="left">Smoking</td>
<td valign="bottom" align="center">91 (32.6)</td>
<td valign="bottom" align="center">15 (25.0)</td>
<td valign="bottom" align="center">76 (34.7)</td>
<td valign="bottom" align="center">0.155</td>
</tr>
<tr>
<td valign="bottom" align="left">Drinking</td>
<td valign="bottom" align="center">22 (7.9)</td>
<td valign="bottom" align="center">5 (8.3)</td>
<td valign="bottom" align="center">17 (7.8)</td>
<td valign="bottom" align="center">1.000</td>
</tr>
<tr>
<td valign="bottom" align="left">Surgery and trauma</td>
<td valign="bottom" align="center">169 (60.6)</td>
<td valign="bottom" align="center">39 (65.0)</td>
<td valign="bottom" align="center">130 (59.4)</td>
<td valign="bottom" align="center">0.428</td>
</tr>
<tr>
<td valign="bottom" align="left">Prior hospitalization</td>
<td valign="bottom" align="center">179 (64.2)</td>
<td valign="bottom" align="center">29 (48.3)</td>
<td valign="bottom" align="center">150 (68.5)</td>
<td valign="bottom" align="center">
<bold>0.004</bold>
</td>
</tr>
<tr>
<th valign="bottom" colspan="5" align="left">Epidemiology</th>
</tr>
<tr>
<td valign="bottom" align="left">Community-acquired</td>
<td valign="bottom" align="center">204 (73.1)</td>
<td valign="bottom" align="center">38 (63.3)</td>
<td valign="bottom" align="center">166 (75.8)</td>
<td valign="bottom" align="center">0.054</td>
</tr>
<tr>
<td valign="bottom" align="left">Emergency admission</td>
<td valign="bottom" align="center">222 (79.6)</td>
<td valign="bottom" align="center">49 (81.7)</td>
<td valign="bottom" align="center">173 (79.0)</td>
<td valign="bottom" align="center">0.649</td>
</tr>
<tr>
<td valign="bottom" align="left">
<bold>Invasive operation</bold>
</td>
<td valign="bottom" align="center"/>
<td valign="bottom" align="center"/>
<td valign="bottom" align="center"/>
<td valign="bottom" align="center"/>
</tr>
<tr>
<td valign="bottom" align="left">Invasive line</td>
<td valign="bottom" align="center">87 (31.2)</td>
<td valign="bottom" align="center">50 (83.3)</td>
<td valign="bottom" align="center">37 (16.9)</td>
<td valign="bottom" align="center">
<bold>&lt;0.001</bold>
</td>
</tr>
<tr>
<td valign="bottom" align="left">Ventilation</td>
<td valign="bottom" align="center">101 (36.2)</td>
<td valign="bottom" align="center">48 (80.0)</td>
<td valign="bottom" align="center">53 (24.2)</td>
<td valign="bottom" align="center">
<bold>&lt;0.001</bold>
</td>
</tr>
<tr>
<th valign="bottom" colspan="5" align="left">Appropriate antimicrobial strategy</th>
</tr>
<tr>
<td valign="bottom" align="left">Empirical treatment</td>
<td valign="bottom" align="center">45 (16.1)</td>
<td valign="bottom" align="center">9 (15.0)</td>
<td valign="bottom" align="center">36 (16.4)</td>
<td valign="bottom" align="center">0.788</td>
</tr>
<tr>
<td valign="bottom" align="left">Combination therapy</td>
<td valign="bottom" align="center">224 (80.3)</td>
<td valign="bottom" align="center">53 (88.3)</td>
<td valign="bottom" align="center">171 (78.1)</td>
<td valign="bottom" align="center">0.077</td>
</tr>
<tr>
<td valign="bottom" align="left">Treatment with BLBLIs</td>
<td valign="bottom" align="center">144 (51.6)</td>
<td valign="bottom" align="center">40 (66.7)</td>
<td valign="bottom" align="center">104 (47.5)</td>
<td valign="bottom" align="center">
<bold>0.008</bold>
</td>
</tr>
<tr>
<td valign="bottom" align="left">Treatment with Carbapenems</td>
<td valign="bottom" align="center">58 (20.8)</td>
<td valign="bottom" align="center">17 (28.3)</td>
<td valign="bottom" align="center">41 (18.7)</td>
<td valign="bottom" align="center">0.104</td>
</tr>
<tr>
<td valign="bottom" align="left">Treatment with Quinolones</td>
<td valign="bottom" align="center">161 (57.7)</td>
<td valign="bottom" align="center">32 (53.3)</td>
<td valign="bottom" align="center">129 (58.9)</td>
<td valign="bottom" align="center">0.439</td>
</tr>
<tr>
<td valign="bottom" align="left">More than two categories of antibiotics</td>
<td valign="middle" align="center">111 (39.8)</td>
<td valign="middle" align="center">14 (42.4)</td>
<td valign="middle" align="center">97 (39.4)</td>
<td valign="middle" align="center">0.741</td>
</tr>
<tr>
<th valign="bottom" colspan="5" align="left">Related agent use</th>
</tr>
<tr>
<td valign="bottom" align="left">Vasoactive agent use</td>
<td valign="bottom" align="center">74 (26.5)</td>
<td valign="bottom" align="center">49 (81.7)</td>
<td valign="bottom" align="center">25 (11.4)</td>
<td valign="bottom" align="center">
<bold>&lt;0.001</bold>
</td>
</tr>
<tr>
<td valign="bottom" align="left">Statins use</td>
<td valign="bottom" align="center">91 (32.6)</td>
<td valign="bottom" align="center">17 (28.3)</td>
<td valign="bottom" align="center">74 (33.8)</td>
<td valign="bottom" align="center">0.424</td>
</tr>
<tr>
<th valign="bottom" colspan="5" align="left">Outcome</th>
</tr>
<tr>
<td valign="bottom" align="left">death within 28 days</td>
<td valign="bottom" align="center">33 (11.8)</td>
<td valign="bottom" align="center">18 (30.0)</td>
<td valign="bottom" align="center">15 (6.8)</td>
<td valign="bottom" align="center">
<bold>&lt;0.001</bold>
</td>
</tr>
<tr>
<td valign="bottom" align="left">Days of hospital stay (IQR)</td>
<td valign="bottom" align="center">6.3 (4.0-11.3)</td>
<td valign="bottom" align="center">6.5 (3.9-15.5)</td>
<td valign="bottom" align="center">6.2 (4.0-10.8)</td>
<td valign="bottom" align="center">0.423</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>IQR, interquartile range; CCI, Charlson comorbidity index; VTE, Venous thromboembolism; BLBLI, beta-lactam-beta-lactamase inhibitor; OR, odds ratio; CI, confidence interval. The P-values less than 0.05 are in bold.</p>
</fn>
</table-wrap-foot>
</table-wrap>
<p>As the results of multivariate analysis, pulmonary disease (OR 2.348, 95% CI 1.126-4.897, p = 0.023), bleeding and coagulation disorders (OR 3.626, 95% CI 1.783-7.372, p &lt; 0.001), respiratory failure (OR 2.823, 95% CI 0.178-6.767, p = 0.020) and kidney dysfunction (OR 2.450, 95% CI 1.189-5.047, p&#xa0;= 0.015) were independent risk factors while male (OR 0.333, 95% CI 0.161-0.688, p =0.003) and hypertension (OR 0.363, 95% CI 0.180-0.733, p = 0.005) was protective factors for patients suffered from septic shock (<xref ref-type="table" rid="T4">
<bold>Table&#xa0;4</bold>
</xref>).</p>
<table-wrap id="T4" position="float">
<label>Table&#xa0;4</label>
<caption>
<p>Predictors for 28-day mortality in septic shock and non-septic shock group by univariate and multivariate analysis.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="bottom" rowspan="2" align="left">Variables, n (%)</th>
<th valign="bottom" colspan="2" align="center">Univariate</th>
<th valign="bottom" colspan="2" align="center">Multivariate</th>
</tr>
<tr>
<th valign="bottom" align="center">OR (95%CI)</th>
<th valign="bottom" align="center">P-value</th>
<th valign="bottom" align="center">OR (95%CI)</th>
<th valign="bottom" align="center">P-value</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="bottom" align="left">Age, years</td>
<td valign="bottom" align="center">&#x2013;</td>
<td valign="bottom" align="center">0.769</td>
<td valign="top" align="center">0.992 (0.968-1.016)</td>
<td valign="top" align="center">0.497</td>
</tr>
<tr>
<td valign="bottom" align="left">Male</td>
<td valign="bottom" align="center">0.622 (0.350-1.104)</td>
<td valign="bottom" align="center">0.103</td>
<td valign="top" align="center">0.333 (0.161-0.688)</td>
<td valign="top" align="center">
<bold>0.003</bold>
</td>
</tr>
<tr>
<td valign="bottom" align="left">Pulmonary disease</td>
<td valign="top" align="center">3.043 (1.676-5.526)</td>
<td valign="bottom" align="center">&lt;0.001</td>
<td valign="top" align="center">2.348 (1.126-4.897)</td>
<td valign="top" align="center">
<bold>0.023</bold>
</td>
</tr>
<tr>
<td valign="bottom" align="left">Hypertension</td>
<td valign="top" align="center">0.494 (0.277-0.882)</td>
<td valign="bottom" align="center">0.016</td>
<td valign="top" align="center">0.363 (0.180-0.733)</td>
<td valign="top" align="center">
<bold>0.005</bold>
</td>
</tr>
<tr>
<td valign="bottom" align="left">Bleeding and coagulation disorders</td>
<td valign="top" align="center">4.505 (2.453-8.274)</td>
<td valign="bottom" align="center">&lt;0.001</td>
<td valign="top" align="center">3.626 (1.783-7.372)</td>
<td valign="top" align="center">
<bold>&lt;0.001</bold>
</td>
</tr>
<tr>
<td valign="bottom" align="left">Respiratory failure</td>
<td valign="top" align="center">5.861 (2.946-11.660)</td>
<td valign="bottom" align="center">&lt;0.001</td>
<td valign="top" align="center">2.823 (1.178-6.767)</td>
<td valign="top" align="center">
<bold>0.020</bold>
</td>
</tr>
<tr>
<td valign="bottom" align="left">Kidney dysfunction</td>
<td valign="top" align="center">2.726 (1.477-5.031)</td>
<td valign="bottom" align="center">0.001</td>
<td valign="top" align="center">2.450 (1.189-5.047)</td>
<td valign="top" align="center">
<bold>0.015</bold>
</td>
</tr>
<tr>
<td valign="bottom" align="left">Prior hospitalization</td>
<td valign="top" align="center">0.430 (0.241-0.769)</td>
<td valign="bottom" align="center">0.004</td>
<td valign="top" align="center">0.601 (0.300-1.204)</td>
<td valign="top" align="center">0.151</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>OR: odds ratio; CI: confidence interval. The P-values less than 0.05 are in bold.</p>
</fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="s3_4">
<title>Comparison of antimicrobial susceptibility</title>
<p>For the 279 isolates, meropenem exerted the highest susceptibility rate (98.9%), followed by piperacillin/tazobactam (94.3%), and the last was ampicillin/sulbactam (80.6). The survival group had an obviously higher ampicillin/sulbactam and cefazolin susceptibility rate than the non-survival group (<xref ref-type="fig" rid="f2">
<bold>Figure&#xa0;2A</bold>
</xref>). Actually, except for ceftazidime and trimethoprim/sulfa, the susceptibility rates of all drugs were relatively higher in the survival group than in the non-survival group. Except for meropenem, the septic shock group had a higher susceptibility rate of all drugs than the non-septic shock group (<xref ref-type="fig" rid="f2">
<bold>Figure&#xa0;2B</bold>
</xref>).</p>
<fig id="f2" position="float">
<label>Figure&#xa0;2</label>
<caption>
<p>The antimicrobial susceptibility of BSI-KP in 279 patients. <bold>(A)</bold> The antimicrobial susceptibility of BSI-KP in survival and non-survival group. <bold>(B)</bold> The antimicrobial susceptibility of BSI-KP in septic shock and non-septic shock group. SAM, ampicillin/sulbactam; CFZ, cefazolin; FEP, cefepime; CAZ, ceftazidime; CRO, ceftriaxone; TZP, piperacillin/tazobactam; CIP, ciprofloxacin; MEM, meropenem; TOB, tobramycin; GEN, gentamicin; SMZ, trimethoprim/sulfa.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fcimb-12-1087701-g002.tif"/>
</fig>
</sec>
<sec id="s3_5">
<title>Effect of statins use on 28-day mortality and septic shock</title>
<p>In univariate analysis (<xref ref-type="table" rid="T1">
<bold>Table&#xa0;1</bold>
</xref>), the hyperlipidemic group had a lower risk of death within 28 days than the non-hyperlipidemic group (OR 0.309, 95% CI 0.123-0.774, p = 0.009). However, there was no significant difference in the risk of septic shock (OR 0.664, 95% CI 0.362-1.219, p = 0.185) between the two groups (<xref ref-type="table" rid="T3">
<bold>Table&#xa0;3</bold>
</xref>).</p>
<p>Consequently, we explored the relationship between statins use and 28-day mortality in all patients. As a result of multivariate analysis (<xref ref-type="table" rid="T2">
<bold>Table&#xa0;2</bold>
</xref>), patients with statins use had a lower risk of death within 28 days than those without statins use (OR 0.220, 95% CI 0.060-0.801, p = 0.022). Similarly, no significant difference was found in the risk of septic shock between the two groups (OR 0.775, 95% CI 0.414-1.451, p = 0.424) (<xref ref-type="table" rid="T3">
<bold>Table&#xa0;3</bold>
</xref>).</p>
<p>There were totally 91 patients used statins which varied from Simvastatin, Pravastatin, Atorvastatin, and Rosuvastatin during the hospitalization. It was observed that only three patients using Atorvastatin and two using Rosuvastatin died within 28-day (<xref ref-type="fig" rid="f3">
<bold>Figure&#xa0;3A</bold>
</xref>). The statins duration time of the patients could be divided into four groups as showed in <xref ref-type="fig" rid="f3">
<bold>Figure&#xa0;3B</bold>
</xref>, in which four and one patient died within 28-day in group 7-28 days and group more than 28 days, respectively.</p>
<fig id="f3" position="float">
<label>Figure&#xa0;3</label>
<caption>
<p>The use of statins and death within 28-day in statins use patients. <bold>(A)</bold> The distribution of statins categories and death within 28-day in statins use patients. <bold>(B)</bold> The distribution of statins duration and death within 28-day in statins use patients. A, Atorvastatin; P, Pravastatin; R, Rosuvastatin; S, Simvastatin.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fcimb-12-1087701-g003.tif"/>
</fig>
</sec>
<sec id="s3_6">
<title>Risk prediction model for 28-day mortality and sepsis/septic shock</title>
<p>We created a logistic regression model to predict the probability of 28-day mortality based on the results of multivariate analysis. The Omnibus test and the Hosmer-Lemeshow test demonstrated that this model was generally meaningful (p &lt; 0.001) and had a good fit to the data (p = 0.799), respectively. ROC curve showed that the integration of quinolones uses, vasoactive agent use, statins use was good for prediction with an AUROC of 0.855 (95% CI = 0.796&#x2013;0.914) (p &lt; 0.001) (<xref ref-type="fig" rid="f4">
<bold>Figure&#xa0;4A</bold>
</xref>). We also established a model for evaluating the risk of septic shock and confirmed the model quality with the Omnibus test (p &lt; 0.001) and the Hosmer-Lemeshow test (p = 1.000). ROC curve showed that the integration of gender, pulmonary disease, bleeding and coagulation disorders, respiratory failure, kidney dysfunction and hypertension could assess risk of septic-shock to some extent, with an AUROC of 0.815 (95% CI = 0.755&#x2013;0.874) (p &lt; 0.001) (<xref ref-type="fig" rid="f4">
<bold>Figure&#xa0;4B</bold>
</xref>).</p>
<fig id="f4" position="float">
<label>Figure&#xa0;4</label>
<caption>
<p>Receiver-operator characteristic curve for predicting the 28-day mortality and septic shock of BSI-KP. <bold>(A)</bold> The model was based on the variables of statins use, quinolones use and vasoactive agent use to predict 28-day mortality. <bold>(B)</bold> The model was based on the variables of gender, pulmonary disease, bleeding and coagulation disorders, respiratory failure, kidney dysfunction and hypertension for predicting septic shock.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fcimb-12-1087701-g004.tif"/>
</fig>
</sec>
</sec>
<sec id="s4" sec-type="discussion">
<title>Discussion</title>
<p>It was the first time for us to investigate the risk factors of mortality in patients with BSI-KP based on the MIMIC IV database. Since a number of improvements have been made, MIMIC IV released a series of new tables, including table <italic>microbiologyevents</italic> that comprised of variety culture results. This study collected data from MIMIC IV v1.0, which was released on March 16th, 2021. Compared to recent research results, the 28-day mortality in our study was relatively lower (11.8%) partly because most BSI-KP isolates were carbapenem-susceptible, and the antimicrobial treatment guided by the culture result was effective. According to the data, isolates from 1/33 patients of the non-survival group were meropenem resistant. Only 3/279 patients were positive with meropenem-resistant KP in blood culture.</p>
<p>As results from <xref ref-type="table" rid="T2">
<bold>Table&#xa0;2</bold>
</xref>, patients with hyperlipidemia and those with statins use had a lower risk of death within 28 days than those without hyperlipidemia and statins use. It was also found that hyperlipidemia patients are more likely to use statins than non-hyperlipidemia patients (<xref ref-type="supplementary-material" rid="SM1">
<bold>Table S2</bold>
</xref>). Then we focused on the effect of statins used in 28-day mortality. The result suggested that statins use presented no statistical difference of mortality analysis neither in hyperlipidemia patients nor in non-hyperlipidemia patients (<xref ref-type="supplementary-material" rid="SM1">
<bold>Table S1</bold>
</xref>), which probably attributed to the sample size and the intergroup heterogeneity. A multivariate analysis was performed to confirm the potential correlation between statins and 28-day mortality. As shown in <xref ref-type="table" rid="T2">
<bold>Table&#xa0;2</bold>
</xref>, patients with statins use had a lower risk of death within 28 days than those without statin use. Therefore, statins use was an independent protective factor for the death of patients with BSI-KP.</p>
<p>Statins have lipid-lowering properties by inhibiting 3-hydroxy-3 methylglutaryl coenzyme A reductase, a rate-limiting enzyme in cholesterol biosynthesis. They also have anti-inflammatory and immunomodulatory effects resulting in improved endothelial function, reduced thrombogenicity, and plaque stabilization (<xref ref-type="bibr" rid="B5">Dobesh and Olsen, 2014</xref>). Fifteen years ago, a meta-analysis published on Lancet (<xref ref-type="bibr" rid="B12">Lewington et&#xa0;al., 2007</xref>) demonstrated that statins use could reduce coronary event rates and total stroke rates in patients with a wide range of ages and blood pressures. Researchers had focused on the functions of statins earlier, especially in patients with sepsis (<xref ref-type="bibr" rid="B1">Almog, 2003</xref>; <xref ref-type="bibr" rid="B16">Novack et&#xa0;al., 2006</xref>). Since then, a series of studies have been performed and showed that statin use improved the outcome of BSI patients with transplantation and cancer (<xref ref-type="bibr" rid="B21">Sun et&#xa0;al., 2009</xref>; <xref ref-type="bibr" rid="B8">Hsu et&#xa0;al., 2009</xref>; <xref ref-type="bibr" rid="B25">Yu et&#xa0;al., 2014</xref>). However, there found no evidence indicating that statins use is associated with a reduction in mortality of sepsis patients (<xref ref-type="bibr" rid="B18">Pasin et&#xa0;al., 2013</xref>; <xref ref-type="bibr" rid="B22">Wan et&#xa0;al., 2014</xref>). In our study, it was observed that statins use had a significant difference in the risk of septic shock and the 28-day mortality of patients admitted into the intensive care unit (ICU) (<xref ref-type="supplementary-material" rid="SM1">
<bold>Table S3</bold>
</xref>). Besides, Mehl et&#xa0;al. (<xref ref-type="bibr" rid="B15">Mehl et&#xa0;al., 2015</xref>) proposed that prior statin use is associated with a lower 90-day total mortality in Gram-negative BSI but not in Gram-positive BSI. However, two studies showed statin treatment in patients with <italic>Staphylococcus aureus</italic> bacteremia was associated with lower mortality (<xref ref-type="bibr" rid="B13">Lopez-Cortes et&#xa0;al., 2013</xref>; <xref ref-type="bibr" rid="B20">Smit et&#xa0;al., 2017</xref>). This study also found that statin use could decrease the 28-day mortality in patients with BSI-KP (<xref ref-type="table" rid="T2">
<bold>Table&#xa0;2</bold>
</xref>).</p>
<p>The effect of statins in patients may be different for individual statins. Lee et&#xa0;al. (<xref ref-type="bibr" rid="B11">Lee et&#xa0;al., 2018</xref>) found that simvastatin (hazard ratio [HR], 0.72; 95% CI, 0.58-0.90) and atorvastatin (HR, 0.78; 95% CI, 0.68-0.90) were associated with improved 30-day survival, whereas rosuvastatin was not. Quellette et&#xa0;al. (<xref ref-type="bibr" rid="B17">Ouellette et&#xa0;al., 2015</xref>) pointed out that atorvastatin use was associated with improved mortality in septic patients compared with pre-hospital simvastatin use. In addition, a study base on patients with chronic obstructive pulmonary disease (COPD) also observed that Fluvastatin and Atorvastatin are more effective in reducing C-reactive protein and pulmonary hypertension (<xref ref-type="bibr" rid="B14">Lu et&#xa0;al., 2019</xref>). It inferred that the drug-specific effect of statins on BSI is not correlated to their lipid-lowering potency. Statins have pleiotropic effects such as anti-inflammatory, antithrombotic, and antioxidant effects and their lipid-lowering effects. In sepsis and septic shock, cytokine release from endothelial cells, procoagulant molecules, and thrombocyte production are encouraged. Statins act on sepsis by providing vascular relaxation in the endothelium and reducing the expression of adhesion molecules and cytokines (<xref ref-type="bibr" rid="B7">Goncuoglu et&#xa0;al., 2021</xref>).</p>
<p>On the other hand, Pawar et&#xa0;al. (<xref ref-type="bibr" rid="B19">Pawar et&#xa0;al., 2018</xref>) found that continued statins use for at least two days after admission provided a survival benefit among bacteremia patients. Similar to our study, about 77% (21/91) of patients continued using statins for more than two days. Thus, more laboratory and clinical data are encouraged, and randomized controlled trials will be needed to define the role of statins in BSI patients.</p>
<p>Based on the binary logistic regression results, we built two ROC curves to predict the risk of 28-day mortality and septic shock in BSI-KP patients. In the model for 28-day mortality, the variable age was considered as a confounding factor because of no clinical significance and was excluded (<xref ref-type="fig" rid="f4">
<bold>Figure&#xa0;4A</bold>
</xref>). Quinolones use was regarded as a protective factor mainly attributed to the majority isolates were sensitive to ciprofloxacin (<xref ref-type="fig" rid="f2">
<bold>Figure&#xa0;2</bold>
</xref>). There were two main reasons for the protective effect of quinolones. One is the combination of drugs in the process of anti-infection treatment, and the other is that the utilization rate of carbapenems (20.8%) is lower than quinolones (57.7%). By contrast, the effectiveness of variable gender in the model for septic shock was still preserved since it was not determined whether males were less likely to suffer from septic shock (<xref ref-type="fig" rid="f4">
<bold>Figure&#xa0;4B</bold>
</xref>). As a result, both of the models had good value for prediction.</p>
<p>This study has some limitations. First, the data were obtained from a single institution during a limited study period, and thus, the results may not be widely representative or generalizable. Second, the data was insufficient to detail the effect of statins use on the 28-day mortality of patients. Third, since it was a retrospective study, the confounders still exist, such as patients&#x2019; comorbidities, and the selection bias may influence the results. With the current knowledge, more studies are needed to confirm the use of statins to benefit from their pleiotropic effects in the treatment of BSI-KP patients.</p>
</sec>
<sec id="s5" sec-type="conclusions">
<title>Conclusion</title>
<p>The use of statins could decrease the risk of 28-day mortality in patients of BSI-KP. The risk factor-based prediction model provided evidence for drug treatment in BSI-KP patients. Paying more attention to the treatment strategy will be an optimal way to improve outcomes in clinical practice.</p>
</sec>
<sec id="s6" sec-type="data-availability">
<title>Data availability statement</title>
<p>The original contributions presented in the study are included in the article/<xref ref-type="supplementary-material" rid="SM1">
<bold>Supplementary Material</bold>
</xref>. Further inquiries can be directed to the corresponding author.</p>
</sec>
<sec id="s7" sec-type="ethics-statement">
<title>Ethics statement</title>
<p>Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.</p>
</sec>
<sec id="s8" sec-type="author-contributions">
<title>Author contributions</title>
<p>QX designed the study and extracted the data. BZ and QX analyzed the data and drafted the manuscript. YX and BZ supervised the study and revised the manuscript. PS contributed to the data analyzation and the figure presentation. All authors contributed to the article and approved the submitted version.</p>
</sec>
</body>
<back>
<sec id="s9" sec-type="funding-information">
<title>Funding</title>
<p>This work was supported by the National Key Research and Development Program of China (2021YFC2300300) and Research Project of Jinan Microecological Biomedicine Shandong Laboratory (JNL-2022027C).</p>
</sec>
<sec id="s10" sec-type="COI-statement">
<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 id="s11" sec-type="disclaimer">
<title>Publisher&#x2019;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>
<sec id="s12" sec-type="supplementary-material">
<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/fcimb.2022.1087701/full#supplementary-material">https://www.frontiersin.org/articles/10.3389/fcimb.2022.1087701/full#supplementary-material</ext-link>
</p>
<supplementary-material xlink:href="Table_1.docx" id="SM1" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document"/>
</sec>
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