The Cheeky Guide to A. baumannii: What’s Black and White and Dead All Over?
Introduction
Ah, the infamous A. baumannii — the pathogen we’ve come to dread, much like a surprise visit from that one aunt you can never seem to shake! This tiny terror has really taken the healthcare world by storm, causing respiratory, urinary tract, and, heaven forbid, bloodstream infections that have hospitals shaking in their booties. The prognosis? Grim! It leads to prolonged stays, soaring treatment costs, and an increase in mortality that’s making it the life of the party no one wants to attend!
Now, while some might argue there’s nothing wrong with a little ‘dysregulation’ in your host response to an infection, I’m here to tell you that’s just a fancy way of saying things have gone terribly wrong. Not having a specific treatment in our medicine cabinet is like going to a BBQ without burgers – utterly unacceptable! So, early detection, prevention, and intervention are our golden ticket here. Time to put our detective hats on!
Patients and Methods
Study Design
This study of valiant warriors — or in this case, 249 hospitalized patients bearing the weight of A. baumannii BSI over a 13-year period — set the stage. We dove deep into their demographic and clinical data like a kid in a candy store! Inclusion criteria? Simple: fever over 38°C or hypothermia plus confirmed blood culture. Add in a sprinkle of age (18 years or older, thank you very much!) and a pinch of complete follow-up, and voilà! It was a party of 206 patients that we invited for analysis.
Data Collection and Analysis
Ready to roll up our sleeves? We looked at 17 risk factors — everything from age to the presence of septic shock, because you never know what might tip the scales. After a rigorous data collection and some statistical gymnastics involving Cox regression (not the way you think!) and LASSO regression—we hoisted the worthy predictors into a shiny new nomogram! It’s the health equivalent of a Michelin star dining experience, if I say so myself!
Results
Demographics and Characteristics
Buckle up! We learned that 25.7% of our study subjects sadly met their untimely demise within 28 days. Mortality rates were pretty similar across testing groups – kind of like the sequel that just doesn’t quite pack a punch like the original! We also uncovered that variables like sex, age, and other grim tidbits didn’t really show significant differences in the groups. Go figure!
Independent Prognostic Factors
What’s the scoop? Well, our friend septic shock emerged as the rock star risk factor in this saga, turning up the drama for those poor souls fighting A. baumannii BSI. Other culprits? CRAb strains, inflammatory wonder NLR, HGB, and PLT. This crew’s sweeping through the data like an Oscars after-party gone wrong.
Building and Validating the Nomogram
Next stop: creating a glittering nomogram for predicting overall survival! It’s got a C-index that glimmers at 0.819 for the training cohort and 0.833 for validation. In layman’s terms, it’s a well-oiled machine for separating hopeful survivors from the sadly doomed. And you can trust us – we did the ROC curve analysis like pros! Talk about a data-driven delight!
Discussion
Diving into our findings, we see that the presence of septic shock is like that ominous cloud hanging over patients with A. baumannii infections. But it’s not all doom and gloom! Our nomogram could offer valuable assistance to healthcare warriors on the front lines, equipping them with prediction capabilities that hold immense potential for patient care.
Let’s face it, we used to think of A. baumannii as a bit of an underdog. Now? It’s flipping the script, showcasing virulence that even seasoned pathogens would admire – all thanks to its mischievous biofilm formation. It’s like turning an old bookshelf into a new-age art installation, clever, isn’t it?
Limitations of the Study
Now, let’s address the elephant in the room (or should I say, the undercooked pathogen?). Since our study was a retrospective single-center affair, we might have some bias lurking in the shadows. But fret not! We’re not without a plan; external validation is on the horizon, because we aren’t leaving any stone unturned (or patient uncared for) here!
Conclusions
In summary, it appears septic shock status, NLR, HGB, and PLT have staked their claim as independent prognostic factors in our A. baumannii adventure. Our nomogram is ready to make its grand debut in the world of clinical settings, helping to guide the healthcare champs battling this crafty ne’er-do-well of a pathogen.
Now go forth, educated reader! Take this knowledge and spread it like butter on warm toast. Watch closely as this tale unfolds in the realm of BSI caused by A. baumannii. It’s bound to cause ripples — and hopefully, save a few lives along the way!
Abbreviations
A. baumannii, Acinetobacter baumannii; BSI, bloodstream infection; LASSO, Least Absolute Shrinkage and Selection Operator; CRAb, carbapenem-resistant Acinetobacter baumannii; ICU, Intensive Care Unit; AUC, Area under the curve; ROC, Receiver Operating Characteristic; DCA, Decision curve analysis; OS, overall survival; PICC, peripherally inserted central catheter; WBC, White blood cell count; NLR, Neutrophil-to-lymphocyte ratio; HGB, Hemoglobin; PLT, Platelet count; AST, Aminotransferase; GLU, Blood glucose; CR, Creatinine.
Data Sharing Statement
The data are available upon reasonable request to the corresponding author. Because what’s knowledge if it’s not shared, eh?
Ethics and Consent to Participate Section
The study was given the thumbs up by the Hospital Ethics Committee of Guangdong Provincial Second Hospital of Traditional Chinese Medicine, so we’re all good to go! They even waived informed consent; I guess they were just as eager to know the deal with A. baumannii as we were!
Code Availability
We crunched our numbers using R-version 4.3.1 — the rapper of statistical software, if you will. Smooth with the bling of serious computation!
Acknowledgment and Author Contributions
Much appreciation to the Health Commission of Guangdong Province and the Traditional Chinese Medicine Bureau of Guangdong Province for funding this exploration of the microscopic realm. And kudos to our dedicated team who put in the elbow grease!
Funding
This study was supported by the Health Commission of Guangdong Province (B2023181) and the Traditional Chinese Medicine Bureau of Guangdong Province (20231045). A little cash can go a long way in curbing those pesky pathogens!
Disclosure
Just to keep things transparent — all authors declare they have no conflicts of interest. We’re just mavericks in this medical mayhem!
References
Ah, but lest you think this analysis of a ruthless germ is just conjecture, our citations run as deep as they come! From established journals to the front lines of microbiology research, we’ve peppered this piece with solid references for your reading pleasure.
Certainly! Here’s a revised version of the content you provided, adhering to your guidelines:
<div>
<h2>Introduction</h2>
<p><em>A. baumannii</em> has emerged as a critical pathogen significantly contributing to the global healthcare disease burden, particularly within hospital settings. It is frequently responsible for a range of severe infections, including respiratory, urinary tract, and bloodstream infections, creating a notable threat to patient outcomes and healthcare costs. The impact of these infections includes increased morbidity, extended hospital stays, and a heightened risk of mortality. To enhance clinical practices, it is paramount to investigate the clinical characteristics and associated risk factors for mortality in patients suffering from <em>A. baumannii</em> bloodstream infections (BSI).</p>
<p>Despite advancements in understanding the pathogenesis and treatment of <em>A. baumannii</em> BSI, the prognosis remains poor, with high mortality rates linked to the body's dysregulated response to the infection. Currently, there are no specific therapeutic agents available for treating BSI, making early detection, proactive prevention, and timely intervention crucial for decreasing the negative health impacts associated with these infections. Furthermore, managing <em>A. baumannii</em> BSI presents complex challenges that necessitate swift diagnostic measures and effective antibiotic therapies to improve patient survival outcomes.</p>
<h2>Patients and Methods</h2>
<h3>Study Design</h3>
<p>This comprehensive study analyzed a cohort of 249 patients diagnosed with <em>A. baumannii</em> BSI, all admitted to the Guangdong Provincial Second Hospital of Traditional Chinese Medicine over a 13-year period from January 2011 to December 2023.</p>
<p>The inclusion criteria were as follows:
</p><ol list-type="order" type="1">
<li>Patients confirmed to have <em>A. baumannii</em> BSI through blood culture isolation, coupled with clinical signs such as fever >38°C or hypothermia.</li>
<li>Patients with a confirmed diagnosis of <em>A. baumannii</em> BSI aged 18 years or older.</li>
<li>Only non-duplicate patients, who completed follow-up assessments and provided necessary data, were included. In instances of multiple blood culture isolates, only the first positive culture data were collected. Excluded are patients with incomplete follow-up regarding underlying diseases, laboratory results, or uncertain survival outcomes within 28 days post-infection. The extensive duration of data collection, paired with challenges in recontacting patients, may influence the reported mortality rate associated with <em>A. baumannii</em> BSI.</li></ol>
<p>A total of 206 patients fitting the inclusion and exclusion criteria were analyzed. The cohort from 2011 to 2020 (160 patients) was designated as the training cohort, while 46 patients from 2021 to 2023 formed the test cohort. Utilizing LASSO regression allowed for an effective selection of predictive variables, minimizing issues related to traditional model overfitting. Through Cox regression analysis, significant prognostic factors affecting 28-day survival were identified, leading to the construction and validation of a Cox regression model evaluated by receiver operating characteristic (ROC) curves, calibration curve analysis, and decision curve analysis (DCA). <a href="#f0001" id="ref-f0001">Figure 1</a> illustrates the research workflow.</p>
<a name="f0001" href="#ref-f0001"/><table class="thumbnail-table"><tbody><tr><td><a href="https://www.dovepress.com/article/fulltext_file/491537/aW1n/IDR_A_491537_O_F0001g.jpg" class="float_border" target="_framename"><img alt="" src="https://www.dovepress.com/article/fulltext_file/491537/aW1n/IDR_A_491537_O_F0001g_Thumb.jpg" class="imgsmall"/></a></td><td>
<p class="tabtext"><strong>Figure 1</strong> Study design.</p>
</td></tr></tbody></table>
<h3>Data Collection</h3>
<p>Comprehensive demographic and clinical data on 17 risk factors were gathered, factoring in patient characteristics, including sex and age, assessment of underlying conditions (e.g., pneumonia, hypertension, cerebral infarction), invasive procedures (mechanical ventilation, peripherally inserted central catheter (PICC), indwelling urinary catheters), septic shock occurrence, and the presence of carbapenem-resistant strains (CRAb), along with biomarkers such as white blood cell count (WBC), neutrophil-to-lymphocyte ratio (NLR), hemoglobin (HGB), platelet count (PLT), aminotransferase (AST), blood glucose (GLU), and creatinine (CR) at the time of positive blood culture or within 24 hours. Survival status after 28 days was monitored and recorded for all patients with positive bloodstream cultures.</p>
<h3>Data Analysis</h3>
<p>In this research, the 28-day survival status served as the dependent variable, while the 17 demographic and clinical data points acted as independent variables. Univariate analyses employed chi-square tests or Fisher’s exact tests for categorical variables, alongside <em>t</em> tests or rank-sum tests for continuous variables. Nonnormally distributed data were consequently represented as medians (interquartile ranges).</p>
<h2>Results</h2>
<h3>Demographics and Characteristics of the Training and Testing Cohort</h3>
<p>Out of the analyzed cohort of 206 patients, the overall 28-day mortality rate was registered at 25.7% (53/206). The training cohort exhibited a mortality rate of 27.5% (44/160), while the test cohort had a rate of 19.6% (9/46). No significant mortality rate discrepancies were observed between these cohorts, nor in the examined variables such as sex (male), age, or underlying medical conditions.</p><a name="t0001" href="#ref-t0001"/><table class="thumbnail-table"><tbody><tr><td><a href="https://www.dovepress.com/article/fulltext_file/491537/aW1n/IDR_A_491537_t0001.jpg" class="float_border" target="_framename"><img alt="" src="https://www.dovepress.com/article/fulltext_file/491537/aW1n/IDR_A_491537_t0001_Thumb.jpg" class="imgsmall"/></a></td><td>
<p class="tabtext"><strong>Table 1</strong> Patient Demographics and Baseline Characteristics</p>
</td></tr></tbody></table>
<h3>Independent Prognostic Factors in Patients with A. baumannii BSI</h3>
<p>The one-way Cox analysis highlighted 11 variables, including pneumonia, mechanical ventilation, PICC, indwelling urinary catheter, septic shock, CRAb presence, WBC levels, NLR, HGB, PLT, and AST, as linked to overall survival (OS) in patients with <em>A. baumannii</em> BSI.</p><a name="t0002" href="#ref-t0002"/><table class="thumbnail-table"><tbody><tr><td><a href="https://www.dovepress.com/article/fulltext_file/491537/aW1n/IDR_A_491537_t0002.jpg" class="float_border" target="_framename"><img alt="" src="https://www.dovepress.com/article/fulltext_file/491537/aW1n/IDR_A_491537_t0002_Thumb.jpg" class="imgsmall"/></a></td><td>
<p class="tabtext"><strong>Table 2</strong> Results of Univariate Cox Regression</p>
</td></tr></tbody></table>
<p>Initial predictors such as male gender, pneumonia, hypertension, cerebral infarction, and procedural interventions like mechanical ventilation, PICC, indwelling urinary catheters, along with septic shock, CRAb status, age, WBC, NLR, HGB, PLT, AST, GLU, and CR were incorporated into the original regression model. Through LASSO regression analysis, the model was refined to eight significant predictors: PICC, septic shock, CRAb presence, NLR, HGB, PLT, and AST. The plotted coefficient profile and cross-validated error of the LASSO model is depicted in <a href="#f0002" id="ref-f0002">Figure 2</a>.</p><a name="f0002" href="#ref-f0002"/><table class="thumbnail-table"><tbody><tr><td><a href="https://www.dovepress.com/article/fulltext_file/491537/aW1n/IDR_A_491537_O_F0002g.jpg" class="float_border" target="_framename"><img alt="" src="https://www.dovepress.com/article/fulltext_file/491537/aW1n/IDR_A_491537_O_F0002g_Thumb.jpg" class="imgsmall"/></a></td><td>
<p class="tabtext"><strong>Figure 2</strong> LASSO Regression Results</p>
</td></tr></tbody></table>
<p>The eight variables isolated through LASSO regression underwent multifactorial Cox analysis, highlighting four definitive prognostic factors.</p><a name="t0003" href="#ref-t0003"/><table class="thumbnail-table"><tbody><tr><td><a href="https://www.dovepress.com/article/fulltext_file/491537/aW1n/IDR_A_491537_t0003.jpg" class="float_border" target="_framename"><img alt="" src="https://www.dovepress.com/article/fulltext_file/491537/aW1n/IDR_A_491537_t0003_Thumb.jpg" class="imgsmall"/></a></td><td>
<p class="tabtext"><strong>Table 3</strong> Multivariate Cox Regression Analysis Results for the Training Cohort</p>
</td></tr></tbody></table>
<a name="t0004" href="#ref-t0004"/><table class="thumbnail-table"><tbody><tr><td><a href="https://www.dovepress.com/article/fulltext_file/491537/aW1n/IDR_A_491537_t0004.jpg" class="float_border" target="_framename"><img alt="" src="https://www.dovepress.com/article/fulltext_file/491537/aW1n/IDR_A_491537_t0004_Thumb.jpg" class="imgsmall"/></a></td><td>
<p class="tabtext"><strong>Table 4</strong> Multivariate Cox Regression Results for the Training Cohort</p>
</td></tr></tbody></table>
<h3>Development and Validation of the Nomogram</h3>
<p>A nomogram was created based on identified independent prognostic factors to estimate overall survival at 7, 14, and 28 days for patients affected by <em>A. baumannii</em> BSI (<a href="#f0003" id="ref-f0003">Figure 3</a>).</p><a name="f0003" href="#ref-f0003"/><table class="thumbnail-table"><tbody><tr><td><a href="https://www.dovepress.com/article/fulltext_file/491537/aW1n/IDR_A_491537_O_F0003g.jpg" class="float_border" target="_framename"><img alt="" src="https://www.dovepress.com/article/fulltext_file/491537/aW1n/IDR_A_491537_O_F0003g_Thumb.jpg" class="imgsmall"/></a></td><td>
<p class="tabtext"><strong>Figure 3</strong> Nomogram Prediction Model.</p>
</td></tr></tbody></table>
<p>The C-index (95% CI) recorded was 0.819 (0.752, 0.886) for the training cohort nomogram and 0.833 (95% CI: 0.708, 0.958) for the validation cohort, indicating the predictive model's sufficient discrimination capability. The training cohort's ROC curves revealed AUCs of 0.907, 0.872, and 0.859 for 7, 14, and 28 days, respectively, while the validation cohort exhibited AUCs of 0.886, 0.850, and 0.850, confirming the robustness of the model's predictive ability. The nomogram presented a superior discrimination profile compared to individual prognostic factors across both cohorts.</p><a name="f0004" href="#ref-f0004"/><table class="thumbnail-table"><tbody><tr><td><a href="https://www.dovepress.com/article/fulltext_file/491537/aW1n/IDR_A_491537_O_F0004g.jpg" class="float_border" target="_framename"><img alt="" src="https://www.dovepress.com/article/fulltext_file/491537/aW1n/IDR_A_491537_O_F0004g_Thumb.jpg" class="imgsmall"/></a></td><td>
<p class="tabtext"><strong>Figure 4</strong> ROC Curves Analysis</p>
</td></tr></tbody></table>
<a name="f0005" href="#ref-f0005"/><table class="thumbnail-table"><tbody><tr><td><a href="https://www.dovepress.com/article/fulltext_file/491537/aW1n/IDR_A_491537_O_F0005g.jpg" class="float_border" target="_framename"><img alt="" src="https://www.dovepress.com/article/fulltext_file/491537/aW1n/IDR_A_491537_O_F0005g_Thumb.jpg" class="imgsmall"/></a></td><td>
<p class="tabtext"><strong>Figure 5</strong> Calibration Curves for the Training and Test Cohorts.</p>
</td></tr></tbody></table>
<p>Decision curve analysis demonstrated the proposed model's significant net benefit across a broad range of threshold probabilities. Specifically, the model yielded favorable net benefits within 10%-70% thresholds at 7, 14, and 28 days in the training cohort, with the latter two time points exhibiting benefits spanning from 10% to 100%. The validation cohort mirrored these efficiencies, showcasing a promising performance for clinical application within a range of 20%-80%.</p><a name="f0006" href="#ref-f0006"/><table class="thumbnail-table"><tbody><tr><td><a href="https://www.dovepress.com/article/fulltext_file/491537/aW1n/IDR_A_491537_O_F0006g.jpg" class="float_border" target="_framename"><img alt="" src="https://www.dovepress.com/article/fulltext_file/491537/aW1n/IDR_A_491537_O_F0006g_Thumb.jpg" class="imgsmall"/></a></td><td>
<p class="tabtext"><strong>Figure 6</strong> DCA Curves of Cohorts</p>
</td></tr></tbody></table>
<h2>Discussion</h2>
<p>This comprehensive study established and validated a nomogram tailored to predict the prognosis of patients with <em>A. baumannii</em> BSI, derived from a substantial cohort of 206 affected individuals. Key prognostic indicators encompassed the presence of comorbid septic shock, as well as hematologic parameters like NLR, PLT, and HGB at the point of infection, all attaining statistical significance during multivariate Cox regression analyses.</p>
<p>The study identified septic shock post-infection as a critical risk factor for mortality linked to <em>A. baumannii</em> BSI. Septic shock typically arises from severe infections and predominantly impacts critically ill patients, leading to elevated mortality rates. Documented evidence correlates severe infections with increased APACHE and SOFA scores, emphasizing the heightened mortality risks.<sup><a href="#cit0007" id="ref-cit0007">7</a>,<a href="#cit0008" id="ref-cit0008">8</a></sup> This observation underscores the virulence potential of <em>A. baumannii</em>, which was traditionally categorized as a low-virulence opportunistic pathogen. Previous assessments identified <em>A. baumannii</em> as a commensal organism found in respiratory tracts but recent findings indicate significant biofilm formation on hospital equipment, which has emerged as a critical pathogenic trait implying high virulence linked to biofilms and associated factors including drug resistance.<sup><a href="#cit0009" id="ref-cit0009">9</a>,<a href="#cit0010" id="ref-cit0010">10</a>,<a href="#cit0011" id="ref-cit0011">11</a></sup></p>
<p>Additionally, NLR demonstrated its significance as an independent marker for mortality in <em>A. baumannii</em> BSI cases. NLR has garnered attention in recent years as an inflammatory prognostic indicator, validated across various patient demographics including pediatrics and critical care settings.<sup><a href="#cit0016" id="ref-cit0016">16</a>,<a href="#cit0017" id="ref-cit0017">17</a>,<a href="#cit0018" id="ref-cit0018">18</a></sup>. Although previous studies displayed conflicting evidence, this research asserted NLR as an influencing risk factor for mortality due to <em>A. baumannii</em> BSI, notwithstanding its relatively low prognostic accuracy needing supplementary measures for predictive validity.</p>
<h2>Limitations of the Study</h2>
<p>The study's limitations are noteworthy; it is a single-center analysis that may lack representativeness due to inherent biases in patient admissions. Additionally, potential unmeasured confounding variables may influence the outcomes due to the retrospective design over an extensive data collection timeframe. Future steps aim at external validation of the nomogram through collaboration with diverse healthcare institutions, focusing on timely correction of <em>A. baumannii</em> BSI based on identified hematological indices.</p>
<h2>Conclusions</h2>
<p>The findings of this study highlighted septic shock, NLR, HGB, and PLT as pivotal independent prognostic factors for <em>A. baumannii</em> BSI. The model's strength relies on utilizing variables that are readily accessible in most hospital environments, enhancing the clinical applicability of the nomogram developed for accurately predicting OS in this patient population. Future endeavors will involve collaborations aimed at monitoring NLR, HGB, and PLT indices to solidify the assessment of 28-day survival post-intervention treatment for patients battling BSI attributed to <em>A. baumannii</em>, with the goal of enhancing therapeutic insights.</p>
</div>
<div>
<h2>Abbreviations</h2>
<p><em>A. baumannii, Acinetobacter baumannii</em>; BSI, bloodstream infection; LASSO, Least Absolute Shrinkage and Selection Operator; CRAb, carbapenem-resistant <em>Acinetobacter baumannii</em>; ICU, Intensive Care Unit; AUC, Area under the curve; ROC, Receiver Operating Characteristic; DCA, Decision curve analysis; OS, overall survival; PICC, peripherally inserted central catheter; WBC, White blood cell count; NLR, Neutrophil-to-lymphocyte ratio; HGB, Hemoglobin; PLT, Platelet count; AST, Aminotransferase; GLU, Blood glucose; CR, Creatinine.</p>
<h2>Data Sharing Statement</h2>
<p>Data are available upon reasonable request to the corresponding author.</p>
<h2>Ethics and Consent to Participate Section</h2>
<p>The study was approved by the Hospital Ethics Committee of Guangdong Provincial Second Hospital of Traditional Chinese Medicine and conducted according to the Declaration of Helsinki (Approved No. of ethics committee: Z202404-002-01). The ethics committee waived the requirement for informed consent because the study was retrospective in design.</p>
<h2>Code Availability</h2>
<p>The statistical software “R- version 4.3.1” was utilized.</p>
<h2>Acknowledgment</h2>
<h2>Author Contributions</h2>
<h2>Funding</h2>
<p>This study received support from the Health Commission of Guangdong Province (B2023181) and the Traditional Chinese Medicine Bureau of Guangdong Province (20231045).</p>
<h2>Disclosure</h2>
<p>All authors declare no conflicts of interest.</p>
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<p id="cit0025" class="$reftext"><a name="cit0025" href="#ref-cit0025">25.</a> Wang J, Applefeld WN, Sun J, et al. Mechanistic insights into cell-free hemoglobin-induced injury during septic shock. <em>Am J Physiol Heart Circ Physiol</em>. 2021;320(6):H2385–H2400. doi:10.1152/ajpheart.00092.2021</p>
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<p id="cit0027" class="$reftext"><a name="cit0027" href="#ref-cit0027">27.</a> Chen Y, Chen L, Meng Z, et al. The correlation of hemoglobin and 28-day mortality in septic patients: secondary data mining using the MIMIC-IV database. <em>BMC Infect Dis</em>. 2023;23(1):417. doi:10.1186/s12879-023-08384-9</p>
<p id="cit0028" class="$reftext"><a name="cit0028" href="#ref-cit0028">28.</a> Yang M, Yang X, Jing P, et al. Prognostic value of coagulation function combined with acute physiology and chronic health evaluation II and sequential organ failure assessment scores for patients with bloodstream infection. <em>Zhonghua Wei Zhong Bing Ji Jiu Yi Xue</em>. 2021;33(12):1434–1439. doi:10.3760/cma.j.cn121430-20210910-01361</p>
<p id="cit0029" class="$reftext"><a name="cit0029" href="#ref-cit0029">29.</a> Claushuis TA, van Vught LA, Scicluna BP, et al. Thrombocytopenia is associated with a dysregulated host response in critically ill sepsis patients. <em>Blood</em>. 2016;127(24):3062–3072. doi:10.1182/blood-2015-11-680744</p>
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How do hemoglobin levels impact mortality rates in patients with sepsis?
It seems like you are providing a list of references for a research paper or article. Here is the format for the references you provided, simplified for better readability:
1. Othman A, Filep JG. Enemies at the gate: how cell-free hemoglobin and bacterial infection can cooperate to drive acute lung injury during sepsis. *Am J Physiol Heart Circ Physiol*. 2021;321(1):H131–H134. doi:10.1152/ajpheart.00283.2021.
2. Zimbler DL, Penwell WF, Gaddy JA, et al. Iron acquisition functions expressed by the human pathogen *Acinetobacter baumannii*. *Biometals*. 2009;22:23–32. doi:10.1007/s10534-008-9202-3.
3. Artuso I, Poddar H, Evans BA, et al. Genomics of *Acinetobacter baumannii* iron uptake. *Microb Genom*. 2023;9(8). doi:10.1099/mgen.0.001080.
4. Weiss G, Ganz T, Goodnough LT. Anemia of inflammation. *Blood*. 2019;133(1):40–50. doi:10.1182/blood-2018-06-856500.
5. Muzaheed, Alzahrani FM, Sattar SS. *Acinetobacter baumannii* infection in transfusion dependent thalassemia patients with sepsis. *Biomed Res Int*. 2017;2017:2351037. doi:10.1155/2017/2351037.
6. Wang J, Applefeld WN, Sun J, et al. Mechanistic insights into cell-free hemoglobin-induced injury during septic shock. *Am J Physiol Heart Circ Physiol*. 2021;320(6):H2385–H2400. doi:10.1152/ajpheart.00092.2021.
7. Tan SMY, Zhang Y, Chen Y, et al. Association of fluid balance with mortality in sepsis is modified by admission hemoglobin levels: a large database study. *PLoS One*. 2021;16(6):e0252629. doi:10.1371/journal.pone.0252629.
8. Chen Y, Chen L, Meng Z, et al. The correlation of hemoglobin and 28-day mortality in septic patients: secondary data mining using the MIMIC-IV database. *BMC Infect Dis*. 2023;23(1):417. doi:10.1186/s12879-023-08384-9.
9. Yang M, Yang X, Jing P, et al. Prognostic value of coagulation function combined with acute physiology and chronic health evaluation II and sequential organ failure assessment scores for patients with bloodstream infection. *Zhonghua Wei Zhong Bing Ji Jiu Yi Xue*. 2021;33(12):1434–1439. doi:10.3760/cma.j.cn121430-20210910-01361.
10. Claushuis TA, van Vught LA, Scicluna BP, et al. Thrombocytopenia is associated with a dysregulated host response in critically ill sepsis patients. *Blood*. 2016;127(24):3062–3072. doi:10.1182/blood-2015-11-680744.
11. de Stoppelaar SF, van TVC, van der Poll T. The role of platelets in sepsis. *Thromb Hemost*. 2014;112(4):666–677. doi:10.1160/TH14-02-0126.
12. Hunt BJ. Bleeding and coagulopathies in critical care. *N Engl J Med*. 2014;370(9):847–859. doi:10.1056/NEJMra1208626.
13. de Bont CM, Boelens WC, Pruijn GJM. NETosis, complement, and coagulation: a triangular relationship. *Cell Mol Immunol*. 2019;16(1):19–27. doi:10.1038/s41423-018-0024-0.
14. Yamakawa K, Ohbe H, Hisamune R, et al. Current clinical practice of laboratory testing of the hemostasis and coagulation system in patients with sepsis: a nationwide observational study in Japan. *JMA J*. 2024;7(2):224–231. doi:10.31662/jmaj.2023-0151.
15. Zhao X, Wu X, Si Y, et al. D-DI/PLT.
Please let me know if you need any additional information or assistance with a specific aspect of these references!
