A Retrospective Cohort Study: Predictive Value of Biomarkers for Mortality in Bloodstream Infection Patients

A Retrospective Cohort Study: Predictive Value of Biomarkers for Mortality in Bloodstream Infection Patients | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Retrospective Cohort Study: Predictive Value of Biomarkers for Mortality in Bloodstream Infection Patients Bo Li, Zuoxiang Fu, Qiaoling Ye, Jiawen Li, Jijun Zhao, Xue Li, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6621760/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract Objective To investigate the predictive value of procalcitonin (PCT), uric acid, platelet count, and neutrophil-to-lymphocyte ratio (NLR) for mortality risk in blood culture-positive sepsis patients. Methods A retrospective analysis was conducted on 500 patients with positive blood cultures from both upper and lower limbs treated in the emergency department of our hospital from January 2023 to February 2025. Based on clinical outcomes, patients were divided into a rehabilitation discharge group (n = 416) and a death group (n = 84). Peripheral blood PCT, platelet count, uric acid, and NLR levels within 24 hours of admission were compared. Receiver operating characteristic (ROC) curves were used to evaluate the predictive value of these biomarkers for mortality. Binary logistic regression was employed to identify independent risk factors influencing mortality in blood culture-positive sepsis patients. Results Logistic regression analysis revealed that platelet count (OR = 0.996, 95% CI: 0.993–1.000), absolute lymphocyte count (OR = 0.901, 95% CI: 0.489–1.661), absolute neutrophil count (OR = 1.025, 95% CI: 0.976–1.077), PCT (OR = 1.012, 95% CI: 1.002–1.021), uric acid (OR = 1.010, 95% CI: 1.007–1.012), and NLR (OR = 1.040, 95% CI: 1.009–1.071) were significant risk factors for mortality ( p < 0.05). ROC analysis demonstrated that AUC for platelet count, PCT, uric acid, NLR, and their combination were 0.622, 0.747, 0.759, 0.650, and 0.823, respectively, with the combined model showing the highest predictive value. Conclusion Elevated PCT, uric acid, and NLR levels, along with reduced platelet counts, are predictive of mortality in blood culture-positive sepsis patients. Clinicians should closely monitor these biomarkers for early intervention to reduce mortality. Sepsis Retrospective Cohort Study Mortality Prediction Figures Figure 1 1. Introduction Sepsis is defined as life-threatening organ dysfunction caused by a dysregulated host response to infection [ 1 ] . Septic shock requires vasopressors to maintain a mean arterial pressure > 65 mmHg and a serum lactate level > 2 mmol/L. Sepsis is a global health burden with high morbidity and mortality [ 2 ] . In 2017, nearly 50 million cases of sepsis were reported worldwide, resulting in approximately 11 million deaths [ 3 ] . Mortality rates for severe sepsis and septic shock exceed 30–50% and 50%, respectively [ 4 ] . Blood culture-positive sepsis patients face even higher mortality, posing a significant public health challenge [ 5 ] . As a developing country with the largest population in the world, China has a serious aging population, which indirectly increases the incidence and mortality of sepsis. The emergency department is the window of the hospital, so a comprehensive grasp of the clinical characteristics of sepsis, a correct assessment of the severity of the disease, and early active treatment may be the key to improve the cure rate and reduce the fatality rate. Uric acid is the end product of purine metabolism and exhibits a dual role in the human body: it functions as a crucial endogenous antioxidant but may also trigger pathological processes at elevated concentrations, closely associated with diseases such as renal and cardiovascular disorders [ 6 , 7 ] . Peripheral blood neutrophils and lymphocytes are common infection markers, and the neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) reflect systemic inflammatory and infectious states [ 8 ] . Procalcitonin (PCT) serves as a diagnostic biomarker for sepsis and guides clinical treatment [ 9 ] . This suggests that platelet count, PCT, uric acid, and NLR may correlate with mortality in blood culture-positive sepsis. However, current research on the combined use of PCT, PLR, and NLR for predicting outcomes in blood culture-positive sepsis remains insufficient. Therefore, this retrospective clinical study comprehensively analyzed the predictive value of platelet count, PCT, uric acid, and NLR in blood culture-positive sepsis patients, aiming to provide early prognostic insights and address the research gap in this field. 2. Methods 2.1 Research Subject This retrospective study included 500 patients who visited the emergency department of our hospital between January 2023 and February 2025, with positive results from blood cultures collected from both upper and lower limbs within 24 hours of admission. Based on clinical outcomes, eligible cases were categorized into a rehabilitation discharge group (n = 416) and a death group (n = 84). The study protocol was approved by the Institutional Ethics Committee. As this retrospective analysis utilized anonymized data from clinical records without identifiable patient information, informed consent was waived. Sepsis was defined as a complex inflammatory response of the host to infection [ 10 ] . In this study, blood culture-positive sepsis patients were those diagnosed with sepsis and confirmed by positive blood cultures within 24 hours of admission. Diagnostic Criteria : According to the definition and diagnostic standards for sepsis [ 10 ] , patients must fulfill the following: confirmed infection, Sequential Organ Failure Assessment score ≥ 2, and positive blood cultures. I. Confirmation of Infection Confirmed via microbiological evidence. II. Systemic Inflammatory Response Syndrome Criteria (≥ 2 required): Body temperature: ≥38°C or ≤ 36°C; Tachycardia: ≥90 beats/min; Tachypnea: ≥20 breaths/min; Respiratory alkalosis: PaCO₂ ≤32 mmHg (< 4.3 kPa); White blood cell (WBC) count: Leukocytosis (≥ 12/nL) or leukopenia (≤ 4/nL) or band forms ≥ 10% (indicating a left shift, i.e., increased percentage of immature neutrophils and granulocyte precursors). Inclusion Criteria : 1) Aged 18–80 years. (2) Complete clinical data to ensure study reliability and accuracy. (3) Patients with confirmed infection and SIRS, and positive blood cultures from both upper and lower limbs within 24 hours of admission. Exclusion Criteria : 1) Pre-admission diagnosis of sepsis with prior treatment (to ensure first-time treatment). (2) History of malignancy (to minimize interference with inflammatory markers). (3) Severe organ failure (to ensure research accuracy and reliability). (4) Hematologic disorders (due to potential effects on inflammatory markers). 2.2 Sample Size This study was designed based on the sample size calculation principle of binary logistic regression analysis. The sample size requirement for binary logistic regression analysis stipulates that each independent variable should have at least 10 events to ensure the stability and accuracy of the model [ 11 ] . Given that 15 independent variables had been identified before determining the sample size, 150 samples were sufficient. Considering the availability of clinical data and the research time limit requirements, the sample size was expanded to 500 patients with blood culture-confirmed sepsis. This sample size ensures a sufficient number of positive blood culture sepsis death events, meets the requirements for model stability, and accurately assesses the relationship between the predictive variables and positive blood culture sepsis death. 2.3 Observational Indicators Patient medical records were reviewed to collect general patient data and relevant laboratory data within 24 hours of admission. Specific testing methods were as follows: First, 5 mL of peripheral venous blood was collected at patient admission. Second, two tubes were used: one tube was used to detect neutrophils (NE, normal range: (2.0–7.0)×10 9 /L), lymphocytes (LYM, normal range: (0.8-4.0)×10 9 /L), white blood cells (WBC, normal range: (4.0–10.0)×10 9 /L), and platelets (PLT, normal range: (10.0–40.0)×10 9 /L), as well as NLR (normal range: 0.78–3.53) and PLR (normal range: 0.10–0.20). The other tube was centrifuged at 3000r/min for 15 minutes, after which procalcitonin (PCT, normal range: <0.05 ng/mL) and uric acid (normal range: 143-416umol/L) were tested using chemiluminescence. The reagent kit (batch number: 20192400468, Shanghai, China) was provided by Siemens, and operations were strictly performed according to the kit instructions. Simultaneously, 5 mL of radial arterial blood was collected, and calcium ion concentration (normal range: 1.15-1.29mmol/L) was measured using a blood gas analyzer (Radiometer Medical Company, National Medical Device Registration No. 20172402212), with operations strictly following the kit instructions. Additionally, 10 mL each of cubital vein and dorsalis pedis vein blood was injected into blood culture bottles (bioMérieux SA, REF424066) for culturing, and culture results were observed. 2.4 Statistical Analysis Data processing and statistical analysis were performed using SPSS 25.0 software. The statistical significance threshold was set at α = 0.05 (two-tailed). Measurement data were tested for normality using the Shapiro-Wilk test. Normally distributed data were expressed as mean \(\:\pm\:\) standard deviation and analyzed using independent samples t-tests. Non-normally distributed data were expressed as median (interquartile range) and analyzed using the rank sum test. Categorical data were expressed as percentages and analyzed using chi-square tests. Binary logistic regression was used to analyze the relationships between platelet count, absolute lymphocyte count, absolute neutrophil count, PCT, uric acid, NLR, and mortality in blood culture-positive sepsis patients. Receiver operating characteristic (ROC) curves were plotted, and the area under the curve (AUC) was calculated to evaluate the predictive value of PCT, uric acid, and NLR for mortality in blood culture-positive sepsis patients. AUC values > 0.9 indicated high predictive performance, 0.71–0.90 indicated moderate predictive performance, 0.5–0.7 indicated low predictive performance, and < 0.5 indicated no predictive performance. A p < 0.05 was considered statistically significant. 3 Results 3.1 Baseline Characteristics of Patients In the blood culture-positive sepsis death group, platelet count, absolute lymphocyte count, absolute neutrophil count, PCT, uric acid, and NLR levels were higher than those in the blood culture-positive sepsis survival group. The platelet count in the death group was lower than that in the survival group. These differences were statistically significant ( p < 0.05), as shown in Table 1 . Table 1 Baseline Characteristics of Patients Variable Clinical Outcome Statistics p Rehabilitation discharge (n = 416) Death (n = 84) Age (years)(M(P25 P75) ) 65.00 (54.00, 73.00) 67.00 (58.00, 75.00) Z = 1.615 0.106 Gender n(%) χ 2 = 2.214 0.073 Man 213 (51.20%) 52 (61.90%) Woman 203 (48.80%) 32 (38.91%) Diabetesn(%) χ 2 = 0.621 0.431 Yes 116 (27.88%) 27 (32.14%) No 300 (72.12%) 57 (67.86%) Hypertension n(%) χ 2 = 1.011 0.315 Yes 154 (37.02%) 36 (42.86%) No 262 (62.98%) 48 (57.14%) Coronary heart disease n(%) χ 2 = 0.412 0.521 Yes 66 (15.87%) 11 (13.10%) No 350 (84.13%) 73 (86.90%) Chronic renal failure n(%) χ 2 = 2.461 0.117 Yes 19 (4.57%) 8 (9.52%) No 397 (95.43%) 76 (90.48%) Platelet count(*10 9 /L) (M(P25 P75) ) 140.00 (94.00, 211.00) 106.00 (38.50, 171.00) Z=-3.525 < 0.001 Absolute value of lymphocytes(*10 9 /L) (M(P25 P75) ) 9.75 (6.39, 13.97) 11.93 (7.57, 16.05) Z = 2.761 0.006 Calcium ion (mmol/L) (M(P25 P75) ) 1.12 (1.08, 1.16) 1.11 (1.08, 1.14) Z=-0.873 0.382 PCT (ng/ mL) (M(P25 P75) ) 7.90 (1.70, 26.00) 28.50 (12.50, 52.00) Z = 7.160 < 0.001 Uric acid (umol/L)(M(P25 P75) ) 287.50 (218.50, 366.00) 389.50 (319.50, 516.50) Z = 7.491 < 0.001 NLR(M(P25 P75) ) 15.42 (9.22, 25.22) 21.79 (12.73, 32.02) Z = 4.330 < 0.001 PLR(M(P25 P75) ) 215.27 (126.05, 369.07) 205.72 (70.77, 364.85) Z=-1.362 0.173 PCT: procalcitonin; PLR: platelet-to-lymphocyte ratio; NLR: neutrophil-to-lymphocyte ratio 3.2 Multivariate Analysis The concurrent status of blood culture-positive sepsis patients was set as the dependent variable ("0" = sepsis survival group, "1" = sepsis death group). Variables with statistically significant differences in Table 1 (platelet count, absolute lymphocyte count, absolute neutrophil count, uric acid, PCT, and NLR) were included in the logistic regression analysis. The results showed that platelet count, PCT, uric acid, and NLR were independent risk factors for mortality in blood culture-positive sepsis patients, as detailed in Table 2 . Table 2 Logistic Regression Results ꞵ Standard error Wald value p OR 95% confidence interval for OR Floor limit Upper limit Platelet count -0.004 0.002 4.94 0.026 0.996 0.993 1.000 Absolute value of lymphocytes -0.104 0.312 0.111 0.739 0.901 0.489 1.661 Absolute value of neutrophils 0.025 0.025 0.975 0.323 1.025 0.976 1.077 PCT 0.012 0.005 5.572 0.018 1.012 1.002 1.021 Uric acid 0.010 0.001 47.486 0.000 1.010 1.007 1.012 NLR 0.039 0.015 6.369 0.012 1.040 1.009 1.071 Constant -5.921 0.659 70.606 0.000 0.003 PCT: procalcitonin; NLR: neutrophil-to-lymphocyte ratio 3.3 Predictive Value of Combined Platelet Count, PCT, Uric Acid, and NLR for Mortality in Blood Culture-Positive Sepsis The mortality status of emergency department blood culture-positive sepsis patients was set as the dependent variable (coded as "0" = non-sepsis, "1" = sepsis). Platelet count, PCT, uric acid, and NLR were included as test variables to plot ROC curves. Results showed that the AUC values for platelet count, PCT, uric acid, NLR individually, and their combination in predicting mortality in emergency blood culture-positive sepsis patients were all > 0.50, indicating moderate predictive value. Among them, PCT, uric acid, and the combination of all four markers achieved the highest AUC values, as detailed in Table 3 and Fig. 1 . Notably, the odds ratio (OR) for platelet count was close to 1, suggesting a weak but statistically significant negative correlation with mortality risk. However, its clinical significance requires comprehensive evaluation in conjunction with other indicators and study context. Table 3 Value of PCT, PLR and NLR alone or in combination in predicting postoperative complications of ureteral calculi AUC Cutoff value p 95%CI Sensitivity Specificity Platelet count 0.622 129.50 < 0.001 0.553–0.691 0.667 0.565 PCT 0.747 65.550 < 0.001 0.700-0.795 0.952 0.478 Uric acid 0.759 334.500 < 0.001 0.702–0.816 0.714 0.673 NLR 0.650 24.475 < 0.001 0.587–0.712 0.536 0.683 Platelet + PCT + uric acid + NLR 0.824 0.023 < 0.001 0.779–0.869 0.821 0.675 PCT: procalcitonin; NLR: neutrophil-to-lymphocyte ratio 4 Discussion Sepsis is defined as a life-threatening syndrome caused by dysregulated host responses to infection, leading to physiological, pathological, and biological abnormalities [ 1 ] . Global disease studies indicate that this syndrome is associated with a high mortality risk, accounting for approximately 20% of global deaths [ 12 ] . Over the past 14 years, overall mortality rates among patients hospitalized with sepsis for the first time have declined, with comorbidities, infection sites, and acute organ dysfunction identified as mortality-related patient characteristics [ 13 ] . Notably, studies have shown no significant changes in short- or long-term mortality trends for sepsis patients admitted to ICUs [ 14 , 15 ] , highlighting the urgent need for a precise early-warning system. Therefore, identifying biomarkers associated with blood culture-positive sepsis is critical to reducing mortality risk. This study focused on blood culture-confirmed sepsis patients and found that platelet count, serum PCT, uric acid, and the NLR have predictive value for mortality. Clinicians should closely monitor these indicators for early intervention to reduce mortality, shorten hospital stays, and alleviate patient economic burdens. Our results demonstrated that the death group exhibited significantly higher NLR, uric acid, and PCT levels but lower platelet counts compared to the survival group ( p < 0.05). These biomarkers showed high sensitivity and specificity in predicting mortality, underscoring their clinical significance. Studies suggest that hyperuricemia, prevalent in sepsis patients, exacerbates systemic inflammation by activating the NLRP3 inflammasome and promoting pro-inflammatory cytokine release, thereby contributing to multi-organ dysfunction [ 16 , 17 ] . Thus, uric acid may aid in assessing sepsis severity. Neutrophils, the first line of defense against bacterial invasion, are recruited to infection sites via chemotactic signals, leading to elevated serum neutrophil levels during inflammation [ 18 ] . However, in sepsis, the imbalance between pro- and anti-inflammatory responses induces immunosuppression, impairing neutrophil chemotaxis. Concurrently, excessive platelet activation due to dysregulated inflammation and coagulation promotes irreversible platelet aggregation, exacerbating endothelial injury—particularly in critical capillaries—resulting in microvascular occlusion, tissue ischemia, and multi-organ failure [ 19 , 20 ] . Qi et al. [ 21 ] further reported that neutrophil percentages are significantly higher in patients with urosepsis compared to non-septic individuals. Additionally, increased apoptosis reduces lymphocyte counts, while platelet aggregation contributes to thrombus formation. Consequently, NLR and platelet count may assist in evaluating sepsis severity. PCT, a precursor of calcitonin produced by thyroid parafollicular cells, lacks hormonal activity under physiological conditions. In healthy individuals, PCT is cleaved by proteases into calcitonin, maintaining negligible serum levels. However, during infections or multi-organ dysfunction, PCT synthesis is rapidly upregulated by interleukin-6, C-reactive protein, and other mediators, leading to a sharp rise in serum PCT within 3–4 hours [ 22 ] . Notably, PCT levels remain unaffected by allergic or autoimmune reactions, making it a highly specific marker for systemic inflammation. A meta-analysis by Tan et al. [ 23 ] demonstrated that PCT predicts sepsis AUC of 0.85 (95% CI: 0.82–0.88), indicating strong diagnostic performance. This study highlights the prognostic utility of platelet count, NLR, uric acid, and PCT in blood culture-positive sepsis. While PCT exhibits high specificity but moderate sensitivity, NLR shows higher sensitivity with lower specificity. Individually, these markers lack sufficient predictive power for early mortality warning. However, their combined use improves accuracy and sensitivity, offering superior prognostic value. Clinicians may leverage these findings to refine management strategies, such as enhancing infection surveillance and initiating timely antibiotic therapy. 5. Limitations This study has the following limitations: This was a retrospective study, and the sample size needs to be expanded with prospective, multicenter studies to further validate the results. The specificity corresponding to the cutoff values of individual biomarkers is relatively low, necessitating the combination of other laboratory indicators to improve early warning efficacy. 6. Conclusion In conclusion, platelet count, PCT, uric acid, NLR, and their combined detection can serve as early warning indicators for mortality in blood culture-positive sepsis patients. The combined detection of platelet count, PCT, uric acid, and NLR demonstrates superior predictive value, providing clinicians with early alerts to reduce mortality in blood culture-positive sepsis patients and ultimately improve outcomes. Declarations Ethics approval and consent to participate Ethics approval was obtained from biomedical research ethic committee of the General Hospital of Ningxia Medical University. The retrospective noninterventional design led to the waiver of written informed consent, and study conduction was in accordance with the Helsinki Declaration. The confidentiality of all patient data was strictly maintained. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Funding Not applicable. Availability of data and material All data generated or analyzed during this study are included in the article. Authors' contributions BL, ZXF, JLW, and LM contributed equally. BL, ZXF, JLW, and LM contributed to the conception and design of the research; QLY, JWL, JJZ and XL contributed to the acquisition, analysis, and interpretation of the data; BL and ZXF drafted the manuscript; JLW, and LM critically revised the manuscript, All authors read and approved the final manuscript. References Singer M, Deutschman C S, Seymour C W, et al. The third international consensus definitions for sepsis and septic shock (Sepsis-3)[J]. Jama, 2016, 315(8): 801-810. Ginting F, Sugianli A K, Barimbing M, et al. Appropriateness of diagnosis and antibiotic use in sepsis patients admitted to a tertiary hospital in Indonesia[J]. Postgraduate medicine, 2021, 133(6): 674-679. Teggert A, Datta H, Ali Z. Biomarkers for point-of-care diagnosis of sepsis[J]. Micromachines, 2020, 11(3): 286. Paoli C J, Reynolds M A, Sinha M, et al. Epidemiology and costs of sepsis in the United States—an analysis based on timing of diagnosis and severity level[J]. 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Therapeutics and Clinical Risk Management, 2020: 999-1006. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 20 Feb, 2026 Reviews received at journal 19 Feb, 2026 Reviewers agreed at journal 02 Feb, 2026 Reviewers agreed at journal 31 Jan, 2026 Reviews received at journal 25 Jun, 2025 Reviewers agreed at journal 16 Jun, 2025 Reviewers invited by journal 02 Jun, 2025 Editor assigned by journal 18 May, 2025 Submission checks completed at journal 18 May, 2025 First submitted to journal 08 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6621760","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":465581732,"identity":"1031fb26-064d-40f0-92ba-342e32057acd","order_by":0,"name":"Bo Li","email":"","orcid":"","institution":"General Hospital of Ningxia Medical University","correspondingAuthor":false,"prefix":"","firstName":"Bo","middleName":"","lastName":"Li","suffix":""},{"id":465581733,"identity":"97b01628-78be-4f36-858e-44b7acdb57ac","order_by":1,"name":"Zuoxiang Fu","email":"","orcid":"","institution":"The Second People's Hospital of Yinchuan City","correspondingAuthor":false,"prefix":"","firstName":"Zuoxiang","middleName":"","lastName":"Fu","suffix":""},{"id":465581735,"identity":"02664490-aad2-4ae0-8341-d30d2aa5f11c","order_by":2,"name":"Qiaoling Ye","email":"","orcid":"","institution":"The Fourth People's Hospital of Ningxia Hui Autonomous Region","correspondingAuthor":false,"prefix":"","firstName":"Qiaoling","middleName":"","lastName":"Ye","suffix":""},{"id":465581737,"identity":"233dcd22-32b3-4c59-84b7-206bf81dd40e","order_by":3,"name":"Jiawen Li","email":"","orcid":"","institution":"General Hospital of Ningxia Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jiawen","middleName":"","lastName":"Li","suffix":""},{"id":465581738,"identity":"6a929515-b34c-45c3-a696-26aae8668030","order_by":4,"name":"Jijun Zhao","email":"","orcid":"","institution":"General Hospital of Ningxia Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jijun","middleName":"","lastName":"Zhao","suffix":""},{"id":465581739,"identity":"ac703192-9fb7-40a6-966f-64dc66bdab80","order_by":5,"name":"Xue Li","email":"","orcid":"","institution":"General Hospital of Ningxia Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xue","middleName":"","lastName":"Li","suffix":""},{"id":465581740,"identity":"b4ba3247-4bd7-49b5-92f5-5daa07b11343","order_by":6,"name":"Jiali Wu","email":"","orcid":"","institution":"General Hospital of Ningxia Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jiali","middleName":"","lastName":"Wu","suffix":""},{"id":465581741,"identity":"71d73e2c-9517-4736-a9db-1dc30192de70","order_by":7,"name":"Lei Ma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIiWNgGAWjYBACNv7mg49//JCQY2NvbHyQUFFDWAufxLFkY8YeG2M+nsPNBg/OHCOsRY4hx0yagS0tUU7CvU3yYQszEQ5jOJYmXcBzOIFNgrGtIrGBjYG/vTsBvxbm5sPWMywO57FJN7bdSNwhwyBx5uwGQrYk3uDhOVzMJnMQqOUMG4OBRC4hLTkGEjxshxPbJBLbChLbmInSYiTNA/Q+SAsDcVqAgWw4ExjIbDwHmyUSzhzjIegX+f7mgw8+AKNSvr394ccfFTVy/O29+LVgAB7SlI+CUTAKRsEowAoAHg1KYb9FwPIAAAAASUVORK5CYII=","orcid":"","institution":"General Hospital of Ningxia Medical University","correspondingAuthor":true,"prefix":"","firstName":"Lei","middleName":"","lastName":"Ma","suffix":""}],"badges":[],"createdAt":"2025-05-08 14:53:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6621760/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6621760/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84201365,"identity":"86ef8377-2115-42b2-8c93-34cce7de4ad5","added_by":"auto","created_at":"2025-06-09 08:31:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":57064,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves of platelet count, PCT, uric acid, and NLR alone and in combination predict death in patients with blood cultural-positive sepsis\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6621760/v1/c98d3a1346ed3a35242e50a1.png"},{"id":84202600,"identity":"1cd047b7-3c35-406b-b7ff-74cb6b347dd7","added_by":"auto","created_at":"2025-06-09 08:39:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":877750,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6621760/v1/ef4be0e1-682e-40f0-8268-ad442a68516f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Retrospective Cohort Study: Predictive Value of Biomarkers for Mortality in Bloodstream Infection Patients","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSepsis is defined as life-threatening organ dysfunction caused by a dysregulated host response to infection\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Septic shock requires vasopressors to maintain a mean arterial pressure\u0026thinsp;\u0026gt;\u0026thinsp;65 mmHg and a serum lactate level\u0026thinsp;\u0026gt;\u0026thinsp;2 mmol/L. Sepsis is a global health burden with high morbidity and mortality\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. In 2017, nearly 50\u0026nbsp;million cases of sepsis were reported worldwide, resulting in approximately 11\u0026nbsp;million deaths\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Mortality rates for severe sepsis and septic shock exceed 30\u0026ndash;50% and 50%, respectively\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Blood culture-positive sepsis patients face even higher mortality, posing a significant public health challenge\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. As a developing country with the largest population in the world, China has a serious aging population, which indirectly increases the incidence and mortality of sepsis. The emergency department is the window of the hospital, so a comprehensive grasp of the clinical characteristics of sepsis, a correct assessment of the severity of the disease, and early active treatment may be the key to improve the cure rate and reduce the fatality rate.\u003c/p\u003e \u003cp\u003eUric acid is the end product of purine metabolism and exhibits a dual role in the human body: it functions as a crucial endogenous antioxidant but may also trigger pathological processes at elevated concentrations, closely associated with diseases such as renal and cardiovascular disorders\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Peripheral blood neutrophils and lymphocytes are common infection markers, and the neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) reflect systemic inflammatory and infectious states\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Procalcitonin (PCT) serves as a diagnostic biomarker for sepsis and guides clinical treatment\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. This suggests that platelet count, PCT, uric acid, and NLR may correlate with mortality in blood culture-positive sepsis. However, current research on the combined use of PCT, PLR, and NLR for predicting outcomes in blood culture-positive sepsis remains insufficient. Therefore, this retrospective clinical study comprehensively analyzed the predictive value of platelet count, PCT, uric acid, and NLR in blood culture-positive sepsis patients, aiming to provide early prognostic insights and address the research gap in this field.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Research Subject\u003c/h2\u003e\n \u003cp\u003eThis retrospective study included 500 patients who visited the emergency department of our hospital between January 2023 and February 2025, with positive results from blood cultures collected from both upper and lower limbs within 24 hours of admission. Based on clinical outcomes, eligible cases were categorized into a rehabilitation discharge group (n\u0026thinsp;=\u0026thinsp;416) and a death group (n\u0026thinsp;=\u0026thinsp;84). The study protocol was approved by the Institutional Ethics Committee. As this retrospective analysis utilized anonymized data from clinical records without identifiable patient information, informed consent was waived.\u003c/p\u003e\n \u003cp\u003eSepsis was defined as a complex inflammatory response of the host to infection\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. In this study, blood culture-positive sepsis patients were those diagnosed with sepsis and confirmed by positive blood cultures within 24 hours of admission.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eDiagnostic Criteria\u003c/strong\u003e: According to the definition and diagnostic standards for sepsis\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e, patients must fulfill the following: confirmed infection, Sequential Organ Failure Assessment score\u0026thinsp;\u0026ge;\u0026thinsp;2, and positive blood cultures.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eI. Confirmation of Infection\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eConfirmed via microbiological evidence.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eII. Systemic Inflammatory Response Syndrome Criteria\u003c/strong\u003e (\u0026ge;\u0026thinsp;2 required):\u003c/p\u003e\n \u003cp\u003eBody temperature: \u0026ge;38\u0026deg;C or \u0026le;\u0026thinsp;36\u0026deg;C; Tachycardia: \u0026ge;90 beats/min; Tachypnea: \u0026ge;20 breaths/min; Respiratory alkalosis: PaCO₂ \u0026le;32 mmHg (\u0026lt;\u0026thinsp;4.3 kPa); White blood cell (WBC) count: Leukocytosis (\u0026ge;\u0026thinsp;12/nL) or leukopenia (\u0026le;\u0026thinsp;4/nL) or band forms\u0026thinsp;\u0026ge;\u0026thinsp;10% (indicating a left shift, i.e., increased percentage of immature neutrophils and granulocyte precursors).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eInclusion Criteria\u003c/strong\u003e:\u003c/p\u003e\n \u003cp\u003e1) Aged 18\u0026ndash;80 years. (2) Complete clinical data to ensure study reliability and accuracy. (3) Patients with confirmed infection and SIRS, and positive blood cultures from both upper and lower limbs within 24 hours of admission.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eExclusion Criteria\u003c/strong\u003e:\u003c/p\u003e\n \u003cp\u003e1) Pre-admission diagnosis of sepsis with prior treatment (to ensure first-time treatment). (2) History of malignancy (to minimize interference with inflammatory markers). (3) Severe organ failure (to ensure research accuracy and reliability). (4) Hematologic disorders (due to potential effects on inflammatory markers).\u003c/p\u003e\n \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Sample Size\u003c/h2\u003e\n \u003cp\u003eThis study was designed based on the sample size calculation principle of binary logistic regression analysis. The sample size requirement for binary logistic regression analysis stipulates that each independent variable should have at least 10 events to ensure the stability and accuracy of the model\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Given that 15 independent variables had been identified before determining the sample size, 150 samples were sufficient. Considering the availability of clinical data and the research time limit requirements, the sample size was expanded to 500 patients with blood culture-confirmed sepsis. This sample size ensures a sufficient number of positive blood culture sepsis death events, meets the requirements for model stability, and accurately assesses the relationship between the predictive variables and positive blood culture sepsis death.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Observational Indicators\u003c/h2\u003e\n \u003cp\u003ePatient medical records were reviewed to collect general patient data and relevant laboratory data within 24 hours of admission. Specific testing methods were as follows: First, 5 mL of peripheral venous blood was collected at patient admission. Second, two tubes were used: one tube was used to detect neutrophils (NE, normal range: (2.0\u0026ndash;7.0)\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L), lymphocytes (LYM, normal range: (0.8-4.0)\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L), white blood cells (WBC, normal range: (4.0\u0026ndash;10.0)\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L), and platelets (PLT, normal range: (10.0\u0026ndash;40.0)\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L), as well as NLR (normal range: 0.78\u0026ndash;3.53) and PLR (normal range: 0.10\u0026ndash;0.20). The other tube was centrifuged at 3000r/min for 15 minutes, after which procalcitonin (PCT, normal range: \u0026lt;0.05 ng/mL) and uric acid (normal range: 143-416umol/L) were tested using chemiluminescence. The reagent kit (batch number: 20192400468, Shanghai, China) was provided by Siemens, and operations were strictly performed according to the kit instructions. Simultaneously, 5 mL of radial arterial blood was collected, and calcium ion concentration (normal range: 1.15-1.29mmol/L) was measured using a blood gas analyzer (Radiometer Medical Company, National Medical Device Registration No. 20172402212), with operations strictly following the kit instructions. Additionally, 10 mL each of cubital vein and dorsalis pedis vein blood was injected into blood culture bottles (bioM\u0026eacute;rieux SA, REF424066) for culturing, and culture results were observed.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4 Statistical Analysis\u003c/h2\u003e\n \u003cp\u003eData processing and statistical analysis were performed using SPSS 25.0 software. The statistical significance threshold was set at \u0026alpha;\u0026thinsp;=\u0026thinsp;0.05 (two-tailed). Measurement data were tested for normality using the Shapiro-Wilk test. Normally distributed data were expressed as mean\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\)\u003c/span\u003e\u003c/span\u003estandard deviation\u0026nbsp;\u003cimg src=\"data:image/png;base64,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\"\u003e and analyzed using independent samples t-tests. Non-normally distributed data were expressed as median (interquartile range) and analyzed using the rank sum test. Categorical data were expressed as percentages and analyzed using chi-square tests. Binary logistic regression was used to analyze the relationships between platelet count, absolute lymphocyte count, absolute neutrophil count, PCT, uric acid, NLR, and mortality in blood culture-positive sepsis patients. Receiver operating characteristic (ROC) curves were plotted, and the area under the curve (AUC) was calculated to evaluate the predictive value of PCT, uric acid, and NLR for mortality in blood culture-positive sepsis patients. AUC values \u0026gt;\u0026thinsp;0.9 indicated high predictive performance, 0.71\u0026ndash;0.90 indicated moderate predictive performance, 0.5\u0026ndash;0.7 indicated low predictive performance, and \u0026lt;\u0026thinsp;0.5 indicated no predictive performance. A \u003cem\u003ep\u003c/em\u003e \u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Baseline Characteristics of Patients\u003c/h2\u003e \u003cp\u003eIn the blood culture-positive sepsis death group, platelet count, absolute lymphocyte count, absolute neutrophil count, PCT, uric acid, and NLR levels were higher than those in the blood culture-positive sepsis survival group. The platelet count in the death group was lower than that in the survival group. These differences were statistically significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline Characteristics of Patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eClinical Outcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStatistics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRehabilitation discharge (n\u0026thinsp;=\u0026thinsp;416)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDeath (n\u0026thinsp;=\u0026thinsp;84)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)(M(P25 P75) )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e65.00 (54.00, 73.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e67.00 (58.00, 75.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZ\u0026thinsp;=\u0026thinsp;1.615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;2.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e213 (51.20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52 (61.90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWoman\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e203 (48.80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32 (38.91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetesn(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.431\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e116 (27.88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27 (32.14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e300 (72.12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57 (67.86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;1.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.315\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e154 (37.02%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36 (42.86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e262 (62.98%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48 (57.14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoronary heart disease n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.521\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e66 (15.87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11 (13.10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e350 (84.13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e73 (86.90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic renal failure n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;2.461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19 (4.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8 (9.52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e397 (95.43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e76 (90.48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet count(*10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003cp\u003e(M(P25 P75) )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e140.00 (94.00, 211.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e106.00 (38.50, 171.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZ=-3.525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsolute value of lymphocytes(*10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003cp\u003e(M(P25 P75) )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.75 (6.39, 13.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.93 (7.57, 16.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZ\u0026thinsp;=\u0026thinsp;2.761\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalcium ion (mmol/L)\u003c/p\u003e \u003cp\u003e(M(P25 P75) )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.12 (1.08, 1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.11 (1.08, 1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZ=-0.873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.382\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCT (ng/ mL)\u003c/p\u003e \u003cp\u003e(M(P25 P75) )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.90 (1.70, 26.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.50 (12.50, 52.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZ\u0026thinsp;=\u0026thinsp;7.160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUric acid (umol/L)(M(P25 P75) )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e287.50 (218.50, 366.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e389.50 (319.50, 516.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZ\u0026thinsp;=\u0026thinsp;7.491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR(M(P25 P75) )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.42 (9.22, 25.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.79 (12.73, 32.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZ\u0026thinsp;=\u0026thinsp;4.330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLR(M(P25 P75) )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e215.27 (126.05, 369.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e205.72 (70.77, 364.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZ=-1.362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.173\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003ePCT: procalcitonin; PLR: platelet-to-lymphocyte ratio;\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNLR: neutrophil-to-lymphocyte ratio\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Multivariate Analysis\u003c/h2\u003e \u003cp\u003eThe concurrent status of blood culture-positive sepsis patients was set as the dependent variable (\"0\" = sepsis survival group, \"1\" = sepsis death group). Variables with statistically significant differences in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (platelet count, absolute lymphocyte count, absolute neutrophil count, uric acid, PCT, and NLR) were included in the logistic regression analysis. The results showed that platelet count, PCT, uric acid, and NLR were independent risk factors for mortality in blood culture-positive sepsis patients, as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLogistic Regression Results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eꞵ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStandard error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eWald value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e95% confidence interval for OR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFloor limit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUpper limit\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsolute value of lymphocytes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.661\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsolute value of neutrophils\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.077\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUric acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47.486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.071\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-5.921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e70.606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003ePCT: procalcitonin; NLR: neutrophil-to-lymphocyte ratio\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e3.3 Predictive Value of Combined Platelet Count, PCT, Uric Acid, and NLR for Mortality in Blood Culture-Positive Sepsis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe mortality status of emergency department blood culture-positive sepsis patients was set as the dependent variable (coded as \"0\" = non-sepsis, \"1\" = sepsis). Platelet count, PCT, uric acid, and NLR were included as test variables to plot ROC curves. Results showed that the AUC values for platelet count, PCT, uric acid, NLR individually, and their combination in predicting mortality in emergency blood culture-positive sepsis patients were all \u0026gt;\u0026thinsp;0.50, indicating moderate predictive value. Among them, PCT, uric acid, and the combination of all four markers achieved the highest AUC values, as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Notably, the odds ratio (OR) for platelet count was close to 1, suggesting a weak but statistically significant negative correlation with mortality risk. However, its clinical significance requires comprehensive evaluation in conjunction with other indicators and study context.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eValue of PCT, PLR and NLR alone or in combination in predicting postoperative complications of ureteral calculi\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCutoff value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e129.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.553\u0026ndash;0.691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.565\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65.550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.700-0.795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.478\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUric acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e334.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.702\u0026ndash;0.816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.673\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.587\u0026ndash;0.712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.536\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.683\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet\u0026thinsp;+\u0026thinsp;PCT\u0026thinsp;+\u0026thinsp;uric acid\u0026thinsp;+\u0026thinsp;NLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.779\u0026ndash;0.869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.675\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003ePCT: procalcitonin; NLR: neutrophil-to-lymphocyte ratio\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eSepsis is defined as a life-threatening syndrome caused by dysregulated host responses to infection, leading to physiological, pathological, and biological abnormalities\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Global disease studies indicate that this syndrome is associated with a high mortality risk, accounting for approximately 20% of global deaths\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Over the past 14 years, overall mortality rates among patients hospitalized with sepsis for the first time have declined, with comorbidities, infection sites, and acute organ dysfunction identified as mortality-related patient characteristics\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Notably, studies have shown no significant changes in short- or long-term mortality trends for sepsis patients admitted to ICUs\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e, highlighting the urgent need for a precise early-warning system. Therefore, identifying biomarkers associated with blood culture-positive sepsis is critical to reducing mortality risk. This study focused on blood culture-confirmed sepsis patients and found that platelet count, serum PCT, uric acid, and the NLR have predictive value for mortality. Clinicians should closely monitor these indicators for early intervention to reduce mortality, shorten hospital stays, and alleviate patient economic burdens.\u003c/p\u003e \u003cp\u003eOur results demonstrated that the death group exhibited significantly higher NLR, uric acid, and PCT levels but lower platelet counts compared to the survival group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These biomarkers showed high sensitivity and specificity in predicting mortality, underscoring their clinical significance. Studies suggest that hyperuricemia, prevalent in sepsis patients, exacerbates systemic inflammation by activating the NLRP3 inflammasome and promoting pro-inflammatory cytokine release, thereby contributing to multi-organ dysfunction\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Thus, uric acid may aid in assessing sepsis severity. Neutrophils, the first line of defense against bacterial invasion, are recruited to infection sites via chemotactic signals, leading to elevated serum neutrophil levels during inflammation\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. However, in sepsis, the imbalance between pro- and anti-inflammatory responses induces immunosuppression, impairing neutrophil chemotaxis. Concurrently, excessive platelet activation due to dysregulated inflammation and coagulation promotes irreversible platelet aggregation, exacerbating endothelial injury\u0026mdash;particularly in critical capillaries\u0026mdash;resulting in microvascular occlusion, tissue ischemia, and multi-organ failure\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Qi et al.\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e further reported that neutrophil percentages are significantly higher in patients with urosepsis compared to non-septic individuals. Additionally, increased apoptosis reduces lymphocyte counts, while platelet aggregation contributes to thrombus formation. Consequently, NLR and platelet count may assist in evaluating sepsis severity.\u003c/p\u003e \u003cp\u003ePCT, a precursor of calcitonin produced by thyroid parafollicular cells, lacks hormonal activity under physiological conditions. In healthy individuals, PCT is cleaved by proteases into calcitonin, maintaining negligible serum levels. However, during infections or multi-organ dysfunction, PCT synthesis is rapidly upregulated by interleukin-6, C-reactive protein, and other mediators, leading to a sharp rise in serum PCT within 3\u0026ndash;4 hours\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Notably, PCT levels remain unaffected by allergic or autoimmune reactions, making it a highly specific marker for systemic inflammation. A meta-analysis by Tan et al.\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e demonstrated that PCT predicts sepsis AUC of 0.85 (95% CI: 0.82\u0026ndash;0.88), indicating strong diagnostic performance.\u003c/p\u003e \u003cp\u003eThis study highlights the prognostic utility of platelet count, NLR, uric acid, and PCT in blood culture-positive sepsis. While PCT exhibits high specificity but moderate sensitivity, NLR shows higher sensitivity with lower specificity. Individually, these markers lack sufficient predictive power for early mortality warning. However, their combined use improves accuracy and sensitivity, offering superior prognostic value. Clinicians may leverage these findings to refine management strategies, such as enhancing infection surveillance and initiating timely antibiotic therapy.\u003c/p\u003e"},{"header":"5. Limitations","content":"\u003cp\u003eThis study has the following limitations: This was a retrospective study, and the sample size needs to be expanded with prospective, multicenter studies to further validate the results. The specificity corresponding to the cutoff values of individual biomarkers is relatively low, necessitating the combination of other laboratory indicators to improve early warning efficacy.\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eIn conclusion, platelet count, PCT, uric acid, NLR, and their combined detection can serve as early warning indicators for mortality in blood culture-positive sepsis patients. The combined detection of platelet count, PCT, uric acid, and NLR demonstrates superior predictive value, providing clinicians with early alerts to reduce mortality in blood culture-positive sepsis patients and ultimately improve outcomes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthics approval was obtained from biomedical research ethic committee of the General Hospital of Ningxia Medical University. The retrospective noninterventional design led to the waiver of written informed consent, and study conduction was in accordance with the Helsinki Declaration. The confidentiality of all patient data was strictly maintained.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in the article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBL, ZXF, JLW, and LM contributed equally. BL, ZXF, JLW, and LM contributed to the conception and design of the research; QLY, JWL, JJZ and XL contributed to the acquisition, analysis, and interpretation of the data; BL and ZXF drafted the manuscript; JLW, and LM critically revised the manuscript, All authors read and approved the final manuscript.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSinger M, Deutschman C S, Seymour C W, et al. The third international consensus definitions for sepsis and septic shock (Sepsis-3)[J]. Jama, 2016, 315(8): 801-810.\u003c/li\u003e\n\u003cli\u003eGinting F, Sugianli A K, Barimbing M, et al. Appropriateness of diagnosis and antibiotic use in sepsis patients admitted to a tertiary hospital in Indonesia[J]. Postgraduate medicine, 2021, 133(6): 674-679.\u003c/li\u003e\n\u003cli\u003eTeggert A, Datta H, Ali Z. Biomarkers for point-of-care diagnosis of sepsis[J]. Micromachines, 2020, 11(3): 286.\u003c/li\u003e\n\u003cli\u003ePaoli C J, Reynolds M A, Sinha M, et al. Epidemiology and costs of sepsis in the United States\u0026mdash;an analysis based on timing of diagnosis and severity level[J]. Critical care medicine, 2018, 46(12): 1889-1897.\u003c/li\u003e\n\u003cli\u003eReinhart K, Daniels R, Kissoon N, et al. Recognizing sepsis as a global health priority\u0026mdash;a WHO resolution[J]. New England Journal of Medicine, 2017, 377(5): 414-417.\u003c/li\u003e\n\u003cli\u003eMuhammad F , Dik G , Kolak S ,et al.Design of highly selective, and sensitive screen-printed electrochemical sensor for detection of uric acid with uricase immobilized polycaprolactone/polyethylene imine electrospun nanofiber[J].Electrochimica Acta, 2022.DOI:10.1016/j.electacta.2022.141675.\u003c/li\u003e\n\u003cli\u003eGherghina M E, Peride I, Tiglis M, et al. Uric acid and oxidative stress\u0026mdash;relationship with cardiovascular, metabolic, and renal impairment[J]. International Journal of Molecular Sciences, 2022, 23(6): 3188.\u003c/li\u003e\n\u003cli\u003eLevy MM, Evans LE, Rhodes A. The Surviving Sepsis Campaign Bundle: 2018 update. Critical Care Medicine. 2018, 46: 925\u0026ndash;928.\u003c/li\u003e\n\u003cli\u003eAsik Z. The role of the NLR and PLR in urinary tract infection[J]. Clinical laboratory, 2021, 67(10).\u003c/li\u003e\n\u003cli\u003eWagenlehner FM, Lichtenstern C, Rolfes C, Mayer K, Uhle F, Weidner W, et al. Diagnosis and management for urosepsis. International Journal of Urology: Official Journal of the Japanese Urological Association. 2013, 20: 963\u0026ndash;970.\u003c/li\u003e\n\u003cli\u003ePeduzzi P, Concato J, Kemper E, et al. A simulation study of the number of events per variable in logistic regression analysis[J]. Journal of clinical epidemiology, 1996, 49(12): 1373-1379.\u003c/li\u003e\n\u003cli\u003eRudd K E, Johnson S C, Agesa K M, et al. Global, regional, and national sepsis incidence and mortality, 1990\u0026ndash;2017: analysis for the Global Burden of Disease Study[J]. The Lancet, 2020, 395(10219): 200-211.\u003c/li\u003e\n\u003cli\u003eSkei N V, Nilsen T I L, Mohus R M, et al. Trends in mortality after a sepsis hospitalization: a nationwide prospective registry study from 2008 to 2021[J]. Infection, 2023, 51(6): 1773-1786.\u003c/li\u003e\n\u003cli\u003eVesteinsdottir E, Sigurdsson M I, Gottfredsson M, et al. Temporal trends in the epidemiology, management, and outcome of sepsis\u0026mdash;A nationwide observational study[J]. Acta Anaesthesiologica Scandinavica, 2022, 66(4): 497-506.\u003c/li\u003e\n\u003cli\u003eStrandberg G, Walther S, Agvald \u0026Ouml;hman C, et al. Mortality after Severe Sepsis and Septic Shock in Swedish Intensive Care Units 2008‐2016\u0026mdash;A nationwide observational study[J]. Acta Anaesthesiologica Scandinavica, 2020, 64(7): 967-975.\u003c/li\u003e\n\u003cli\u003eAlshehri M A ,Alrashed M ,Shawaqfeh M , et al.Impact of Hyperuricemia on Clinical Outcomes in Sepsis Patients: A Retrospective Cohort Study[J].Journal of Clinical Medicine,2024,13(21):6548-6548.\u003c/li\u003e\n\u003cli\u003eMatsuoka M ,Yamaguchi J ,Kinoshita K .Clinical Significance of Elevated Xanthine Dehydrogenase Levels and Hyperuricemia in Patients with Sepsis[J].International Journal of Molecular Sciences,2023,24(18):\u003c/li\u003e\n\u003cli\u003eHolmbom M, Andersson M, Grabe M, et al. Community-onset urosepsis: incidence and risk factors for 30-day mortality\u0026ndash;a retrospective cohort study[J]. Scandinavian journal of urology, 2022, 56(5-6): 414-420.\u003c/li\u003e\n\u003cli\u003ePeerapornratana S, Manrique-Caballero CL, G\u0026oacute;mez H, Kellum JA. Acute kidney injury from sepsis: current concepts, epidemiology, pathophysiology, prevention and treatment. Kidney International. 2019, 96: 1083\u0026ndash;1099.\u003c/li\u003e\n\u003cli\u003eZhang H, Wang Y, Qu M, Li W, Wu D, Cata JP, et al. Neutrophil, neutrophil extracellular traps and endothelial cell dysfunction in sepsis. Clinical and Translational Medicine. 2023, 13: e1170.\u003c/li\u003e\n\u003cli\u003eJacobi J. The pathophysiology of sepsis\u0026mdash;2021 update: Part 2, organ dysfunction and assessment. American Journal of HealthSystem Pharmacy: AJHP: Official Journal of the American Society of Health-System Pharmacists. 2022, 79: 424\u0026ndash;436.\u003c/li\u003e\n\u003cli\u003eHashem H E, Abdel Halim R M, El Masry S A, et al. The utility of neutrophil CD64 and presepsin as diagnostic, prognostic, and monitoring biomarkers in neonatal sepsis[J]. International journal of microbiology, 2020, 2020(1): 8814892.\u003c/li\u003e\n\u003cli\u003eGe S, Lin S, Zhang L, et al. The association of red blood cell distribution width to platelet count ratio and 28-day mortality of patients with sepsis: a retrospective cohort study[J]. Therapeutics and Clinical Risk Management, 2020: 999-1006.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Sepsis, Retrospective Cohort Study, Mortality Prediction","lastPublishedDoi":"10.21203/rs.3.rs-6621760/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6621760/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo investigate the predictive value of procalcitonin (PCT), uric acid, platelet count, and neutrophil-to-lymphocyte ratio (NLR) for mortality risk in blood culture-positive sepsis patients.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA retrospective analysis was conducted on 500 patients with positive blood cultures from both upper and lower limbs treated in the emergency department of our hospital from January 2023 to February 2025. Based on clinical outcomes, patients were divided into a rehabilitation discharge group (n\u0026thinsp;=\u0026thinsp;416) and a death group (n\u0026thinsp;=\u0026thinsp;84). Peripheral blood PCT, platelet count, uric acid, and NLR levels within 24 hours of admission were compared. Receiver operating characteristic (ROC) curves were used to evaluate the predictive value of these biomarkers for mortality. Binary logistic regression was employed to identify independent risk factors influencing mortality in blood culture-positive sepsis patients.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eLogistic regression analysis revealed that platelet count (OR\u0026thinsp;=\u0026thinsp;0.996, 95% CI: 0.993\u0026ndash;1.000), absolute lymphocyte count (OR\u0026thinsp;=\u0026thinsp;0.901, 95% CI: 0.489\u0026ndash;1.661), absolute neutrophil count (OR\u0026thinsp;=\u0026thinsp;1.025, 95% CI: 0.976\u0026ndash;1.077), PCT (OR\u0026thinsp;=\u0026thinsp;1.012, 95% CI: 1.002\u0026ndash;1.021), uric acid (OR\u0026thinsp;=\u0026thinsp;1.010, 95% CI: 1.007\u0026ndash;1.012), and NLR (OR\u0026thinsp;=\u0026thinsp;1.040, 95% CI: 1.009\u0026ndash;1.071) were significant risk factors for mortality (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). ROC analysis demonstrated that AUC for platelet count, PCT, uric acid, NLR, and their combination were 0.622, 0.747, 0.759, 0.650, and 0.823, respectively, with the combined model showing the highest predictive value.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eElevated PCT, uric acid, and NLR levels, along with reduced platelet counts, are predictive of mortality in blood culture-positive sepsis patients. Clinicians should closely monitor these biomarkers for early intervention to reduce mortality.\u003c/p\u003e","manuscriptTitle":"A Retrospective Cohort Study: Predictive Value of Biomarkers for Mortality in Bloodstream Infection Patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-09 08:30:56","doi":"10.21203/rs.3.rs-6621760/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-20T11:25:47+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-19T20:41:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"273636864824996109066054610779872806046","date":"2026-02-02T06:36:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"186585393068969929982839330516185684623","date":"2026-01-31T08:38:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-25T08:28:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"15152415063392923993642671108517967916","date":"2025-06-16T04:03:47+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-02T20:35:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-19T03:56:15+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-19T03:55:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Infectious Diseases","date":"2025-05-08T14:41:54+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d94b9247-b281-4b4c-8bc7-981b4b46b5ca","owner":[],"postedDate":"June 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-02-20T11:40:09+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-09 08:30:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6621760","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6621760","identity":"rs-6621760","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}