Prognostic Role of Third-Day CRP, Lactate and MechanicalVentilation in Predicting ICU Mortality in Sepsis: A Single-Center Study

Prognostic Role of Third-Day CRP, Lactate and MechanicalVentilation in Predicting ICU Mortality in Sepsis: A Single-Center Study | 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 Prognostic Role of Third-Day CRP, Lactate and MechanicalVentilation in Predicting ICU Mortality in Sepsis: A Single-Center Study mustafa uğuz, berfin çirkin doruk, nur zafer kırdağ This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7558369/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Sepsis remains a leading cause of morbidity and mortality in intensive care units (ICUs). Early identification of high-risk patients is essential for optimizing management. While C-reactive protein (CRP), procalcitonin (PCT), and lactate are widely used biomarkers, evidence on the prognostic value of their dynamic changes, particularly on the third day of ICU stay, is limited. This study aimed to evaluate the prognostic role of third-day CRP, lactate, and mechanical ventilation requirement in predicting 30-day mortality in septic ICU patients. Methods We conducted a retrospective cohort study of adult patients with sepsis admitted to the ICUs of Mersin City Training and Research Hospital between January 2018 and December 2023. Patients younger than 18 years, those with incomplete data, early deaths within 24 hours, and recurrent admissions were excluded. Clinical and laboratory parameters were recorded at admission and on the third day. The primary outcome was all-cause 30-day mortality. Multivariable logistic regression was used to identify independent predictors, and model performance was assessed using receiver operating characteristic (ROC) analysis. Results A total of 200 patients were included (mean age 68.5 years; 55% male). The 30-day mortality rate was 41.5%. Baseline APACHE II and SOFA scores were not predictive of mortality. By contrast, third-day CRP and lactate levels were significantly higher in non-survivors (18.0 ± 11.7 vs. 9.1 ± 7.2 mg/dL, p < 0.001; 3.22 ± 2.58 vs. 1.73 ± 0.81 mmol/L, p < 0.001, respectively). Mechanical ventilation was required in 67.1% of non-survivors compared with 4.3% of survivors (p < 0.001). In multivariable analysis, third-day CRP (OR 1.10, 95% CI 1.04–1.17), third-day lactate (OR 2.10, 95% CI 1.36–3.24), and mechanical ventilation (OR 42.9, 95% CI 12.9–143.0) remained independent predictors. The combined model demonstrated excellent discriminatory ability (AUC = 0.919; sensitivity = 84.1%; specificity = 84.6%). Conclusion Third-day CRP and lactate levels, together with mechanical ventilation requirement, provide a robust and clinically applicable model for predicting 30-day mortality in septic ICU patients. Incorporating dynamic biomarkers into prognostic assessment may improve early risk stratification, guide timely treatment, optimize ICU resource use, and support palliative care decisions. Conclusion Third-day CRP and lactate levels, together with mechanical ventilation requirement, provide a robust and clinically applicable model for predicting 30-day mortality in septic ICU patients. Incorporating dynamic biomarkers into prognostic assessment may improve early risk stratification, guide timely treatment escalation, optimize ICU resource allocation, and support palliative care decisions in appropriate cases. These findings highlight the value of combining laboratory and clinical parameters in the prognostic evaluation of sepsis Sepsis CRP Lactate Mechanicalventilation Mortality Prognostic model Figures Figure 1 Figure 2 Introduction Sepsis remains one of the most critical clinical syndromes in intensive care units (ICUs), leading to high mortality and morbidity. Current global data indicate that sepsis accounts for nearly one-fifth of all deaths, underscoring its persistent role as a major public health concern [1]. Early identification of high-risk patients is crucial for the timely initiation of appropriate antimicrobial therapy and organ support. Therefore, the identification of reliable prognostic markers has become a primary goal in clinical management [2,3]. C-reactive protein (CRP) and procalcitonin (PCT) are among the most commonly used biomarkers for the diagnosis of sepsis and for monitoring treatment response [4,5]. However, increasing emphasis has been placed not on single measurements, but rather on the prognostic value of dynamic changes in these biomarkers over time. Several studies have reported that insufficient reduction in CRP within the first 72 hours of admission, or failure of PCT levels to decrease as expected, are independently associated with mortality [6–8]. Similarly, lactate levels, which reflect tissue hypoperfusion and metabolic dysfunction, have been strongly linked to poor outcomes when adequate lactate clearance is not achieved during serial measurements in the first 48–72 hours [9,10]. The third day of ICU stay is considered a particularly critical time point in clinical practice. At this stage, antimicrobial therapy is often reassessed, organ support strategies are reevaluated, and strategic decisions regarding clinical management are made. Thus, the dynamic changes in third-day biomarkers may reflect not only the initial severity of illness but also the patient’s response to therapy and ongoing inflammatory processes, offering a more reliable indicator for predicting mortality risk. In addition to biomarkers, the need for invasive mechanical ventilation (MV) represents an important clinical marker of disease severity and mortality risk. Large-scale studies have demonstrated a strong association between MV requirement and mortality in septic patients [11,12]. While MV is often a life-saving intervention, it also reflects the severity of infection and the extent of multi-organ dysfunction. Therefore, evaluating the dynamic changes of biomarkers together with clinical parameters may provide a more reliable and comprehensive approach to mortality risk stratification. Methods Study Design and Setting This retrospective cohort study was conducted at Mersin City Training and Research Hospital between January 2018 and December 2023. The hospital is a tertiary care center with a capacity of 2,200 beds, including 12 intensive care units (ICUs). The study included adult patients who were diagnosed with sepsis and admitted to the ICUs during the study period. Patient Selection Sepsis was defined according to the Sepsis-3 criteria. Patients aged ≥18 years with a diagnosis of sepsis and treated in the ICU were eligible for inclusion. Patients under 18 years of age, those with incomplete clinical or laboratory data, those who died within the first 24 hours of ICU admission, and those with recurrent ICU admissions (only the first admission was considered) were excluded from the study. A total of 254 patients were screened, of whom 54 were excluded based on these criteria. The final analysis included 200 patients (83 non-survivors and 117 survivors). No a priori sample size calculation was performed; instead, all eligible patients were included due to the retrospective nature of the study. The patient selection process is presented in a STROBE-compliant flow diagram. Data Collection Data were obtained from the hospital’s electronic medical records. Demographic characteristics (age, sex), comorbidities, and clinical/laboratory parameters were recorded. Baseline (day 0) measurements included CRP, procalcitonin, lactate, creatinine, ALT, WBC, neutrophil count, lymphocyte count, platelet count, and arterial blood gas parameters (pCO₂, fCOHb, lactate). On the third day (72 hours), the same biomarkers were re-measured. Clinical variables included APACHE II and SOFA scores (on admission and on day 3), requirement for invasive mechanical ventilation, renal replacement therapy, and 30-day all-cause mortality. The mechanical ventilation variable referred exclusively to invasive ventilation; non-invasive respiratory support was not included. Primary Endpoint The primary endpoint was all-cause 30-day mortality following ICU admission. Handling of Missing Data Patients with missing data were excluded from the analysis. No imputation methods were applied, and analyses were performed only on patients with complete datasets. Ethical Approval The study was approved by the Toros University Scientific Research and Publication Ethics Committee (Approval no: E-66792640-929, Date: 01.11.2023). All patient data were anonymized prior to analysis. The study was conducted in accordance with the principles of the Declaration of Helsinki. Statistical Analysis Descriptive data were presented as mean ± standard deviation (SD) or median with interquartile range (IQR), as appropriate. Comparisons between groups were performed using the Mann–Whitney U test for continuous variables and the Chi-square test for categorical variables. To identify independent risk factors associated with mortality, a multivariable logistic regression model was constructed. Variables that were statistically significant in univariable analysis (p < 0.05) and those considered clinically relevant were included in the model. The variables entered into the model were age, sex, third-day CRP, third-day procalcitonin, third-day lactate, APACHE II score, SOFA score, and the requirement for mechanical ventilation. Multicollinearity was assessed using the variance inflation factor (VIF), and all variables demonstrated VIF values < 2. The discriminatory ability of the model was evaluated using receiver operating characteristic (ROC) curve analysis, with the area under the curve (AUC), sensitivity, and specificity values reported. The model was developed to ensure an events-per-variable (EPV) ratio of ≥10. A p-value of < 0.05 was considered statistically significant. Results The demographic and baseline laboratory parameters of the patients were compared between survivors and non-survivors (Table 1). No significant differences were observed in terms of age or sex (p > 0.05). Similarly, baseline APACHE II and SOFA scores were not significantly associated with mortality (p = 0.059 and p = 0.917, respectively). Among laboratory parameters, only lactate levels were significantly higher in the non-survivor group (3.90 ± 2.38 vs. 3.19 ± 1.96 mmol/L; p = 0.008). Baseline CRP and procalcitonin levels did not differ significantly between groups (p = 0.818 and p = 0.074, respectively). Table 1. Baseline demographic, clinical, and laboratory parameters according to 30-day mortality status Variable Non-survivors (Mean ± SD / n, %) Survivors (Mean ± SD / n, %) p-value Age (years) 69.41 ± 13.38 67.84 ± 15.59 0.505 (Mann-Whitney U) Sex (male) 62 / 117 (53.0%) 48 / 83 (57.8%) 0.594 (Chi-square) APACHE II score 18.52 ± 9.11 15.80 ± 5.91 0.059 (Mann-Whitney U) SOFA score 7.02 ± 3.87 6.68 ± 2.78 0.917 (Mann-Whitney U) Lactate (mmol/L) 3.90 ± 2.38 3.19 ± 1.96 0.008 (Mann-Whitney U) CRP (mg/dL) 17.40 ± 11.78 17.48 ± 11.19 0.818 (Mann-Whitney U) Procalcitonin (ng/mL) 36.69 ± 29.76 42.75 ± 26.56 0.074 (Mann-Whitney U) Data are presented as mean ± SD or n (%). Mann–Whitney U test was used for continuous variables, Chi-square test for categorical variables. On the third day of ICU stay, creatinine, ALT, neutrophil count, CRP, procalcitonin, and lactate levels were significantly higher in non-survivors, whereas platelet counts were significantly lower compared to survivors (Table 2). In particular, third-day CRP (18.0 ± 11.7 mg/dL vs. 9.1 ± 7.2 mg/dL; p < 0.001) and third-day lactate (3.22 ± 2.58 mmol/L vs. 1.73 ± 0.81 mmol/L; p < 0.001) demonstrated the strongest associations with mortality.(Table 3) Table 2. Third-day laboratory findings according to 30-day mortality status Variable Non-survivors (Mean ± SD) Survivors (Mean ± SD) p-value Creatinine (mg/dL) 2.79 ± 3.49 1.65 ± 1.48 p < 0.001 (Mann-Whitney U) ALT (U/L) 95.40 ± 157.36 202.02 ± 1415.12 0.002 (Mann-Whitney U) Neutrophil (cells/µL) 15539.99 ± 26797.21 10825.26 ± 9687.69 0.016 (Mann-Whitney U) Platelet (10³/µL) 168.73 ± 118.46 199.21 ± 130.19 0.024 (Mann-Whitney U) Procalcitonin (ng/mL) 23.83 ± 25.66 14.99 ± 19.61 0.011 (Mann-Whitney U) CRP (mg/dL) 17.94 ± 11.66 9.10 ± 7.17 p < 0.001 (Mann-Whitney U) Lactate (mmol/L) 3.22 ± 2.58 1.73 ± 0.81 0.000 (Mann-Whitney U) Data are presented as mean ± SD. Mann–Whitney U test was used for comparisons. Table 3. Multivariable logistic regression analysis for predictors of 30-day mortality Variable OR 95% CI p-value CRP, 3rd day (mg/dL) 1.10 1.04 – 1.17 0.002 Lactate, 3rd day (mmol/L) 2.10 1.36 – 3.24 <0.001 Mechanical ventilation 42.9 12.9 – 143.0 <0.001 Multivariable logistic regression model included age, sex, third-day CRP, third-day procalcitonin, third-day lactate, APACHE II, SOFA, and mechanical ventilation. Only variables remaining significant are shown. Among clinical variables, the requirement for mechanical ventilation was strongly associated with mortality (χ² = 90.3; p < 0.001). In the non-survivor group, 67.1% of patients required mechanical ventilation, compared with only 4.3% in survivors. These data were available for 199 patients; information for one patient was missing. Multivariable logistic regression analysis included age, sex, third-day CRP, third-day lactate, and the requirement for mechanical ventilation. Other parameters that were significant in univariable analysis (platelet count, ALT, neutrophil count) did not remain independent predictors in the multivariable model. The combined model demonstrated excellent discriminatory ability for predicting 30-day mortality, with an area under the curve (AUC) of 0.919. The optimal cut-off value was determined as 0.30, corresponding to a sensitivity of 84.1% and a specificity of 84.6%. ROC curves for CRP, lactate, and the combined model are shown in Figure 1. Discussion In this study, we evaluated clinical and laboratory parameters predicting mortality in 200 ICU patients diagnosed with sepsis. The mean age was 68.5 years, and 55% of the patients were male. The 30-day mortality rate was 41.5%, which is consistent with global data reporting mortality rates between 25% and 50% [13]. Our findings demonstrated that third-day CRP and lactate levels, together with the requirement for mechanical ventilation, were independent predictors of mortality. Previous studies have shown that sepsis-related mortality increases with advanced age, while sex generally has limited prognostic impact [14,15]. In our cohort, age was not significantly associated with mortality, and sex had no prognostic value. This likely reflects the older age distribution of our patient population, where sex-related differences may be less pronounced. It is noteworthy that baseline APACHE II and SOFA scores were not predictive of mortality. Although the prognostic value of these severity scores has been highlighted in multiple studies [16,17], differences in patient populations, comorbidities, treatment heterogeneity, and cutoff values may explain the variability in results. In addition, these scores were recorded only at admission and may not capture dynamic changes during the ICU stay. Our findings suggest that single time-point severity scores may be limited in predicting outcomes, whereas dynamic follow-up of biomarkers may provide more reliable prognostic information. Third-day CRP and lactate levels were significantly higher among non-survivors. In clinical practice, the third day represents a critical point when antimicrobial therapies are reassessed and organ support strategies are adjusted. CRP trajectories are known to reflect treatment response, with persistently elevated levels indicating poor prognosis [18]. In our study, sustained elevation of CRP at 72 hours in the non-survivor group likely reflects ongoing inflammation or inadequate treatment response. Similarly, lactate is a marker of tissue hypoperfusion, and persistently elevated levels are strongly associated with mortality [19,20]. Our findings support the use of third-day biomarker dynamics as important indicators to guide clinical decision-making. Nonetheless, serial measurements at later time points (e.g., days 5 or 7) may provide additional prognostic information. The requirement for invasive mechanical ventilation emerged as the strongest clinical predictor of mortality. Previous studies have shown that mechanical ventilation reflects both the severity of organ dysfunction and contributes to poor outcomes due to related complications [21,22]. In our cohort, 67.1% of non-survivors required ventilation compared to only 4.3% of survivors. The odds ratio for mechanical ventilation in multivariable analysis was strikingly high (OR = 42.9). This likely reflects both the profound severity of illness among ventilated patients and strong interactions within the model. However, such a high odds ratio may dominate the model and obscure the contributions of other variables. Furthermore, the independent effects of CRP and lactate in non-ventilated subgroups were not analyzed, representing a limitation of our study. Future prospective subgroup analyses are needed to better clarify the prognostic role of mechanical ventilation. Although procalcitonin (PCT) was significantly associated with mortality in univariable analysis, it did not retain independence in multivariable models. This suggests that its prognostic association may be overshadowed by stronger predictors such as CRP and lactate. Previous studies have also reported heterogeneous findings regarding the prognostic role of PCT [24], and our results align with this variability. Multicollinearity testing showed VIF values < 2 for all variables, indicating no significant multicollinearity effect on the model (Supplementary Table 1). In the multivariable logistic regression model, platelet count, ALT, and neutrophil count—though significant in univariable analyses—lost their independent associations, while third-day CRP, third-day lactate, and mechanical ventilation remained as independent predictors of mortality. This highlights the importance of close monitoring of these three parameters in clinical practice. The discriminatory power of our model was excellent (AUC = 0.919). Compared with prior prognostic models, where AUC values typically range from 0.75 to 0.85 [23,24], our model demonstrated superior performance. High sensitivity (84.1%) and specificity (84.6%) further support the clinical reliability of our model. However, the model was tested only with internal validation; no cross-validation or bootstrap methods were applied, which poses a risk of overfitting. Independent external validation is required to confirm these findings. When compared with similar studies in the literature, our results show both parallels and distinctions. Previous prospective studies have demonstrated that insufficient reductions in third-day CRP and lactate levels are strongly associated with mortality [18–20], consistent with our findings. However, the lack of predictive value of APACHE II and SOFA in our cohort suggests that biomarker dynamics may provide more reliable prognostic information in elderly, comorbid ICU populations. The high odds ratio of mechanical ventilation as a predictor is also consistent with prior studies [21,22], but this should not be interpreted as mortality risk being solely attributable to ventilation itself; rather, it reflects a combination of disease severity, complications, and treatment processes. The main clinical implication of our study is that integrating third-day biomarkers with mechanical ventilation status may provide practical benefits for patient management. This combination allows early identification of high-risk patients, timely escalation of treatment (e.g., advanced organ support), prioritization of ICU resources, and consideration of palliative care where appropriate. Thus, our findings have not only prognostic value but also direct clinical applicability. It is also noteworthy that APACHE II and SOFA scores alone were not predictive of mortality. However, combining these traditional severity scores with third-day CRP and lactate levels may enhance risk stratification. Prior studies have similarly reported that integrating biomarker dynamics with clinical scoring systems improves prognostic accuracy [25]. Accordingly, CRP and lactate may be integrated into existing scoring systems to provide clinicians with more precise decision-support tools for early risk identification in sepsis. Our patient population was predominantly elderly (mean age: 68 years). Therefore, our results mainly reflect outcomes in older, comorbid patients. The prognostic role of biomarkers may differ in younger or immunocompromised subgroups (e.g., hematologic malignancies, solid organ transplant recipients), and our findings may not be directly generalizable to such populations. Future prospective studies including younger and immunocompromised patients are warranted. Strengths of our study include the systematic evaluation of third-day biomarkers in septic ICU patients, integration of laboratory and clinical variables, and validation of model performance using ROC analysis. However, several limitations should be acknowledged. Conclusion The combination of third-day CRP and lactate levels with the requirement for mechanical ventilation provides a robust and clinically applicable model for predicting 30-day mortality in septic ICU patients. This approach enables early identification of high-risk patients, timely escalation of therapy, prioritization of ICU resources, and appropriate initiation of palliative care discussions. Our findings therefore hold both prognostic and practical value for critical care management. Furthermore, the lack of prognostic value of traditional severity scores (APACHE II and SOFA) alone, and the added predictive accuracy achieved by integrating third-day biomarkers, suggest that CRP and lactate may enhance existing scoring systems and support more precise clinical decision-making. Our study primarily reflects outcomes in an elderly, comorbid ICU population. Caution is warranted in generalizing these results to younger or immunocompromised patients, and further prospective multicenter studies are needed to validate these findings across diverse populations. Ultimately, integrating third-day biomarkers and clinical parameters into prognostic models may improve early risk stratification and patient management in sepsis. Limitations Several limitations of our study should be acknowledged. First, the single-center, retrospective design limits the generalizability of our findings. Although the sample size (n = 200) was statistically adequate in terms of events-per-variable ratio, larger multicenter cohorts would provide more robust validation. Second, no a priori power analysis was performed; all eligible patients were included due to the retrospective design. Third, biomarker measurements were limited to the first three days of ICU stay, and serial measurements at later time points (e.g., days 5 or 7) were not available, restricting assessment of longer-term biomarker dynamics. Fourth, the high AUC of our model was achieved through internal validation only, without cross-validation or bootstrapping, raising the possibility of overfitting. The lack of external validation further limits generalizability. Finally, heterogeneity in treatment protocols and the single-center setting may reduce the applicability of our findings to other clinical contexts. Declarations Ethics approval and consent to participate The study was approved by the Toros University Scientific Research and Publication Ethics Committee (Approval no: E-66792640-929, Date: 01.11.2023). Written informed consent was waived due to the retrospective design of the study. Consent for publication Not applicable. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Authors’ contributions MU : Study conception and design, data collection, statistical analysis, manuscript drafting. BCD : Data collection, literature review, manuscript editing. NZK : Clinical data interpretation, critical revision of the manuscript. All authors read and approved the final manuscript. 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Has mortalityfromacuterespiratorydistresssyndromedecreased over time? A systematic review. Am J RespirCritCareMed. 2009;179(3):220–7. Chen YX, Li CS. Lactateclearance in criticallyillpatients with sepsis: a prospectivestudy. J CritCare. 2015;30(2):280–5. Jekarl DW, Lee SY, Lee J, Park YJ, Kim Y, Park JH, et al. Procalcitonin as a prognostic marker for sepsis based on Sepsis-3. J Clin Lab Anal. 2019;33(9):e22996. Song JU, Sin CK, Park HK, Shim SR, Lee J. Performance of the SOFA score and APACHE II score for predictingmortality in criticallyillpatients with sepsis: A systematic review and meta-analysis. J Clin Med. 2022;11(19):5810. doi: 10.3390/jcm11195810 Additional Declarations No competing interests reported. Supplementary Files SupplementaryFigure1STROBEfinal.tiff Supplementary Figure 1.STROBE flow diagram illustrating the patient selection process. A total of 254 patients with sepsis were screened for eligibility. After excluding patients who were younger than 18 years, had incomplete data, died within the first 24 hours, or had recurrent admissions, 200 patients were included in the final analysis (83 non-survivors and 117 survivors). suplement.docx Supplementary Table 1.Multicollinearity analysis (VIF values) of the variables included in the multivariable logistic regression model. All variables demonstrated VIF values < 2, indicating no significant multicollinearity. Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. 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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-7558369","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":511661544,"identity":"836f9353-48a7-41f7-9226-d27bb19a8908","order_by":0,"name":"mustafa uğuz","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIiWNgGAWjYDCCAxCKH0JVADEzcwMRWhIYJCHKzoC0MJKihbENTOLXwnf7dJrkzx92Evwz0q9J/pxXG83fDtTyo2IbTi2S53K3SfMkJEtI3Mgpk5Dcdjx3xmHGBsaeM7dxajE4w7tNmiGBuY7hRk6ahOG2Y7kNQC3MjG34tUj+SKiXkAdpSZxzLHc+MVokeBIOSxjcSD8mcbChJncDIS2SZ3g3W/OkHZcwPPOG2bLh2IHcjUAtB/H5he8M78abP2yqJeSOpz+8+aOmLnfe+cMHH/yowK0FCfAYAInDYOYBYtQDAfsDIFFHpOJRMApGwSgYSQAAcS9eIXjoNfQAAAAASUVORK5CYII=","orcid":"","institution":"mersin şehir eğitim ve araştırma hastanesi","correspondingAuthor":true,"prefix":"","firstName":"mustafa","middleName":"","lastName":"uğuz","suffix":""},{"id":511661545,"identity":"d03333fe-2b8b-4422-9d4a-02a9bc3fdc31","order_by":1,"name":"berfin çirkin doruk","email":"","orcid":"","institution":"mersin şehir eğitim ve araştırma hastanesi","correspondingAuthor":false,"prefix":"","firstName":"berfin","middleName":"çirkin","lastName":"doruk","suffix":""},{"id":511661546,"identity":"66cfe98e-4a6a-4e4e-b942-9a172d15f371","order_by":2,"name":"nur zafer kırdağ","email":"","orcid":"","institution":"mersin şehir eğitim ve araştırma hastanesi","correspondingAuthor":false,"prefix":"","firstName":"nur","middleName":"zafer","lastName":"kırdağ","suffix":""}],"badges":[],"createdAt":"2025-09-07 20:08:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7558369/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7558369/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90913792,"identity":"9b1d6c41-11c1-4e86-9626-049f2d7bf6ea","added_by":"auto","created_at":"2025-09-09 13:55:04","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":49576,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic (ROC) curves for predicting 30-day mortality in septic ICU patients.\u003c/p\u003e\n\u003cp\u003e(A) ROC curve for third-day CRP (AUC = 0.77).\u003cbr\u003e\n(B) ROC curve for third-day lactate (AUC = 0.81).\u003cbr\u003e\n(C) ROC curve for the combined model including third-day CRP, lactate, and mechanical ventilation requirement. The combined model demonstrated excellent discriminatory power (AUC = 0.919). The optimal cut-off point determined by the Youden index (cut-off = 0.30; sensitivity = 84.1%; specificity = 84.6%) is indicated on the curve.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7558369/v1/fce1d5b4f99254ca7a3b30de.jpeg"},{"id":90912193,"identity":"fa186fdd-5788-46e9-922a-57f0ed8241b2","added_by":"auto","created_at":"2025-09-09 13:47:04","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":113793,"visible":true,"origin":"","legend":"\u003cp\u003eUnnumbered image in the Methods section\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7558369/v1/11e3a3932d9a1f3b133c3f43.jpeg"},{"id":106315514,"identity":"9133b3c6-4290-4c67-9007-ccbbf4fc544e","added_by":"auto","created_at":"2026-04-07 11:14:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":975397,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7558369/v1/79a27d31-5fde-41ae-a692-bdc8423174a3.pdf"},{"id":90912214,"identity":"c0f0ba41-860f-4f52-a3a9-1e45820ac831","added_by":"auto","created_at":"2025-09-09 13:47:05","extension":"tiff","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":9757814,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 1.\u003c/strong\u003eSTROBE flow diagram illustrating the patient selection process. A total of 254 patients with sepsis were screened for eligibility. After excluding patients who were younger than 18 years, had incomplete data, died within the first 24 hours, or had recurrent admissions, 200 patients were included in the final analysis (83 non-survivors and 117 survivors).\u003c/p\u003e","description":"","filename":"SupplementaryFigure1STROBEfinal.tiff","url":"https://assets-eu.researchsquare.com/files/rs-7558369/v1/01acf7081bd00177e97ad3ff.tiff"},{"id":90912197,"identity":"22f0767d-580a-47e5-90e3-fcd87d5af56e","added_by":"auto","created_at":"2025-09-09 13:47:04","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":44505,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table 1.\u003c/strong\u003eMulticollinearity analysis (VIF values) of the variables included in the multivariable logistic regression model. All variables demonstrated VIF values \u0026lt; 2, indicating no significant multicollinearity.\u003c/p\u003e","description":"","filename":"suplement.docx","url":"https://assets-eu.researchsquare.com/files/rs-7558369/v1/7e4d88183e97296ad3e6ca5c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prognostic Role of Third-Day CRP, Lactate and MechanicalVentilation in Predicting ICU Mortality in Sepsis: A Single-Center Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSepsis remains one of the most critical clinical syndromes in intensive care units (ICUs), leading to high mortality and morbidity. Current global data indicate that sepsis accounts for nearly one-fifth of all deaths, underscoring its persistent role as a major public health concern [1]. Early identification of high-risk patients is crucial for the timely initiation of appropriate antimicrobial therapy and organ support. Therefore, the identification of reliable prognostic markers has become a primary goal in clinical management [2,3].\u003c/p\u003e\n\u003cp\u003eC-reactive protein (CRP) and procalcitonin (PCT) are among the most commonly used biomarkers for the diagnosis of sepsis and for monitoring treatment response [4,5]. However, increasing emphasis has been placed not on single measurements, but rather on the prognostic value of dynamic changes in these biomarkers over time. Several studies have reported that insufficient reduction in CRP within the first 72 hours of admission, or failure of PCT levels to decrease as expected, are independently associated with mortality [6\u0026ndash;8]. Similarly, lactate levels, which reflect tissue hypoperfusion and metabolic dysfunction, have been strongly linked to poor outcomes when adequate lactate clearance is not achieved during serial measurements in the first 48\u0026ndash;72 hours [9,10].\u003c/p\u003e\n\u003cp\u003eThe third day of ICU stay is considered a particularly critical time point in clinical practice. At this stage, antimicrobial therapy is often reassessed, organ support strategies are reevaluated, and strategic decisions regarding clinical management are made. Thus, the dynamic changes in third-day biomarkers may reflect not only the initial severity of illness but also the patient\u0026rsquo;s response to therapy and ongoing inflammatory processes, offering a more reliable indicator for predicting mortality risk.\u003c/p\u003e\n\u003cp\u003eIn addition to biomarkers, the need for invasive mechanical ventilation (MV) represents an important clinical marker of disease severity and mortality risk. Large-scale studies have demonstrated a strong association between MV requirement and mortality in septic patients [11,12]. While MV is often a life-saving intervention, it also reflects the severity of infection and the extent of multi-organ dysfunction. Therefore, evaluating the dynamic changes of biomarkers together with clinical parameters may provide a more reliable and comprehensive approach to mortality risk stratification.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Design and Setting\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective cohort study was conducted at Mersin City Training and Research Hospital between January 2018 and December 2023. The hospital is a tertiary care center with a capacity of 2,200 beds, including 12 intensive care units (ICUs). The study included adult patients who were diagnosed with sepsis and admitted to the ICUs during the study period.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient Selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSepsis was defined according to the Sepsis-3 criteria. Patients aged \u0026ge;18 years with a diagnosis of sepsis and treated in the ICU were eligible for inclusion. Patients under 18 years of age, those with incomplete clinical or laboratory data, those who died within the first 24 hours of ICU admission, and those with recurrent ICU admissions (only the first admission was considered) were excluded from the study.\u003c/p\u003e\n\u003cp\u003eA total of 254 patients were screened, of whom 54 were excluded based on these criteria. The final analysis included 200 patients (83 non-survivors and 117 survivors). No a priori sample size calculation was performed; instead, all eligible patients were included due to the retrospective nature of the study. The patient selection process is presented in a STROBE-compliant flow diagram.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData were obtained from the hospital\u0026rsquo;s electronic medical records. Demographic characteristics (age, sex), comorbidities, and clinical/laboratory parameters were recorded. Baseline (day 0) measurements included CRP, procalcitonin, lactate, creatinine, ALT, WBC, neutrophil count, lymphocyte count, platelet count, and arterial blood gas parameters (pCO₂, fCOHb, lactate). On the third day (72 hours), the same biomarkers were re-measured. Clinical variables included APACHE II and SOFA scores (on admission and on day 3), requirement for invasive mechanical ventilation, renal replacement therapy, and 30-day all-cause mortality. The mechanical ventilation variable referred exclusively to invasive ventilation; non-invasive respiratory support was not included.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrimary Endpoint\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe primary endpoint was all-cause 30-day mortality following ICU admission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHandling of Missing Data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients with missing data were excluded from the analysis. No imputation methods were applied, and analyses were performed only on patients with complete datasets.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Toros University Scientific Research and Publication Ethics Committee (Approval no: E-66792640-929, Date: 01.11.2023). All patient data were anonymized prior to analysis. The study was conducted in accordance with the principles of the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDescriptive data were presented as mean \u0026plusmn; standard deviation (SD) or median with interquartile range (IQR), as appropriate. Comparisons between groups were performed using the Mann\u0026ndash;Whitney U test for continuous variables and the Chi-square test for categorical variables. To identify independent risk factors associated with mortality, a multivariable logistic regression model was constructed. Variables that were statistically significant in univariable analysis (p \u0026lt; 0.05) and those considered clinically relevant were included in the model. The variables entered into the model were age, sex, third-day CRP, third-day procalcitonin, third-day lactate, APACHE II score, SOFA score, and the requirement for mechanical ventilation. Multicollinearity was assessed using the variance inflation factor (VIF), and all variables demonstrated VIF values \u0026lt; 2. The discriminatory ability of the model was evaluated using receiver operating characteristic (ROC) curve analysis, with the area under the curve (AUC), sensitivity, and specificity values reported. The model was developed to ensure an events-per-variable (EPV) ratio of \u0026ge;10. A p-value of \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe demographic and baseline laboratory parameters of the patients were compared between survivors and non-survivors (Table 1). No significant differences were observed in terms of age or sex (p \u0026gt; 0.05). Similarly, baseline APACHE II and SOFA scores were not significantly associated with mortality (p = 0.059 and p = 0.917, respectively). Among laboratory parameters, only lactate levels were significantly higher in the non-survivor group (3.90 \u0026plusmn; 2.38 vs. 3.19 \u0026plusmn; 1.96 mmol/L; p = 0.008). Baseline CRP and procalcitonin levels did not differ significantly between groups (p = 0.818 and p = 0.074, respectively).\u003c/p\u003e\n\u003ch3\u003eTable 1. Baseline demographic, clinical, and laboratory parameters according to 30-day mortality status\u003c/h3\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"626\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-survivors (Mean \u0026plusmn; SD / n, %)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurvivors (Mean \u0026plusmn; SD / n, %)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e69.41 \u0026plusmn; 13.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e67.84 \u0026plusmn; 15.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e0.505 (Mann-Whitney U)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex (male)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e62 / 117 (53.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e48 / 83 (57.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e0.594 (Chi-square)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAPACHE II score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e18.52 \u0026plusmn; 9.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e15.80 \u0026plusmn; 5.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e0.059 (Mann-Whitney U)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSOFA score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e7.02 \u0026plusmn; 3.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e6.68 \u0026plusmn; 2.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e0.917 (Mann-Whitney U)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLactate (mmol/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e3.90 \u0026plusmn; 2.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e3.19 \u0026plusmn; 1.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e0.008 (Mann-Whitney U)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCRP (mg/dL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e17.40 \u0026plusmn; 11.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e17.48 \u0026plusmn; 11.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e0.818 (Mann-Whitney U)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProcalcitonin (ng/mL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e36.69 \u0026plusmn; 29.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e42.75 \u0026plusmn; 26.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e0.074 (Mann-Whitney U)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eData are presented as mean \u0026plusmn; SD or n (%). Mann\u0026ndash;Whitney U test was used for continuous variables, Chi-square test for categorical variables.\u003c/p\u003e\n\u003cp\u003eOn the third day of ICU stay, creatinine, ALT, neutrophil count, CRP, procalcitonin, and lactate levels were significantly higher in non-survivors, whereas platelet counts were significantly lower compared to survivors (Table 2). In particular, third-day CRP (18.0 \u0026plusmn; 11.7 mg/dL vs. 9.1 \u0026plusmn; 7.2 mg/dL; p \u0026lt; 0.001) and third-day lactate (3.22 \u0026plusmn; 2.58 mmol/L vs. 1.73 \u0026plusmn; 0.81 mmol/L; p \u0026lt; 0.001) demonstrated the strongest associations with mortality.(Table 3)\u003c/p\u003e\n\u003ch3\u003eTable 2. Third-day laboratory findings according to 30-day mortality status\u003c/h3\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"622\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-survivors (Mean \u0026plusmn; SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurvivors (Mean \u0026plusmn; SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCreatinine (mg/dL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e2.79 \u0026plusmn; 3.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e1.65 \u0026plusmn; 1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003ep \u0026lt; 0.001 (Mann-Whitney U)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eALT (U/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e95.40 \u0026plusmn; 157.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e202.02 \u0026plusmn; 1415.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e0.002 (Mann-Whitney U)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNeutrophil (cells/\u0026micro;L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e15539.99 \u0026plusmn; 26797.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e10825.26 \u0026plusmn; 9687.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e0.016 (Mann-Whitney U)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlatelet (10\u0026sup3;/\u0026micro;L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e168.73 \u0026plusmn; 118.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e199.21 \u0026plusmn; 130.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e0.024 (Mann-Whitney U)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProcalcitonin (ng/mL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e23.83 \u0026plusmn; 25.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e14.99 \u0026plusmn; 19.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e0.011 (Mann-Whitney U)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCRP (mg/dL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e17.94 \u0026plusmn; 11.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e9.10 \u0026plusmn; 7.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003ep \u0026lt; 0.001 (Mann-Whitney U)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLactate (mmol/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e3.22 \u0026plusmn; 2.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e1.73 \u0026plusmn; 0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e0.000 (Mann-Whitney U)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eData are presented as mean \u0026plusmn; SD. Mann\u0026ndash;Whitney U test was used for comparisons.\u003c/p\u003e\n\u003ch3\u003eTable 3. Multivariable logistic regression analysis for predictors of \u0026nbsp; 30-day mortality\u003c/h3\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCRP, 3rd day (mg/dL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e1.04 \u0026ndash; 1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLactate, 3rd day (mmol/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e2.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e1.36 \u0026ndash; 3.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMechanical ventilation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e42.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e12.9 \u0026ndash; 143.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eMultivariable logistic regression model included age, sex, third-day CRP, third-day procalcitonin, third-day lactate, APACHE II, SOFA, and mechanical ventilation. Only variables remaining significant are shown.\u003c/p\u003e\n\u003cp\u003eAmong clinical variables, the requirement for mechanical ventilation was strongly associated with mortality (\u0026chi;\u0026sup2; = 90.3; p \u0026lt; 0.001). In the non-survivor group, 67.1% of patients required mechanical ventilation, compared with only 4.3% in survivors. These data were available for 199 patients; information for one patient was missing.\u003c/p\u003e\n\u003cp\u003eMultivariable logistic regression analysis included age, sex, third-day CRP, third-day lactate, and the requirement for mechanical ventilation. Other parameters that were significant in univariable analysis (platelet count, ALT, neutrophil count) did not remain independent predictors in the multivariable model. The combined model demonstrated excellent discriminatory ability for predicting 30-day mortality, with an area under the curve (AUC) of 0.919. The optimal cut-off value was determined as 0.30, corresponding to a sensitivity of 84.1% and a specificity of 84.6%. ROC curves for CRP, lactate, and the combined model are shown in Figure 1.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we evaluated clinical and laboratory parameters predicting mortality in 200 ICU patients diagnosed with sepsis. The mean age was 68.5 years, and 55% of the patients were male. The 30-day mortality rate was 41.5%, which is consistent with global data reporting mortality rates between 25% and 50% [13]. Our findings demonstrated that third-day CRP and lactate levels, together with the requirement for mechanical ventilation, were independent predictors of mortality.\u003c/p\u003e\n\u003cp\u003ePrevious studies have shown that sepsis-related mortality increases with advanced age, while sex generally has limited prognostic impact [14,15]. In our cohort, age was not significantly associated with mortality, and sex had no prognostic value. This likely reflects the older age distribution of our patient population, where sex-related differences may be less pronounced.\u003c/p\u003e\n\u003cp\u003eIt is noteworthy that baseline APACHE II and SOFA scores were not predictive of mortality. Although the prognostic value of these severity scores has been highlighted in multiple studies [16,17], differences in patient populations, comorbidities, treatment heterogeneity, and cutoff values may explain the variability in results. In addition, these scores were recorded only at admission and may not capture dynamic changes during the ICU stay. Our findings suggest that single time-point severity scores may be limited in predicting outcomes, whereas dynamic follow-up of biomarkers may provide more reliable prognostic information.\u003c/p\u003e\n\u003cp\u003eThird-day CRP and lactate levels were significantly higher among non-survivors. In clinical practice, the third day represents a critical point when antimicrobial therapies are reassessed and organ support strategies are adjusted. CRP trajectories are known to reflect treatment response, with persistently elevated levels indicating poor prognosis [18]. In our study, sustained elevation of CRP at 72 hours in the non-survivor group likely reflects ongoing inflammation or inadequate treatment response. Similarly, lactate is a marker of tissue hypoperfusion, and persistently elevated levels are strongly associated with mortality [19,20]. Our findings support the use of third-day biomarker dynamics as important indicators to guide clinical decision-making. Nonetheless, serial measurements at later time points (e.g., days 5 or 7) may provide additional prognostic information.\u003c/p\u003e\n\u003cp\u003eThe requirement for invasive mechanical ventilation emerged as the strongest clinical predictor of mortality. Previous studies have shown that mechanical ventilation reflects both the severity of organ dysfunction and contributes to poor outcomes due to related complications [21,22]. In our cohort, 67.1% of non-survivors required ventilation compared to only 4.3% of survivors. The odds ratio for mechanical ventilation in multivariable analysis was strikingly high (OR = 42.9). This likely reflects both the profound severity of illness among ventilated patients and strong interactions within the model. However, such a high odds ratio may dominate the model and obscure the contributions of other variables. Furthermore, the independent effects of CRP and lactate in non-ventilated subgroups were not analyzed, representing a limitation of our study. Future prospective subgroup analyses are needed to better clarify the prognostic role of mechanical ventilation.\u003c/p\u003e\n\u003cp\u003eAlthough procalcitonin (PCT) was significantly associated with mortality in univariable analysis, it did not retain independence in multivariable models. This suggests that its prognostic association may be overshadowed by stronger predictors such as CRP and lactate. Previous studies have also reported heterogeneous findings regarding the prognostic role of PCT [24], and our results align with this variability. Multicollinearity testing showed VIF values \u0026lt; 2 for all variables, indicating no significant multicollinearity effect on the model (Supplementary Table 1).\u003c/p\u003e\n\u003cp\u003eIn the multivariable logistic regression model, platelet count, ALT, and neutrophil count\u0026mdash;though significant in univariable analyses\u0026mdash;lost their independent associations, while third-day CRP, third-day lactate, and mechanical ventilation remained as independent predictors of mortality. This highlights the importance of close monitoring of these three parameters in clinical practice.\u003c/p\u003e\n\u003cp\u003eThe discriminatory power of our model was excellent (AUC = 0.919). Compared with prior prognostic models, where AUC values typically range from 0.75 to 0.85 [23,24], our model demonstrated superior performance. High sensitivity (84.1%) and specificity (84.6%) further support the clinical reliability of our model. However, the model was tested only with internal validation; no cross-validation or bootstrap methods were applied, which poses a risk of overfitting. Independent external validation is required to confirm these findings.\u003c/p\u003e\n\u003cp\u003eWhen compared with similar studies in the literature, our results show both parallels and distinctions. Previous prospective studies have demonstrated that insufficient reductions in third-day CRP and lactate levels are strongly associated with mortality [18\u0026ndash;20], consistent with our findings. However, the lack of predictive value of APACHE II and SOFA in our cohort suggests that biomarker dynamics may provide more reliable prognostic information in elderly, comorbid ICU populations. The high odds ratio of mechanical ventilation as a predictor is also consistent with prior studies [21,22], but this should not be interpreted as mortality risk being solely attributable to ventilation itself; rather, it reflects a combination of disease severity, complications, and treatment processes.\u003c/p\u003e\n\u003cp\u003eThe main clinical implication of our study is that integrating third-day biomarkers with mechanical ventilation status may provide practical benefits for patient management. This combination allows early identification of high-risk patients, timely escalation of treatment (e.g., advanced organ support), prioritization of ICU resources, and consideration of palliative care where appropriate. Thus, our findings have not only prognostic value but also direct clinical applicability.\u003c/p\u003e\n\u003cp\u003eIt is also noteworthy that APACHE II and SOFA scores alone were not predictive of mortality. However, combining these traditional severity scores with third-day CRP and lactate levels may enhance risk stratification. Prior studies have similarly reported that integrating biomarker dynamics with clinical scoring systems improves prognostic accuracy [25]. Accordingly, CRP and lactate may be integrated into existing scoring systems to provide clinicians with more precise decision-support tools for early risk identification in sepsis.\u003c/p\u003e\n\u003cp\u003eOur patient population was predominantly elderly (mean age: 68 years). Therefore, our results mainly reflect outcomes in older, comorbid patients. The prognostic role of biomarkers may differ in younger or immunocompromised subgroups (e.g., hematologic malignancies, solid organ transplant recipients), and our findings may not be directly generalizable to such populations. Future prospective studies including younger and immunocompromised patients are warranted.\u003c/p\u003e\n\u003cp\u003eStrengths of our study include the systematic evaluation of third-day biomarkers in septic ICU patients, integration of laboratory and clinical variables, and validation of model performance using ROC analysis. However, several limitations should be acknowledged.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe combination of third-day CRP and lactate levels with the requirement for mechanical ventilation provides a robust and clinically applicable model for predicting 30-day mortality in septic ICU patients. This approach enables early identification of high-risk patients, timely escalation of therapy, prioritization of ICU resources, and appropriate initiation of palliative care discussions. Our findings therefore hold both prognostic and practical value for critical care management.\u003c/p\u003e\n\u003cp\u003eFurthermore, the lack of prognostic value of traditional severity scores (APACHE II and SOFA) alone, and the added predictive accuracy achieved by integrating third-day biomarkers, suggest that CRP and lactate may enhance existing scoring systems and support more precise clinical decision-making.\u003c/p\u003e\n\u003cp\u003eOur study primarily reflects outcomes in an elderly, comorbid ICU population. Caution is warranted in generalizing these results to younger or immunocompromised patients, and further prospective multicenter studies are needed to validate these findings across diverse populations. Ultimately, integrating third-day biomarkers and clinical parameters into prognostic models may improve early risk stratification and patient management in sepsis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSeveral limitations of our study should be acknowledged. First, the single-center, retrospective design limits the generalizability of our findings. Although the sample size (n = 200) was statistically adequate in terms of events-per-variable ratio, larger multicenter cohorts would provide more robust validation. Second, no a priori power analysis was performed; all eligible patients were included due to the retrospective design. Third, biomarker measurements were limited to the first three days of ICU stay, and serial measurements at later time points (e.g., days 5 or 7) were not available, restricting assessment of longer-term biomarker dynamics. Fourth, the high AUC of our model was achieved through internal validation only, without cross-validation or bootstrapping, raising the possibility of overfitting. The lack of external validation further limits generalizability. Finally, heterogeneity in treatment protocols and the single-center setting may reduce the applicability of our findings to other clinical contexts.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The study was approved by the Toros University Scientific Research and Publication Ethics Committee (Approval no: E-66792640-929, Date: 01.11.2023). Written informed consent was waived due to the retrospective design of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eMU\u003c/strong\u003e: Study conception and design, data collection, statistical analysis, manuscript drafting.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eBCD\u003c/strong\u003e: Data collection, literature review, manuscript editing.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eNZK\u003c/strong\u003e: Clinical data interpretation, critical revision of the manuscript.\u003cbr\u003e\u0026nbsp;All authors read and approved the final manuscript.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The authors would like to thank the staff of the intensive care units of Mersin City Hospital for their support in data collection.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eSinger M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, et al. The Third International ConsensusDefinitions for Sepsis and SepticShock (Sepsis-3). JAMA. 2016;315(8):801\u0026ndash;10.\u003c/li\u003e\n \u003cli\u003eR Core Team. R: A language and environment for statisticalcomputing. R Foundation for Statistical Computing, Vienna, Austria. 2021. Availablefrom: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.R-project.org/\u003c/span\u003e\u003c/span\u003e.\u003c/li\u003e\n \u003cli\u003eThe jamoviproject (2022). jamovi (Version 2.3) [Computer Software]. Retrievedfrom \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.jamovi.org\u003c/span\u003e\u003c/span\u003e.\u003c/li\u003e\n \u003cli\u003eVincent JL, Marshall JC, \u0026Ntilde;amendys-Silva SA, Fran\u0026ccedil;ois B, Martin-Loeches I, Lipman J, et al. Assessment of the worldwideburden of criticalillness: the intensivecare over nations (ICON) audit. Lancet RespirMed. 2014;2(5):380\u0026ndash;6.\u003c/li\u003e\n \u003cli\u003eCohen J, Vincent JL, Adhikari NK, Machado FR, Angus DC, Calandra T, et al. Sepsis: a roadmap for futureresearch. Lancet Infect Dis. 2015;15(5):581\u0026ndash;614.\u003c/li\u003e\n \u003cli\u003eHotchkiss RS, Monneret G, Payen D. Immunosuppression in sepsis: a novelunderstanding of the disorder and a newtherapeuticapproach. Lancet Infect Dis. 2013;13(3):260\u0026ndash;8.\u003c/li\u003e\n \u003cli\u003eDellinger RP, Levy MM, Rhodes A, Annane D, Gerlach H, Opal SM, et al. Surviving Sepsis Campaign: internationalguidelines for management of severe sepsis and septicshock: 2012. CritCareMed. 2013;41(2):580\u0026ndash;637.\u003c/li\u003e\n \u003cli\u003eAngus DC, van der Poll T. Severe sepsis and septicshock. N Engl J Med. 2013;369(9):840\u0026ndash;51.\u003c/li\u003e\n \u003cli\u003eRhodes A, Evans LE, Alhazzani W, Levy MM, Antonelli M, Ferrer R, et al. Surviving Sepsis Campaign: internationalguidelines for management of sepsis and septicshock: 2016. IntensiveCareMed. 2017;43(3):304\u0026ndash;77.\u003c/li\u003e\n \u003cli\u003eKaukonen KM, Bailey M, Pilcher D, Cooper DJ, Bellomo R. Systemicinflammatoryresponsesyndromecriteria in defining severe sepsis. N Engl J Med. 2015;372(17):1629\u0026ndash;38.\u003c/li\u003e\n \u003cli\u003ePierrakos C, Vincent JL. Sepsis biomarkers: a review. CritCare. 2010;14(1):R15.\u003c/li\u003e\n \u003cli\u003eLiu VX, Fielding-Singh V, Greene JD, Baker JM, Iwashyna TJ, Bhattacharya J, et al. The timing of earlyantibiotics and hospitalmortality in sepsis. Am J RespirCritCareMed. 2017;196(7):856\u0026ndash;63.\u003c/li\u003e\n \u003cli\u003eRudd KE, Johnson SC, Agesa KM, Shackelford KA, Tsoi D, Kievlan DR, et al. Global, regional, and national sepsis incidence and mortality, 1990\u0026ndash;2017: analysis for the Global Burden of DiseaseStudy. Lancet. 2020;395(10219):200\u0026ndash;11.\u003c/li\u003e\n \u003cli\u003eShankar-Hari M, Phillips GS, Levy ML, Seymour CW, Liu VX, Deutschman CS, et al. Developing a newdefinition and assessingnewclinicalcriteria for septicshock: for the Third International ConsensusDefinitions for Sepsis and SepticShock (Sepsis-3). JAMA. 2016;315(8):775\u0026ndash;87.\u003c/li\u003e\n \u003cli\u003eMartin GS, Mannino DM, Eaton S, Moss M. The epidemiology of sepsis in the United Statesfrom 1979 through 2000. N Engl J Med. 2003;348(16):1546\u0026ndash;54.\u003c/li\u003e\n \u003cli\u003eKnaus WA, Draper EA, Wagner DP, Zimmerman JE. APACHE II: a severity of diseaseclassificationsystem. CritCareMed. 1985;13(10):818\u0026ndash;29.\u003c/li\u003e\n \u003cli\u003eVincent JL, Moreno R, Takala J, Willatts S, De Mendon\u0026ccedil;a A, Bruining H, et al. The SOFA (Sepsis-related Organ FailureAssessment) scoretodescribe organ dysfunction/failure. IntensiveCareMed. 1996;22(7):707\u0026ndash;10.\u003c/li\u003e\n \u003cli\u003eP\u0026oacute;voa P, Coelho L, Almeida E, Fernandes A, Mealha R, Moreira P, et al. C-reactive protein as a marker of infection in criticallyillpatients. Clin MicrobiolInfect. 2005;11(2):101\u0026ndash;8.\u003c/li\u003e\n \u003cli\u003eBakker J, Nijsten MW, Jansen TC. Clinical use of lactatemonitoring in criticallyillpatients. Ann IntensiveCare. 2013;3:12.\u003c/li\u003e\n \u003cli\u003eSinger M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, et al. The Third International ConsensusDefinitions for Sepsis and SepticShock (Sepsis-3). JAMA. 2016;315(8):801\u0026ndash;10.\u003c/li\u003e\n \u003cli\u003eEsteban A, Anzueto A, Frutos F, Al\u0026iacute;a I, Brochard L, Stewart TE, et al. Characteristics and outcomes in adultpatientsreceivingmechanicalventilation: a 28-day internationalstudy. JAMA. 2002;287(3):345\u0026ndash;55.\u003c/li\u003e\n \u003cli\u003ePhua J, Badia JR, Adhikari NK, Friedrich JO, Fowler RA, Singh JM, et al. Has mortalityfromacuterespiratorydistresssyndromedecreased over time? A systematic review. Am J RespirCritCareMed. 2009;179(3):220\u0026ndash;7.\u003c/li\u003e\n \u003cli\u003eChen YX, Li CS. Lactateclearance in criticallyillpatients with sepsis: a prospectivestudy. J CritCare. 2015;30(2):280\u0026ndash;5.\u003c/li\u003e\n \u003cli\u003eJekarl DW, Lee SY, Lee J, Park YJ, Kim Y, Park JH, et al. Procalcitonin as a prognostic marker for sepsis based on Sepsis-3. J Clin Lab Anal. 2019;33(9):e22996.\u003c/li\u003e\n \u003cli\u003eSong JU, Sin CK, Park HK, Shim SR, Lee J. Performance of the SOFA score and APACHE II score for predictingmortality in criticallyillpatients with sepsis: A systematic review and meta-analysis. \u003cem\u003eJ Clin Med.\u003c/em\u003e 2022;11(19):5810. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/jcm11195810\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Sepsis, CRP, Lactate, Mechanicalventilation, Mortality, Prognostic model","lastPublishedDoi":"10.21203/rs.3.rs-7558369/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7558369/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eSepsis remains a leading cause of morbidity and mortality in intensive care units (ICUs). Early identification of high-risk patients is essential for optimizing management. While C-reactive protein (CRP), procalcitonin (PCT), and lactate are widely used biomarkers, evidence on the prognostic value of their dynamic changes, particularly on the third day of ICU stay, is limited. This study aimed to evaluate the prognostic role of third-day CRP, lactate, and mechanical ventilation requirement in predicting 30-day mortality in septic ICU patients.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe conducted a retrospective cohort study of adult patients with sepsis admitted to the ICUs of Mersin City Training and Research Hospital between January 2018 and December 2023. Patients younger than 18 years, those with incomplete data, early deaths within 24 hours, and recurrent admissions were excluded. Clinical and laboratory parameters were recorded at admission and on the third day. The primary outcome was all-cause 30-day mortality. Multivariable logistic regression was used to identify independent predictors, and model performance was assessed using receiver operating characteristic (ROC) analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eA total of 200 patients were included (mean age 68.5 years; 55% male). The 30-day mortality rate was 41.5%. Baseline APACHE II and SOFA scores were not predictive of mortality. By contrast, third-day CRP and lactate levels were significantly higher in non-survivors (18.0\u0026thinsp;\u0026plusmn;\u0026thinsp;11.7 vs. 9.1\u0026thinsp;\u0026plusmn;\u0026thinsp;7.2 mg/dL, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; 3.22\u0026thinsp;\u0026plusmn;\u0026thinsp;2.58 vs. 1.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81 mmol/L, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, respectively). Mechanical ventilation was required in 67.1% of non-survivors compared with 4.3% of survivors (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In multivariable analysis, third-day CRP (OR 1.10, 95% CI 1.04\u0026ndash;1.17), third-day lactate (OR 2.10, 95% CI 1.36\u0026ndash;3.24), and mechanical ventilation (OR 42.9, 95% CI 12.9\u0026ndash;143.0) remained independent predictors. The combined model demonstrated excellent discriminatory ability (AUC\u0026thinsp;=\u0026thinsp;0.919; sensitivity\u0026thinsp;=\u0026thinsp;84.1%; specificity\u0026thinsp;=\u0026thinsp;84.6%).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThird-day CRP and lactate levels, together with mechanical ventilation requirement, provide a robust and clinically applicable model for predicting 30-day mortality in septic ICU patients. Incorporating dynamic biomarkers into prognostic assessment may improve early risk stratification, guide timely treatment, optimize ICU resource use, and support palliative care decisions.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThird-day CRP and lactate levels, together with mechanical ventilation requirement, provide a robust and clinically applicable model for predicting 30-day mortality in septic ICU patients. Incorporating dynamic biomarkers into prognostic assessment may improve early risk stratification, guide timely treatment escalation, optimize ICU resource allocation, and support palliative care decisions in appropriate cases. These findings highlight the value of combining laboratory and clinical parameters in the prognostic evaluation of sepsis\u003c/p\u003e","manuscriptTitle":"Prognostic Role of Third-Day CRP, Lactate and MechanicalVentilation in Predicting ICU Mortality in Sepsis: A Single-Center Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-09 13:46:59","doi":"10.21203/rs.3.rs-7558369/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"149eaa5b-3bd9-4b86-8a22-137f3738b1c6","owner":[],"postedDate":"September 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-07T11:12:50+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-09 13:46:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7558369","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7558369","identity":"rs-7558369","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}