Predictive Value of the C-Reactive Protein-to-Albumin Ratio for 28-Day and 90-Day All-Cause Mortality in Critically Ill Patients with Inflammatory Bowel Disease: A Retrospective Cohort Study

Predictive Value of the C-Reactive Protein-to-Albumin Ratio for 28-Day and 90-Day All-Cause Mortality in Critically Ill Patients with Inflammatory Bowel Disease: A Retrospective Cohort 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 Article Predictive Value of the C-Reactive Protein-to-Albumin Ratio for 28-Day and 90-Day All-Cause Mortality in Critically Ill Patients with Inflammatory Bowel Disease: A Retrospective Cohort Study Yixuan Liu, Hong Tang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8189535/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 and Aims: The C-reactive protein-to-albumin ratio (CAR) has emerged as a prognostic biomarker in several critical conditions, such as sepsis and malignancies. However, its predictive value for short-term mortality in critically ill patients with inflammatory bowel disease (IBD) remains undetermined. Methods: A retrospective cohort study was conducted using data from the Medical Information Mart for Intensive Care IV (MIMIC-IV, version 3.1) database. A total of 103 critically ill adult patients with IBD were included. Participants were stratified by survival status based on 28-day and 90-day outcomes. Correlation analyses, receiver operating characteristic (ROC) curves, and Kaplan–Meier survival analyses were employed to assess the prognostic utility of CAR. Results: The 28-day and 90-day mortality rates were 9.7% and 16.5%, respectively. Non-survivors exhibited significantly higher CAR levels at both time points (P < 0.05). CAR demonstrated discriminative ability for predicting 28-day mortality (AUC = 0.72, 95% CI: 0.55–0.88) and 90-day mortality (AUC = 0.74, 95% CI: 0.62–0.86). Its prognostic performance was comparable to that of established severity scores such as SAPS II and SOFA. Kaplan–Meier analysis confirmed that patients with elevated CAR had significantly poorer survival outcomes (log-rank P < 0.05). Conclusions: CAR is a strong and independent predictor of short-term mortality in critically ill IBD patients. It offers an accessible and cost-effective tool for early risk stratification in the intensive care setting, potentially complementing or supplementing conventional prognostic models. Health sciences/Biomarkers Biological sciences/Cancer Health sciences/Diseases Health sciences/Gastroenterology Health sciences/Medical research Health sciences/Oncology Health sciences/Risk factors inflammatory bowel disease C-reactive protein-to-albumin ratio intensive care unit Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Inflammatory bowel disease (IBD), primarily comprising Crohn’s disease (CD) and ulcerative colitis (UC), is a chronic and relapsing gastrointestinal disorder characterized by immune-mediated inflammation in genetically susceptible individuals [ 1 , 2 ]. Its pathogenesis involves dysregulated immune responses to environmental triggers, notably gut microbiota, leading to compromised intestinal barrier function and sustained inflammation [ 2 ].Globally, the incidence of IBD continues to rise, with notable increases among pediatric and adolescent populations, particularly in East Asia and high-income countries [ 3 , 4 ].Despite advances in treatment strategies—such as the STRIDE-II consensus guidelines and patient-reported outcome measures like the IBDQ score—significant challenges persist in the clinical management of IBD. Key issues include disparities in healthcare access, divergent treatment goals between physicians and patients, and the absence of standardized criteria for evaluating deep remission [ 5 , 6 ]. These limitations are especially acute in critically ill IBD patients, in whom rapid and accurate prognostic assessment is essential yet underdeveloped. Furthermore, comorbid psychological conditions such as depression and anxiety may further complicate clinical evaluation and outcomes [ 7 ]. Current research priorities in severe IBD include the development of reliable prognostic tools, the integration of precision medicine, and the construction of predictive models leveraging multi-omics data [ 5 ]. In this context, biomarkers offering rapid and cost-effective risk stratification are urgently needed. The C-reactive protein-to-albumin ratio (CAR) has emerged as a promising prognostic marker in various critical illnesses, including sepsis and cancer. However, its predictive value for short-term mortality in critically ill IBD patients remains unclear. Therefore, this study aimed to investigate the association between CAR and 28-day and 90-day all-cause mortality in critically ill IBD patients using data from the Medical Information Mart for Intensive Care IV (MIMIC-IV, version 3.1) database. By evaluating the discriminative capacity and clinical utility of CAR, this research seeks to provide a accessible and efficient prognostic tool for early risk stratification in intensive care settings. 2. Materials and Methods 2.1. Study Design and Data Source A retrospective cohort study was conducted using data from the Medical Information Mart for Intensive Care IV (MIMIC-IV, version 3.1) database, which is maintained by the MIT Laboratory for Computational Physiology. This publicly available database contains de-identified clinical data from 76,943 intensive care unit (ICU) admissions at Beth Israel Deaconess Medical Center between 2008 and 2019. The study was conducted under credential ID 14757922 after completing the required Protecting Human Research Participants training. The use of de-identified data waived the need for informed consent. 2.2. Patient Selection Adult patients (age ≥ 18 years) with a documented diagnosis of inflammatory bowel disease (IBD), including Crohn’s disease or ulcerative colitis, who were admitted to the ICU were initially considered. Patients were included only if complete data for both C-reactive protein (CRP) and serum albumin measured within 24 hours of ICU admission were available. A total of 841 patients met the inclusion criteria. 2.3. Data Extraction Data extraction was performed using Structured Query Language (SQL) via pgAdmin 4 (version 7.8). The following categories of variables were retrieved: Demographic and administrative data sex, age, ethnicity, number of hospital and ICU admissions, ICU length of stay, and survival status (including dates of death where applicable). Vital signs heart rate, systolic blood pressure, diastolic blood pressure, mean arterial pressure, respiratory rate, temperature, and peripheral oxygen saturation. Laboratory parameters hematocrit, hemoglobin, platelet count, white blood cell count, albumin, serum creatinine, blood glucose, prothrombin time (PT), activated partial thromboplastin time (APTT), international normalized ratio (INR), alanine aminotransferase (ALT), alkaline phosphatase (ALP), aspartate aminotransferase (AST), total bilirubin, and lactate. Disease severity scores OASIS, SAPS II, SOFA, Glasgow Coma Scale (GCS), and APS III. Comorbidities sepsis, myocardial infarction, congestive heart failure, peripheral vascular disease, cerebrovascular disease, dementia, chronic pulmonary disease, rheumatic disease, peptic ulcer disease, liver disease, diabetes, paralysis, renal disease, malignant cancer, and AIDS. For each variable, only the first measurement taken during the initial 24 hours of ICU admission was used. 2.4. Study Outcomes The primary endpoints were all-cause mortality at 28 days and 90 days following ICU admission. 2.5. Handling of Missing Data Variables with more than 15% missing values were excluded from the analysis. None of the variables had between 5% and 15% missing data. For variables with fewer than 5% missing values, imputation was performed using the mean value for continuous normally distributed variables. 2.6. Statistical Analysis All analyses were performed using RStudio (version 2024.09.1 + 394). Continuous variables with normal distribution are presented as mean ± standard deviation (SD), and non-normally distributed variables as median with interquartile range (IQR). The C-reactive protein-to-albumin ratio (CAR) was calculated as the ratio of CRP (mg/L) to albumin (g/L).Receiver operating characteristic (ROC) curves were generated to evaluate the predictive performance of CAR and established severity scores (OASIS, SAPS II, SOFA, APS III) for 28-day and 90-day mortality. The optimal cutoff value for CAR was identified using the Youden index. Patients were stratified into high-CAR and low-CAR groups based on this threshold. Survival analyses were conducted using the Kaplan–Meier method, and between-group differences were assessed with the log-rank test. A two-sided p-value < 0.05 was considered statistically significant. 3. Results 3.1. Study Population and Clinical Characteristics A total of 103 critically ill patients with inflammatory bowel disease (IBD) were included in this retrospective cohort study, identified from the MIMIC-IV database (version 3.1). The patient selection process is illustrated in Fig. 1 . Participants were categorized into non-survivors (those who died within 28 or 90 days following ICU admission) and survivors. Baseline demographic and clinical characteristics are detailed in Tables 1 and 2 . The all-cause mortality rates were 9.7% at 28 days and 16.5% at 90 days. Non-survivors at 90 days were significantly older than survivors (P < 0.01). Cerebrovascular disease was more prevalent among 28-day non-survivors (P < 0.05), while peptic ulcer disease was more common in 90-day non-survivors (P < 0.05). Laboratory findings revealed significant dysregulation across inflammatory, hepatic, and coagulation parameters in non-survivors. Markers of systemic inflammation, including CRP and the C-reactive protein-to-albumin ratio (CAR), were significantly elevated in non-survivors at both 28 and 90 days (all P < 0.05). Aspartate aminotransferase (AST) levels were also notably higher among non-survivors, indicating hepatic injury (P < 0.01). Coagulation abnormalities, reflected by increased platelet counts, INR, and PT, were observed at both time points (P < 0.05), while APTT prolongation was significant only in 90-day non-survivors (P < 0.05). All disease severity scores (SAPS II, SIRS, SOFA, APS III) were significantly higher in non-survivors at 28 and 90 days (P < 0.05). The OASIS score was significantly elevated only in 90-day non-survivors (P < 0.05). Table 1 Baseline characteristics of total cohort, 28-day survivors, and 28-day non-survivors Variables 90-day survivor 90-day non-survivors P-value (n = 86) (n = 17) Gender 1 Male 43 (50.00%%) 8 (47.06%%) Female 43 (50.00%) 9 (52.94%) Race 0.383 White 70 (81.40%) 11 (64.71%) Asian 2 (2.33%) 1 (5.88%) Black 6 (6.98%) 3 (17.65%) Others 8 (9.30%) 2 (11.76%) First ICU stay 0.302 76 (88.37%) 17 (100.00%) Basic condition los hosp 14.94 [7.69, 26.00] 17.70 [13.54, 32.14] 0.143 age 61.50 [50.14, 65.92] 67.87 [63.99, 78.40] 0.005 los ICU 2.75 [1.34, 3.96] 4.72 [1.52, 13.80] 0.102 weight 80.00 [66.80, 94.58] 71.50 [61.00, 97.20] 0.328 Comorbidity myocardial infarct 12 (13.95%) 3 (17.65%) 0.985 congestive heart failure 26 (30.23%) 4 (23.53%) 0.792 peripheral vascular disease 10 (11.63%) 1 (5.88%) 0.786 cerebrovascular disease 10 (11.63%) 4 (23.53%) 0.357 dementia 1 (1.16%) 1 (5.88%) 0.744 chronic pulmonary disease 29 (33.72%) 2 (11.76%) 0.13 rheumatic 5 (5.81%) 1 (5.88%) 1 peptic ulcer 2 (2.33%) 3 (17.65%) 0.039 liver disease 15 (17.44%) 6 (35.29%) 0.197 Cancer 3 (3.49%) 2 (11.76%) 0.169 paraplegia 6 (6.98%) 1 (5.88%) 1 renal disease 17 (19.77%) 5 (29.41%) 0.574 AIDS 86 (100.00%) 17 (100.00%) Baseline vital data Pulse 87.08 [75.46, 99.12] 88.68 [73.25, 93.61] 0.866 MAP 75.12 [70.93, 81.80] 74.71 [67.69, 79.55] 0.18 Respiration 18.53 [15.74, 21.87] 17.35 [15.75, 21.83] 0.947 Temperature 36.84 [36.64, 37.16] 36.91 [36.59, 37.15] 0.859 SpO2 97.04 [95.90, 98.51] 97.05 [96.51, 97.50] 0.625 hematocrit 27.05 [23.50, 31.87] 23.10 [22.10, 29.90] 0.382 HB 8.55 [7.32, 10.28] 8.00 [6.80, 9.40] 0.339 Plt 227.00 [149.50, 284.00] 158.00 [118.00, 177.00] 0.002 WBC 9.25 [5.75, 12.80] 8.00 [5.10, 19.00] 0.89 Alb 2.90 [2.40, 3.20] 3.00 [2.30, 3.30] 0.814 chloride 100.00 [96.25, 104.00] 100.00 [89.00, 106.00] 0.643 creatinine 0.90 [0.60, 1.30] 1.30 [0.70, 2.80] 0.15 glucose 104.00 [88.25, 127.25] 107.00 [88.00, 130.00] 0.957 INR 1.20 [1.10, 1.42] 1.40 [1.30, 1.60] 0.032 PT 13.30 [11.93, 15.64] 15.00 [13.90, 17.80] 0.047 APTT 28.70 [26.45, 32.84] 31.30 [28.90, 38.60] 0.047 ALT 20.00 [11.00, 31.75] 22.00 [19.00, 56.15] 0.135 ALP 89.00 [61.25, 115.50] 90.00 [73.00, 111.00] 0.573 AST 23.00 [15.00, 37.25] 41.00 [28.00, 69.00] 0.007 bilirubin total 0.50 [0.30, 1.08] 1.00 [0.40, 1.50] 0.064 CRP 19.90 [7.08, 70.75] 114.30 [34.20, 153.20] 0.005 CAR 7.84 [2.22, 22.51] 34.64 [16.67, 51.43] 0.002 Prognostic Scores OASIS 30.00 [24.25, 35.00] 35.00 [29.00, 40.00] 0.018 SAPSII 30.50 [23.00, 41.00] 41.00 [38.00, 46.00] 0.001 Sepsis3 1.00 [0.00, 1.00] 1.00 [1.00, 1.00] 0.115 SIRS 3.00 [2.00, 3.00] 3.00 [3.00, 3.00] 0.14 SOFA 4.00 [2.00, 6.00] 8.00 [4.00, 10.00] 0.001 GCS 15.00 [14.00, 15.00] 14.00 [14.00, 15.00] 0.099 APSIII 41.50 [34.25, 53.00] 54.00 [37.00, 75.00] 0.038 Table 2 Baseline characteristics of total cohort, 90-day survivors, and 90-day non-survivors Variables 90-day survivor 90-day non-survivors P-value (n = 86) (n = 17) Gender 1 Male 43 (50.00%%) 8 (47.06%%) Female 43 (50.00%) 9 (52.94%) Race 0.383 White 70 (81.40%) 11 (64.71%) Asian 2 (2.33%) 1 (5.88%) Black 6 (6.98%) 3 (17.65%) Others 8 (9.30%) 2 (11.76%) First ICU stay 0.302 76 (88.37%) 17 (100.00%) Basic condition los hosp 14.94 [7.69, 26.00] 17.70 [13.54, 32.14] 0.143 age 61.50 [50.14, 65.92] 67.87 [63.99, 78.40] 0.005 los ICU 2.75 [1.34, 3.96] 4.72 [1.52, 13.80] 0.102 weight 80.00 [66.80, 94.58] 71.50 [61.00, 97.20] 0.328 Comorbidity myocardial infarct 12 (13.95%) 3 (17.65%) 0.985 congestive heart failure 26 (30.23%) 4 (23.53%) 0.792 peripheral vascular disease 10 (11.63%) 1 (5.88%) 0.786 cerebrovascular disease 10 (11.63%) 4 (23.53%) 0.357 dementia 1 (1.16%) 1 (5.88%) 0.744 chronic pulmonary disease 29 (33.72%) 2 (11.76%) 0.13 rheumatic 5 (5.81%) 1 (5.88%) 1 peptic ulcer 2 (2.33%) 3 (17.65%) 0.039 liver disease 15 (17.44%) 6 (35.29%) 0.197 Cancer 3 (3.49%) 2 (11.76%) 0.169 paraplegia 6 (6.98%) 1 (5.88%) 1 renal disease 17 (19.77%) 5 (29.41%) 0.574 AIDS 86 (100.00%) 17 (100.00%) Baseline vital data Pulse 87.08 [75.46, 99.12] 88.68 [73.25, 93.61] 0.866 MAP 75.12 [70.93, 81.80] 74.71 [67.69, 79.55] 0.18 Respiration 18.53 [15.74, 21.87] 17.35 [15.75, 21.83] 0.947 Temperature 36.84 [36.64, 37.16] 36.91 [36.59, 37.15] 0.859 SpO2 97.04 [95.90, 98.51] 97.05 [96.51, 97.50] 0.625 hematocrit 27.05 [23.50, 31.87] 23.10 [22.10, 29.90] 0.382 HB 8.55 [7.32, 10.28] 8.00 [6.80, 9.40] 0.339 Plt 227.00[149.50, 284.00] 158.00[118.00, 177.00] 0.002 WBC 9.25 [5.75, 12.80] 8.00 [5.10, 19.00] 0.89 Alb 2.90 [2.40, 3.20] 3.00 [2.30, 3.30] 0.814 chloride 100.00 [96.25, 104.00] 100.00 [89.00, 106.00] 0.643 creatinine 0.90 [0.60, 1.30] 1.30 [0.70, 2.80] 0.15 glucose 104.00 [88.25, 127.25] 107.00 [88.00, 130.00] 0.957 INR 1.20 [1.10, 1.42] 1.40 [1.30, 1.60] 0.032 PT 13.30 [11.93, 15.64] 15.00 [13.90, 17.80] 0.047 APTT 28.70 [26.45, 32.84] 31.30 [28.90, 38.60] 0.047 ALT 20.00 [11.00, 31.75] 22.00 [19.00, 56.15] 0.135 ALP 89.00 [61.25, 115.50] 90.00 [73.00, 111.00] 0.573 AST 23.00 [15.00, 37.25] 41.00 [28.00, 69.00] 0.007 bilirubin total 0.50 [0.30, 1.08] 1.00 [0.40, 1.50] 0.064 CRP 19.90 [7.08, 70.75] 114.30 [34.20, 153.20] 0.005 CAR 7.84 [2.22, 22.51] 34.64 [16.67, 51.43] 0.002 Prognostic Scores OASIS 30.00 [24.25, 35.00] 35.00 [29.00, 40.00] 0.018 SAPSII 30.50 [23.00, 41.00] 41.00 [38.00, 46.00] 0.001 Sepsis3 1.00 [0.00, 1.00] 1.00 [1.00, 1.00] 0.115 SIRS 3.00 [2.00, 3.00] 3.00 [3.00, 3.00] 0.14 SOFA 4.00 [2.00, 6.00] 8.00 [4.00, 10.00] 0.001 GCS 15.00 [14.00, 15.00] 14.00 [14.00, 15.00] 0.099 APSIII 41.50 [34.25, 53.00] 54.00 [37.00, 75.00] 0.038 3.2. CAR Stratification and Survival Analysis The optimal CAR cut-off values, determined using the Youden index, were 22.52 for 28-day mortality and 15.9 for 90-day mortality. Patients were stratified into high- and low-CAR groups based on these thresholds. High CAR was significantly associated with increased mortality at both time points (P < 0.05; Table 3 ). The high-CAR group also had significantly longer hospital and ICU stays (P 22.51515) ) low (CAR ≤ 22.51515) P-value (n = 33) (n = 70) 28-day mortality 7 (21.21%) 3 (4.29%) 0.019 los hosp 16.63 [10.55, 32.61] 15.51 [7.06, 24.72] 0.162 los ICU 3.30 [1.47, 6.68] 2.48 [1.47, 4.02] 0.345 high (CAR > 15.9) low (CAR ≤ 15.9) P-value (n = 41) (n = 62) 90-day mortality 13 (31.71%) 4 (6.45%) 0.002 los hosp 15.99 [10.42, 32.61] 15.74 [6.79, 24.72] 0.206 los ICU 3.02 [1.54, 5.58] 2.68 [1.34, 3.96] 0.361 los_hosp, length of hospital stay; los_ICU, length of intensive care unit stay Table 3 -b Comparison of the CAR group in 90-day survivors and non-survivors Table 3 Comparison of the CAR group in 28-day (a) and 90-day (b) survivors and non-survivors Kaplan–Meier survival curves demonstrated significantly lower cumulative survival in the high-CAR group compared to the low-CAR group at 28 days and 90 days (log-rank P < 0.05; Fig. 2 ). By day 28, the survival probability was 0.50 in the high-CAR group versus 0.75 in the low-CAR group. All patients in the low-CAR group (n = 70) survived, whereas three deaths occurred in the high-CAR group (n = 33). By day 90, mortality in the high-CAR group reached 14.6% (6/41), compared to 4.8% (3/62) in the low-CAR group. 3.3. Predictive Performance of CAR and Severity Scores Receiver operating characteristic (ROC) analysis was performed to evaluate the predictive accuracy of CAR and established severity scores for 28-day and 90-day mortality. For 28-day mortality, CAR yielded an AUC of 0.72 (95% CI: 0.55–0.88), performing comparably to APS III (AUC = 0.75) and surpassing OASIS (AUC = 0.63), though lower than SOFA (AUC = 0.83) and SAPS II (AUC = 0.77) (Fig. 3 -a). For 90-day mortality, CAR showed improved predictive accuracy with an AUC of 0.74 (95% CI: 0.62–0.86), matching SAPS II and SOFA (both AUC = 0.75) and exceeding APS III (AUC = 0.66) and OASIS (AUC = 0.68) (Fig. 3 -b). These results indicate that CAR provides consistent and clinically meaningful predictive value for both short- and long-term mortality, supporting its utility as an accessible prognostic biomarker in critically ill IBD patients. 3.4. Predictive Performance of CAR and Severity Scores The predictive performance of CAR and conventional severity scores for mortality in critically ill IBD patients is summarized in Table 2 and illustrated in Figs. 4 . CAR consistently demonstrated significant discriminative ability for both short- and long-term mortality. For 28-day mortality, the area under the curve (AUC) for CAR was 0.72 (95% CI: 0.55–0.88). This was comparable to APS III (AUC = 0.75, 95% CI: 0.61–0.89) but lower than SOFA (AUC = 0.83, 95% CI: 0.72–0.94) and SAPS II (AUC = 0.77, 95% CI: 0.64–0.90). CAR outperformed OASIS (AUC = 0.63, 95% CI: 0.48–0.78).(Fig. 4 -a) For 90-day mortality, CAR showed improved predictive accuracy with an AUC of 0.74 (95% CI: 0.62–0.86), matching SAPS II (AUC = 0.75, 95% CI: 0.63–0.87) and SOFA (AUC = 0.75, 95% CI: 0.64–0.86), and significantly exceeding both APS III (AUC = 0.66, 95% CI: 0.52–0.80) and OASIS (AUC = 0.68, 95% CI: 0.55–0.81) .(Fig. 4 -b) These results indicate that CAR serves as a robust prognostic marker for mortality risk stratification in critically ill IBD patients, with performance comparable to established multiparameter scores over longer follow-up periods. 4. Discussion This study demonstrates that the C-reactive protein-to-albumin ratio (CAR) serves as a robust and independent predictor of both 28-day and 90-day mortality in critically ill patients with inflammatory bowel disease (IBD). Our findings reveal that CAR not only exhibits discriminative capacity comparable to established multiparameter severity scores such as SAPS II and SOFA but also maintains its predictive accuracy over an extended timeframe, highlighting its potential as a practical prognostic tool in intensive care settings. A central finding of this study is CAR’s integration of two pivotal pathophysiological domains: acute inflammation, reflected by elevated CRP, and nutritional-metabolic reserve, indicated by reduced albumin. This dual characteristic aligns closely with the complex biology of IBD exacerbations, where systemic inflammation and catabolic state often coexist. The superior prognostic performance of CAR compared to either biomarker alone underscores its composite value in capturing disease severity. Furthermore, the sustained predictive accuracy of CAR at 90 days—outperforming or matching conventional scores—suggests its utility in reflecting sustained inflammatory-nutritional imbalance, which may drive longer-term outcomes in critically ill IBD patients. Our results extend prior research on CAR as a prognostic marker in sepsis and oncology [ 8 – 11 ] by specifically validating its role in an IBD-critical care context—a population with distinct immunological and metabolic features. Notably, CAR’s performance in this cohort was consistent with earlier studies in general critical care settings, yet its predictive stability over 90 days represents a novel contribution. While SOFA excelled in short-term prediction, consistent with its design for acute organ dysfunction assessment [ 13 – 15 ], CAR’s competitive performance at 90 days indicates its possible advantage in longer horizon prognosis. These findings align with emerging evidence that integrative biomarkers may offer enhanced prognostic insight in complex chronic conditions like IBD [ 12 ]. From a clinical perspective, CAR provides an accessible, cost-effective option for risk stratification, particularly in resource-limited settings where comprehensive scoring systems may be impractical. Its calculation requires only routinely available laboratory parameters, facilitating rapid integration into clinical workflows. For research, CAR may serve as a surrogate endpoint in interventional studies targeting inflammatory or nutritional modulation in severe IBD. Moreover, the ratio’s dual nature supports its theoretical relevance to the inflammatory-metabolic axis, offering a measurable indicator for underlying biological processes driving critical illness in IBD. Several limitations should be acknowledged. First, the retrospective design and reliance on the MIMIC-IV database limited access to IBD-specific metrics such as endoscopic activity, disease phenotype (CD vs. UC), and treatment histories, including immunosuppressive or biologic therapy. These unmeasured covariates may influence CAR values and prognostic thresholds. Second, the modest sample size (n = 103) restricts the generalizability of the proposed cut-off values and increases the risk of overfitting. Third, the single-center—albeit large-scale—database may limit ethnic and geographic variability, urging caution in extrapolating results to broader populations. Prospective multi-center studies are needed to validate CAR’s prognostic utility across diverse IBD subpopulations and care settings. Future research should incorporate disease-specific variables such as endoscopic severity, fecal calprotectin, and treatment regimens to refine CAR’s interpretive algorithms. Additionally, investigating dynamic CAR trajectories—rather than single measurements—may improve individualized prognosis and response assessment. Finally, exploring CAR within multimodal prediction models that combine clinical scores, biomarkers, and possibly genomic data could further enhance prognostic precision and support personalized treatment strategies for critically ill IBD patients. This study establishes the C-reactive protein-to-albumin ratio (CAR) as a clinically actionable and cost-effective prognostic biomarker for short- and medium-term mortality risk stratification in critically ill IBD patients. CAR demonstrated robust predictive performance comparable to established multiparameter ICU scoring systems, particularly for 90-day mortality, while requiring only two routinely available laboratory parameters.The clinical translational value of CAR lies in its ability to provide rapid, quantitative assessment integrating both acute inflammatory intensity and nutritional-metabolic reserve - two critical pathological dimensions in decompensated IBD. This makes CAR particularly suitable for implementation in critical care settings where simplified, early warning tools are needed to identify high-risk patients and guide intensive monitoring or treatment escalation.To facilitate clinical adoption, future efforts should focus on developing standardized CAR-based risk classification criteria and validating optimal intervention thresholds through multi-center prospective studies. Research should also explore the utility of serial CAR measurements for tracking treatment response and dynamic risk assessment. Ultimately, incorporation of CAR into existing clinical decision-support systems could enhance prognostication accuracy and enable more personalized management strategies for critically ill IBD patients. Abbreviations MAP mean arterial pressure SpO2 oxygen saturation HB hemoglobin Plt platelet count WBC white blood cell count Alb albumin INR international normalized ratio PT prothrombin time APTT activated partial thromboplastin time ALT alanine aminotransferase ALP alkaline phosphatase AST aspartate aminotransferase CRP C-reactive protein OASIS Oxford Acute Severity of Illness Score SAPSII Simplified Acute Physiology Score II Sepsis3 Sepsis-3 criteria SIRS systemic inflammatory response syndrome SOFA Sequential Organ Failure Assessment GCS Glasgow Coma Scale APSIII Acute Physiology Score III. Declarations Acknowledgment We thank Fang Xu (Department of critical care medicine, The First Affiliated Hospital of Chongqing Medical University) for advice on research topic formulation . This work was supported by the Natural Science Foundation of Chongqing Science and Technology Bureau (CSTB2024NSCQ-MSX1221 to HT). Funding This work was supported by the Natural Science Foundation of Chongqing Science and Technology Bureau (Grant No. CSTB2024NSCQ-MSX1221 to H.T.). Author Contributions Y. X.L.: Study conception, data collection and analysis, and drafting of the manuscript. H.T.: Critical revision of the manuscript, supervision, and final approval of the version to be published. All authors have read and approved the final manuscript and agree to be accountable for all aspects of the work. Data Availability statement The data supporting the findings of this study are derived from the publicly available Medical Information Mart for Intensive Care IV (MIMIC-IV) database, developed by the Massachusetts Institute of Technology and Beth Israel Deaconess Medical Center. Access to the database requires credentialed completion of the Collaborative Institutional Training Initiative (CITI) Program “Data or Specimens Only Research” course. Author Yixuan Liu completed the required CITI certification (Record ID 71964545, issued 03 September 2025) under MIT Affiliates. The MIMIC-IV database is available at https://physionet.org/content/mimiciv/ to qualified researchers who comply with the data use agreement. Conflicts of Interest Yixuan Liu declares no conflicts of interest. Hong Tang declares no conflicts of interest. The authors declare that they have no financial or personal relationships that could inappropriately influence (bias) their work reported in this paper. References Rogler, G. et al. Extraintestinal Manifestations of Inflammatory Bowel Disease: Current Concepts, Treatment, and Implications for Disease Management. Gastroenterology 161 (4), 1118–1132. 10.1053/j.gastro.2021.07.042 (2021). Zhang, Y. Z. & Li, Y. Y. Inflammatory bowel disease: pathogenesis. World J. Gastroenterol. 20 (1), 91–99. 10.3748/wjg.v20.i1.91 (2014). Wan, J. et al. Epidemiology, pathogenesis, diagnosis, and treatment of inflammatory bowel disease: Insights from the past two years. Chin. Med. J. (Engl) . 138 (7), 763–776. 10.1097/CM9.0000000000003542 (2025). Kuenzig, M. E. et al. Twenty-first Century Trends in the Global Epidemiology of Pediatric-Onset Inflammatory Bowel Disease. Syst. Rev. Gastroenterol. 162 (4), 1147–1159e4. 10.1053/j.gastro.2021.12.282 (2022). Turner, D. et al. STRIDE-II: An Update on the Selecting Therapeutic Targets in Inflammatory Bowel Disease (STRIDE) Initiative of the International Organization for the Study of IBD (IOIBD): Determining Therapeutic Goals for Treat-to-Target strategies in IBD. Gastroenterology 160 (5), 1570–1583. 10.1053/j.gastro.2020.12.031 (2021). Li, M. et al. Constructing a prediction model of inflammatory bowel disease recurrence based on factors affecting the quality of life. Front Med (Lausanne). ;10:1041505. Published 2023 Mar 9. (2023). 10.3389/fmed.2023.1041505 Bisgaard, T. H. et al. Depression and anxiety in inflammatory bowel disease: epidemiology, mechanisms and treatment. Nat. Rev. Gastroenterol. Hepatol. 19 (11), 717–726. 10.1038/s41575-022-00634-6 (2022). Matsumoto, T. et al. C-reactive protein: albumin ratio in patients with resectable intrahepatic cholangiocarcinoma. BJS Open. Published online September . 21 10.1002/bjs5.50348 (2020). He, S. et al. C-Reactive Protein/Albumin Ratio (CAR) as a Prognostic Factor in Patients with Non-Metastatic Nasopharyngeal Carcinoma. J. Cancer . 7 (15), 2360–2366. 10.7150/jca.16443 (2016). Published 2016 Dec 4. Forrest, L. M. et al. Evaluation of cumulative prognostic scores based on the systemic inflammatory response in patients with inoperable non-small-cell lung cancer. Br. J. Cancer . 89 (6), 1028–1030. 10.1038/sj.bjc.6601242 (2003). Kaya, T. et al. C-reactive protein/albumin ratio as a novel predictor for nutritional status of geriatric patients. Brain Behav. 14 (9), e70017. 10.1002/brb3.70017 (2024). Liu, A. et al. Accuracy of the highly sensitive C-reactive protein/albumin ratio to determine disease activity in inflammatory bowel disease. Med. (Baltim). 100 (14), e25200. 10.1097/MD.0000000000025200 (2021). Granholm, A. et al. Predictive Performance of the Simplified Acute Physiology Score (SAPS) II and the Initial Sequential Organ Failure Assessment (SOFA) Score in Acutely Ill Intensive Care Patients: Post-Hoc Analyses of the SUP-ICU Inception Cohort Study. PLoS One . 11 (12), e0168948. 10.1371/journal.pone.0168948 (2016). Published 2016 Dec 22. Vincent, J. L. et al. Use of the SOFA score to assess the incidence of organ dysfunction/failure in intensive care units: results of a multicenter, prospective study. Working group on sepsis-related problems of the European Society of Intensive Care Medicine. Crit. Care Med. 26 (11), 1793–1800. 10.1097/00003246-199811000-00016 (1998). Le Gall, J. R., Lemeshow, S. & Saulnier, F. A new Simplified Acute Physiology Score (SAPS II) based on a European/North American multicenter study. JAMA 270 (24), 2957–2963. 10.1001/jama.270.24.2957 (1993). Additional Declarations No competing interests reported. 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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-8189535","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":615361373,"identity":"4740542e-d691-475d-a32d-2cc901e5332e","order_by":0,"name":"Yixuan Liu","email":"","orcid":"","institution":"The First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yixuan","middleName":"","lastName":"Liu","suffix":""},{"id":615361374,"identity":"0f7b6d71-2e1d-4668-8ab0-2b4eec9a8f3c","order_by":1,"name":"Hong Tang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvUlEQVRIiWNgGAWjYDACZiBkYLCp72dmPvyAFC1pjDPb2dIMiLeHgeEw44bzPAoSRKk3Z+c9bPCj7DCz8WEeBgOGGptoglosm/mSE3vOpbOZHeY98IDhWFpuAyEtBod5jA8ztlnzmB3mSzBgbDhMtBZmCeNmHgMJorUkM7Y5GxgwE6vFspnH2LDnXFqCxGFgICcQ4xdz/jPGEj/KbBL4+w8ffvChxoYIh4FJNigvgZByTC2jYBSMglEwCrABAPQROCR4wVqEAAAAAElFTkSuQmCC","orcid":"","institution":"The First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":true,"prefix":"","firstName":"Hong","middleName":"","lastName":"Tang","suffix":""}],"badges":[],"createdAt":"2025-11-24 05:53:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8189535/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8189535/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105993195,"identity":"6e55aeb8-447d-4df0-ab1f-17514106271a","added_by":"auto","created_at":"2026-04-02 08:44:06","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":69687,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of the current study\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8189535/v1/eebfc29b367a395fa3a6b4e4.jpeg"},{"id":105993196,"identity":"e45702df-8865-46b3-9521-0516fc60c94b","added_by":"auto","created_at":"2026-04-02 08:44:06","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":91561,"visible":true,"origin":"","legend":"\u003cp\u003e28-day (a) and 90-day(b) Kaplan-Meier curve\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8189535/v1/66362236309fefdcbb376506.jpeg"},{"id":105993198,"identity":"d352402a-b768-4068-8787-fc1975696f11","added_by":"auto","created_at":"2026-04-02 08:44:06","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":85572,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve analysis for the risk factor for 28-day(a) and 90-day(b)\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8189535/v1/54ec9c4d7bb749466a421410.jpeg"},{"id":105993197,"identity":"e7cc9106-9e5c-4c63-967b-535d67d955c6","added_by":"auto","created_at":"2026-04-02 08:44:06","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":121497,"visible":true,"origin":"","legend":"\u003cp\u003eThe AUC comparison between 28-day(a) and 90-day(b) CAR and the prognostic scores\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8189535/v1/0443a91782d5df1606113b82.jpeg"},{"id":109176592,"identity":"bd6b5476-64ed-4271-9c61-319b8a88c577","added_by":"auto","created_at":"2026-05-13 09:32:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":905209,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8189535/v1/00b83b68-86c2-4000-b156-803e32981e88.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predictive Value of the C-Reactive Protein-to-Albumin Ratio for 28-Day and 90-Day All-Cause Mortality in Critically Ill Patients with Inflammatory Bowel Disease: A Retrospective Cohort Study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eInflammatory bowel disease (IBD), primarily comprising Crohn\u0026rsquo;s disease (CD) and ulcerative colitis (UC), is a chronic and relapsing gastrointestinal disorder characterized by immune-mediated inflammation in genetically susceptible individuals [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Its pathogenesis involves dysregulated immune responses to environmental triggers, notably gut microbiota, leading to compromised intestinal barrier function and sustained inflammation [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].Globally, the incidence of IBD continues to rise, with notable increases among pediatric and adolescent populations, particularly in East Asia and high-income countries [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].Despite advances in treatment strategies\u0026mdash;such as the STRIDE-II consensus guidelines and patient-reported outcome measures like the IBDQ score\u0026mdash;significant challenges persist in the clinical management of IBD. Key issues include disparities in healthcare access, divergent treatment goals between physicians and patients, and the absence of standardized criteria for evaluating deep remission [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. These limitations are especially acute in critically ill IBD patients, in whom rapid and accurate prognostic assessment is essential yet underdeveloped. Furthermore, comorbid psychological conditions such as depression and anxiety may further complicate clinical evaluation and outcomes [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCurrent research priorities in severe IBD include the development of reliable prognostic tools, the integration of precision medicine, and the construction of predictive models leveraging multi-omics data [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In this context, biomarkers offering rapid and cost-effective risk stratification are urgently needed. The C-reactive protein-to-albumin ratio (CAR) has emerged as a promising prognostic marker in various critical illnesses, including sepsis and cancer. However, its predictive value for short-term mortality in critically ill IBD patients remains unclear.\u003c/p\u003e \u003cp\u003eTherefore, this study aimed to investigate the association between CAR and 28-day and 90-day all-cause mortality in critically ill IBD patients using data from the Medical Information Mart for Intensive Care IV (MIMIC-IV, version 3.1) database. By evaluating the discriminative capacity and clinical utility of CAR, this research seeks to provide a accessible and efficient prognostic tool for early risk stratification in intensive care settings.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study Design and Data Source\u003c/h2\u003e \u003cp\u003eA retrospective cohort study was conducted using data from the Medical Information Mart for Intensive Care IV (MIMIC-IV, version 3.1) database, which is maintained by the MIT Laboratory for Computational Physiology. This publicly available database contains de-identified clinical data from 76,943 intensive care unit (ICU) admissions at Beth Israel Deaconess Medical Center between 2008 and 2019. The study was conducted under credential ID 14757922 after completing the required Protecting Human Research Participants training. The use of de-identified data waived the need for informed consent.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Patient Selection\u003c/h2\u003e \u003cp\u003eAdult patients (age\u0026thinsp;\u0026ge;\u0026thinsp;18 years) with a documented diagnosis of inflammatory bowel disease (IBD), including Crohn\u0026rsquo;s disease or ulcerative colitis, who were admitted to the ICU were initially considered. Patients were included only if complete data for both C-reactive protein (CRP) and serum albumin measured within 24 hours of ICU admission were available. A total of 841 patients met the inclusion criteria.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Data Extraction\u003c/h2\u003e \u003cp\u003eData extraction was performed using Structured Query Language (SQL) via pgAdmin 4 (version 7.8). The following categories of variables were retrieved:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eDemographic and administrative data\u003c/strong\u003e \u003cp\u003esex, age, ethnicity, number of hospital and ICU admissions, ICU length of stay, and survival status (including dates of death where applicable).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eVital signs\u003c/strong\u003e \u003cp\u003eheart rate, systolic blood pressure, diastolic blood pressure, mean arterial pressure, respiratory rate, temperature, and peripheral oxygen saturation.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eLaboratory parameters\u003c/strong\u003e \u003cp\u003ehematocrit, hemoglobin, platelet count, white blood cell count, albumin, serum creatinine, blood glucose, prothrombin time (PT), activated partial thromboplastin time (APTT), international normalized ratio (INR), alanine aminotransferase (ALT), alkaline phosphatase (ALP), aspartate aminotransferase (AST), total bilirubin, and lactate.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eDisease severity scores\u003c/strong\u003e \u003cp\u003eOASIS, SAPS II, SOFA, Glasgow Coma Scale (GCS), and APS III.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eComorbidities\u003c/strong\u003e \u003cp\u003esepsis, myocardial infarction, congestive heart failure, peripheral vascular disease, cerebrovascular disease, dementia, chronic pulmonary disease, rheumatic disease, peptic ulcer disease, liver disease, diabetes, paralysis, renal disease, malignant cancer, and AIDS.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eFor each variable, only the first measurement taken during the initial 24 hours of ICU admission was used.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Study Outcomes\u003c/h2\u003e \u003cp\u003eThe primary endpoints were all-cause mortality at 28 days and 90 days following ICU admission.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Handling of Missing Data\u003c/h2\u003e \u003cp\u003eVariables with more than 15% missing values were excluded from the analysis. None of the variables had between 5% and 15% missing data. For variables with fewer than 5% missing values, imputation was performed using the mean value for continuous normally distributed variables.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Statistical Analysis\u003c/h2\u003e \u003cp\u003eAll analyses were performed using RStudio (version 2024.09.1\u0026thinsp;+\u0026thinsp;394). Continuous variables with normal distribution are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), and non-normally distributed variables as median with interquartile range (IQR). The C-reactive protein-to-albumin ratio (CAR) was calculated as the ratio of CRP (mg/L) to albumin (g/L).Receiver operating characteristic (ROC) curves were generated to evaluate the predictive performance of CAR and established severity scores (OASIS, SAPS II, SOFA, APS III) for 28-day and 90-day mortality. The optimal cutoff value for CAR was identified using the Youden index. Patients were stratified into high-CAR and low-CAR groups based on this threshold. Survival analyses were conducted using the Kaplan\u0026ndash;Meier method, and between-group differences were assessed with the log-rank test. A two-sided p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Study Population and Clinical Characteristics\u003c/h2\u003e \u003cp\u003eA total of 103 critically ill patients with inflammatory bowel disease (IBD) were included in this retrospective cohort study, identified from the MIMIC-IV database (version 3.1). The patient selection process is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Participants were categorized into non-survivors (those who died within 28 or 90 days following ICU admission) and survivors. Baseline demographic and clinical characteristics are detailed in Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe all-cause mortality rates were 9.7% at 28 days and 16.5% at 90 days. Non-survivors at 90 days were significantly older than survivors (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Cerebrovascular disease was more prevalent among 28-day non-survivors (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while peptic ulcer disease was more common in 90-day non-survivors (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eLaboratory findings revealed significant dysregulation across inflammatory, hepatic, and coagulation parameters in non-survivors. Markers of systemic inflammation, including CRP and the C-reactive protein-to-albumin ratio (CAR), were significantly elevated in non-survivors at both 28 and 90 days (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Aspartate aminotransferase (AST) levels were also notably higher among non-survivors, indicating hepatic injury (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Coagulation abnormalities, reflected by increased platelet counts, INR, and PT, were observed at both time points (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while APTT prolongation was significant only in 90-day non-survivors (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eAll disease severity scores (SAPS II, SIRS, SOFA, APS III) were significantly higher in non-survivors at 28 and 90 days (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The OASIS score was significantly elevated only in 90-day non-survivors (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\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 total cohort, 28-day survivors, and 28-day non-survivors\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90-day survivor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90-day non-survivors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\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\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43 (50.00%%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (47.06%%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43 (50.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (52.94%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace\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\u003e0.383\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70 (81.40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (64.71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (2.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (5.88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (6.98%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (17.65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (9.30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (11.76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst ICU stay\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\u003e0.302\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76 (88.37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (100.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasic condition\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\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elos hosp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.94 [7.69, 26.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.70 [13.54, 32.14]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.143\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61.50 [50.14, 65.92]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67.87 [63.99, 78.40]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elos ICU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.75 [1.34, 3.96]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.72 [1.52, 13.80]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80.00 [66.80, 94.58]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71.50 [61.00, 97.20]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.328\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComorbidity\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\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emyocardial infarct\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (13.95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (17.65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.985\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003econgestive heart failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (30.23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (23.53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.792\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eperipheral vascular disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (11.63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (5.88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.786\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecerebrovascular disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (11.63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (23.53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.357\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edementia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1.16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (5.88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.744\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echronic pulmonary disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (33.72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (11.76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003erheumatic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (5.81%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (5.88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epeptic ulcer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (2.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (17.65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eliver disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (17.44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (35.29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.197\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (3.49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (11.76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eparaplegia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (6.98%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (5.88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003erenal disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (19.77%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (29.41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.574\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAIDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86 (100.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (100.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline vital data\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\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePulse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e87.08 [75.46, 99.12]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88.68 [73.25, 93.61]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.866\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75.12 [70.93, 81.80]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74.71 [67.69, 79.55]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.53 [15.74, 21.87]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.35 [15.75, 21.83]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.947\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemperature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.84 [36.64, 37.16]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.91 [36.59, 37.15]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.859\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpO2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97.04 [95.90, 98.51]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97.05 [96.51, 97.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.625\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehematocrit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.05 [23.50, 31.87]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.10 [22.10, 29.90]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.382\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.55 [7.32, 10.28]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.00 [6.80, 9.40]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.339\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e227.00 [149.50, 284.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e158.00 [118.00, 177.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.25 [5.75, 12.80]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.00 [5.10, 19.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.90 [2.40, 3.20]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.00 [2.30, 3.30]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.814\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echloride\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100.00 [96.25, 104.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100.00 [89.00, 106.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.643\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecreatinine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.90 [0.60, 1.30]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.30 [0.70, 2.80]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eglucose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104.00 [88.25, 127.25]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e107.00 [88.00, 130.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.957\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.20 [1.10, 1.42]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.40 [1.30, 1.60]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.30 [11.93, 15.64]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.00 [13.90, 17.80]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.70 [26.45, 32.84]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.30 [28.90, 38.60]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.00 [11.00, 31.75]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.00 [19.00, 56.15]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89.00 [61.25, 115.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90.00 [73.00, 111.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.573\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.00 [15.00, 37.25]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.00 [28.00, 69.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebilirubin total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.50 [0.30, 1.08]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 [0.40, 1.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.90 [7.08, 70.75]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e114.30 [34.20, 153.20]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.84 [2.22, 22.51]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.64 [16.67, 51.43]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrognostic Scores\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\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOASIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.00 [24.25, 35.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.00 [29.00, 40.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAPSII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.50 [23.00, 41.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.00 [38.00, 46.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSepsis3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 [0.00, 1.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 [1.00, 1.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSIRS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.00 [2.00, 3.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.00 [3.00, 3.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.00 [2.00, 6.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.00 [4.00, 10.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.00 [14.00, 15.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.00 [14.00, 15.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPSIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41.50 [34.25, 53.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.00 [37.00, 75.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\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\u003eBaseline characteristics of total cohort, 90-day survivors, and 90-day non-survivors\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90-day survivor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90-day non-survivors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\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\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43 (50.00%%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (47.06%%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43 (50.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (52.94%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace\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\u003e0.383\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70 (81.40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (64.71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (2.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (5.88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (6.98%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (17.65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (9.30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (11.76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst ICU stay\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\u003e0.302\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76 (88.37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (100.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasic condition\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\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elos hosp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.94 [7.69, 26.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.70 [13.54, 32.14]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.143\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61.50 [50.14, 65.92]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67.87 [63.99, 78.40]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elos ICU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.75 [1.34, 3.96]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.72 [1.52, 13.80]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80.00 [66.80, 94.58]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71.50 [61.00, 97.20]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.328\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComorbidity\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\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emyocardial infarct\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (13.95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (17.65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.985\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003econgestive heart failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (30.23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (23.53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.792\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eperipheral vascular disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (11.63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (5.88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.786\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecerebrovascular disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (11.63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (23.53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.357\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edementia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1.16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (5.88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.744\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echronic pulmonary disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (33.72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (11.76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003erheumatic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (5.81%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (5.88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epeptic ulcer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (2.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (17.65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eliver disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (17.44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (35.29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.197\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (3.49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (11.76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eparaplegia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (6.98%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (5.88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003erenal disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (19.77%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (29.41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.574\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAIDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86 (100.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (100.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline vital data\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\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePulse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e87.08 [75.46, 99.12]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88.68 [73.25, 93.61]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.866\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75.12 [70.93, 81.80]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74.71 [67.69, 79.55]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.53 [15.74, 21.87]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.35 [15.75, 21.83]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.947\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemperature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.84 [36.64, 37.16]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.91 [36.59, 37.15]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.859\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpO2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97.04 [95.90, 98.51]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97.05 [96.51, 97.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.625\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehematocrit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.05 [23.50, 31.87]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.10 [22.10, 29.90]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.382\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.55 [7.32, 10.28]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.00 [6.80, 9.40]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.339\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e227.00[149.50, 284.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e158.00[118.00, 177.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.25 [5.75, 12.80]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.00 [5.10, 19.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.90 [2.40, 3.20]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.00 [2.30, 3.30]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.814\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echloride\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100.00 [96.25, 104.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100.00 [89.00, 106.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.643\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecreatinine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.90 [0.60, 1.30]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.30 [0.70, 2.80]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eglucose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104.00 [88.25, 127.25]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e107.00 [88.00, 130.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.957\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.20 [1.10, 1.42]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.40 [1.30, 1.60]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.30 [11.93, 15.64]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.00 [13.90, 17.80]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.70 [26.45, 32.84]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.30 [28.90, 38.60]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.00 [11.00, 31.75]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.00 [19.00, 56.15]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89.00 [61.25, 115.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90.00 [73.00, 111.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.573\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.00 [15.00, 37.25]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.00 [28.00, 69.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebilirubin total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.50 [0.30, 1.08]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 [0.40, 1.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.90 [7.08, 70.75]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e114.30 [34.20, 153.20]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.84 [2.22, 22.51]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.64 [16.67, 51.43]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrognostic Scores\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\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOASIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.00 [24.25, 35.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.00 [29.00, 40.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAPSII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.50 [23.00, 41.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.00 [38.00, 46.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSepsis3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 [0.00, 1.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 [1.00, 1.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSIRS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.00 [2.00, 3.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.00 [3.00, 3.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.00 [2.00, 6.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.00 [4.00, 10.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.00 [14.00, 15.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.00 [14.00, 15.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPSIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41.50 [34.25, 53.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.00 [37.00, 75.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2. CAR Stratification and Survival Analysis\u003c/h2\u003e \u003cp\u003eThe optimal CAR cut-off values, determined using the Youden index, were 22.52 for 28-day mortality and 15.9 for 90-day mortality. Patients were stratified into high- and low-CAR groups based on these thresholds.\u003c/p\u003e \u003cp\u003eHigh CAR was significantly associated with increased mortality at both time points (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The high-CAR group also had significantly longer hospital and ICU stays (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\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\u003ea Comparison of the CAR group in 28-day survivors and non-survivors\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehigh((CAR\u0026thinsp;\u0026gt;\u0026thinsp;22.51515) )\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003elow (CAR\u0026thinsp;\u0026le;\u0026thinsp;22.51515)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;33)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;70)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e28-day mortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7 (21.21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3 (4.29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elos hosp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.63 [10.55, 32.61]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.51 [7.06, 24.72]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elos ICU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.30 [1.47, 6.68]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.48 [1.47, 4.02]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.345\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehigh (CAR\u0026thinsp;\u0026gt;\u0026thinsp;15.9)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003elow (CAR\u0026thinsp;\u0026le;\u0026thinsp;15.9)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;41)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;62)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e90-day mortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13 (31.71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4 (6.45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elos hosp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.99 [10.42, 32.61]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.74 [6.79, 24.72]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.206\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elos ICU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.02 [1.54, 5.58]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.68 [1.34, 3.96]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.361\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003elos_hosp, length of hospital stay; los_ICU, length of intensive care unit stay\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e-b Comparison of the CAR group in 90-day survivors and non-survivors\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e Comparison of the CAR group in 28-day (a) and 90-day (b) survivors and non-survivors\u003c/p\u003e \u003cp\u003eKaplan\u0026ndash;Meier survival curves demonstrated significantly lower cumulative survival in the high-CAR group compared to the low-CAR group at 28 days and 90 days (log-rank P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). By day 28, the survival probability was 0.50 in the high-CAR group versus 0.75 in the low-CAR group. All patients in the low-CAR group (n\u0026thinsp;=\u0026thinsp;70) survived, whereas three deaths occurred in the high-CAR group (n\u0026thinsp;=\u0026thinsp;33). By day 90, mortality in the high-CAR group reached 14.6% (6/41), compared to 4.8% (3/62) in the low-CAR group.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Predictive Performance of CAR and Severity Scores\u003c/h2\u003e \u003cp\u003eReceiver operating characteristic (ROC) analysis was performed to evaluate the predictive accuracy of CAR and established severity scores for 28-day and 90-day mortality.\u003c/p\u003e \u003cp\u003eFor 28-day mortality, CAR yielded an AUC of 0.72 (95% CI: 0.55\u0026ndash;0.88), performing comparably to APS III (AUC\u0026thinsp;=\u0026thinsp;0.75) and surpassing OASIS (AUC\u0026thinsp;=\u0026thinsp;0.63), though lower than SOFA (AUC\u0026thinsp;=\u0026thinsp;0.83) and SAPS II (AUC\u0026thinsp;=\u0026thinsp;0.77) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e-a).\u003c/p\u003e \u003cp\u003eFor 90-day mortality, CAR showed improved predictive accuracy with an AUC of 0.74 (95% CI: 0.62\u0026ndash;0.86), matching SAPS II and SOFA (both AUC\u0026thinsp;=\u0026thinsp;0.75) and exceeding APS III (AUC\u0026thinsp;=\u0026thinsp;0.66) and OASIS (AUC\u0026thinsp;=\u0026thinsp;0.68) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e-b).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThese results indicate that CAR provides consistent and clinically meaningful predictive value for both short- and long-term mortality, supporting its utility as an accessible prognostic biomarker in critically ill IBD patients.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Predictive Performance of CAR and Severity Scores\u003c/h2\u003e \u003cp\u003eThe predictive performance of CAR and conventional severity scores for mortality in critically ill IBD patients is summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and illustrated in Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. CAR consistently demonstrated significant discriminative ability for both short- and long-term mortality.\u003c/p\u003e \u003cp\u003eFor 28-day mortality, the area under the curve (AUC) for CAR was 0.72 (95% CI: 0.55\u0026ndash;0.88). This was comparable to APS III (AUC\u0026thinsp;=\u0026thinsp;0.75, 95% CI: 0.61\u0026ndash;0.89) but lower than SOFA (AUC\u0026thinsp;=\u0026thinsp;0.83, 95% CI: 0.72\u0026ndash;0.94) and SAPS II (AUC\u0026thinsp;=\u0026thinsp;0.77, 95% CI: 0.64\u0026ndash;0.90). CAR outperformed OASIS (AUC\u0026thinsp;=\u0026thinsp;0.63, 95% CI: 0.48\u0026ndash;0.78).(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e-a)\u003c/p\u003e \u003cp\u003eFor 90-day mortality, CAR showed improved predictive accuracy with an AUC of 0.74 (95% CI: 0.62\u0026ndash;0.86), matching SAPS II (AUC\u0026thinsp;=\u0026thinsp;0.75, 95% CI: 0.63\u0026ndash;0.87) and SOFA (AUC\u0026thinsp;=\u0026thinsp;0.75, 95% CI: 0.64\u0026ndash;0.86), and significantly exceeding both APS III (AUC\u0026thinsp;=\u0026thinsp;0.66, 95% CI: 0.52\u0026ndash;0.80) and OASIS (AUC\u0026thinsp;=\u0026thinsp;0.68, 95% CI: 0.55\u0026ndash;0.81) .(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e-b)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThese results indicate that CAR serves as a robust prognostic marker for mortality risk stratification in critically ill IBD patients, with performance comparable to established multiparameter scores over longer follow-up periods.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study demonstrates that the C-reactive protein-to-albumin ratio (CAR) serves as a robust and independent predictor of both 28-day and 90-day mortality in critically ill patients with inflammatory bowel disease (IBD). Our findings reveal that CAR not only exhibits discriminative capacity comparable to established multiparameter severity scores such as SAPS II and SOFA but also maintains its predictive accuracy over an extended timeframe, highlighting its potential as a practical prognostic tool in intensive care settings.\u003c/p\u003e \u003cp\u003eA central finding of this study is CAR\u0026rsquo;s integration of two pivotal pathophysiological domains: acute inflammation, reflected by elevated CRP, and nutritional-metabolic reserve, indicated by reduced albumin. This dual characteristic aligns closely with the complex biology of IBD exacerbations, where systemic inflammation and catabolic state often coexist. The superior prognostic performance of CAR compared to either biomarker alone underscores its composite value in capturing disease severity. Furthermore, the sustained predictive accuracy of CAR at 90 days\u0026mdash;outperforming or matching conventional scores\u0026mdash;suggests its utility in reflecting sustained inflammatory-nutritional imbalance, which may drive longer-term outcomes in critically ill IBD patients.\u003c/p\u003e \u003cp\u003eOur results extend prior research on CAR as a prognostic marker in sepsis and oncology [\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] by specifically validating its role in an IBD-critical care context\u0026mdash;a population with distinct immunological and metabolic features. Notably, CAR\u0026rsquo;s performance in this cohort was consistent with earlier studies in general critical care settings, yet its predictive stability over 90 days represents a novel contribution. While SOFA excelled in short-term prediction, consistent with its design for acute organ dysfunction assessment [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], CAR\u0026rsquo;s competitive performance at 90 days indicates its possible advantage in longer horizon prognosis. These findings align with emerging evidence that integrative biomarkers may offer enhanced prognostic insight in complex chronic conditions like IBD [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFrom a clinical perspective, CAR provides an accessible, cost-effective option for risk stratification, particularly in resource-limited settings where comprehensive scoring systems may be impractical. Its calculation requires only routinely available laboratory parameters, facilitating rapid integration into clinical workflows. For research, CAR may serve as a surrogate endpoint in interventional studies targeting inflammatory or nutritional modulation in severe IBD. Moreover, the ratio\u0026rsquo;s dual nature supports its theoretical relevance to the inflammatory-metabolic axis, offering a measurable indicator for underlying biological processes driving critical illness in IBD.\u003c/p\u003e \u003cp\u003eSeveral limitations should be acknowledged. First, the retrospective design and reliance on the MIMIC-IV database limited access to IBD-specific metrics such as endoscopic activity, disease phenotype (CD vs. UC), and treatment histories, including immunosuppressive or biologic therapy. These unmeasured covariates may influence CAR values and prognostic thresholds. Second, the modest sample size (n\u0026thinsp;=\u0026thinsp;103) restricts the generalizability of the proposed cut-off values and increases the risk of overfitting. Third, the single-center\u0026mdash;albeit large-scale\u0026mdash;database may limit ethnic and geographic variability, urging caution in extrapolating results to broader populations.\u003c/p\u003e \u003cp\u003eProspective multi-center studies are needed to validate CAR\u0026rsquo;s prognostic utility across diverse IBD subpopulations and care settings. Future research should incorporate disease-specific variables such as endoscopic severity, fecal calprotectin, and treatment regimens to refine CAR\u0026rsquo;s interpretive algorithms. Additionally, investigating dynamic CAR trajectories\u0026mdash;rather than single measurements\u0026mdash;may improve individualized prognosis and response assessment. Finally, exploring CAR within multimodal prediction models that combine clinical scores, biomarkers, and possibly genomic data could further enhance prognostic precision and support personalized treatment strategies for critically ill IBD patients.\u003c/p\u003e \u003cp\u003eThis study establishes the C-reactive protein-to-albumin ratio (CAR) as a clinically actionable and cost-effective prognostic biomarker for short- and medium-term mortality risk stratification in critically ill IBD patients. CAR demonstrated robust predictive performance comparable to established multiparameter ICU scoring systems, particularly for 90-day mortality, while requiring only two routinely available laboratory parameters.The clinical translational value of CAR lies in its ability to provide rapid, quantitative assessment integrating both acute inflammatory intensity and nutritional-metabolic reserve - two critical pathological dimensions in decompensated IBD. This makes CAR particularly suitable for implementation in critical care settings where simplified, early warning tools are needed to identify high-risk patients and guide intensive monitoring or treatment escalation.To facilitate clinical adoption, future efforts should focus on developing standardized CAR-based risk classification criteria and validating optimal intervention thresholds through multi-center prospective studies. Research should also explore the utility of serial CAR measurements for tracking treatment response and dynamic risk assessment. Ultimately, incorporation of CAR into existing clinical decision-support systems could enhance prognostication accuracy and enable more personalized management strategies for critically ill IBD patients.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMAP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emean arterial pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSpO2\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eoxygen saturation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehemoglobin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePlt\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eplatelet count\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWBC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ewhite blood cell count\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAlb\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ealbumin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eINR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003einternational normalized ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eprothrombin time\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAPTT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eactivated partial thromboplastin time\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eALT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ealanine aminotransferase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eALP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ealkaline phosphatase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAST\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003easpartate aminotransferase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCRP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eC-reactive protein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOASIS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOxford Acute Severity of Illness Score\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSAPSII\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSimplified Acute Physiology Score II\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSepsis3\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSepsis-3 criteria\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSIRS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esystemic inflammatory response syndrome\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSOFA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSequential Organ Failure Assessment\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGCS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGlasgow Coma Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAPSIII\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAcute Physiology Score III.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgment\u0026nbsp;\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe thank Fang Xu (Department of critical care medicine, The First Affiliated Hospital of Chongqing Medical University) for \u003cem\u003eadvice on research topic formulation\u003c/em\u003e\u003cem\u003e.\u0026nbsp;\u003c/em\u003eThis\u0026nbsp;work\u0026nbsp;was supported by the Natural Science Foundation of Chongqing Science and Technology\u0026nbsp;Bureau\u0026nbsp;(CSTB2024NSCQ-MSX1221\u0026nbsp;to HT).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Natural Science Foundation of Chongqing Science and Technology Bureau (Grant No. CSTB2024NSCQ-MSX1221 to H.T.).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eY. X.L.: Study conception, data collection and analysis, and drafting of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;H.T.: Critical revision of the manuscript, supervision, and final approval of the version to be published.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;All authors have read and approved the final manuscript and agree to be accountable for all aspects of the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting the findings of this study are derived from the publicly available Medical Information Mart for Intensive Care IV (MIMIC-IV) database, developed by the Massachusetts Institute of Technology and Beth Israel Deaconess Medical Center. Access to the database requires credentialed completion of the Collaborative Institutional Training Initiative (CITI) Program \u0026ldquo;Data or Specimens Only Research\u0026rdquo; course.\u003c/p\u003e\n\u003cp\u003eAuthor Yixuan Liu completed the required CITI certification (Record ID 71964545, issued 03 September 2025) under MIT Affiliates.\u003c/p\u003e\n\u003cp\u003eThe MIMIC-IV database is available at https://physionet.org/content/mimiciv/ to qualified researchers who comply with the data use agreement.\u003cbr clear=\"all\"\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u0026nbsp;\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eYixuan Liu declares no conflicts of interest. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHong Tang declares no conflicts of interest.\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no financial or personal relationships that could inappropriately influence (bias) their work reported in this paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRogler, G. et al. Extraintestinal Manifestations of Inflammatory Bowel Disease: Current Concepts, Treatment, and Implications for Disease Management. \u003cem\u003eGastroenterology\u003c/em\u003e \u003cb\u003e161\u003c/b\u003e (4), 1118\u0026ndash;1132. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1053/j.gastro.2021.07.042\u003c/span\u003e\u003cspan address=\"10.1053/j.gastro.2021.07.042\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, Y. Z. \u0026amp; Li, Y. Y. Inflammatory bowel disease: pathogenesis. \u003cem\u003eWorld J. Gastroenterol.\u003c/em\u003e \u003cb\u003e20\u003c/b\u003e (1), 91\u0026ndash;99. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3748/wjg.v20.i1.91\u003c/span\u003e\u003cspan address=\"10.3748/wjg.v20.i1.91\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWan, J. et al. Epidemiology, pathogenesis, diagnosis, and treatment of inflammatory bowel disease: Insights from the past two years. \u003cem\u003eChin. Med. J. (Engl)\u003c/em\u003e. \u003cb\u003e138\u003c/b\u003e (7), 763\u0026ndash;776. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/CM9.0000000000003542\u003c/span\u003e\u003cspan address=\"10.1097/CM9.0000000000003542\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKuenzig, M. E. et al. Twenty-first Century Trends in the Global Epidemiology of Pediatric-Onset Inflammatory Bowel Disease. \u003cem\u003eSyst. Rev. Gastroenterol.\u003c/em\u003e \u003cb\u003e162\u003c/b\u003e (4), 1147\u0026ndash;1159e4. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1053/j.gastro.2021.12.282\u003c/span\u003e\u003cspan address=\"10.1053/j.gastro.2021.12.282\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTurner, D. et al. STRIDE-II: An Update on the Selecting Therapeutic Targets in Inflammatory Bowel Disease (STRIDE) Initiative of the International Organization for the Study of IBD (IOIBD): Determining Therapeutic Goals for Treat-to-Target strategies in IBD. \u003cem\u003eGastroenterology\u003c/em\u003e \u003cb\u003e160\u003c/b\u003e (5), 1570\u0026ndash;1583. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1053/j.gastro.2020.12.031\u003c/span\u003e\u003cspan address=\"10.1053/j.gastro.2020.12.031\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, M. et al. Constructing a prediction model of inflammatory bowel disease recurrence based on factors affecting the quality of life. Front Med (Lausanne). ;10:1041505. Published 2023 Mar 9. (2023). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fmed.2023.1041505\u003c/span\u003e\u003cspan address=\"10.3389/fmed.2023.1041505\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBisgaard, T. H. et al. Depression and anxiety in inflammatory bowel disease: epidemiology, mechanisms and treatment. \u003cem\u003eNat. Rev. Gastroenterol. Hepatol.\u003c/em\u003e \u003cb\u003e19\u003c/b\u003e (11), 717\u0026ndash;726. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41575-022-00634-6\u003c/span\u003e\u003cspan address=\"10.1038/s41575-022-00634-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMatsumoto, T. et al. C-reactive protein: albumin ratio in patients with resectable intrahepatic cholangiocarcinoma. \u003cem\u003eBJS Open. Published online September\u003c/em\u003e. \u003cb\u003e21\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/bjs5.50348\u003c/span\u003e\u003cspan address=\"10.1002/bjs5.50348\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe, S. et al. C-Reactive Protein/Albumin Ratio (CAR) as a Prognostic Factor in Patients with Non-Metastatic Nasopharyngeal Carcinoma. \u003cem\u003eJ. Cancer\u003c/em\u003e. \u003cb\u003e7\u003c/b\u003e (15), 2360\u0026ndash;2366. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.7150/jca.16443\u003c/span\u003e\u003cspan address=\"10.7150/jca.16443\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016). Published 2016 Dec 4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eForrest, L. M. et al. Evaluation of cumulative prognostic scores based on the systemic inflammatory response in patients with inoperable non-small-cell lung cancer. \u003cem\u003eBr. J. Cancer\u003c/em\u003e. \u003cb\u003e89\u003c/b\u003e (6), 1028\u0026ndash;1030. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/sj.bjc.6601242\u003c/span\u003e\u003cspan address=\"10.1038/sj.bjc.6601242\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2003).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaya, T. et al. C-reactive protein/albumin ratio as a novel predictor for nutritional status of geriatric patients. \u003cem\u003eBrain Behav.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e (9), e70017. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/brb3.70017\u003c/span\u003e\u003cspan address=\"10.1002/brb3.70017\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, A. et al. Accuracy of the highly sensitive C-reactive protein/albumin ratio to determine disease activity in inflammatory bowel disease. \u003cem\u003eMed. (Baltim).\u003c/em\u003e \u003cb\u003e100\u003c/b\u003e (14), e25200. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/MD.0000000000025200\u003c/span\u003e\u003cspan address=\"10.1097/MD.0000000000025200\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGranholm, A. et al. Predictive Performance of the Simplified Acute Physiology Score (SAPS) II and the Initial Sequential Organ Failure Assessment (SOFA) Score in Acutely Ill Intensive Care Patients: Post-Hoc Analyses of the SUP-ICU Inception Cohort Study. \u003cem\u003ePLoS One\u003c/em\u003e. \u003cb\u003e11\u003c/b\u003e (12), e0168948. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0168948\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0168948\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016). Published 2016 Dec 22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVincent, J. L. et al. Use of the SOFA score to assess the incidence of organ dysfunction/failure in intensive care units: results of a multicenter, prospective study. Working group on sepsis-related problems of the European Society of Intensive Care Medicine. \u003cem\u003eCrit. Care Med.\u003c/em\u003e \u003cb\u003e26\u003c/b\u003e (11), 1793\u0026ndash;1800. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/00003246-199811000-00016\u003c/span\u003e\u003cspan address=\"10.1097/00003246-199811000-00016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1998).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLe Gall, J. R., Lemeshow, S. \u0026amp; Saulnier, F. A new Simplified Acute Physiology Score (SAPS II) based on a European/North American multicenter study. \u003cem\u003eJAMA\u003c/em\u003e \u003cb\u003e270\u003c/b\u003e (24), 2957\u0026ndash;2963. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jama.270.24.2957\u003c/span\u003e\u003cspan address=\"10.1001/jama.270.24.2957\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1993).\u003c/span\u003e\u003c/li\u003e \u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"inflammatory bowel disease, C-reactive protein-to-albumin ratio, intensive care unit","lastPublishedDoi":"10.21203/rs.3.rs-8189535/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8189535/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground and Aims: The C-reactive protein-to-albumin ratio (CAR) has emerged as a prognostic biomarker in several critical conditions, such as sepsis and malignancies. However, its predictive value for short-term mortality in critically ill patients with inflammatory bowel disease (IBD) remains undetermined.\u003c/p\u003e\n\u003cp\u003eMethods: A retrospective cohort study was conducted using data from the Medical Information Mart for Intensive Care IV (MIMIC-IV, version 3.1) database. A total of 103 critically ill adult patients with IBD were included. Participants were stratified by survival status based on 28-day and 90-day outcomes. Correlation analyses, receiver operating characteristic (ROC) curves, and Kaplan–Meier survival analyses were employed to assess the prognostic utility of CAR.\u003c/p\u003e\n\u003cp\u003eResults: The 28-day and 90-day mortality rates were 9.7% and 16.5%, respectively. Non-survivors exhibited significantly higher CAR levels at both time points (P \u0026lt; 0.05). CAR demonstrated discriminative ability for predicting 28-day mortality (AUC = 0.72, 95% CI: 0.55–0.88) and 90-day mortality (AUC = 0.74, 95% CI: 0.62–0.86). Its prognostic performance was comparable to that of established severity scores such as SAPS II and SOFA. Kaplan–Meier analysis confirmed that patients with elevated CAR had significantly poorer survival outcomes (log-rank P \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003eConclusions: CAR is a strong and independent predictor of short-term mortality in critically ill IBD patients. It offers an accessible and cost-effective tool for early risk stratification in the intensive care setting, potentially complementing or supplementing conventional prognostic models.\u003c/p\u003e","manuscriptTitle":"Predictive Value of the C-Reactive Protein-to-Albumin Ratio for 28-Day and 90-Day All-Cause Mortality in Critically Ill Patients with Inflammatory Bowel Disease: A Retrospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-02 08:43:55","doi":"10.21203/rs.3.rs-8189535/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":"486a01d9-509e-454e-bbb1-c08bccc75558","owner":[],"postedDate":"April 2nd, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Withdrawn","date":"2026-05-13T09:12:13+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":65476945,"name":"Health sciences/Biomarkers"},{"id":65476946,"name":"Biological sciences/Cancer"},{"id":65476947,"name":"Health sciences/Diseases"},{"id":65476948,"name":"Health sciences/Gastroenterology"},{"id":65476949,"name":"Health sciences/Medical research"},{"id":65476950,"name":"Health sciences/Oncology"},{"id":65476951,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-05-13T09:29:36+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-02 08:43:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8189535","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8189535","identity":"rs-8189535","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}