C-Reactive Protein-to-Albumin Ratio in Predicting Mortality in Patients with Ileus: A Comparative Analytical Study with the Glasgow Prognostic Score

C-Reactive Protein-to-Albumin Ratio in Predicting Mortality in Patients with Ileus: A Comparative Analytical Study with the Glasgow Prognostic Score | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article C-Reactive Protein-to-Albumin Ratio in Predicting Mortality in Patients with Ileus: A Comparative Analytical Study with the Glasgow Prognostic Score Serdar Özdemir, İbrahim Altunok This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7476696/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 Objective: To evaluate the predictability of in-hospital mortality in patients followed up in the emergency department with a diagnosis of ileus using the C-reactive protein (CRP)-to-albumin ratio (CAR) and the Glasgow Prognostic Score (GPS) and to compare the diagnostic performance of these two biomarkers. Materials and Methods: The study included 270 patients aged ≥18 years, diagnosed with ileus in the emergency department of a university hospital between January 2022 and December 2024, for whom complete laboratory data were available. CAR was calculated by dividing CRP by albumin, while GPS was scored based on CRP > 10 mg/L and albumin < 35 g/L values. Factors associated with mortality were evaluated using univariate and multivariate logistic regression analyses, and diagnostic accuracy was determined using the area under the receiver operating characteristic (ROC) curve (AUC). Results: Among the patients, 7.4% had in-hospital mortality. CAR and GPS values were found to be significantly higher in the mortality group. In ROC analysis, the AUC value of CAR was calculated as 0.729, while that of GPS was 0.676, indicating a higher discriminatory power for CAR. In multivariate logistic regression analyses, CAR demonstrated a statistically significant and independent association with mortality (odds ratio [OR] = 0.859; 95% confidence interval [CI]: 0.747–0.988; p = 0.033). GPS was also identified as an independent predictor (OR = 0.393; 95% CI: 0.174–0.892; p = 0.025). Conclusion: CAR and GPS are valuable markers for predicting mortality risk in patients with ileus. However, the continuous nature of CAR suggests that it is superior to GPS in terms of diagnostic accuracy and clinical applicability. C-reactive protein albumin mortality prognosis ileus Introduction Ileus is a significant group of diseases characterized by impaired gastrointestinal motility, resulting in the cessation or delayed forward movement of intestinal contents. It encompasses a broad range of surgical and medical conditions and is primarily classified into two main types: mechanical and paralytic. It presents symptoms such as abdominal distension, vomiting, constipation, and abdominal pain, and if not promptly managed, it may lead to severe complications, such as bowel ischemia, perforation, sepsis, and multi-organ failure ( 1 – 3 ). Mortality rates are particularly high in elderly patients, those with multiple comorbidities, and in cases of delayed presentation ( 4 ). There is therefore an increasing need for reliable, rapid, accessible, and cost-effective biomarkers to predict prognosis in patients with ileus. Certain parameters derived from conventional laboratory tests have recently gained attention as indicators reflecting systemic inflammation ( 5 – 7 ). In this context, C-reactive protein (CRP), an acute-phase reactant, along with albumin, a negative acute-phase reactant and an indicator of nutritional status, have been evaluated together to derive the CRP-to-albumin ratio (CAR), which has been proposed as a prognostic marker in various malignant and non-malignant diseases ( 8 – 10 ). A high CAR value has been associated with adverse clinical outcomes by reflecting both systemic inflammation and poor nutritional status due to low albumin levels. The Glasgow Prognostic Score (GPS), calculated based on CRP and albumin levels, was initially introduced as a prognostic marker in malignancies but has since demonstrated applicability in various acute and chronic conditions. GPS is scored between 0 and 2 based on CRP > 10 mg/L and albumin < 35 g/L, and higher scores have been associated with poor prognosis ( 11 , 12 ). However, there are only a limited number of studies that simultaneously evaluate CAR and GPS and examine their comparative strength in predicting mortality, particularly in urgent conditions such as ileus. In light of the abovementioned information, the current study aimed to assess and compare the diagnostic performance of CAR and GPS in predicting in-hospital mortality among patients diagnosed with ileus in the emergency department. Materials and Methods Study design and population This study was conducted using a retrospective, diagnostic validity, observational design. Adult patients diagnosed with ileus and monitored in the emergency department of a tertiary university hospital between January 2022 and December 2024, with complete laboratory data available, were included. The study was planned in accordance with the Declaration of Helsinki and approved by the local ethics committee. It was designed and reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines ( 13 ). Data collection Patient data were retrospectively retrieved from the electronic medical record system of the hospital. Variables recorded included age, sex, comorbidities, history of abdominal surgery, vital signs, and laboratory parameters (complete blood count, biochemistry, CRP, albumin, electrolytes, and blood gas analysis). Comorbidities were recorded as hypertension, diabetes mellitus, presence of malignancy, chronic kidney disease, chronic heart failure, coronary artery disease, history of stroke, and chronic obstructive pulmonary disease. The patients were categorized into two groups based on the presence or absence of in-hospital mortality: the survivor group and the mortality group. Key variables included CRP, albumin, glucose, lactate, international normalized ratio (INR), calcium, and inflammatory indices such as CAR and GPS. CAR was calculated by dividing CRP (mg/L) by albumin (g/L). GPS was scored in three levels (0–2) based on CRP > 10 mg/L and albumin < 35 g/L, in accordance with the existing literature. Statistical analysis Statistical analyses were conducted using the Jamovi Version 2.3 software (Queensland University, Brisbane, Australia). Descriptive statistics were expressed as frequencies and percentages for categorical variables and as medians (minimum–maximum) for continuous variables. Normality was assessed using the Shapiro-Wilk test. For comparisons between two groups, the Pearson chi-square or Fisher’s exact chi-square test was used for categorical variables, while the Mann–Whitney U test was used for continuous variables. To identify biomarkers potentially associated with mortality, univariate analyses were first conducted. Variables with a p-value of < 0.05 in these analyses were considered candidates for inclusion in the multivariate model. The clinical relevance, presence in the literature, and biological rationale of these variables were also taken into account. Before constructing the multiple logistic regression model, potential multicollinearity among variables was assessed using Pearson correlation coefficients. Variables that were computationally derived or directly influenced one another were not included in the same model. Specifically, as CRP and albumin are components of CAR, they were not included in the multivariate model. The same model excluded GPS, which is strongly correlated with CAR. Accordingly, the variables included in the multivariate logistic regression analyses were as follows: glucose, INR, eosinophils, calcium, and CAR for Model 1 and glucose, INR, eosinophils, calcium, and GPS for Model 2. These variables were selected based on both statistical significance and pathophysiological relevance to mortality. Model fit was evaluated using the Hosmer-Lemeshow test. Diagnostic accuracy was assessed by the area under the receiver operating characteristic (ROC) curve (AUC) ( 14 ). Differences in diagnostic performance between the models were compared using the DeLong test. A p-value of < 0.05 was considered statistically significant. Results Among the 270 patients with ileus included in the study, 92.6% (n = 250) survived, while 7.4% (n = 20) had in-hospital mortality. The patients in the mortality group were significantly older compared to survivors (78.5 vs. 65 years; p = 0.015). No statistically significant difference was found between the groups in terms of sex distribution (p = 1.000). Although malignancy was more frequently observed in the mortality group, this difference did not reach statistical significance (p = 0.053). However, the history of previous abdominal surgery was significantly more common in the mortality group (65% vs. 40%; p = 0.029) (Table 1 ). Table 1 Baseline Characteristics of the Study Sample and Comparison of Groups Total, n (%) Survivor group n = 250 (92.6%) Mortality group n = 20 (7.4%) Total n = 270 Age, years 65 (52 to 74) 78.5 (60.5 to 87.2) 65 (52 to 74) 0.015 Sex 1.000 Male, n (%) 133 (53.2%) 11 (55%) 144 (53.3%) Female, n (%) 117 (46.8%) 9 (45%) 126 (46.7%) Hypertension, n (%) 96 (38.4%) 9 (45%) 105 (38.9%) 0.731 Diabetes mellitus, n (%) 52 (20.8%) 4 (20%) 56 (20.7%) 1.000 Presence of malignancy, n (%) 90 (36%) 7 (35%) 97 (35.9%) 1.000 Chronic kidney disease, n (%) 5 (2%) 2 (10%) 7 (2.6%) 0.151 Chronic heart failure, n (%) 5 (2%) 2 (10%) 7 (2.6%) 0.151 Coronary artery disease, n (%) 20 (8%) 3 (15%) 23 (8.5%) 0.507 History of cerebrovascular accident, n (%) 9 (3.6%) 1 (5%) 10 (3.7%) 1.000 Chronic obstructive pulmonary disease, n (%) 12 (4.8%) 2 (10%) 14 (5.2%) 0.628 White blood cell count, ×10³/µL 11.3 (8.6 to 16.0) 12.3 (8.2 to 17.3) 11.4 (8.6 to 16.1) 0.734 Neutrophil count, ×10³/µL 9.4 (6.4 to 13.2) 11.0 (6.6 to 14.5) 9.4 (6.4 to 13.2) 0.579 Monocyte count, ×10³/µL 0.6 (0.4 to 0.9) 0.7 (0.5 to 1.1) 0.6 (0.4 to 0.9) 0.114 Lymphocyte count, ×10³/µL 1.5 (1.0 to 2.1) 1.1 (0.7 to 1.7) 1.4 (1.0 to 2.1) 0.141 Eosinophil count, ×10³/µL 0.04 (0.01 to 0.09) 0.015 (0.0 to 0.0225) 0.04 (0.01 to 0.09) < 0.001 Red blood cell count, ×10⁶/µL 4.9 (4.4 to 5.5) 4.8 (4.2 to 5) 4.9 (4.3 to 5.5) 0.171 Hemoglobin level, g/dL 13.9 (12.2 to 15.7) 13.3 (11.6 to 14.5) 13.9 (12.2 to 15.7) 0.162 Hematocrit,% 41.9 (37.2 to 47.5) 41.1 (35.9 to 43.5) 41.9 (37.2 to 47.2) 0.160 Mean corpuscular volume, fL 87.3 (83.2 to 93.2) 88.1 (79.3 to 91) 87.5 (83.1 to 93) 0.783 Red cell distribution width,% 14.4 (13.5 to 16.9) 14.6 (13.8 to 16.2) 14.4 (13.5 to 16.9) 0.819 Platelet count, ×10³/µL 287 (237 to 420) 295 (255.5 to 371.2) 289 (237 to 420) 0.854 Mean platelet volume, fL 9.9 (9 to 10.8) 9.4 (8.8 to 10.3) 9.8 (8.9 to 10.8) 0.330 Plateletcrit,% 0.3 (0.2 to 0.4) 0.3 (0.2 to 0.3) 0.3 (0.2 to 0.4) 0.813 Platelet distribution width, fL 16.2 (15.9 to 16.6) 16.1 (15.8 to 16.4) 16.2 (15.9 to 16.6) 0.549 Alanine aminotransferase, U/L 17 (12 to 23) 14 (8 to 27.5) 17 (12 to 23.8) 0.534 Albumin level, g/L 43 (39 to 46) 35.5 (32 to 40.2) 42.4 (38.1 to 45.9) < 0.001 Alkaline phosphatase, U/L 87 (70 to 111) 72 (61.5 to 104.5) 86 (69 to 110.5) 0.224 Amylase, U/L 58 (38 to 79) 63.5 (28.8 to 72.8) 58 (38 to 79) 0.617 Aspartate aminotransferase, U/L 22 (17 to 29) 21 (16.2 to 30) 22 (17 to 29) 0.809 C-reactive protein, mg/L 13.4 (4.7 to 37.8) 55.9 (16.2 to 105.8) 15.1 (4.8 to 44.8) 0.001 Serum glucose level, mg/dL 128 (107 to 147) 166 (126 to 196.2) 129 (109 to 150.5) 0.002 Serum calcium level, mg/dL 9.0 (8.5 to 9.5) 8.3 (7.7 to 9.3) 8.9 (8.4 to 9.4) 0.032 Blood urea nitrogen, mg/dL 38.7 (28.1 to 53.2) 73.5 (42.2 to 113.5) 39.8 (28.5 to 57.9) < 0.001 Serum chloride, mmol/L 100.1 (96.7 to 102.8) 97.5 (92.4 to 101.6) 100 (96.4 to 102.7) 0.066 Serum creatinine, mg/dL 0.9 (0.7 to 1.1) 1.3 (0.7 to 1.8) 0.9 (0.7 to 1.2) 0.058 Lipase, U/L 24.2 (17.0 to 34) 21 (15.0 to 44.2) 24.1 (17 to 35.1) 0.698 Serum magnesium, mg/dL 2 (1.8 to 2.1) 2 (2.0 to 2.1) 2.0 (1.8 to 2.1) 0.417 Serum potassium, mmol/L 4.4 (4.0 to 4.7) 4.5 (3.8 to 4.8) 4.4 (4.0 to 4.7) 0.962 Total protein, g/L 71.5 (66.0 to 76.2) 65.0 (57.8 to 69.6) 71.0 (65.7 to 76.0) 0.001 Serum sodium, mmol/L 137.7 (135 to 140) 137.8 (132.8 to 139.6) 137.7 (135 to 140) 0.657 Total bilirubin, mg/dL 0.6 (0.4 to 0.9) 0.8 (0.5 to 1.1) 0.6 (0.4 to 0.9) 0.268 Gamma-glutamyl transferase, U/L 19 (13 to 32.2) 17 (12.5 to 28.5) 19 (13 to 32) 0.527 Direct bilirubin, mg/dL 0.2 (0.1 to 0.3) 0.3 (0.2 to 0.4) 0.2 (0.1 to 0.3) 0.013 International normalized ratio 1.1 (1.0 to 1.2) 1.2 (1.1 to 1.3) 1.1 (1.0 to 1.2) < 0.001 Partial pressure of carbon dioxide, mmHg 39.5 (34.8 to 43.5) 34.3 (31.4 to 42.7) 39.3 (34.5 to 43.3) 0.116 Arterial pH 7.4 (7.4 to 7.4) 7.4 (7.4 to 7.4) 7.4 (7.4 to 7.4) 0.854 Oxygen saturation,% 67.9 (50.6 to 84.8) 59.2 (48.5 to 87.0) 67.2 (50.0 to 85.2) 0.700 Partial pressure of oxygen, mmHg 36.4 (28.1 to 50) 34.1 (28.1 to 53.8) 36.2 (28.1 to 50.2) 0.962 Bicarbonate, mmol/L 23.7 (21.9 to 26.2) 21.4 (20.5 to 24.4) 23.6 (21.6 to 26.1) 0.043 Base excess, mmol/L -0.6 (-2.2 to 1.4) -2.4 (-5.2 to 0.7) -0.7 (-2.5 to 1.4) 0.052 Lactate, mmol/L 2.0 (1.6 to 2.6) 2.4 (2.0 to 4.5) 2.1 (1.6 to 2.6) 0.015 Ionized calcium, mmol/L 1.1 (1 to 1.1) 1.0 (1.0 to 1.1) 1.1 (1.0 to 1.1) 0.012 C-reactive protein-to-albumin ratio 0.3 (0.1 to 0.9) 1.5 (0.4 to 2.9) 0.3 (0.1 to 1.1) 0.001 Glasgow Prognostic Score 0.010 0 105 (42%) 3 (15%) 108 (40%) 1 118 (47.2%) 11 (55%) 129 (47.8%) 2 27 (10.8%) 6 (30%) 33 (12.2%) Upon examination of laboratory parameters, the mortality group was found to have significantly lower eosinophil levels (p < 0.001) and significantly higher CRP levels (55.9 mg/L vs. 13.4 mg/L; p = 0.001). This group also had significantly lower albumin levels (35.5 g/L vs. 43 g/L; p < 0.001) and serum calcium levels (8.3 mg/dL vs. 9 mg/dL; p = 0.032). Glucose (166 mg/dL vs. 128 mg/dL; p = 0.002), blood urea nitrogen (73.5 mg/dL vs. 38.7 mg/dL; p < 0.001), and lactate levels (2.4 mmol/L vs. 2.0 mmol/L; p = 0.015) were significantly higher among non-survivors. INR was similarly found to be significantly elevated in the mortality group (1.2 vs. 1.1; p < 0.001) (Table 1 ). With respect to inflammatory markers, CAR was significantly higher in the mortality group (1.5 vs. 0.3; p = 0.001). Similarly, GPS scores were clustered at higher levels in this group, with the proportion of patients with a GPS score of 2 being 30% among non-survivors and 10.8% among survivors (p = 0.010). Both the CAR (cut-off ≥ 0.34) and GPS (cut-off ≥ 0.5) demonstrated high sensitivity (85%; 95% confidence interval [CI]: 62.11–96.79) and negative predictive value (97.22%; 95% CI: 92.43–99.01) for mortality risk stratification, supporting their utility as rule-out tools. However, specificity (42%; 95% CI: 35.81–48.38) and positive predictive value (10.49%; 95% CI: 8.66–12.66) were low for both, limiting their utility in confirming mortality. Overall diagnostic accuracy was identical for both indices (45.19%; 95% CI: 39.15–51.33) at a prevalence of 7.41% (95% CI: 4.58–11.21). Critically, CAR showed superior discriminative power with an AUC value of 0.729 (95% CI: 0.613–0.844) compared to GPS (AUR: 0.676; 95% CI: 0.567–0.786) (Table 2 ). DeLong’s test, which was conducted to compare the ROC curves, revealed no statistically significant difference in discriminative ability between GPS and CAR for predicting mortality (AUC difference: −0.0527; 95% CI: −0.121 to 0.0154; z = − 1.52; p = 0.129). Positive likelihood ratios were weak (1.466; 95% CI: 1.185–1.81), while negative likelihood ratios (0.357; 95% CI: 0.125–1.02) indicated moderate usefulness for ruling out mortality. Table 2 Diagnostic Performance Characteristics for Mortality Prediction Parameters C-reactive protein-to-albumin ratio Glasgow Prognostic Score Area under the curve 0.729 (0.613–0.844) 0.676 (0.567–0.786) Cut-off ≥ 0.34 ≥ 0.5 ≥ 1.5 Sensitivity 85% (62–96%) 85% (62–96%) 30% (11.89–54.28%) Specificity 42% (35–48%) 42% (35–48%) 89.2% (84.68–92.76%) Positive likelihood ratio 1.466 (1.185–1.81) 1.466 (1.185–1.81) 2.78 (1.30–5.93) Negative likelihood ratio 0.357 (0.125–1.02) 0.357 (0.125–1.02) 0.78 (0.58–1.04) Prevalence 7.41% (4.58–11.21%) 7.41% (4.58–11.21%) 7.41% (4.58–11.21%) Positive predictive value 10.49% (8.66–12.66%) 10.49% (8.66–12.66%) 18.18% (9.43–32.18%) Negative predictive value 97.22% (92.43–99.01%) 97.22% (92.43–99.01%) 94.09% (92.26–95.51%) Accuracy 45.19% (39.15–51.33%) 45.19% (39.15–51.33%) 84.81% (79.97–88.88%) In the multivariate logistic regression analysis designed to predict mortality, glucose, INR, eosinophil, calcium, and CAR were included in Model 1. The overall model fit was calculated as deviance = 96.8, Akaike Information Criterion (AIC) = 109, and McFadden R² = 0.233. According to the results of this analysis, CAR demonstrated a statistically significant and positive association with mortality (β = 0.152, p = 0.033, odds ratio [OR] = 0.859, 95% CI: 0.747–0.988) (Table 3 ). For Model 1, the sensitivity was calculated as 99.1%, the specificity as 16.7%, and the AUC as 0.832. Table 3 Multivariable Logistic Regression Analysis for Mortality Variable Coefficient (β) Standard error p-value Odds ratio 95% confidence interval Model 1 Glucose (mg/dl) 0.00986 0.00512 0.054 0.990 0.980–1.000 International normalized ratio 0.64665 0.56959 0.256 0.524 0.172–1.600 Eosinophil count (×10⁹/L) -2,421.450 1,066.816 0.023 3.28 × 10¹⁰ 2.72 × 10⁶–3.95 × 10¹⁹ Calcium (mmol/L) -0.48363 0.32085 0.132 1.622 0.865–3.042 C-reactive protein-to-albumin ratio 0.15200 0.07149 0.033 0.859 0.747–0.988 Model 2 Glucose (mg/dl) 0.0107 0.00524 0.040 0.989 0.979–1.000 International normalized ratio 0.7425 0.54041 0.169 0.476 0.165–1.373 Eosinophil count (×10⁹/L) -255.012 1085.212 0.019 1.19 × 10¹¹ 6.88 × 10⁶–2.05 × 10²⁰ Calcium (mmol/L) -0.5026 0.33127 0.129 1.653 0.864–3.164 Glasgow Prognostic Score 0.9329 0.41744 0.025 0.393 0.174–0.892 The second multivariate logistic regression analysis, which aimed to predict mortality, included glucose, INR, eosinophil, calcium, and GPS were included in Model 2. The overall model fit was evaluated with a deviance value of 95.7, an AIC of 108, and a McFadden R² of 0.241. The analysis determined that the GPS score was a statistically significant positive predictor of mortality (β = 0.9329, p = 0.025, OR = 0.393, 95% CI: 0.174–0.892) (Table 3 ). For Model 2, the sensitivity was 99.1%, the specificity was 11.1%, and the AUC was 0.846. Discussion This is one of the first analytical observational studies to compare the performance of CAR and GPS in predicting in-hospital mortality in patients diagnosed with ileus. Our findings suggest that both indices have high negative predictive value, indicating their clinical utility in ruling out mortality. Nevertheless, CAR demonstrated a higher discriminative capacity and superior performance compared to GPS in mortality prediction. In addition, multivariate analyses revealed that both CAR and GPS were independently associated with mortality. CAR emerged as a particularly practical and effective biomarker that could be integrated into clinical decision-making processes. In the pathogenesis of ileus, early recognition of the inflammatory response and objective evaluation of the body’s capacity to manage it are of critical importance for clinical management. Inflammation-based indices such as CAR and GPS are potential biomarkers reflecting this pathophysiological process. The current study found significantly elevated CAR values in the mortality group, and multivariate analysis revealed CAR as an independent predictor. This suggests that the combined assessment of systemic inflammation and low albumin levels provides stronger prognostic information than inflammatory parameters used in isolation. Previous literature has reported that CAR is associated with both short- and long-term mortality in various disease states, including malignancies, sepsis, acute pancreatitis, and stroke. Fairclough et al. reported that elevated CAR values adversely affected survival in the emergency care setting ( 15 ). Similarly, studies have linked CAR to both mortality and the need for urgent surgical intervention in patients receiving surgical intensive care ( 16 ). Consistent with these findings, our study supports the utility of CAR as a valuable prognostic parameter in acute abdominal conditions such as ileus. To understand the relationship between CAR and GPS scores and mortality, it is essential to assess the underlying biological mechanisms. Ileus is a clinical condition that develops due to impaired intestinal passage and triggers systemic inflammation. During this process, impaired tissue perfusion, a weakened intestinal barrier, and bacterial translocation augment the proinflammatory response ( 17 ). The increase in acute-phase reactants such as CRP reflects this response, while albumin levels decrease due to inflammation, capillary leakage, and malnutrition ( 18 ). CAR integrates these two parameters to reflect both the inflammatory and nutritional aspects of the disease. Although GPS is based on similar biomarkers, its categorical classification may limit its sensitivity in capturing clinical variability between patient groups compared to CAR ( 19 , 20 ). The ROC analysis in our study demonstrated that CAR had higher discriminative power than GPS (AUC: 0.729 vs. 0.676). This finding suggests that the limited scoring range of GPS may reduce sensitivity, whereas the continuous nature of CAR allows for more detailed risk stratification. This study has several limitations. First, due to the retrospective and observational design, it is not possible to establish causal relationships among the variables. Second, as data were collected retrospectively from the hospital information system, there may be missing or biased details regarding certain clinical variables. In particular, the inability to standardize the time between symptom onset and laboratory sampling may have affected the levels of inflammatory markers. Furthermore, the study was conducted at a single center, and the patient population was limited to the characteristics of that institution, which reduces generalizability. Third, due to the relatively low mortality rate (7.4%), some variables may not have reached statistical significance in multivariate analyses. Lastly, potential confounding factors not evaluated in this study (e.g., corticosteroid use, concurrent infections, and immunosuppressive conditions) could have influenced inflammation levels and outcomes, possibly introducing bias. In conclusion, the findings of this study indicate that CAR is a more sensitive, flexible, and clinically applicable marker than GPS in acute abdominal conditions such as ileus. The ease of calculating CAR using commonly available parameters such as CRP and albumin renders it a practical tool for risk stratification in emergency departments and intensive care units. Particularly, identifying elevated CAR levels during the initial assessment may benefit from closer monitoring of patients and early implementation of advanced treatment planning. Declarations Conflicts of interest/Competing interests The authors declare that they have no conflicts of interest. Ethics approval This retrospective study was approved by the Institutional Ethics Committee of Ümraniye Training and Research Hospital (Approval No: 126, 10 July 2025). Consent to participate The requirement for informed consent was waived due to the retrospective nature of the study. Written consent for publication Not applicable. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Author Contribution Serdar Özdemir and İbrahim Altunok conceived and designed the study, collected and analyzed the data, and drafted the manuscript. Both authors read and approved the final version of the manuscript. Acknowledgement none Data Availability The datasets used and analyzed during the current study are available from the corresponding author on reasonable request. References Vilz TO, Stoffels B, Strassburg C, Schild HH, Kalff JC. Ileus in Adults. Dtsch Arztebl Int. 2017;114(29–30):508–518. doi: 10.3238/arztebl.2017.0508 . PMID: 28818187; PMCID: PMC5569564. Ullah S, Khan M, Mumtaz N, Naseer A. Intestinal Obstruction: A Spectrum of Causes. J Postgrad Med Inst. 2011;23(2). Jackson P, Vigiola Cruz M. Intestinal Obstruction: Evaluation and Management. Am Fam Physician. 2018;98(6):362–367. PMID: 30215917. Koşar MN, Görgülü Ö. Incidence and mortality results of intestinal obstruction in geriatric and adult patients: 10 years retrospective analysis. 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Int Surg. 2014 Sep-Oct;99(5):512–7. doi: 10.9738/INTSURG-D-13-00118.1 . PMID: 25216413; PMCID: PMC4253916. Altay S, Gürdoğan M, Keskin M, Kardaş F, Çakır B. The Inflammation-Based Glasgow Prognostic Score as a Prognostic Factor in Patients with Intensive Cardiovascular Care Unit. Medicina (Kaunas). 2019;55(5):139. doi: 10.3390/medicina55050139 . PMID: 31096693; PMCID: PMC6572028. Babaoğlu AB, Tekindal M, Büyükuysal MÇ, Tözün M, Elmalı F, Bayraktaroğlu T, Tekindal MA. Epidemiyolojide Gözlemsel Çalışmaların Raporlanması: STROBE Kriterlerinin Türkçe Uyarlaması. Med J West Black Sea. 2021;5(1):86–93. Özdemir S, Algın A. Interpretation of the Area Under the Receiver Operating Characteristic Curve. Exp Appl Med Sci. 2022;3(1):310–311. Fairclough E, Cairns E, Hamilton J, Kelly C. Evaluation of a modified early warning system for acute medical admissions and comparison with C-reactive protein/albumin ratio as a predictor of patient outcome. Clin Med (Lond). 2009;9(1):30–3. doi: 10.7861/clinmedicine.9-1-30 . PMID: 19271597; PMCID: PMC5922628. Özçiftci Yılmaz P, Karacan E. The effects of C-reactive protein/albumin ratio and hematologic parameters on predicting the prognosis for emergency surgical patients in intensive care. Ulus Travma Acil Cerrahi Derg. 2021;27(1):67–72. English. doi: 10.14744/tjtes.2020.45758 . PMID: 33394472. Hussain Z, Park H. Inflammation and Impaired Gut Physiology in Post-operative Ileus: Mechanisms and the Treatment Options. J Neurogastroenterol Motil. 2022;28(4):517–530. doi: 10.5056/jnm22100 . PMID: 36250359; PMCID: PMC9577567. Ge X, Cao Y, Wang H, Ding C, Tian H, Zhang X, Gong J, Zhu W, Li N. Diagnostic accuracy of the postoperative ratio of C-reactive protein to albumin for complications after colorectal surgery. World J Surg Oncol. 2017;15(1):15. doi: 10.1186/s12957-016-1092-1 . PMID: 28069031; PMCID: PMC5223565. Kinoshita A, Onoda H, Imai N, Iwaku A, Oishi M, Tanaka K, Fushiya N, Koike K, Nishino H, Matsushima M. The C-reactive protein/albumin ratio, a novel inflammation-based prognostic score, predicts outcomes in patients with hepatocellular carcinoma. Ann Surg Oncol. 2015;22(3):803 – 10. doi: 10.1245/s10434-014-4048-0 . Epub 2014 Sep 5. PMID: 25190127. Zhou T, Zhan J, Hong S, Hu Z, Fang W, Qin T, Ma Y, Yang Y, He X, Zhao Y, Huang Y, Zhao H, Zhang L. Ratio of C-Reactive Protein/Albumin is An Inflammatory Prognostic Score for Predicting Overall Survival of Patients with Small-cell Lung Cancer. Sci Rep. 2015;5:10481. doi: 10.1038/srep10481 . PMID: 26084991; PMCID: PMC4471724. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-7476696","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":516339571,"identity":"5dc5dab2-7667-411b-a1c4-eed164d1693b","order_by":0,"name":"Serdar Özdemir","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYDCCA2CSmYFB/vzDxxA2cwORWiR4mI0ZGAyAbEbitbBJg7UwENDCd/sA84ePe6zlzWf3HqsuqPgTzd8O1PKjYhtOLZLnEtgkZzxLN5xz51za7RlnDHJnHGZsYOw5cxunFoMzDGzMPAcOM85gSDC7zdtmkNsA1MLM2IZXC/PnPwcO24O0FIO0zCdCC4M0w4HDiTMkcsyYQVo2ENIieYaxTbLnQHryDJ5jydI8Z4xzNwK1HMTnF74zzIc//DhgbTuDvfngZ54Kudx55w8ffPCjArcW7LFwAI/6UTAKRsEoGAVEAAD+alnY/YwfPgAAAABJRU5ErkJggg==","orcid":"","institution":"University of Health Sciences Umraniye Training and Research Hospital","correspondingAuthor":true,"prefix":"","firstName":"Serdar","middleName":"","lastName":"Özdemir","suffix":""},{"id":516339572,"identity":"ea692503-a251-434a-8943-466b7024f59b","order_by":1,"name":"İbrahim Altunok","email":"","orcid":"","institution":"University of Health Sciences Umraniye Training and Research Hospital","correspondingAuthor":false,"prefix":"","firstName":"İbrahim","middleName":"","lastName":"Altunok","suffix":""}],"badges":[],"createdAt":"2025-08-28 06:08:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7476696/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7476696/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":98427118,"identity":"724d71ce-327e-4c68-bebc-7a8ef5bedb91","added_by":"auto","created_at":"2025-12-17 16:39:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":897855,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7476696/v1/269f7411-5f0f-4de9-a0df-e79b12159cd0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"C-Reactive Protein-to-Albumin Ratio in Predicting Mortality in Patients with Ileus: A Comparative Analytical Study with the Glasgow Prognostic Score","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIleus is a significant group of diseases characterized by impaired gastrointestinal motility, resulting in the cessation or delayed forward movement of intestinal contents. It encompasses a broad range of surgical and medical conditions and is primarily classified into two main types: mechanical and paralytic. It presents symptoms such as abdominal distension, vomiting, constipation, and abdominal pain, and if not promptly managed, it may lead to severe complications, such as bowel ischemia, perforation, sepsis, and multi-organ failure (\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Mortality rates are particularly high in elderly patients, those with multiple comorbidities, and in cases of delayed presentation (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). There is therefore an increasing need for reliable, rapid, accessible, and cost-effective biomarkers to predict prognosis in patients with ileus.\u003c/p\u003e\u003cp\u003eCertain parameters derived from conventional laboratory tests have recently gained attention as indicators reflecting systemic inflammation (\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). In this context, C-reactive protein (CRP), an acute-phase reactant, along with albumin, a negative acute-phase reactant and an indicator of nutritional status, have been evaluated together to derive the CRP-to-albumin ratio (CAR), which has been proposed as a prognostic marker in various malignant and non-malignant diseases (\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). A high CAR value has been associated with adverse clinical outcomes by reflecting both systemic inflammation and poor nutritional status due to low albumin levels.\u003c/p\u003e\u003cp\u003eThe Glasgow Prognostic Score (GPS), calculated based on CRP and albumin levels, was initially introduced as a prognostic marker in malignancies but has since demonstrated applicability in various acute and chronic conditions. GPS is scored between 0 and 2 based on CRP\u0026thinsp;\u0026gt;\u0026thinsp;10 mg/L and albumin\u0026thinsp;\u0026lt;\u0026thinsp;35 g/L, and higher scores have been associated with poor prognosis (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). However, there are only a limited number of studies that simultaneously evaluate CAR and GPS and examine their comparative strength in predicting mortality, particularly in urgent conditions such as ileus.\u003c/p\u003e\u003cp\u003eIn light of the abovementioned information, the current study aimed to assess and compare the diagnostic performance of CAR and GPS in predicting in-hospital mortality among patients diagnosed with ileus in the emergency department.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy design and population\u003c/h2\u003e\u003cp\u003eThis study was conducted using a retrospective, diagnostic validity, observational design. Adult patients diagnosed with ileus and monitored in the emergency department of a tertiary university hospital between January 2022 and December 2024, with complete laboratory data available, were included. The study was planned in accordance with the Declaration of Helsinki and approved by the local ethics committee. It was designed and reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003ePatient data were retrospectively retrieved from the electronic medical record system of the hospital. Variables recorded included age, sex, comorbidities, history of abdominal surgery, vital signs, and laboratory parameters (complete blood count, biochemistry, CRP, albumin, electrolytes, and blood gas analysis). Comorbidities were recorded as hypertension, diabetes mellitus, presence of malignancy, chronic kidney disease, chronic heart failure, coronary artery disease, history of stroke, and chronic obstructive pulmonary disease.\u003c/p\u003e\u003cp\u003eThe patients were categorized into two groups based on the presence or absence of in-hospital mortality: the survivor group and the mortality group. Key variables included CRP, albumin, glucose, lactate, international normalized ratio (INR), calcium, and inflammatory indices such as CAR and GPS. CAR was calculated by dividing CRP (mg/L) by albumin (g/L). GPS was scored in three levels (0\u0026ndash;2) based on CRP\u0026thinsp;\u0026gt;\u0026thinsp;10 mg/L and albumin\u0026thinsp;\u0026lt;\u0026thinsp;35 g/L, in accordance with the existing literature.\u003c/p\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses were conducted using the Jamovi Version 2.3 software (Queensland University, Brisbane, Australia). Descriptive statistics were expressed as frequencies and percentages for categorical variables and as medians (minimum\u0026ndash;maximum) for continuous variables. Normality was assessed using the Shapiro-Wilk test. For comparisons between two groups, the Pearson chi-square or Fisher\u0026rsquo;s exact chi-square test was used for categorical variables, while the Mann\u0026ndash;Whitney U test was used for continuous variables.\u003c/p\u003e\u003cp\u003eTo identify biomarkers potentially associated with mortality, univariate analyses were first conducted. Variables with a p-value of \u0026lt;\u0026thinsp;0.05 in these analyses were considered candidates for inclusion in the multivariate model. The clinical relevance, presence in the literature, and biological rationale of these variables were also taken into account.\u003c/p\u003e\u003cp\u003eBefore constructing the multiple logistic regression model, potential multicollinearity among variables was assessed using Pearson correlation coefficients. Variables that were computationally derived or directly influenced one another were not included in the same model. Specifically, as CRP and albumin are components of CAR, they were not included in the multivariate model. The same model excluded GPS, which is strongly correlated with CAR. Accordingly, the variables included in the multivariate logistic regression analyses were as follows: glucose, INR, eosinophils, calcium, and CAR for Model 1 and glucose, INR, eosinophils, calcium, and GPS for Model 2. These variables were selected based on both statistical significance and pathophysiological relevance to mortality. Model fit was evaluated using the Hosmer-Lemeshow test. Diagnostic accuracy was assessed by the area under the receiver operating characteristic (ROC) curve (AUC) (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Differences in diagnostic performance between the models were compared using the DeLong test. A p-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eAmong the 270 patients with ileus included in the study, 92.6% (n\u0026thinsp;=\u0026thinsp;250) survived, while 7.4% (n\u0026thinsp;=\u0026thinsp;20) had in-hospital mortality. The patients in the mortality group were significantly older compared to survivors (78.5 vs. 65 years; p\u0026thinsp;=\u0026thinsp;0.015). No statistically significant difference was found between the groups in terms of sex distribution (p\u0026thinsp;=\u0026thinsp;1.000). Although malignancy was more frequently observed in the mortality group, this difference did not reach statistical significance (p\u0026thinsp;=\u0026thinsp;0.053). However, the history of previous abdominal surgery was significantly more common in the mortality group (65% vs. 40%; p\u0026thinsp;=\u0026thinsp;0.029) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline Characteristics of the Study Sample and Comparison of Groups\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal, n (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSurvivor group\u003c/p\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;250 (92.6%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMortality group\u003c/p\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;20 (7.4%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;270\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e65 (52 to 74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e78.5 (60.5 to 87.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e65 (52 to 74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e133 (53.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11 (55%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e144 (53.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e117 (46.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9 (45%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e126 (46.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e96 (38.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9 (45%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e105 (38.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.731\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes mellitus, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e52 (20.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (20%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e56 (20.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePresence of malignancy, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e90 (36%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7 (35%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e97 (35.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChronic kidney disease, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 (2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (10%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7 (2.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.151\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChronic heart failure, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 (2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (10%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7 (2.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.151\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCoronary artery disease, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20 (8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (15%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23 (8.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.507\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHistory of cerebrovascular accident, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9 (3.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10 (3.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChronic obstructive pulmonary disease, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12 (4.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (10%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14 (5.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.628\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhite blood cell count, \u0026times;10\u0026sup3;/\u0026micro;L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.3 (8.6 to 16.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.3 (8.2 to 17.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.4 (8.6 to 16.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.734\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeutrophil count, \u0026times;10\u0026sup3;/\u0026micro;L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.4 (6.4 to 13.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.0 (6.6 to 14.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.4 (6.4 to 13.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.579\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMonocyte count, \u0026times;10\u0026sup3;/\u0026micro;L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.6 (0.4 to 0.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.7 (0.5 to 1.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.6 (0.4 to 0.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.114\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLymphocyte count, \u0026times;10\u0026sup3;/\u0026micro;L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.5 (1.0 to 2.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.1 (0.7 to 1.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.4 (1.0 to 2.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.141\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEosinophil count, \u0026times;10\u0026sup3;/\u0026micro;L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.04 (0.01 to 0.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.015 (0.0 to 0.0225)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.04 (0.01 to 0.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRed blood cell count, \u0026times;10⁶/\u0026micro;L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.9 (4.4 to 5.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.8 (4.2 to 5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.9 (4.3 to 5.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.171\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHemoglobin level, g/dL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13.9 (12.2 to 15.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.3 (11.6 to 14.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.9 (12.2 to 15.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.162\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\u003e41.9 (37.2 to 47.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41.1 (35.9 to 43.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e41.9 (37.2 to 47.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.160\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean corpuscular volume, fL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e87.3 (83.2 to 93.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e88.1 (79.3 to 91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e87.5 (83.1 to 93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.783\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRed cell distribution width,%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.4 (13.5 to 16.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.6 (13.8 to 16.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14.4 (13.5 to 16.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.819\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlatelet count, \u0026times;10\u0026sup3;/\u0026micro;L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e287 (237 to 420)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e295 (255.5 to 371.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e289 (237 to 420)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.854\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean platelet volume, fL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.9 (9 to 10.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.4 (8.8 to 10.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.8 (8.9 to 10.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.330\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlateletcrit,%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.3 (0.2 to 0.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.3 (0.2 to 0.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.3 (0.2 to 0.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.813\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlatelet distribution width, fL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16.2 (15.9 to 16.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.1 (15.8 to 16.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16.2 (15.9 to 16.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.549\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlanine aminotransferase, U/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17 (12 to 23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14 (8 to 27.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17 (12 to 23.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.534\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlbumin level, g/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e43 (39 to 46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35.5 (32 to 40.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e42.4 (38.1 to 45.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlkaline phosphatase, U/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e87 (70 to 111)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e72 (61.5 to 104.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e86 (69 to 110.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.224\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAmylase, U/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58 (38 to 79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e63.5 (28.8 to 72.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e58 (38 to 79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.617\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAspartate aminotransferase, U/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22 (17 to 29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21 (16.2 to 30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22 (17 to 29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.809\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC-reactive protein, mg/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13.4 (4.7 to 37.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e55.9 (16.2 to 105.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.1 (4.8 to 44.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerum glucose level, mg/dL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e128 (107 to 147)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e166 (126 to 196.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e129 (109 to 150.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerum calcium level, mg/dL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.0 (8.5 to 9.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.3 (7.7 to 9.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.9 (8.4 to 9.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.032\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlood urea nitrogen, mg/dL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e38.7 (28.1 to 53.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e73.5 (42.2 to 113.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e39.8 (28.5 to 57.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerum chloride, mmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e100.1 (96.7 to 102.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e97.5 (92.4 to 101.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e100 (96.4 to 102.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.066\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerum creatinine, mg/dL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.9 (0.7 to 1.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.3 (0.7 to 1.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9 (0.7 to 1.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.058\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLipase, U/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24.2 (17.0 to 34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21 (15.0 to 44.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24.1 (17 to 35.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.698\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerum magnesium, mg/dL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (1.8 to 2.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (2.0 to 2.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.0 (1.8 to 2.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.417\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerum potassium, mmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.4 (4.0 to 4.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.5 (3.8 to 4.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.4 (4.0 to 4.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.962\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal protein, g/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e71.5 (66.0 to 76.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e65.0 (57.8 to 69.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e71.0 (65.7 to 76.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerum sodium, mmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e137.7 (135 to 140)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e137.8 (132.8 to 139.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e137.7 (135 to 140)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.657\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal bilirubin, mg/dL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.6 (0.4 to 0.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.8 (0.5 to 1.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.6 (0.4 to 0.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.268\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGamma-glutamyl transferase, U/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19 (13 to 32.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17 (12.5 to 28.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19 (13 to 32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.527\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDirect bilirubin, mg/dL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.2 (0.1 to 0.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.3 (0.2 to 0.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.2 (0.1 to 0.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.013\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInternational normalized ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.1 (1.0 to 1.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.2 (1.1 to 1.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.1 (1.0 to 1.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePartial pressure of carbon dioxide, mmHg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e39.5 (34.8 to 43.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34.3 (31.4 to 42.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e39.3 (34.5 to 43.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.116\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArterial pH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.4 (7.4 to 7.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.4 (7.4 to 7.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.4 (7.4 to 7.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.854\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOxygen saturation,%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e67.9 (50.6 to 84.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e59.2 (48.5 to 87.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e67.2 (50.0 to 85.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.700\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePartial pressure of oxygen, mmHg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36.4 (28.1 to 50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34.1 (28.1 to 53.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36.2 (28.1 to 50.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.962\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBicarbonate, mmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.7 (21.9 to 26.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21.4 (20.5 to 24.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23.6 (21.6 to 26.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.043\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBase excess, mmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.6 (-2.2 to 1.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-2.4 (-5.2 to 0.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.7 (-2.5 to 1.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.052\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLactate, mmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.0 (1.6 to 2.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.4 (2.0 to 4.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.1 (1.6 to 2.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.015\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIonized calcium, mmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.1 (1 to 1.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.0 (1.0 to 1.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.1 (1.0 to 1.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.012\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC-reactive protein-to-albumin ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.3 (0.1 to 0.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.5 (0.4 to 2.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.3 (0.1 to 1.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eGlasgow Prognostic Score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.010\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e105 (42%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (15%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e108 (40%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e118 (47.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11 (55%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e129 (47.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e27 (10.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 (30%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33 (12.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eUpon examination of laboratory parameters, the mortality group was found to have significantly lower eosinophil levels (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and significantly higher CRP levels (55.9 mg/L vs. 13.4 mg/L; p\u0026thinsp;=\u0026thinsp;0.001). This group also had significantly lower albumin levels (35.5 g/L vs. 43 g/L; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and serum calcium levels (8.3 mg/dL vs. 9 mg/dL; p\u0026thinsp;=\u0026thinsp;0.032). Glucose (166 mg/dL vs. 128 mg/dL; p\u0026thinsp;=\u0026thinsp;0.002), blood urea nitrogen (73.5 mg/dL vs. 38.7 mg/dL; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and lactate levels (2.4 mmol/L vs. 2.0 mmol/L; p\u0026thinsp;=\u0026thinsp;0.015) were significantly higher among non-survivors. INR was similarly found to be significantly elevated in the mortality group (1.2 vs. 1.1; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWith respect to inflammatory markers, CAR was significantly higher in the mortality group (1.5 vs. 0.3; p\u0026thinsp;=\u0026thinsp;0.001). Similarly, GPS scores were clustered at higher levels in this group, with the proportion of patients with a GPS score of 2 being 30% among non-survivors and 10.8% among survivors (p\u0026thinsp;=\u0026thinsp;0.010). Both the CAR (cut-off \u0026ge;\u0026thinsp;0.34) and GPS (cut-off \u0026ge;\u0026thinsp;0.5) demonstrated high sensitivity (85%; 95% confidence interval [CI]: 62.11\u0026ndash;96.79) and negative predictive value (97.22%; 95% CI: 92.43\u0026ndash;99.01) for mortality risk stratification, supporting their utility as rule-out tools. However, specificity (42%; 95% CI: 35.81\u0026ndash;48.38) and positive predictive value (10.49%; 95% CI: 8.66\u0026ndash;12.66) were low for both, limiting their utility in confirming mortality. Overall diagnostic accuracy was identical for both indices (45.19%; 95% CI: 39.15\u0026ndash;51.33) at a prevalence of 7.41% (95% CI: 4.58\u0026ndash;11.21).\u003c/p\u003e\u003cp\u003eCritically, CAR showed superior discriminative power with an AUC value of 0.729 (95% CI: 0.613\u0026ndash;0.844) compared to GPS (AUR: 0.676; 95% CI: 0.567\u0026ndash;0.786) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). DeLong\u0026rsquo;s test, which was conducted to compare the ROC curves, revealed no statistically significant difference in discriminative ability between GPS and CAR for predicting mortality (AUC difference: \u0026minus;0.0527; 95% CI: \u0026minus;0.121 to 0.0154; z\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;1.52; p\u0026thinsp;=\u0026thinsp;0.129). Positive likelihood ratios were weak (1.466; 95% CI: 1.185\u0026ndash;1.81), while negative likelihood ratios (0.357; 95% CI: 0.125\u0026ndash;1.02) indicated moderate usefulness for ruling out mortality.\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\u003eDiagnostic Performance Characteristics for Mortality Prediction\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\u003eParameters\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC-reactive protein-to-albumin ratio\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eGlasgow Prognostic Score\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArea under the curve\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.729 (0.613\u0026ndash;0.844)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e0.676 (0.567\u0026ndash;0.786)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCut-off\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;1.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSensitivity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e85% (62\u0026ndash;96%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e85% (62\u0026ndash;96%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30% (11.89\u0026ndash;54.28%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpecificity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42% (35\u0026ndash;48%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42% (35\u0026ndash;48%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e89.2% (84.68\u0026ndash;92.76%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePositive likelihood ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.466 (1.185\u0026ndash;1.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.466 (1.185\u0026ndash;1.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.78 (1.30\u0026ndash;5.93)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNegative likelihood ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.357 (0.125\u0026ndash;1.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.357 (0.125\u0026ndash;1.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.78 (0.58\u0026ndash;1.04)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrevalence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.41% (4.58\u0026ndash;11.21%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.41% (4.58\u0026ndash;11.21%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.41% (4.58\u0026ndash;11.21%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePositive predictive value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.49% (8.66\u0026ndash;12.66%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.49% (8.66\u0026ndash;12.66%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18.18% (9.43\u0026ndash;32.18%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNegative predictive value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e97.22% (92.43\u0026ndash;99.01%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e97.22% (92.43\u0026ndash;99.01%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e94.09% (92.26\u0026ndash;95.51%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAccuracy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45.19% (39.15\u0026ndash;51.33%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45.19% (39.15\u0026ndash;51.33%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e84.81% (79.97\u0026ndash;88.88%)\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\u003eIn the multivariate logistic regression analysis designed to predict mortality, glucose, INR, eosinophil, calcium, and CAR were included in Model 1. The overall model fit was calculated as deviance\u0026thinsp;=\u0026thinsp;96.8, Akaike Information Criterion (AIC)\u0026thinsp;=\u0026thinsp;109, and McFadden R\u0026sup2; = 0.233. According to the results of this analysis, CAR demonstrated a statistically significant and positive association with mortality (β\u0026thinsp;=\u0026thinsp;0.152, p\u0026thinsp;=\u0026thinsp;0.033, odds ratio [OR]\u0026thinsp;=\u0026thinsp;0.859, 95% CI: 0.747\u0026ndash;0.988) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). For Model 1, the sensitivity was calculated as 99.1%, the specificity as 16.7%, and the AUC as 0.832.\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\u003eMultivariable Logistic Regression Analysis for Mortality\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCoefficient (β)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStandard error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOdds ratio\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e95% confidence interval\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e\u003cb\u003eModel 1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGlucose (mg/dl)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.00986\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.00512\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.054\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.990\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.980\u0026ndash;1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInternational normalized ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.64665\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.56959\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.256\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.524\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.172\u0026ndash;1.600\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEosinophil count (\u0026times;10⁹/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-2,421.450\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1,066.816\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.023\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.28 \u0026times; 10\u0026sup1;⁰\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.72 \u0026times; 10⁶\u0026ndash;3.95 \u0026times; 10\u0026sup1;⁹\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCalcium (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.48363\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.32085\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.622\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.865\u0026ndash;3.042\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC-reactive protein-to-albumin ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.15200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.07149\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.033\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.859\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.747\u0026ndash;0.988\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e\u003cb\u003eModel 2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGlucose (mg/dl)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.00524\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.040\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.989\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.979\u0026ndash;1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInternational normalized ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.7425\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.54041\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.169\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.476\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.165\u0026ndash;1.373\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEosinophil count (\u0026times;10⁹/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-255.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1085.212\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.019\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.19 \u0026times; 10\u0026sup1;\u0026sup1;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e6.88 \u0026times; 10⁶\u0026ndash;2.05 \u0026times; 10\u0026sup2;⁰\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCalcium (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.5026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.33127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.129\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.653\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.864\u0026ndash;3.164\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGlasgow Prognostic Score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.9329\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.41744\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.025\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.393\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.174\u0026ndash;0.892\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\u003eThe second multivariate logistic regression analysis, which aimed to predict mortality, included glucose, INR, eosinophil, calcium, and GPS were included in Model 2. The overall model fit was evaluated with a deviance value of 95.7, an AIC of 108, and a McFadden R\u0026sup2; of 0.241. The analysis determined that the GPS score was a statistically significant positive predictor of mortality (β\u0026thinsp;=\u0026thinsp;0.9329, p\u0026thinsp;=\u0026thinsp;0.025, OR\u0026thinsp;=\u0026thinsp;0.393, 95% CI: 0.174\u0026ndash;0.892) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). For Model 2, the sensitivity was 99.1%, the specificity was 11.1%, and the AUC was 0.846.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis is one of the first analytical observational studies to compare the performance of CAR and GPS in predicting in-hospital mortality in patients diagnosed with ileus. Our findings suggest that both indices have high negative predictive value, indicating their clinical utility in ruling out mortality. Nevertheless, CAR demonstrated a higher discriminative capacity and superior performance compared to GPS in mortality prediction. In addition, multivariate analyses revealed that both CAR and GPS were independently associated with mortality. CAR emerged as a particularly practical and effective biomarker that could be integrated into clinical decision-making processes.\u003c/p\u003e\u003cp\u003eIn the pathogenesis of ileus, early recognition of the inflammatory response and objective evaluation of the body\u0026rsquo;s capacity to manage it are of critical importance for clinical management. Inflammation-based indices such as CAR and GPS are potential biomarkers reflecting this pathophysiological process. The current study found significantly elevated CAR values in the mortality group, and multivariate analysis revealed CAR as an independent predictor. This suggests that the combined assessment of systemic inflammation and low albumin levels provides stronger prognostic information than inflammatory parameters used in isolation. Previous literature has reported that CAR is associated with both short- and long-term mortality in various disease states, including malignancies, sepsis, acute pancreatitis, and stroke. Fairclough et al. reported that elevated CAR values adversely affected survival in the emergency care setting (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Similarly, studies have linked CAR to both mortality and the need for urgent surgical intervention in patients receiving surgical intensive care (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Consistent with these findings, our study supports the utility of CAR as a valuable prognostic parameter in acute abdominal conditions such as ileus.\u003c/p\u003e\u003cp\u003eTo understand the relationship between CAR and GPS scores and mortality, it is essential to assess the underlying biological mechanisms. Ileus is a clinical condition that develops due to impaired intestinal passage and triggers systemic inflammation. During this process, impaired tissue perfusion, a weakened intestinal barrier, and bacterial translocation augment the proinflammatory response (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). The increase in acute-phase reactants such as CRP reflects this response, while albumin levels decrease due to inflammation, capillary leakage, and malnutrition (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). CAR integrates these two parameters to reflect both the inflammatory and nutritional aspects of the disease. Although GPS is based on similar biomarkers, its categorical classification may limit its sensitivity in capturing clinical variability between patient groups compared to CAR (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). The ROC analysis in our study demonstrated that CAR had higher discriminative power than GPS (AUC: 0.729 vs. 0.676). This finding suggests that the limited scoring range of GPS may reduce sensitivity, whereas the continuous nature of CAR allows for more detailed risk stratification.\u003c/p\u003e\u003cp\u003eThis study has several limitations. First, due to the retrospective and observational design, it is not possible to establish causal relationships among the variables. Second, as data were collected retrospectively from the hospital information system, there may be missing or biased details regarding certain clinical variables. In particular, the inability to standardize the time between symptom onset and laboratory sampling may have affected the levels of inflammatory markers. Furthermore, the study was conducted at a single center, and the patient population was limited to the characteristics of that institution, which reduces generalizability. Third, due to the relatively low mortality rate (7.4%), some variables may not have reached statistical significance in multivariate analyses. Lastly, potential confounding factors not evaluated in this study (e.g., corticosteroid use, concurrent infections, and immunosuppressive conditions) could have influenced inflammation levels and outcomes, possibly introducing bias.\u003c/p\u003e\u003cp\u003eIn conclusion, the findings of this study indicate that CAR is a more sensitive, flexible, and clinically applicable marker than GPS in acute abdominal conditions such as ileus. The ease of calculating CAR using commonly available parameters such as CRP and albumin renders it a practical tool for risk stratification in emergency departments and intensive care units. Particularly, identifying elevated CAR levels during the initial assessment may benefit from closer monitoring of patients and early implementation of advanced treatment planning.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eConflicts of interest/Competing interests\u003c/h2\u003e\u003cp\u003eThe authors declare that they have no conflicts of interest.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003cp\u003e This retrospective study was approved by the Institutional Ethics Committee of \u0026Uuml;mraniye Training and Research Hospital (Approval No: 126, 10 July 2025).\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003cp\u003e The requirement for informed consent was waived due to the retrospective nature of the study.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eWritten consent for publication\u003c/h2\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eSerdar \u0026Ouml;zdemir and İbrahim Altunok conceived and designed the study, collected and analyzed the data, and drafted the manuscript. Both authors read and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003enone\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eVilz TO, Stoffels B, Strassburg C, Schild HH, Kalff JC. Ileus in Adults. Dtsch Arztebl Int. 2017;114(29\u0026ndash;30):508\u0026ndash;518. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3238/arztebl.2017.0508\u003c/span\u003e\u003cspan address=\"10.3238/arztebl.2017.0508\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PMID: 28818187; PMCID: PMC5569564.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eUllah S, Khan M, Mumtaz N, Naseer A. Intestinal Obstruction: A Spectrum of Causes. 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PMID: 26084991; PMCID: PMC4471724.\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":"C-reactive protein, albumin, mortality, prognosis, ileus","lastPublishedDoi":"10.21203/rs.3.rs-7476696/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7476696/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate the predictability of in-hospital mortality in patients followed up in the emergency department with a diagnosis of ileus using the C-reactive protein (CRP)-to-albumin ratio (CAR) and the Glasgow Prognostic Score (GPS) and to compare the diagnostic performance of these two biomarkers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterials and Methods:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study included 270 patients aged ≥18 years, diagnosed with ileus in the emergency department of a university hospital between January 2022 and December 2024, for whom complete laboratory data were available. CAR was calculated by dividing CRP by albumin, while GPS was scored based on CRP \u0026gt; 10 mg/L and albumin \u0026lt; 35 g/L values. Factors associated with mortality were evaluated using univariate and multivariate logistic regression analyses, and diagnostic accuracy was determined using the area under the receiver operating characteristic (ROC) curve (AUC).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong the patients, 7.4% had in-hospital mortality. CAR and GPS values were found to be significantly higher in the mortality group. In ROC analysis, the AUC value of CAR was calculated as 0.729, while that of GPS was 0.676, indicating a higher discriminatory power for CAR. In multivariate logistic regression analyses, CAR demonstrated a statistically significant and independent association with mortality (odds ratio [OR] = 0.859; 95% confidence interval [CI]: 0.747–0.988; p = 0.033). GPS was also identified as an independent predictor (OR = 0.393; 95% CI: 0.174–0.892; p = 0.025).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCAR and GPS are valuable markers for predicting mortality risk in patients with ileus. However, the continuous nature of CAR suggests that it is superior to GPS in terms of diagnostic accuracy and clinical applicability.\u003c/p\u003e","manuscriptTitle":"C-Reactive Protein-to-Albumin Ratio in Predicting Mortality in Patients with Ileus: A Comparative Analytical Study with the Glasgow Prognostic Score","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-18 12:27:47","doi":"10.21203/rs.3.rs-7476696/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":"9f53a6ca-d6a0-4cdf-9921-b2db2d7a227f","owner":[],"postedDate":"September 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-12T00:23:32+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-18 12:27:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7476696","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7476696","identity":"rs-7476696","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}