Clinical Prediction of Intra-Abdominal Infection in Patients with Severe Acute Pancreatitis: Logistic Regression and Nomogram

Clinical Prediction of Intra-Abdominal Infection in Patients with Severe Acute Pancreatitis: Logistic Regression and Nomogram | 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 Clinical Prediction of Intra-Abdominal Infection in Patients with Severe Acute Pancreatitis: Logistic Regression and Nomogram Rui Qi, Hebin Wang, Renying Luo, Jing Li, Li Su This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6805714/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: The goal is to create a clinical prediction model for individuals suffering from severe acute pancreatitis (SAP) who may have an intra-abdominal infection (IAI). Methods: Patients with acute severe pancreatitis at our institution from January 2020 to December 2023 were retrospectively analyzed. The study population was carefully split into two groups: a training set and a validation set, using a 70:30 ratio. This division was designed to facilitate a thorough development and assessment of the predictive model. In the training set, we identified predictive features utilizing LASSO regression, a method known for its ability to enhance model accuracy by selecting the most relevant variables. Following this, we established both a prediction model and a nomogram through multivariable logistic regression analysis, allowing for a comprehensive assessment of the identified risk factors. To assess the diagnostic performance of our model, we utilized receiver operating characteristic (ROC) curves for both the training and validation cohorts. This analysis yielded valuable information regarding the sensitivity and specificity of our predictive model. Furthermore, we conducted decision curve analysis (DCA) and created calibration plots to enhance our evaluation of the model's accuracy and its practical relevance in clinical settings. Results: A total of 415 participants were included in the analysis, with baseline demographic and clinical characteristics documented. The cohort consisted of 291 individuals in the training set and 124 in the validation set. LASSO regression identified four significant predictors with non-zero coefficients (HCT, PCT, APACHE II, NLR) for subsequent modeling. The prediction model's AUC was 0.853 (95% CI: 0.804-0.901) in the training set and 0.858 (95% CI: 0.786-0.930) in the validation set, according to ROC curve analysis. The calibration curve closely resembled the ideal line, and calibration plots demonstrated a strong alignment between the observed instances of IAI and the predicted values. The DCA demonstrated substantial net benefits for clinical application. Conclusion: The clinical prediction model integrating HCT, PCT, APACHE II, and NLR effectively predicts the risk of IAI in patients with SAP, thereby enhancing patient management strategies. Acute pancreatitis Severe acute pancreatitis Intra-abdominal infection Nomogram decision curve analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Acute pancreatitis (AP) is a serious clinical condition marked by the sudden inflammation of the pancreas and adjacent tissues. This inflammatory response is primarily triggered by the self-activation of pancreatic enzymes and the autophagic processes occurring within pancreatic tissue. The leading causes of AP are often gallstones and excessive alcohol intake, making it a common yet significant form of acute abdominal pain that requires urgent medical attention [ 1 ]. Severe acute pancreatitis (SAP) stands out as a particularly important subset within this spectrum. It is distinguished by the existence of chronic organ failure that lasts longer than 48 hours and can impact one or more organ systems [ 2 ]. This designation underscores the severity of the condition and its potential to lead to life-threatening complications. Understanding the underlying mechanisms and risk factors associated with both AP and SAP is essential for effective diagnosis and management. SAP is an unpredictable and potentially life-threatening condition, with prognosis closely associated with complications such as multiple organ dysfunction, infected pancreatic necrosis, and issues related to surgical or endoscopic interventions [ 3 ]. One of SAP's most common complications and a major cause of death for afflicted patients is intra-abdominal infection (IAI). When IAI develops in the setting of SAP, it can worsen multi-organ failure and cause intra-abdominal hypertension, which can lead to mortality rates that range from 47–69% [ 4 ]. Therefore, early identification and prediction of risk factors for IAI in SAP patients are crucial for improving clinical management and patient outcomes. Currently, research on predictive indicators and models for IAI in SAP remains limited. C-reactive protein (CRP) and interleukin-6 (IL-6) are two well-known inflammatory markers connected to the emergence of infections in a variety of clinical contexts [ 5 ]. A number of other biomarkers have also been suggested as possible predictors of infectious complications in SAP patients. Furthermore, in SAP patients, the platelet count (PLT), hematocrit (HCT), and neutrophil-to-lymphocyte ratio (NLR) are significant predictors of prognosis and inflammation [ 6 ]. However, these markers generally exhibit low specificity for diagnosing intra-abdominal infections, as they are often elevated in both mild SAP (MSAP) and SAP [ 7 ]. Numerous characteristics can aid in the diagnosis of intra-abdominal infections associated with acute pancreatitis, according to research. Intra-abdominal pressure, the Computed Tomography Severity Index (CTSI), the APACHE II score, the overall severity of pancreatitis, and particular predictive models for intensive care unit admission are important markers [ 8 ]. To assess the severity of SAP, a number of biomarkers and scoring methods are also essential. These include the neutrophil-to-high-density lipoprotein cholesterol ratio (NHR), procalcitonin (PCT), interleukin-6 (IL-6), platelet-to-lymphocyte ratio (PLR), and the Acute Physiology and Chronic Health Evaluation II (APACHE-II) [ 9 , 10 ]. These tools collectively enhance our ability to assess the condition and guide clinical decision-making. However, their predictive efficacy in SAP with concurrent intra-abdominal infection has not been extensively studied. In this investigation, we aim to evaluate the predictive value of CRP, IL-6, NLR, HCT, and PLT in assessing the likelihood of intra-abdominal infection in SAP patients. By employing logistic regression analysis and developing a nomogram, we aspire to provide clinicians with a practical tool for identifying high-risk patients, thereby facilitating timely interventions to enhance clinical outcomes. 2. Methods 2.1 Study Population This research involved a retrospective examination of patients diagnosed with SAP at our institution from January 2020 to December 2023. We collected various clinical and demographic data, including age and gender, from the participants. All individuals provided informed consent, and the study protocol received approval from the hospital's ethics committee. 2.2 Inclusion and Exclusion Criteria Inclusion criteria : Age between 18 and 80 years; Diagnosis of SAP according to established classification criteria for acute pancreatitis; Symptom onset to hospital admission occurring within 72 hours; Availability of comprehensive clinical data, including CRP, IL-6, NLR, HCT, and PLT. Exclusion criteria : Patients with malignant tumors, chronic organ dysfunction, or a history of other severe comorbidities; Patients presenting with gastrointestinal bleeding, intestinal obstruction, or other significant gastrointestinal disorders; Pregnant individuals, patients with autoimmune diseases, those with traumatic SAP, and patients with fulminant acute pancreatitis; Patients who underwent open abdominal surgery before or during hospitalization. 2.3 Diagnostic Criteria Diagnosis of IAI: Positive bacterial or fungal cultures from peritoneal fluid—which was extracted from ascitic fluid or pancreatic and peripancreatic tissues—were used to diagnose IAI. Systemic Inflammatory Response Syndrome (SIRS) : If at least two of the following circumstances exist, SIRS can be diagnosed: an increase in body temperature exceeding 38°C or a decrease below 36°C; pCO2 levels below 32 mmHg; a respiratory rate greater than 20 breaths per minute or indications of hyperventilation; a heart rate greater than 94 beats per minute; and a white blood cell count that is either above 12×10 9 /L or below 4×10 9 /L, or the detection of immature white blood cells exceeding 10%. 2.4 Observational Parameters We performed a review of patient cases to collect demographic and clinical information, which included gender, age, history of hypertension, alcohol consumption history, smoking history, history of diabetes, etiological factors for SAP, BMI, NEUT, PLT, HCT, D-dimer levels, CRP, IL-6, PCT, the presence of SIRS, APACHE II score, and the NLR. Additionally, data regarding the diagnosis of IAI were collected. 2.5 Statistical Analysis The dataset underwent random division into training and validation subsets, following a 70:30 ratio for the purpose of variable comparison. Data that failed to meet normal distribution assumptions were expressed as median (interquartile range), while categorical variables were presented as counts and percentages (n%). For univariate analyses, we employed the chi-square test or Fisher's exact test for categorical data, and the rank-sum test for continuous variables. Within the training cohort, we utilized LASSO logistic regression for multivariable analysis, enabling us to pinpoint independent risk factors and develop a nomogram for predicting intra-abdominal infections (IAI). The model's performance was evaluated through receiver operating characteristic (ROC) curves and calibration curves, with the area under the ROC curve (AUC) ranging from 0.5 (no discrimination) to 1 (perfect discrimination). We also conducted decision curve analysis (DCA) to establish the threshold for net clinical benefit. A significance level of p < 0.05 was predetermined for statistical significance. All statistical analyses were executed using R software (version 4.2.2) alongside MSTATA software ( www.mstata.com ). 3. Results 3.1 Demographic and Clinical Characteristics For our prediction analysis, a total of 415 participants' baseline clinical and demographic characteristics were assessed. This sample was divided into a training cohort of 291 individuals and an internal testing cohort of 124 individuals. The study found that the clinical features and demographics of the training and testing cohorts were the same. These findings enhance our understanding of the baseline features of the study population and establish a foundation for subsequent predictive model analyses. (Table 1 ) Table 1 Demographic and Clinical Characteristics of the Training and Test Cohorts Characteristic Cohort p-value Overall N = 415 Training Cohort N = 291 Internal Test Cohort N = 124 Sex 0.235 Female 236 (56.9%) 160 (55.0%) 76 (61.3%) Male 179 (43.1%) 131 (45.0%) 48 (38.7%) Age, years old 48.0 (45.0, 51.5) 48.0 (44.0, 51.5) 48.0 (45.0, 51.3) History of smoking 0.237 No 298 (71.8%) 204 (70.1%) 94 (75.8%) Yes 117 (28.2%) 87 (29.9%) 30 (24.2%) Alcohol consumption 0.951 No 297 (71.6%) 208 (71.5%) 89 (71.8%) Yes 118 (28.4%) 83 (28.5%) 35 (28.2%) History of hypertension 0.517 No 323 (77.8%) 229 (78.7%) 94 (75.8%) Yes 92 (22.2%) 62 (21.3%) 30 (24.2%) History of diabetes 0.689 No 371 (89.4%) 259 (89.0%) 112 (90.3%) Yes 44 (10.6%) 32 (11.0%) 12 (9.7%) Etiology 0.180 Alcoholic 64 (15.4%) 43 (14.8%) 21 (16.9%) Biliary 167 (40.2%) 118 (40.5%) 49 (39.5%) Hypertriglyceridemia 137 (33.0%) 91 (31.3%) 46 (37.1%) Others 47 (11.3%) 39 (13.4%) 8 (6.5%) BMI 25.0 (22.0, 28.0) 25.0 (22.0, 28.0) 24.0 (21.0, 27.0) 0.019 NEUT 86.5 (84.5, 88.6) 86.5 (84.6, 88.6) 86.2 (84.3, 88.4) 0.401 PLT 164 (142, 187) 161 (142, 185) 168 (145, 190) 0.165 NLR 5.3 (2.8, 9.0) 6.0 (2.9, 9.5) 5.0 (2.4, 8.0) 0.106 HCT 38.2 (34.3, 40.3) 38.3 (34.3, 40.4) 37.9 (34.4, 40.2) 0.447 D dimer 2.34 (1.38, 3.18) 2.35 (1.42, 3.21) 2.34 (1.22, 3.02) 0.340 CRP 133 (97, 169) 130 (96, 165) 135 (106, 176) 0.151 IL-6 55 (30, 82) 53 (30, 78) 59 (31, 87) 0.350 PCT 1.19 (0.72, 1.74) 1.23 (0.77, 1.70) 1.09 (0.68, 1.79) 0.671 SIRS 0.824 No 124 (29.9%) 86 (29.6%) 38 (30.6%) Yes 291 (70.1%) 205 (70.4%) 86 (69.4%) APACHEII score 9.00 (8.00, 11.00) 9.00 (8.00, 11.00) 9.00 (8.00, 10.25) 0.444 3.2 Diagnostic Factor Selection The initial model included several candidate predictive factors, such as gender, age, smoking history, alcohol consumption history, history of hypertension, history of diabetes, etiology of SAP, BMI, NEUT, PLT, HCT, D-dimer, CRP, IL-6, PCT, SIRS, APACHE II score, and NLR. Utilizing LASSO regression within the training set, we identified four variables with non-zero coefficients for further modeling. The LASSO regression feature selection process is illustrated in Fig. 1 , and the histogram of non-zero coefficient features from the LASSO regression is presented in Fig. 2 . We assessed the diagnostic value of individual predictors by plotting ROC curves, revealing that the AUC values for PCT, APACHE II, NLR, and HCT in relation to IAI diagnosis were all greater than 0.5. The ROC curves for individual variables in diagnosing IAI are depicted in Fig. 3 , with their corresponding AUC values detailed in Table 2 . Table 2 AUC Values and 95% CI for Variables Variable AUC 95% CI HCT 0.833 (0.781–0.886) PCT 0.675 (0.609–0.741) APACHE-II scores 0.744 (0.687-0.800) NLR 0.813 (0.759–0.868) AUC calculated using the model predictions; CI are estimated using DeLong's method. 3.3 Development of the Predictive Model We assessed the diagnostic utility of PCT, APACHE II, NLR, and HCT for IAI using multivariable logistic regression analysis on the training set. In SAP patients, it was discovered that PCT, APACHE II, NLR, and HCT were independent predictors of IAI. The findings of the multivariable logistic regression analysis with the training set are summarized in Table 3 . The risk calculation formula is as follows: Table 3 Results of Multivariate Logistic regression for Training Cohort Characteristic N Event N OR1 95% CI1 p-value HCT 291 123 0.73 0.65, 0.81 < 0.001 PCT 291 123 2.02 1.22, 3.35 0.006 APACHE-II 291 123 1.19 0.97, 1.45 0.088 NLR 291 123 0.90 0.82, 0.98 0.020 1OR = Odds Ratio, CI = Confidence Interval log[ \(\:\widehat{P}（1-\widehat{P}）\) ] = 9.718−0.319 (HCT) + 0.705 (PCT) −0.11 (NLR) + 0.174 (APACHE-II). Additionally, we developed a nomogram based on clinical risk factors to predict the risk of IAI in SAP patients (see Fig. 4 ). 3.4 Validation of the Predictive Model We assessed the diagnostic capabilities of the predictive model through ROC curve analysis in both the training and validation sets. In the training cohort, the model yielded an AUC of 0.853 (95% CI: 0.804–0.901), while the validation cohort had an AUC of 0.858 (95% CI: 0.786–0.930). The ROC curves, which illustrate the model's diagnostic performance in both cohorts, are displayed in Fig. 5 . Next, we generated calibration curves for the nomogram in both sets, revealing a strong relationship between the observed occurrences of IAI and the predicted values. These results indicate that the nomogram is reliable in the validation cohort, as the calibration curve closely approximates the ideal line. This reinforces the consistency between the predicted outcomes and the actual findings. Calibration curves for both training and validation sets can be found in Fig. 6 . Lastly, we performed a DCA to further evaluate the predictive model. This analysis underscores potential variations in model predictions as clinicians navigate critical thresholds while utilizing the nomogram for diagnostic and decision-making processes. The DCA results indicate that the nomogram offers considerable net clinical benefits, as illustrated by its curve. The DCA for the predictive model is presented in Fig. 7 . 4. Discussion The study successfully identified four independent predictors—PCT, APACHE II, NLR, and HCT—for diagnosing IAI in SAP patients through LASSO regression and multivariable logistic regression. The selection of these variables suggests a meaningful interplay between systemic inflammation, disease severity, and hematological parameters in predicting IAI risk. ROC curve analysis demonstrated that all four selected predictors had an AUC greater than 0.5, indicating their individual discriminatory power for IAI diagnosis. While this confirms their relevance, the relatively moderate AUCs highlight the necessity of a combined predictive model for improved accuracy. The constructed predictive model incorporating PCT, APACHE II, NLR, and HCT exhibited robust discrimination, with AUC values of 0.853 in the training set and 0.858 in the validation set. The high AUC values suggest that the model has strong diagnostic capability, reinforcing the importance of these variables in identifying IAI in SAP patients. The validation results further confirm the model’s generalizability, reducing concerns of overfitting. Calibration curves demonstrated a strong correlation between predicted and observed IAI occurrences, emphasizing the reliability of the model. The close alignment of the calibration curves with the ideal line suggests minimal bias and consistent performance across different datasets. DCA further supports the clinical applicability of the model by highlighting its net clinical benefits. DCA results suggest that using this nomogram in clinical decision-making may enhance diagnostic accuracy, optimize resource allocation, and ultimately improve patient outcomes. Given its high predictive accuracy, this model could be integrated into clinical workflows to stratify SAP patients based on their IAI risk, allowing for timely intervention. The model’s output can help prioritize high-risk patients for intensive monitoring or early empirical treatment, potentially reducing complications. By incorporating readily available clinical and laboratory parameters, the model aligns with precision medicine principles, offering individualized risk assessments. HCT is a straightforward, cost-effective, and readily available hematological parameter that has been employed to assess the severity of acute pancreatitis[ 11 ]. Nevertheless, its predictive efficacy has varied across studies. For example, a study involving 1,612 patients with acute pancreatitis found that elevated HCT at admission, alongside a 24-hour increase in BUN, were superior predictors of persistent organ failure and pancreatic necrosis when compared to other laboratory parameters and the APACHE II score [ 12 ]. Conversely, some research has indicated that admission HCT is not a robust predictor of acute pancreatitis severity [ 13 ]. PCT, a biomarker that rises in response to severe bacterial, fungal, or parasitic infections, as well as sepsis and multiple organ dysfunction syndrome, has been utilized for diagnosing and monitoring various clinically significant infections [ 14 ]. Serum PCT levels typically increase in bacterial and fungal infections, while remaining stable in cases of viral infections or non-infectious inflammation [ 15 , 16 ]. Numerous studies have highlighted PCT's effectiveness in predicting pancreatic infections and evaluating the prognosis of acute pancreatitis. Specifically, PCT can identify clinically significant pancreatic infections and overall prognosis early, with levels ≥ 3.8 ng/mL predicting major complications with 79% sensitivity and 93% specificity on days three and four post-symptom onset. For patients with infectious pancreatic necrosis and concurrent multiple organ dysfunction syndrome, significantly elevated PCT levels have been observed [ 7 ]. A meta-analysis confirmed that PCT is the most reliable predictor of infectious pancreatic necrosis in patients diagnosed with pancreatic necrosis [ 17 ]. When combined with other metrics, such as NLR and MCTSI, PCT significantly enhances the accuracy of predicting infectious pancreatic necrosis [ 18 , 19 ]. Additionally, prior research has established that the APACHE II score, in conjunction with C-reactive protein levels, correlates with the occurrence of secondary pancreatic infections [ 20 ]. In acute pancreatitis, inflammation initiates a cascade of proteolytic enzymes, pro-inflammatory cytokines and anaerobic bacteria, leading to tissue damage. The degree of neutrophil reduction is associated with favorable outcomes in acute pancreatitis, while elevated lymphocyte counts correlate with increased disease severity. NLR serves as a comprehensive inflammatory biomarker, offering rapid assessment of inflammatory progression through the ratio of neutrophil to lymphocyte counts, and has been identified as a useful predictor of acute pancreatitis severity [ 21 ]. In the context of diagnosing IAI in SAP patients, previous studies have similarly identified the relevance of HCT, PCT, APACHE II, and NLR. Zhu et al. utilized the LASSO method to identify five independent predictors of intra-abdominal infection in SAP patients, which included intra-abdominal pressure, severity of pancreatitis, APACHE II score, CTSI, and ICU admission. Their nomogram facilitates clinicians in estimating the risk of intra-abdominal infections and optimizing antibiotic prescriptions for patients with acute pancreatitis [ 22 ]. Furthermore, Sun et al. found that moderate platelet counts could be a positive prognostic factor for intra-abdominal infections in cases of acute pancreatitis [ 23 ]. Meanwhile, Qiu et al. highlighted that HCT is an independent risk factor for IAI in patients with SAP [ 24 – 26 ]. The nomogram presented in this study offers a simple and precise method for predicting the risk of IAI in acute pancreatitis patients, which may improve survival rates during hospitalization. Nonetheless, there are several limitations to this study that should be acknowledged. These limitations may impact the interpretation of the results and should be considered when applying the findings to clinical practice. First, it is a retrospective analysis, which may introduce selection and detection biases. Second, both the training and validation cohorts were derived from the same institution, lacking external validation from independent datasets. Third, parameters such as HCT, PCT, APACHE II, and NLR are dynamic and can exhibit considerable variability depending on the timing of measurements. Although we attempted to adjust for confounding variables through multivariable logistic regression analysis, residual confounding from unmeasured or unknown covariates cannot be entirely excluded. Future work should explore dynamic risk assessment using serial measurements of predictive markers. Additional biomarkers: incorporating emerging biomarkers (e.g., novel inflammatory or metabolic markers) might further enhance predictive accuracy. This predictive model offers a practical, accurate, and clinically relevant tool for assessing IAI risk in SAP patients[ 27 – 29 ]. Its high discrimination ability, strong calibration, and net clinical benefit highlight its potential to enhance decision-making and improve patient management. Future studies should focus on refining and externally validating the model to ensure its broader applicability in diverse clinical settings. Conclusion The clinical predictive model developed using HCT, PCT, APACHE II score, and NLR can accurately assess the risk of IAI in patients with SAP, thereby enhancing patient management strategies. Declarations Ethics approval and consent to participate This study was reviewed and approved by the Ethics Committee of Panzhihua Hospital of Integrated Chinese and Western Medicine (Panzhihua). All procedures involving human participants were conducted in accordance with the ethical standards of the institutional and/or national research committee and with the Declaration of Helsinki. Written informed consent to participate was obtained from all participants prior to their inclusion in the study. Consent for publication: Not applicable. Competing interests: The authors declare that they have no competing interests. Funding: Not applicable Author Contribution Rui Qi conceptualized the study and wrote the initial draft. He-bin Wang, Ren-ying Luo contributed to the literature review and analysis. He-bin Wang, Ren-ying Luo, Jing Li and Li Su revised and finalized the manuscript. All authors read and approved the final manuscript. Acknowledgement Not applicable Data Availability Data Availability StatementThe datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request. Due to the inclusion of sensitive patient information and institutional confidentiality regulations, the raw data are not publicly available. References Jha RK, Ma Q, Sha H, Palikhe M. Acute pancreatitis: a literature review. Med Sci Monit. 2009;15:Ra147–156. Boxhoorn L, Voermans RP, Bouwense SA, Bruno MJ, Verdonk RC, Boermeester MA, et al. Acute pancreatitis. Lancet. 2020;396:726–34. Qin X, Xiang S, Li W. Analysis of factors influencing onset and survival of patients with severe acute pancreatitis: A clinical study. Immun Inflamm Dis. 2024;12:e1267. Kurdia KC, Irrinki S, Chala AV, Bhalla A, Kochhar R, Yadav TD. Early intra-abdominal hypertension: A reliable bedside prognostic marker for severe acute pancreatitis. JGH Open. 2020;4:1091–5. Del Giudice M, Gangestad SW, Rethinking. IL-6 and CRP: Why they are more than inflammatory biomarkers, and why it matters. Brain Behav Immun. 2018;70:61–75. Zahorec R. Neutrophil-to-lymphocyte ratio, past, present and future perspectives. Bratisl Lek Listy. 2021;122:474–88. Rau BM, Kemppainen E, GUMBS A, Büchler MW, Wegscheider K, Bassi C et al. Early Assessment of Pancreatic Infections and Overall Prognosis in Severe Acute Pancreatitis by Procalcitonin (PCT): A Prospective International Multicenter Study. 2007;245:745–54. Zhu C, Zhang S, Zhong H, Gu Z-C, Kang Y, Pan C et al. Intra-abdominal infection in acute pancreatitis in eastern China: microbiological features and a prediction model. 2021;9. Akavipat P, Thinkhamrop J, Thinkhamrop B, Sriraj W, ACUTE PHYSIOLOGY, AND CHRONIC HEALTH EVALUATION (APACHE). II SCORE - THE CLINICAL PREDICTOR IN NEUROSURGICAL INTENSIVE CARE UNIT. Acta Clin Croat. 2019;58:50–6. Cong S, Ma T, Di X, Tian C, Zhao M, Wang K. Diagnostic value of neutrophil CD64, procalcitonin, and interleukin-6 in sepsis: a meta-analysis. BMC Infect Dis. 2021;21:384. Patel ML, Shyam R, Atam V, Bharti H, Sachan R, Parihar A. Clinical profile, etiology, and outcome of acute pancreatitis: Experience at a tertiary care center. Ann Afr Med. 2022;21:118–23. Koutroumpakis E, Wu BU, Bakker OJ, Dudekula A, Singh VK, Besselink MG, et al. Admission Hematocrit and Rise in Blood Urea Nitrogen at 24 h Outperform other Laboratory Markers in Predicting Persistent Organ Failure and Pancreatic Necrosis in Acute Pancreatitis: A Post Hoc Analysis of Three Large Prospective Databases. Am J Gastroenterol. 2015;110:1707–16. Bidari SK, Dhungana M, Panthi RC, Joshi KR, Shrestha R, Neupane D, et al. The Role of Hematocrit Levels in Diagnosing the Severity of Acute Pancreatitis: A Cross-Sectional Study at a Tertiary Care Center in Nepal. Cureus. 2024;16:e68527. van den Berg FF, Boermeester MA. Update on the management of acute pancreatitis. Curr Opin Crit Care. 2023;29:145–51. Watkins RR, Lemonovich TL. Serum procalcitonin in the diagnosis and management of intra-abdominal infections. Expert Rev Anti Infect Ther. 2012;10:197–205. Niu D, Huang Q, Yang F, Tian W, Li C, Ding L et al. Serum biomarkers to differentiate Gram-negative, Gram-positive and fungal infection in febrile patients. J Med Microbiol 2021;70. Yang CJ, Chen J, Phillips AR, Windsor JA, Petrov MS. Predictors of severe and critical acute pancreatitis: a systematic review. Dig Liver Dis. 2014;46:446–51. Zhu QY, Li RM, Zhu YP, Hao DL, Liu Y, Yu J, et al. Early Predictors of Infected Pancreatic Necrosis in Severe Acute Pancreatitis: Implications of Neutrophil to Lymphocyte Ratio, Blood Procalcitonin Concentration, and Modified CT Severity Index. Dig Dis. 2023;41:677–84. Paliwal A, Nawal CL, Meena PD, Singh A. A Study of Procalcitonin as an Early Predictor of Severity in Acute Pancreatitis. J Assoc Physicians India. 2022;70:11–2. Armengol-Carrasco M, Oller B, Escudero LE, Roca JB, Gener J, Rodríguez NAC et al. Specific Prognostic Factors for Secondary Pancreatic Infection in Severe Acute Pancreatitis. 1999;16:125–9. Kong W, He Y, Bao H, Zhang W, Wang X. Diagnostic Value of Neutrophil-Lymphocyte Ratio for Predicting the Severity of Acute Pancreatitis: A Meta-Analysis. Dis Markers. 2020;2020:9731854. Zhu C, Zhang S, Zhong H, Gu Z, Kang Y, Pan C, et al. Intra-abdominal infection in acute pancreatitis in eastern China: microbiological features and a prediction model. Ann Transl Med. 2021;9:477. Sun W, Huang J, Ni T, Wen Y, Menglu G, Yongguo W, et al. Moderate level platelet count might be a good prognostic indicator for intra-abdominal infection in acute pancreatitis: A retrospective cohort study of 1,363 patients. Front Med (Lausanne). 2022;9:1077076. Qiu Q, Nian YJ, Tang L, Guo Y, Wen LZ, Wang B, et al. Artificial neural networks accurately predict intra-abdominal infection in moderately severe and severe acute pancreatitis. J Dig Dis. 2019;20:486–94. Zhang FX, Li ZL, Zhang ZD, Ma XC. Prognostic value of red blood cell distribution width for severe acute pancreatitis. World J Gastroenterol. 2019;25:4739–48. Hu ZD, Lippi G, Montagnana M. Diagnostic and prognostic value of red blood cell distribution width in sepsis: A narrative review. Clin Biochem. 2020;77:1–6. Hong MK, Wang JH, Li MH, Su CC, Chu TY. Progesterone receptor isoform B in the stroma of squamous cervical carcinoma: An independent favorable prognostic marker correlating with hematogenous metastasis. Taiwan J Obstet Gynecol. 2024;63:853–60. Xu Y, Yang W, Qiu J, Zhou K, Yu G, Zhang Y, et al. Metabolic marker-assisted genomic prediction improves hybrid breeding. Plant Commun. 2025;6:101199. Xu R, Chi H, Zhang Q, Li X, Hong Z. Enhancing the diagnostic accuracy of colorectal cancer through the integration of serum tumor markers and hematological indicators with machine learning algorithms. Clin Transl Oncol. 2025;27:299–308. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6805714","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":468844498,"identity":"c2634360-3da8-4f47-9334-06b94166ae35","order_by":0,"name":"Rui Qi","email":"","orcid":"","institution":"Panzhihua Hospital of Integrated Chinese and Western Medicine","correspondingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Qi","suffix":""},{"id":468844501,"identity":"ab9b838a-e9f7-4975-9d3e-46fc81944a13","order_by":1,"name":"Hebin Wang","email":"","orcid":"","institution":"Panzhihua Hospital of Integrated Chinese and Western Medicine","correspondingAuthor":false,"prefix":"","firstName":"Hebin","middleName":"","lastName":"Wang","suffix":""},{"id":468844502,"identity":"b4d226e5-d037-44ab-a956-d2115a373764","order_by":2,"name":"Renying Luo","email":"","orcid":"","institution":"Panzhihua Hospital of Integrated Chinese and Western Medicine","correspondingAuthor":false,"prefix":"","firstName":"Renying","middleName":"","lastName":"Luo","suffix":""},{"id":468844503,"identity":"3c34a08d-989b-4452-8a79-1d2ae7b1bb9c","order_by":3,"name":"Jing Li","email":"","orcid":"","institution":"Panzhihua Hospital of Integrated Chinese and Western Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Li","suffix":""},{"id":468844504,"identity":"685b3a2e-f71d-418c-bcf7-cedc958f7d1e","order_by":4,"name":"Li Su","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvUlEQVRIiWNgGAWjYBACAxiDn5n58APStEi2s6UZ4FOJqcXgPI+CBFFazNl7D78ubDssb3yYB6i/xiaaoBbLnnNp1jPbDhtuO8x74AHDsbTcBoIOu5FjZszbdptx22G+BAPGhsNEaLn/BqzFfnMzj4EEcVpu8Bg/BmpJ3MBMrBbLnhwz5hnn/ifPOAwM5ARi/GLOfsb4c0FZmm1//+HDDz7U2BDWAgRs0nBmAhHKQYD5M5EKR8EoGAWjYKQCAMvDP2ElfiWaAAAAAElFTkSuQmCC","orcid":"","institution":"Panzhihua Hospital of Integrated Chinese and Western Medicine","correspondingAuthor":true,"prefix":"","firstName":"Li","middleName":"","lastName":"Su","suffix":""}],"badges":[],"createdAt":"2025-06-03 00:53:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6805714/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6805714/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84680586,"identity":"73cf792b-278b-4799-8a7a-caf0ed5d0eab","added_by":"auto","created_at":"2025-06-16 08:17:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":101349,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLASSO Regression Process Features\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA: Curve of regression coefficients versus Log(λ);\u003c/p\u003e\n\u003cp\u003eB: Mean squared error versus Log(λ) in LASSO regression.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-6805714/v1/946c99e9bf8c5c6e0fc25034.png"},{"id":84680585,"identity":"31ff2377-08ea-4f5b-a393-40e153b2e3c5","added_by":"auto","created_at":"2025-06-16 08:17:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":13584,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHistogram of Non-Zero Coefficients from LASSO Regression\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-6805714/v1/54a5726142b6c92bd8aabb35.png"},{"id":84680587,"identity":"17e44999-d7cd-4d86-a149-7c0abe296731","added_by":"auto","created_at":"2025-06-16 08:17:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":64844,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC Curves for Individual Variables Diagnosing IAI\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-6805714/v1/5db97ac80a125cc87556fb24.png"},{"id":84682191,"identity":"60b5c114-5de7-4b17-bf69-fd9e669c75cc","added_by":"auto","created_at":"2025-06-16 08:33:00","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":51592,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNomogram for Predicting IAI Risk in SAP Patients Based on Clinical Risk Factors\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-6805714/v1/02e50660fc24819c58aec3c7.png"},{"id":84683774,"identity":"c6a2add4-8244-4121-aef2-a7a9d88a12ba","added_by":"auto","created_at":"2025-06-16 08:41:00","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":41782,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC Curves for the Predictive Model in Training and Validation Cohorts\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-6805714/v1/a19fca54bf400620f412ea62.png"},{"id":84681503,"identity":"58873dab-b58c-4ed7-9db7-149f664ad7e7","added_by":"auto","created_at":"2025-06-16 08:25:00","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":76054,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCalibration Plots for the Nomogram in Training and Validation Cohorts\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-6805714/v1/7cc4a232eb084aa274c9a6c9.png"},{"id":84680589,"identity":"e884eb9a-ec4a-4692-966c-88f8d4959098","added_by":"auto","created_at":"2025-06-16 08:17:00","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":59739,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDecision Curve Analysis (DCA) for the Predictive Model\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-6805714/v1/315513769312f911825a952e.png"},{"id":103836441,"identity":"2dbcc2b8-cda4-40cd-8acf-e685cec329ca","added_by":"auto","created_at":"2026-03-03 13:58:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1403321,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6805714/v1/5ace96cb-04b8-4b25-a066-30718bdc675e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Clinical Prediction of Intra-Abdominal Infection in Patients with Severe Acute Pancreatitis: Logistic Regression and Nomogram","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAcute pancreatitis (AP) is a serious clinical condition marked by the sudden inflammation of the pancreas and adjacent tissues. This inflammatory response is primarily triggered by the self-activation of pancreatic enzymes and the autophagic processes occurring within pancreatic tissue. The leading causes of AP are often gallstones and excessive alcohol intake, making it a common yet significant form of acute abdominal pain that requires urgent medical attention [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Severe acute pancreatitis (SAP) stands out as a particularly important subset within this spectrum. It is distinguished by the existence of chronic organ failure that lasts longer than 48 hours and can impact one or more organ systems [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This designation underscores the severity of the condition and its potential to lead to life-threatening complications. Understanding the underlying mechanisms and risk factors associated with both AP and SAP is essential for effective diagnosis and management. SAP is an unpredictable and potentially life-threatening condition, with prognosis closely associated with complications such as multiple organ dysfunction, infected pancreatic necrosis, and issues related to surgical or endoscopic interventions [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOne of SAP's most common complications and a major cause of death for afflicted patients is intra-abdominal infection (IAI). When IAI develops in the setting of SAP, it can worsen multi-organ failure and cause intra-abdominal hypertension, which can lead to mortality rates that range from 47\u0026ndash;69% [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Therefore, early identification and prediction of risk factors for IAI in SAP patients are crucial for improving clinical management and patient outcomes.\u003c/p\u003e \u003cp\u003eCurrently, research on predictive indicators and models for IAI in SAP remains limited. C-reactive protein (CRP) and interleukin-6 (IL-6) are two well-known inflammatory markers connected to the emergence of infections in a variety of clinical contexts [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. A number of other biomarkers have also been suggested as possible predictors of infectious complications in SAP patients. Furthermore, in SAP patients, the platelet count (PLT), hematocrit (HCT), and neutrophil-to-lymphocyte ratio (NLR) are significant predictors of prognosis and inflammation [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, these markers generally exhibit low specificity for diagnosing intra-abdominal infections, as they are often elevated in both mild SAP (MSAP) and SAP [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNumerous characteristics can aid in the diagnosis of intra-abdominal infections associated with acute pancreatitis, according to research. Intra-abdominal pressure, the Computed Tomography Severity Index (CTSI), the APACHE II score, the overall severity of pancreatitis, and particular predictive models for intensive care unit admission are important markers [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. To assess the severity of SAP, a number of biomarkers and scoring methods are also essential. These include the neutrophil-to-high-density lipoprotein cholesterol ratio (NHR), procalcitonin (PCT), interleukin-6 (IL-6), platelet-to-lymphocyte ratio (PLR), and the Acute Physiology and Chronic Health Evaluation II (APACHE-II) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. These tools collectively enhance our ability to assess the condition and guide clinical decision-making. However, their predictive efficacy in SAP with concurrent intra-abdominal infection has not been extensively studied.\u003c/p\u003e \u003cp\u003eIn this investigation, we aim to evaluate the predictive value of CRP, IL-6, NLR, HCT, and PLT in assessing the likelihood of intra-abdominal infection in SAP patients. By employing logistic regression analysis and developing a nomogram, we aspire to provide clinicians with a practical tool for identifying high-risk patients, thereby facilitating timely interventions to enhance clinical outcomes.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Study Population\u003c/h2\u003e\n \u003cp\u003eThis research involved a retrospective examination of patients diagnosed with SAP at our institution from January 2020 to December 2023. We collected various clinical and demographic data, including age and gender, from the participants. All individuals provided informed consent, and the study protocol received approval from the hospital\u0026apos;s ethics committee.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Inclusion and Exclusion Criteria\u003c/h2\u003e\n \u003cp\u003e\u003cstrong\u003eInclusion criteria\u003c/strong\u003e:\u003c/p\u003e\n \u003col\u003e\n \u003cli\u003e\n \u003cp\u003eAge between 18 and 80 years;\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eDiagnosis of SAP according to established classification criteria for acute pancreatitis;\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eSymptom onset to hospital admission occurring within 72 hours;\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eAvailability of comprehensive clinical data, including CRP, IL-6, NLR, HCT, and PLT.\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ol\u003e\n \u003cp\u003e\u003cstrong\u003eExclusion criteria\u003c/strong\u003e:\u003c/p\u003e\n \u003col\u003e\n \u003cli\u003e\n \u003cp\u003ePatients with malignant tumors, chronic organ dysfunction, or a history of other severe comorbidities;\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003ePatients presenting with gastrointestinal bleeding, intestinal obstruction, or other significant gastrointestinal disorders;\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003ePregnant individuals, patients with autoimmune diseases, those with traumatic SAP, and patients with fulminant acute pancreatitis;\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003ePatients who underwent open abdominal surgery before or during hospitalization.\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ol\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Diagnostic Criteria\u003c/h2\u003e\n \u003cp\u003e\u003cstrong\u003eDiagnosis of IAI:\u0026nbsp;\u003c/strong\u003ePositive bacterial or fungal cultures from peritoneal fluid\u0026mdash;which was extracted from ascitic fluid or pancreatic and peripancreatic tissues\u0026mdash;were used to diagnose IAI.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSystemic Inflammatory Response Syndrome (SIRS)\u003c/strong\u003e: If at least two of the following circumstances exist, SIRS can be diagnosed: an increase in body temperature exceeding 38\u0026deg;C or a decrease below 36\u0026deg;C; pCO2 levels below 32 mmHg; a respiratory rate greater than 20 breaths per minute or indications of hyperventilation; a heart rate greater than 94 beats per minute; and a white blood cell count that is either above 12\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L or below 4\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L, or the detection of immature white blood cells exceeding 10%.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4 Observational Parameters\u003c/h2\u003e\n \u003cp\u003eWe performed a review of patient cases to collect demographic and clinical information, which included gender, age, history of hypertension, alcohol consumption history, smoking history, history of diabetes, etiological factors for SAP, BMI, NEUT, PLT, HCT, D-dimer levels, CRP, IL-6, PCT, the presence of SIRS, APACHE II score, and the NLR. Additionally, data regarding the diagnosis of IAI were collected.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e2.5 Statistical Analysis\u003c/h2\u003e\n \u003cp\u003eThe dataset underwent random division into training and validation subsets, following a 70:30 ratio for the purpose of variable comparison. Data that failed to meet normal distribution assumptions were expressed as median (interquartile range), while categorical variables were presented as counts and percentages (n%). For univariate analyses, we employed the chi-square test or Fisher\u0026apos;s exact test for categorical data, and the rank-sum test for continuous variables. Within the training cohort, we utilized LASSO logistic regression for multivariable analysis, enabling us to pinpoint independent risk factors and develop a nomogram for predicting intra-abdominal infections (IAI). The model\u0026apos;s performance was evaluated through receiver operating characteristic (ROC) curves and calibration curves, with the area under the ROC curve (AUC) ranging from 0.5 (no discrimination) to 1 (perfect discrimination). We also conducted decision curve analysis (DCA) to establish the threshold for net clinical benefit. A significance level of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was predetermined for statistical significance. All statistical analyses were executed using R software (version 4.2.2) alongside MSTATA software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.mstata.com\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Demographic and Clinical Characteristics\u003c/h2\u003e \u003cp\u003e For our prediction analysis, a total of 415 participants' baseline clinical and demographic characteristics were assessed. This sample was divided into a training cohort of 291 individuals and an internal testing cohort of 124 individuals. The study found that the clinical features and demographics of the training and testing cohorts were the same. These findings enhance our understanding of the baseline features of the study population and establish a foundation for subsequent predictive model analyses. (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\u003eDemographic and Clinical Characteristics of the Training and Test Cohorts\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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eCohort\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;415\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTraining Cohort\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;291\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInternal Test Cohort\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;124\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.235\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e236 (56.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e160 (55.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76 (61.3%)\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\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e179 (43.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e131 (45.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48 (38.7%)\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\u003e\u003cb\u003eAge, years old\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.0 (45.0, 51.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.0 (44.0, 51.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48.0 (45.0, 51.3)\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\u003e\u003cb\u003eHistory of smoking\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.237\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e298 (71.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e204 (70.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e94 (75.8%)\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\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e117 (28.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87 (29.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30 (24.2%)\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\u003e\u003cb\u003eAlcohol consumption\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.951\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e297 (71.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e208 (71.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89 (71.8%)\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\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e118 (28.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83 (28.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35 (28.2%)\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\u003e\u003cb\u003eHistory of hypertension\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.517\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e323 (77.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e229 (78.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e94 (75.8%)\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\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92 (22.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62 (21.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30 (24.2%)\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\u003e\u003cb\u003eHistory of diabetes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.689\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e371 (89.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e259 (89.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e112 (90.3%)\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\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44 (10.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (11.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (9.7%)\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\u003e\u003cb\u003eEtiology\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.180\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcoholic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64 (15.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43 (14.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (16.9%)\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\u003eBiliary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e167 (40.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e118 (40.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49 (39.5%)\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\u003eHypertriglyceridemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e137 (33.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91 (31.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46 (37.1%)\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\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47 (11.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (13.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (6.5%)\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\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.0 (22.0, 28.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.0 (22.0, 28.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.0 (21.0, 27.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNEUT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86.5 (84.5, 88.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86.5 (84.6, 88.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86.2 (84.3, 88.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.401\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePLT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e164 (142, 187)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e161 (142, 185)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e168 (145, 190)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNLR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.3 (2.8, 9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.0 (2.9, 9.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.0 (2.4, 8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHCT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.2 (34.3, 40.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.3 (34.3, 40.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.9 (34.4, 40.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.447\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eD dimer\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.34 (1.38, 3.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.35 (1.42, 3.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.34 (1.22, 3.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.340\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCRP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e133 (97, 169)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e130 (96, 165)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e135 (106, 176)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIL-6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55 (30, 82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53 (30, 78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59 (31, 87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.350\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePCT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.19 (0.72, 1.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.23 (0.77, 1.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.09 (0.68, 1.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.671\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSIRS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.824\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e124 (29.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86 (29.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38 (30.6%)\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\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e291 (70.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e205 (70.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86 (69.4%)\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\u003e\u003cb\u003eAPACHEII score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.00 (8.00, 11.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.00 (8.00, 11.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.00 (8.00, 10.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.444\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Diagnostic Factor Selection\u003c/h2\u003e \u003cp\u003eThe initial model included several candidate predictive factors, such as gender, age, smoking history, alcohol consumption history, history of hypertension, history of diabetes, etiology of SAP, BMI, NEUT, PLT, HCT, D-dimer, CRP, IL-6, PCT, SIRS, APACHE II score, and NLR. Utilizing LASSO regression within the training set, we identified four variables with non-zero coefficients for further modeling. The LASSO regression feature selection process is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, and the histogram of non-zero coefficient features from the LASSO regression is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe assessed the diagnostic value of individual predictors by plotting ROC curves, revealing that the AUC values for PCT, APACHE II, NLR, and HCT in relation to IAI diagnosis were all greater than 0.5. The ROC curves for individual variables in diagnosing IAI are depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, with their corresponding AUC values detailed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAUC Values and 95% CI for Variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.781\u0026ndash;0.886)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.609\u0026ndash;0.741)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPACHE-II scores\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.687-0.800)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.813\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.759\u0026ndash;0.868)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eAUC calculated using the model predictions;\u003c/p\u003e \u003cp\u003eCI are estimated using DeLong's method.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Development of the Predictive Model\u003c/h2\u003e \u003cp\u003eWe assessed the diagnostic utility of PCT, APACHE II, NLR, and HCT for IAI using multivariable logistic regression analysis on the training set. In SAP patients, it was discovered that PCT, APACHE II, NLR, and HCT were independent predictors of IAI. The findings of the multivariable logistic regression analysis with the training set are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The risk calculation formula is as follows:\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\u003eResults of Multivariate Logistic regression for Training Cohort\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEvent N\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95% CI1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.65, 0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.22, 3.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPACHE-II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.97, 1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.82, 0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e1OR\u0026thinsp;=\u0026thinsp;Odds Ratio, CI\u0026thinsp;=\u0026thinsp;Confidence Interval\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\u003elog[\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\widehat{P}（1-\\widehat{P}）\\)\u003c/span\u003e\u003c/span\u003e]\u0026thinsp;=\u0026thinsp;9.718\u0026minus;0.319 (HCT)\u0026thinsp;+\u0026thinsp;0.705 (PCT) \u0026minus;0.11 (NLR)\u0026thinsp;+\u0026thinsp;0.174 (APACHE-II).\u003c/p\u003e \u003cp\u003eAdditionally, we developed a nomogram based on clinical risk factors to predict the risk of IAI in SAP patients (see Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Validation of the Predictive Model\u003c/h2\u003e \u003cp\u003eWe assessed the diagnostic capabilities of the predictive model through ROC curve analysis in both the training and validation sets. In the training cohort, the model yielded an AUC of 0.853 (95% CI: 0.804\u0026ndash;0.901), while the validation cohort had an AUC of 0.858 (95% CI: 0.786\u0026ndash;0.930). The ROC curves, which illustrate the model's diagnostic performance in both cohorts, are displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNext, we generated calibration curves for the nomogram in both sets, revealing a strong relationship between the observed occurrences of IAI and the predicted values. These results indicate that the nomogram is reliable in the validation cohort, as the calibration curve closely approximates the ideal line. This reinforces the consistency between the predicted outcomes and the actual findings. Calibration curves for both training and validation sets can be found in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eLastly, we performed a DCA to further evaluate the predictive model. This analysis underscores potential variations in model predictions as clinicians navigate critical thresholds while utilizing the nomogram for diagnostic and decision-making processes. The DCA results indicate that the nomogram offers considerable net clinical benefits, as illustrated by its curve. The DCA for the predictive model is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe study successfully identified four independent predictors—PCT, APACHE II, NLR, and HCT—for diagnosing IAI in SAP patients through LASSO regression and multivariable logistic regression. The selection of these variables suggests a meaningful interplay between systemic inflammation, disease severity, and hematological parameters in predicting IAI risk. ROC curve analysis demonstrated that all four selected predictors had an AUC greater than 0.5, indicating their individual discriminatory power for IAI diagnosis. While this confirms their relevance, the relatively moderate AUCs highlight the necessity of a combined predictive model for improved accuracy.\u003c/p\u003e \u003cp\u003eThe constructed predictive model incorporating PCT, APACHE II, NLR, and HCT exhibited robust discrimination, with AUC values of 0.853 in the training set and 0.858 in the validation set. The high AUC values suggest that the model has strong diagnostic capability, reinforcing the importance of these variables in identifying IAI in SAP patients. The validation results further confirm the model’s generalizability, reducing concerns of overfitting. Calibration curves demonstrated a strong correlation between predicted and observed IAI occurrences, emphasizing the reliability of the model. The close alignment of the calibration curves with the ideal line suggests minimal bias and consistent performance across different datasets.\u003c/p\u003e \u003cp\u003eDCA further supports the clinical applicability of the model by highlighting its net clinical benefits. DCA results suggest that using this nomogram in clinical decision-making may enhance diagnostic accuracy, optimize resource allocation, and ultimately improve patient outcomes. Given its high predictive accuracy, this model could be integrated into clinical workflows to stratify SAP patients based on their IAI risk, allowing for timely intervention. The model’s output can help prioritize high-risk patients for intensive monitoring or early empirical treatment, potentially reducing complications. By incorporating readily available clinical and laboratory parameters, the model aligns with precision medicine principles, offering individualized risk assessments.\u003c/p\u003e \u003cp\u003eHCT is a straightforward, cost-effective, and readily available hematological parameter that has been employed to assess the severity of acute pancreatitis[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Nevertheless, its predictive efficacy has varied across studies. For example, a study involving 1,612 patients with acute pancreatitis found that elevated HCT at admission, alongside a 24-hour increase in BUN, were superior predictors of persistent organ failure and pancreatic necrosis when compared to other laboratory parameters and the APACHE II score [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Conversely, some research has indicated that admission HCT is not a robust predictor of acute pancreatitis severity [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePCT, a biomarker that rises in response to severe bacterial, fungal, or parasitic infections, as well as sepsis and multiple organ dysfunction syndrome, has been utilized for diagnosing and monitoring various clinically significant infections [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Serum PCT levels typically increase in bacterial and fungal infections, while remaining stable in cases of viral infections or non-infectious inflammation [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Numerous studies have highlighted PCT's effectiveness in predicting pancreatic infections and evaluating the prognosis of acute pancreatitis. Specifically, PCT can identify clinically significant pancreatic infections and overall prognosis early, with levels ≥ 3.8 ng/mL predicting major complications with 79% sensitivity and 93% specificity on days three and four post-symptom onset. For patients with infectious pancreatic necrosis and concurrent multiple organ dysfunction syndrome, significantly elevated PCT levels have been observed [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. A meta-analysis confirmed that PCT is the most reliable predictor of infectious pancreatic necrosis in patients diagnosed with pancreatic necrosis [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. When combined with other metrics, such as NLR and MCTSI, PCT significantly enhances the accuracy of predicting infectious pancreatic necrosis [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAdditionally, prior research has established that the APACHE II score, in conjunction with C-reactive protein levels, correlates with the occurrence of secondary pancreatic infections [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In acute pancreatitis, inflammation initiates a cascade of proteolytic enzymes, pro-inflammatory cytokines and anaerobic bacteria, leading to tissue damage. The degree of neutrophil reduction is associated with favorable outcomes in acute pancreatitis, while elevated lymphocyte counts correlate with increased disease severity. NLR serves as a comprehensive inflammatory biomarker, offering rapid assessment of inflammatory progression through the ratio of neutrophil to lymphocyte counts, and has been identified as a useful predictor of acute pancreatitis severity [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the context of diagnosing IAI in SAP patients, previous studies have similarly identified the relevance of HCT, PCT, APACHE II, and NLR. Zhu et al. utilized the LASSO method to identify five independent predictors of intra-abdominal infection in SAP patients, which included intra-abdominal pressure, severity of pancreatitis, APACHE II score, CTSI, and ICU admission. Their nomogram facilitates clinicians in estimating the risk of intra-abdominal infections and optimizing antibiotic prescriptions for patients with acute pancreatitis [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Furthermore, Sun et al. found that moderate platelet counts could be a positive prognostic factor for intra-abdominal infections in cases of acute pancreatitis [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Meanwhile, Qiu et al. highlighted that HCT is an independent risk factor for IAI in patients with SAP [\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e–\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The nomogram presented in this study offers a simple and precise method for predicting the risk of IAI in acute pancreatitis patients, which may improve survival rates during hospitalization.\u003c/p\u003e \u003cp\u003eNonetheless, there are several limitations to this study that should be acknowledged. These limitations may impact the interpretation of the results and should be considered when applying the findings to clinical practice. First, it is a retrospective analysis, which may introduce selection and detection biases. Second, both the training and validation cohorts were derived from the same institution, lacking external validation from independent datasets. Third, parameters such as HCT, PCT, APACHE II, and NLR are dynamic and can exhibit considerable variability depending on the timing of measurements. Although we attempted to adjust for confounding variables through multivariable logistic regression analysis, residual confounding from unmeasured or unknown covariates cannot be entirely excluded. Future work should explore dynamic risk assessment using serial measurements of predictive markers. Additional biomarkers: incorporating emerging biomarkers (e.g., novel inflammatory or metabolic markers) might further enhance predictive accuracy. This predictive model offers a practical, accurate, and clinically relevant tool for assessing IAI risk in SAP patients[\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e–\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Its high discrimination ability, strong calibration, and net clinical benefit highlight its potential to enhance decision-making and improve patient management. Future studies should focus on refining and externally validating the model to ensure its broader applicability in diverse clinical settings.\u003c/p\u003e "},{"header":"Conclusion","content":"\u003cp\u003eThe clinical predictive model developed using HCT, PCT, APACHE II score, and NLR can accurately assess the risk of IAI in patients with SAP, thereby enhancing patient management strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003e This study was reviewed and approved by the Ethics Committee of Panzhihua Hospital of Integrated Chinese and Western Medicine (Panzhihua). All procedures involving human participants were conducted in accordance with the ethical standards of the institutional and/or national research committee and with the Declaration of Helsinki. Written informed consent to participate was obtained from all participants prior to their inclusion in the study.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication:\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting interests:\u003c/strong\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eNot applicable\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eRui Qi conceptualized the study and wrote the initial draft. He-bin Wang, Ren-ying Luo contributed to the literature review and analysis. He-bin Wang, Ren-ying Luo, Jing Li and Li Su revised and finalized the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eNot applicable\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData Availability StatementThe datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request. Due to the inclusion of sensitive patient information and institutional confidentiality regulations, the raw data are not publicly available.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eJha RK, Ma Q, Sha H, Palikhe M. Acute pancreatitis: a literature review. Med Sci Monit. 2009;15:Ra147\u0026ndash;156.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoxhoorn L, Voermans RP, Bouwense SA, Bruno MJ, Verdonk RC, Boermeester MA, et al. Acute pancreatitis. Lancet. 2020;396:726\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQin X, Xiang S, Li W. Analysis of factors influencing onset and survival of patients with severe acute pancreatitis: A clinical study. Immun Inflamm Dis. 2024;12:e1267.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKurdia KC, Irrinki S, Chala AV, Bhalla A, Kochhar R, Yadav TD. Early intra-abdominal hypertension: A reliable bedside prognostic marker for severe acute pancreatitis. JGH Open. 2020;4:1091\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDel Giudice M, Gangestad SW, Rethinking. IL-6 and CRP: Why they are more than inflammatory biomarkers, and why it matters. Brain Behav Immun. 2018;70:61\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZahorec R. Neutrophil-to-lymphocyte ratio, past, present and future perspectives. Bratisl Lek Listy. 2021;122:474\u0026ndash;88.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRau BM, Kemppainen E, GUMBS A, B\u0026uuml;chler MW, Wegscheider K, Bassi C et al. Early Assessment of Pancreatic Infections and Overall Prognosis in Severe Acute Pancreatitis by Procalcitonin (PCT): A Prospective International Multicenter Study. 2007;245:745\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu C, Zhang S, Zhong H, Gu Z-C, Kang Y, Pan C et al. Intra-abdominal infection in acute pancreatitis in eastern China: microbiological features and a prediction model. 2021;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkavipat P, Thinkhamrop J, Thinkhamrop B, Sriraj W, ACUTE PHYSIOLOGY, AND CHRONIC HEALTH EVALUATION (APACHE). II SCORE - THE CLINICAL PREDICTOR IN NEUROSURGICAL INTENSIVE CARE UNIT. Acta Clin Croat. 2019;58:50\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCong S, Ma T, Di X, Tian C, Zhao M, Wang K. Diagnostic value of neutrophil CD64, procalcitonin, and interleukin-6 in sepsis: a meta-analysis. BMC Infect Dis. 2021;21:384.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatel ML, Shyam R, Atam V, Bharti H, Sachan R, Parihar A. Clinical profile, etiology, and outcome of acute pancreatitis: Experience at a tertiary care center. Ann Afr Med. 2022;21:118\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoutroumpakis E, Wu BU, Bakker OJ, Dudekula A, Singh VK, Besselink MG, et al. Admission Hematocrit and Rise in Blood Urea Nitrogen at 24 h Outperform other Laboratory Markers in Predicting Persistent Organ Failure and Pancreatic Necrosis in Acute Pancreatitis: A Post Hoc Analysis of Three Large Prospective Databases. Am J Gastroenterol. 2015;110:1707\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBidari SK, Dhungana M, Panthi RC, Joshi KR, Shrestha R, Neupane D, et al. The Role of Hematocrit Levels in Diagnosing the Severity of Acute Pancreatitis: A Cross-Sectional Study at a Tertiary Care Center in Nepal. Cureus. 2024;16:e68527.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan den Berg FF, Boermeester MA. Update on the management of acute pancreatitis. Curr Opin Crit Care. 2023;29:145\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWatkins RR, Lemonovich TL. Serum procalcitonin in the diagnosis and management of intra-abdominal infections. Expert Rev Anti Infect Ther. 2012;10:197\u0026ndash;205.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNiu D, Huang Q, Yang F, Tian W, Li C, Ding L et al. Serum biomarkers to differentiate Gram-negative, Gram-positive and fungal infection in febrile patients. J Med Microbiol 2021;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang CJ, Chen J, Phillips AR, Windsor JA, Petrov MS. Predictors of severe and critical acute pancreatitis: a systematic review. Dig Liver Dis. 2014;46:446\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu QY, Li RM, Zhu YP, Hao DL, Liu Y, Yu J, et al. Early Predictors of Infected Pancreatic Necrosis in Severe Acute Pancreatitis: Implications of Neutrophil to Lymphocyte Ratio, Blood Procalcitonin Concentration, and Modified CT Severity Index. Dig Dis. 2023;41:677\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePaliwal A, Nawal CL, Meena PD, Singh A. A Study of Procalcitonin as an Early Predictor of Severity in Acute Pancreatitis. J Assoc Physicians India. 2022;70:11\u0026ndash;2.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArmengol-Carrasco M, Oller B, Escudero LE, Roca JB, Gener J, Rodr\u0026iacute;guez NAC et al. Specific Prognostic Factors for Secondary Pancreatic Infection in Severe Acute Pancreatitis. 1999;16:125\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKong W, He Y, Bao H, Zhang W, Wang X. Diagnostic Value of Neutrophil-Lymphocyte Ratio for Predicting the Severity of Acute Pancreatitis: A Meta-Analysis. Dis Markers. 2020;2020:9731854.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu C, Zhang S, Zhong H, Gu Z, Kang Y, Pan C, et al. Intra-abdominal infection in acute pancreatitis in eastern China: microbiological features and a prediction model. Ann Transl Med. 2021;9:477.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun W, Huang J, Ni T, Wen Y, Menglu G, Yongguo W, et al. Moderate level platelet count might be a good prognostic indicator for intra-abdominal infection in acute pancreatitis: A retrospective cohort study of 1,363 patients. Front Med (Lausanne). 2022;9:1077076.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQiu Q, Nian YJ, Tang L, Guo Y, Wen LZ, Wang B, et al. Artificial neural networks accurately predict intra-abdominal infection in moderately severe and severe acute pancreatitis. J Dig Dis. 2019;20:486\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang FX, Li ZL, Zhang ZD, Ma XC. Prognostic value of red blood cell distribution width for severe acute pancreatitis. World J Gastroenterol. 2019;25:4739\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu ZD, Lippi G, Montagnana M. Diagnostic and prognostic value of red blood cell distribution width in sepsis: A narrative review. Clin Biochem. 2020;77:1\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHong MK, Wang JH, Li MH, Su CC, Chu TY. Progesterone receptor isoform B in the stroma of squamous cervical carcinoma: An independent favorable prognostic marker correlating with hematogenous metastasis. Taiwan J Obstet Gynecol. 2024;63:853\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu Y, Yang W, Qiu J, Zhou K, Yu G, Zhang Y, et al. Metabolic marker-assisted genomic prediction improves hybrid breeding. Plant Commun. 2025;6:101199.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu R, Chi H, Zhang Q, Li X, Hong Z. Enhancing the diagnostic accuracy of colorectal cancer through the integration of serum tumor markers and hematological indicators with machine learning algorithms. Clin Transl Oncol. 2025;27:299\u0026ndash;308.\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":"Acute pancreatitis, Severe acute pancreatitis, Intra-abdominal infection, Nomogram, decision curve analysis","lastPublishedDoi":"10.21203/rs.3.rs-6805714/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6805714/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e The goal is to create a clinical prediction model for individuals suffering from severe acute pancreatitis (SAP) who may have an intra-abdominal infection (IAI).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Patients with acute severe pancreatitis at our institution from January 2020 to December 2023 were retrospectively analyzed. The study population was carefully split into two groups: a training set and a validation set, using a 70:30 ratio. This division was designed to facilitate a thorough development and assessment of the predictive model. In the training set, we identified predictive features utilizing LASSO regression, a method known for its ability to enhance model accuracy by selecting the most relevant variables. Following this, we established both a prediction model and a nomogram through multivariable logistic regression analysis, allowing for a comprehensive assessment of the identified risk factors. To assess the diagnostic performance of our model, we utilized receiver operating characteristic (ROC) curves for both the training and validation cohorts. This analysis yielded valuable information regarding the sensitivity and specificity of our predictive model. Furthermore, we conducted decision curve analysis (DCA) and created calibration plots to enhance our evaluation of the model's accuracy and its practical relevance in clinical settings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e A total of 415 participants were included in the analysis, with baseline demographic and clinical characteristics documented. The cohort consisted of 291 individuals in the training set and 124 in the validation set. LASSO regression identified four significant predictors with non-zero coefficients (HCT, PCT, APACHE II, NLR) for subsequent modeling. The prediction model's AUC was 0.853 (95% CI: 0.804-0.901) in the training set and 0.858 (95% CI: 0.786-0.930) in the validation set, according to ROC curve analysis. The calibration curve closely resembled the ideal line, and calibration plots demonstrated a strong alignment between the observed instances of IAI and the predicted values. The DCA demonstrated substantial net benefits for clinical application.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e The clinical prediction model integrating HCT, PCT, APACHE II, and NLR effectively predicts the risk of IAI in patients with SAP, thereby enhancing patient management strategies.\u003c/p\u003e","manuscriptTitle":"Clinical Prediction of Intra-Abdominal Infection in Patients with Severe Acute Pancreatitis: Logistic Regression and Nomogram","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-16 08:16:55","doi":"10.21203/rs.3.rs-6805714/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":"c6e7bd7e-9d62-4a5e-86dd-afd1cd6b0325","owner":[],"postedDate":"June 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-03T13:57:04+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-16 08:16:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6805714","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6805714","identity":"rs-6805714","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}