Objective To investigate the influencing factors for systemic complications secondary to infected pancreatic necrosis (IPN) in patients, and to construct a predictive model and evaluate its predictive performance.
Methods The retrospective cohort study was conducted. The clinical data of 296 patients with IPN who were admitted to Xuanwu Hospital of Capital Medical University and The First Affiliated Hospital of Harbin Medical University from January 2020 to December 2024 were collected. There were 222 males and 74 females, aged 41(34,51) years. The 210 patients who were admitted to Xuanwu Hospital of Capital Medical University were selected as the training set, and the 86 patients who were admitted to The First Affiliated Hospital of Harbin Medical University were selected as the validation set. The training set was used to construct the predictive model, and the validation set was used to validate the predictive model. Observation indicators: (1) analysis of influencing factors for systemic complications secondary to IPN in patients; (2) construction and validation of a prediction model for systemic complications secondary to IPN in patients. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann‑Whitney U test. Comparison of count data between groups was conducted using the chi‑square test or Fisher exact probability. Univariate and multivariate analyses were performed using the Logistic regression model. The accuracy and consistency of the predictive model were evaluated using the area under the receiver operating characteristic curve (AUC) and calibration curves. Decision curve analysis was used to evaluate the net clinical benefit of the predictive model.
Results (1) Analysis of influencing factors for systemic complications secondary to IPN in patients: results of multivariate analysis showed that gender, cholesterol level, white blood cell count, computed tomography severity index (CTSI), and body temperature were independent influencing factors for systemic complications secondary to IPN in patients of the training set odds ratio=0.389, 1.227, 1.144, 4.608, 2.694, 95% confidence interval (CI) as 0.167-0.908, 1.078-1.396, 1.006-1.302, 2.226-9.538, 1.058-6.861, P<0.05. (2) Construction and validation of a prediction model for systemic complications secondary to IPN in patients: based on the results of multivariate analysis, a nomogram model predicting for systemic complications secondary to IPN in patients was constructed. The nomogram model for the training set had an AUC of 0.849 (95%CI as 0.797-0.900), with a sensitivity of 80.2% and a specificity of 73.8%. The nomogram model for validation set had an AUC of 0.809 (95%CI as 0.717-0.901), with a sensitivity of 78.6% and a specificity of 75.0%. The calibration curves of the nomogram model in both the training and validation sets showed good agreement with the observed curve, indicating good model fit. Decision curve analysis demonstrated that the nomogram model had a significant net benefit in both the training and validation sets.
Conclusions Gender, cholesterol level, white blood cell count, CTSI, and body temperature are independent influencing factors for systemic complications secondary to IPN in patients. The nomogram model constructed based on these factors exhibits good predictive performance and high clinical application value.
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