Risk factor analysis and nomogram model construction for mortality in patients following colonic perforation surgery

Colonic perforation is a surgical emergency with high mortality due to rapid progression to septic shock. Early identification of high-risk patients is critical for improving outcomes, yet existing predictive tools are often complex and lack clinical practicality.

To identify the risk factors for postoperative mortality in patients with colonic perforation and develop and validate a predictive model.

A retrospective analysis was conducted on patients who underwent surgery for colonic perforation at the Department of Critical Medicine and General Surgery, Anqing Municipal Hospital, between January 2020 and July 2025. Patients were selected on the basis of inclusion and exclusion criteria and were classified into two groups according to postoperative outcomes: Death and survival. General demographics, laboratory results, and imaging data were collected and compared between the two groups. Univariate analysis was performed initially, followed by multivariate logistic regression analysis for variables with significant differences in the univariate analysis. A predictive model for postoperative mortality was constructed on the basis of the multivariate results. Internal validation was conducted using the bootstrap resampling method. The clinical optimal threshold was identified through decision curve analysis (DCA), and the operability of the dual cut-off strategy was demonstrated using a full-sample confusion matrix and a funnel-type pathway diagram. A nomogram was developed as a personalized prediction tool.

A total of 134 patients were included in the study, with 21 patients in the postoperative death group and 113 in the survival group, yielding a mortality rate of 15.6%. Moreover, no significant differences were found between the two groups concerning sex, history of hypertension, diabetes, cerebrovascular sequelae, cardiac history, haemoglobin level, albumin concentration, intraperitoneal free gas presence, intraperitoneal free fluid presence, perforation site, cause of perforation, operation time, or intraoperative blood loss volume. However, significant differences in age; American Society of Anaesthesiologists classification; Acute Physiology and Chronic Health Evaluation II (APACHE II) score; preoperative peripheral blood white blood cell (WBC) count; platelet count; serum total bilirubin level; serum creatinine level; lactate level; C-reactive protein level; procalcitonin level; the presence of portal venous gas (PVG); and time from onset to surgery (whether > 24 hours) were detected (all P < 0.05). Multivariate logistic regression analysis revealed that the APACHE II score [odds ratio (OR) = 1.24, 95% confidence interval (CI): 1.03-1.48], lactate level (OR = 2.40, 95%CI: 1.34-4.31), and presence of PVG (OR = 20.32, 95%CI: 1.89-218.45) were risk factors for postoperative mortality, whereas an elevated WBC count (OR = 0.68, 95%CI: 0.55-0.85) served as a protective factor. The constructed receiver operating characteristic curve revealed an area under the curve of 0.852 (95%CI: 0.791-0.913), with a Brier score = 0.072 and a slope ≈ 1. DCA demonstrated that within the 1%-40% threshold interval, the net benefit of the three model cut-off points surpassed those of the "full intervention" and "no intervention" scenarios. The full-sample pathway validation showed that the funnel-type process achieved 95.2% sensitivity for mortality screening with a 53% relative reduction in intensive care unit admissions.

The predictive model for postoperative mortality in patients with colonic perforation, which is based on four indicators (APACHE II score, lactate level, WBC count, and PVG presence), demonstrates strong predictive performance. The dual cut-off pathway (0.020 high-sensitivity screen + 0.121 resource-efficient confirmation) markedly reduced intensive care unit resource use while maintaining high sensitivity (95.2%). This investigation offers a replicable decision-making tool that can be integrated into information systems for future prospective studies.