The prognostic nutritional index and a multifactorial nomogram for predicting early stoma-related complications after
Xiao Lin1, Yafei Zhang2, Yuqin Wang3
1Department of Gastrointestinal Surgery, Affiliated Hospital of Xuzhou Medical University, Xuzhou City, Jiangsu Province, China; School of Medical and Health Engineering, Changzhou University, Changzhou City, Jiangsu Province, China.
Purpose:
To evaluate the predictive value of the Prognostic Nutritional Index (PNI) and multiple factors for early stoma-related complications in patients undergoing prophylactic ileostomy.
Methods:
A retrospective analysis was conducted in a cohort of 229 patients who underwent prophylactic ileostomy. The optimal cut-off value of the PNI was determined using receiver operating characteristic (ROC) curve analysis. Based on this threshold, patients were stratified into a low nutritional risk group (PNI >45.55, n = 122) and a high nutritional risk group (PNI ≤45.55, n = 107). Univariate and multivariate binary logistic regression analyses were conducted to identify factors associated with stoma-related complications. A predictive nomogram was subsequently developed and validated. An ROC curve was constructed for the nomogram model, and its predictive performance was compared with that of the PNI-alone model.
Results:
The incidence of early stoma-related complications following prophylactic ileostomy was 47.2 %. Multivariate analysis identified neoadjuvant therapy, absence of preoperative stoma site marking, stoma height < 2cm, and a low PNI as independent risk factors. The nomogram demonstrated excellent predictive performance, with an area under the curve (AUC) of 0.808 (95 % CI: 0.751-0.865), compared to the PNI-alone model (AUC = 0.748; P = 0.007). Internal validation confirmed good calibration, with the calibration curve showing close agreement with the ideal reference.
Conclusion:
The developed nomogram outperforms the PNI-alone model in predicting early stoma-related complications, demonstrating strong discrimination, calibration, and clinical utility. It enables rapid perioperative risk quantification, aids in the early identification of high-risk patients, and facilitates targeted interventions to improve outcomes.
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