eGastroenterology · 2024
Early clinical predictors of infected pancreatic necrosis: a multicentre cohort study.
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Song Kai, He Wenhua, Wu Zuoyan, Meng Jie, Tian Wei, Zheng Shicheng, Mu Dong, Wang Ruifeng, Chen Hongda, Zhu Yin, Wu Dong
Аннотация
BACKGROUND: Infected pancreatic necrosis (IPN) exacerbates complications in patients with acute pancreatitis (AP), increasing mortality rates if not treated promptly. We aimed to evaluate the predictive value of clinical characteristics within 24 hours of admission for IPN prediction.
METHODS: We conducted a retrospective, multicentre cohort study including 3005 patients with AP from eight hospitals in China. Clinical variables collected within 24 hours after admission were analysed using least absolute shrinkage and selection operator regression (10 cross-validations) for variable selection, followed by multivariate logistic regression to develop an IPN prediction model. Internal cross-validation of the development set and validation of the validation set were performed to ensure robustness. Decision curve analysis was used to evaluate its clinical utility.
RESULTS: IPN occurred in 176 patients (176/3005, 5.9%). The final model included temperature, respiratory rate, plasma calcium ion concentration, serum urea nitrogen and serum glucose. The area under the receiver operating characteristics curve (AUC) was 0.85 (95% CI 0.81 to 0.89), outperforming widely used severity scoring systems. The model demonstrated robust performance on the internal validation cohort (mean AUC: 0.84) and external validation cohort (AUC: 0.82, 95% CI 0. 77 to 0.87).
CONCLUSION: We developed a simple and robust model for predicting IPN in patients with AP, demonstrating strong predictive performance and clinical utility.
METHODS: We conducted a retrospective, multicentre cohort study including 3005 patients with AP from eight hospitals in China. Clinical variables collected within 24 hours after admission were analysed using least absolute shrinkage and selection operator regression (10 cross-validations) for variable selection, followed by multivariate logistic regression to develop an IPN prediction model. Internal cross-validation of the development set and validation of the validation set were performed to ensure robustness. Decision curve analysis was used to evaluate its clinical utility.
RESULTS: IPN occurred in 176 patients (176/3005, 5.9%). The final model included temperature, respiratory rate, plasma calcium ion concentration, serum urea nitrogen and serum glucose. The area under the receiver operating characteristics curve (AUC) was 0.85 (95% CI 0.81 to 0.89), outperforming widely used severity scoring systems. The model demonstrated robust performance on the internal validation cohort (mean AUC: 0.84) and external validation cohort (AUC: 0.82, 95% CI 0. 77 to 0.87).
CONCLUSION: We developed a simple and robust model for predicting IPN in patients with AP, demonstrating strong predictive performance and clinical utility.