ArticleEmergency medicine international2026
Development and Internal Validation of a Predictive Nomogram for Assessing Rhabdomyolysis Risk After Wasp Stings: A Multicenter Study in Sichuan, China.
Article in Emergency medicine international, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Development and Internal Validation of a Predictive Nomogram for Assessing Rhabdomyolysis Risk After Wasp Stings: A Multicenter Study in Sichuan, China.Emergency medicine international · 2026Article
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8 authors.
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Abstract
Wasp venom-induced rhabdomyolysis (RM) is a serious complication associated with poor clinical prognosis. However, predictive models for RM after wasp stings remain limited. This study aimed to develop and internally validate a clinical prediction model for RM in patients with wasp stings. This multicenter retrospective cohort study included 607 patients admitted to five tertiary hospitals in Sichuan Province for wasp stings between February 2015 and December 2020. Least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression were used to identify independent predictors of RM. A nomogram incorporating nine predictors was constructed. Model performance was assessed by internal validation and decision curve analysis (DCA). Among the 607 patients, 178 (29.3%) developed RM. Nine predictors were included in the final model: age, sting species, number of stings, tea-colored urine, white blood cell count (WBC), lactate dehydrogenase( LDH), total bilirubin (TBIL), activated partial thromboplastin time (APTT), and month of injury. The nomogram showed excellent discrimination, with an area under the receiver operating characteristic curve (AUC) of 0.949 (95% CI, 0.9319-0.9655) and a concordance index (C-index) of 0.948. DCA suggested good potential clinical utility of the model. We developed a nomogram for early prediction of RM risk after wasp stings using readily available clinical variables. This model may help identify high-risk patients at an early stage and support timely clinical management.
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