Abstract
Acute kidney injury (AKI) following multiple wasp stings is a severe complication with potentially poor outcomes. Despite extensive research on AKI's risk factors, predictive models for wasp sting-related AKI are limited. This study aims to develop and validate a machine learning-based clinical prediction model for AKI in individuals with wasp stings. We retrospectively analyzed clinical data from 214 patients with wasp sting injuries. Among these patients, 34.6% (74/214) developed AKI. Using least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression analysis, the number of stings, presence of gross hematuria, systemic inflammatory response index (SIRI), and platelet count were identified as prognostic factors. A nomogram was constructed and evaluated for its predictive accuracy, showing an area under the curve (AUC) of 0.757 (95% CI 0.711 to 0.804) and a concordance index (C-index) of 0.75. The model's performance was assessed through internal validation, leave-one-out cross-validation, net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA). Validation confirmed the model's reliability and superior discrimination ability over existing models, as demonstrated by NRI, IDI, and DCA. This nomogram accurately predicts the risk of AKI in wasp sting patients, facilitating early identification and management of those at risk.