Related Experiment Videos
Development and Internal Validation of a Nomogram for Predicting Severe Complications After Snakebite: A
Jiahao Guan1, Zhenkai Zhao2, Fei Wang1,3
1Department of Clinical Laboratory, Shaanxi Provincial People's Hospital, Xi'an, Shaanxi, People's Republic of China.
Background:
Snakebite envenomation is a global public health concern with high mortality and morbidity. Given the highly heterogeneous clinical progression and the potential for rapid deterioration in severe cases, early risk stratification is crucial for improving outcomes. This study aimed to develop a nomogram based on simple laboratory indicators to assist primary care clinicians in rapidly identifying high-risk patients, thereby optimizing interventions and reducing mortality and healthcare burden.
Methods:
We conducted a retrospective analysis of 10,505 snakebite envenomation cases admitted to the Medical Laboratory Center of Shaanxi Provincial People's Hospital between March 2014 and October 2025. After applying strict eligibility criteria, 2762 patients were randomly assigned to a training cohort (n=2072) and a validation cohort (n=690) at a 3:1 ratio. LASSO regression and multivariate logistic regression were used to identify independent risk factors for severe complications, which were then incorporated into a nomogram. Model performance was assessed by the area under the receiver operating characteristic curve (AUC), calibration plots, and decision curve analysis (DCA).
Results:
Of the 2762 patients, 64 (2.32%) developed severe complications, most commonly acute myocardial injury, acute liver injury, coagulopathy, and multiple organ dysfunction; the majority presented with two or more. Time from bite to presentation (OR=1.017, 95% CI 1.005-1.027), white blood cell count (OR=1.116, 95% CI 1.043-1.190), and prothrombin time (OR=1.103, 95% CI 1.011-1.203) were identified as independent predictors. The nomogram demonstrated strong discrimination, with AUCs of 0.812 (95% CI 0.741-0.882) and 0.839 (95% CI 0.734-0.945) in the training and validation cohorts, respectively. Calibration was excellent (Hosmer-Lemeshow P>0.05), and decision curve analysis confirmed net clinical benefit.
Conclusion:
This readily accessible nomogram, based on time to presentation, WBC, and PT, demonstrates strong predictive performance, high operability, and clinical accessibility, facilitating early identification of high-risk patients and optimization of treatment decisions.