article · Tropical Medicine and Infectious Disease
A study of 1022 snakebite cases treated between January and June 2024 at the Snakebite Treatment and Research Hospital in Kaltungo, northeastern Nigeria, evaluated the use of machine learning to predict clinical outcomes. The majority of patients were adult males. Key factors linked to severe outcomes, including amputation, tissue debridement, and death, included age, sex, and delays of over four hours in reaching hospital care. Logistic regression, Random Forest, and XGBoost models were evaluated to forecast these outcomes using patient characteristics. While all tested models demonstrated high positive predictive value, the XGBoost model using all available features proved optimal by also maintaining relatively high sensitivity. Such decision-support tools could assist clinicians in triaging high-risk patients and managing scarce antivenom supplies in resource-constrained environments.
Snakebite envenoming is a critical emergency in sub-Saharan Africa, where therapeutic resources like antivenom are often scarce. Identifying which patients face the greatest risk of amputation or death allows medical staff to intervene rapidly and allocate life-saving supplies more effectively, improving patient survival and clinical management in resource-limited rural hospitals.
This work could enable machine-learning-driven clinical decision-support software for healthcare providers in resource-limited treatment centres to triage snakebite patients and direct antivenom usage. The technology is at an early research stage, having been evaluated retrospectively on single-centre hospital data, with further research needed to build better predictive models prior to practical clinical integration.
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The Snakebite Treatment and Research Hospital (SBTRH) is a leading centre for snakebite envenoming care and research in sub-Saharan Africa, treating over 2500 snakebite patients annually. Despite routine data collection, routine analyses are seldom conducted to identify trends or guide clinical practices. This study retrospectively analyzes 1022 snakebite cases at SBTRH from January to June 2024. Most patients were adults (62%) and were predominantly male (72%). Key factors such as age, sex, and time between bite and hospital presentation were associated with outcomes, including recovery, amputation, debridement, and death. Adult males who took more than four hours to arrive to hospital were identified as a high-risk group for poor outcomes. Using patient characteristics, an XGBoost model was developed and was compared to Random Forest and logistic regression models. In general, all models had high positive predictive value and low sensitivity, meaning that if they predicted a patient to experience amputation, debridement, or death, that patient almost always actually experienced amputation, debridement, or death; however, most models rarely made this prediction. The XGBoost model with all features was optimal, given that it had both a high positive predictive value and relatively high sensitivity. This may be of significance to resource-limited settings like SBTRH, where antivenoms can be scarce; however, more research is needed to build better predictive models. These findings underscore the need for targeted interventions for high-risk groups, and further research and integration of machine-learning-driven decision support tools in low-resource-limited clinical settings.
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DOI: 10.3390/tropicalmed10040103
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