Does it Fit? Testing a Novel Pediatric Surgical Risk Calculator in Africa

BACKGROUND: Low- and middle-income countries (LMICs), particularly Sub-Saharan Africa, experience a high burden of surgical disease and a disproportionately high postoperative morbidity and mortality. Although machine learning based pediatric surgical risk calculators (SRCs) have been developed to support clinical decision making, most are derived from high-income settings and may not generalize well to LMIC populations. We previously developed a pediatric surgical risk calculator using multinational LMIC data; however, its performance in independent African cohorts remains unknown. METHODS: We conducted an external validation study of a previously developed pediatric surgical risk calculator using prospective data from children undergoing surgery at four tertiary referral hospitals in Ethiopia and Nigeria between July 2024 and February 2025. Model performance was assessed by evaluating discrimination (area under the receiver operating characteristic curve) and calibration (slope and intercept). Predicted risk was stratified into low, intermediate, and high-risk categories to enhance clinical applicability. Model recalibration was performed to optimize performance in the external cohort. RESULTS: A total of 1,670 pediatric surgical patients were included from the four hospitals. In-hospital mortality was 6.8%. Mortality varied significantly by site, age, weight, sex, prematurity, preoperative sepsis, transfusion, supplemental oxygen use, antibiotic use, ASA class, type of surgery (emergent vs elective), need of ICU postoperatively and reoperation. The risk prediction model had good discrimination overall with an area under the curve (AUC) of 0.84, outperforming ASA classification alone (AUC = 0.77). Discrimination remained high for general pediatric surgery cases (AUC=0.83) and was highest for emergency cases (AUC = 0.89), but was low in neonates (AUC = 0.64). The model demonstrated systematic miscalibration, which improved following logistic calibration. Patients were stratified into meaningful risk categories, which reliably distinguishes between low-and-hgih risk patients (LR=0.1 and LR=5.4 respectively). However, 66.5% fall in the intermediate risk group which is not useful in decision making (LR=1.0). CONCLUSION: The pediatric surgical risk calculator demonstrated good performance in the African context, with improved clinical utility following recalibration and risk stratification. Reduced performance in neonates suggests the need to incorporate additional risk factors and potentially separate models for this subgroup. Future efforts should focus on supporting the use of the SRC in risk communication, clinical decision making, and resource prioritization.