aceDoes 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; h…
In a 1,670-child validation cohort, the machine learning calculator predicted in-hospital mortality better than ASA class alone and separated low-risk from high-risk children.
The calculator performed poorly in neonates and left most patients in an intermediate risk group that gives no useful guidance for decisions.
Performance was low in neonates, the model was systematically miscalibrated until recalibrated, and about two thirds of patients fell in an intermediate risk group that did not help decision making.
Evidence
- Peer-reviewedJournal of Pediatric Surgery2026-10-08
How should this claim be treated?
Truvace Impact Record TRV-2026-1631, v1: “Does it Fit? Testing a Novel Pediatric Surgical Risk Calculator in Africa.” Truvace, 2026-10-16. /record/TRV-2026-1631 (accessed at citation time). sha256 0d8845843ab374cf…
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