Predictability and Stability Testing to Assess Clinical Decision Instrument Performance for Children After Blunt Torso Trauma

Abstract

ObjectiveThe Pediatric Emergency Care Applied Research Network (PECARN) has developed a clinical-decision instrument (CDI) to identify children at very low risk of intra-abdominal injury. However, the CDI has not been externally validated. We sought to vet the PECARN CDI with the Predictability Computability Stability (PCS) data science framework, potentially increasing its chance of a successful external validation.Materials & MethodsWe performed a secondary analysis of two prospectively collected datasets: PECARN (12,044 children from 20 emergency departments) and an independent external validation dataset from the Pediatric Surgical Research Collaborative (PedSRC; 2,188 children from 14 emergency departments). We used PCS to reanalyze the original PECARN CDI along with new interpretable PCS CDIs we developed using the PECARN dataset. External validation was then measured on the PedSRC dataset.ResultsThree predictor variables (abdominal wall trauma, Glasgow Coma Scale Score <14, and abdominal tenderness) were found to be stable. Using only these variables, we developed a PCS CDI which had a lower sensitivity than the original PECARN CDI on internal PECARN validation but performed the same on external PedSRC validation (sensitivity 96.8% and specificity 44%).ConclusionThe PCS data science framework vetted the PECARN CDI and its constituent predictor variables prior to external validation. In this case, the PECARN CDI with 7 predictors, and our PCS-based CDI with 3 stable predictors, had identical performance on independent external validation. This suggests that both CDIs will generalize well to new populations, offering a potential strategy to increase the chance of a successful (costly) prospective validation.