Predictability and stability testing to assess clinical decision instrument performance for children after blunt torso trauma

Aaron E Kornblith, Chandan Singh, Gabriel Devlin, Newton Addo, Christian J Streck, James F Holmes, Nathan Kuppermann, Jacqueline Grupp-Phelan, Jeffrey Fineman, Atul J Butte and Bin Yu

PLOS Digital Health, 2022, vol. 1, issue 8, 1-16

Abstract: Objective: The 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 & methods: We 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 developed using the PECARN dataset. External validation was then measured on the PedSRC dataset. Results: Three predictor variables (abdominal wall trauma, Glasgow Coma Scale Score

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pdig00:0000076

DOI: 10.1371/journal.pdig.0000076

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