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

Author

Listed:
  • 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
  • Bin Yu

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

Suggested Citation

  • 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 & Bin Yu, 2022. "Predictability and stability testing to assess clinical decision instrument performance for children after blunt torso trauma," PLOS Digital Health, Public Library of Science, vol. 1(8), pages 1-16, August.
    • Handle: RePEc:plo:pdig00:0000076
    DOI: 10.1371/journal.pdig.0000076
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    References listed on IDEAS

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    1. Raaz Dwivedi & Yan Shuo Tan & Briton Park & Mian Wei & Kevin Horgan & David Madigan & Bin Yu, 2020. "Stable Discovery of Interpretable Subgroups via Calibration in Causal Studies," International Statistical Review, International Statistical Institute, vol. 88(S1), pages 135-178, December.
    2. Esra Zihni & Vince Istvan Madai & Michelle Livne & Ivana Galinovic & Ahmed A Khalil & Jochen B Fiebach & Dietmar Frey, 2020. "Opening the black box of artificial intelligence for clinical decision support: A study predicting stroke outcome," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-15, April.
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