Cargando…

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

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 Co...

Descripción completa

Detalles Bibliográficos
Autores principales: Kornblith, Aaron E., Singh, Chandan, Devlin, Gabriel, Addo, Newton, Streck, Christian J., Holmes, James F., Kuppermann, Nathan, Grupp-Phelan, Jacqueline, Fineman, Jeffrey, Butte, Atul J., Yu, Bin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9931266/
https://www.ncbi.nlm.nih.gov/pubmed/36812570
http://dx.doi.org/10.1371/journal.pdig.0000076
_version_ 1784889211471527936
author Kornblith, Aaron E.
Singh, Chandan
Devlin, Gabriel
Addo, Newton
Streck, Christian J.
Holmes, James F.
Kuppermann, Nathan
Grupp-Phelan, Jacqueline
Fineman, Jeffrey
Butte, Atul J.
Yu, Bin
author_facet Kornblith, Aaron E.
Singh, Chandan
Devlin, Gabriel
Addo, Newton
Streck, Christian J.
Holmes, James F.
Kuppermann, Nathan
Grupp-Phelan, Jacqueline
Fineman, Jeffrey
Butte, Atul J.
Yu, Bin
author_sort Kornblith, Aaron E.
collection PubMed
description 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 <14, and abdominal tenderness) were found to be stable. A CDI using only these three variables would achieve lower sensitivity than the original PECARN CDI with seven variables on internal PECARN validation but achieve the same performance on external PedSRC validation (sensitivity 96.8% and specificity 44%). 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%). CONCLUSION: The PCS data science framework vetted the PECARN CDI and its constituent predictor variables prior to external validation. We found that the 3 stable predictor variables represented all of the PECARN CDI’s predictive performance on independent external validation. The PCS framework offers a less resource-intensive method than prospective validation to vet CDIs before external validation. We also found that the PECARN CDI will generalize well to new populations and should be prospectively externally validated. The PCS framework offers a potential strategy to increase the chance of a successful (costly) prospective validation.
format Online
Article
Text
id pubmed-9931266
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-99312662023-02-16 Predictability and stability testing to assess clinical decision instrument performance for children after blunt torso trauma Kornblith, Aaron E. Singh, Chandan Devlin, Gabriel Addo, Newton Streck, Christian J. Holmes, James F. Kuppermann, Nathan Grupp-Phelan, Jacqueline Fineman, Jeffrey Butte, Atul J. Yu, Bin PLOS Digit Health Research Article 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 <14, and abdominal tenderness) were found to be stable. A CDI using only these three variables would achieve lower sensitivity than the original PECARN CDI with seven variables on internal PECARN validation but achieve the same performance on external PedSRC validation (sensitivity 96.8% and specificity 44%). 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%). CONCLUSION: The PCS data science framework vetted the PECARN CDI and its constituent predictor variables prior to external validation. We found that the 3 stable predictor variables represented all of the PECARN CDI’s predictive performance on independent external validation. The PCS framework offers a less resource-intensive method than prospective validation to vet CDIs before external validation. We also found that the PECARN CDI will generalize well to new populations and should be prospectively externally validated. The PCS framework offers a potential strategy to increase the chance of a successful (costly) prospective validation. Public Library of Science 2022-08-08 /pmc/articles/PMC9931266/ /pubmed/36812570 http://dx.doi.org/10.1371/journal.pdig.0000076 Text en © 2022 Kornblith et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Kornblith, Aaron E.
Singh, Chandan
Devlin, Gabriel
Addo, Newton
Streck, Christian J.
Holmes, James F.
Kuppermann, Nathan
Grupp-Phelan, Jacqueline
Fineman, Jeffrey
Butte, Atul J.
Yu, Bin
Predictability and stability testing to assess clinical decision instrument performance for children after blunt torso trauma
title Predictability and stability testing to assess clinical decision instrument performance for children after blunt torso trauma
title_full Predictability and stability testing to assess clinical decision instrument performance for children after blunt torso trauma
title_fullStr Predictability and stability testing to assess clinical decision instrument performance for children after blunt torso trauma
title_full_unstemmed Predictability and stability testing to assess clinical decision instrument performance for children after blunt torso trauma
title_short Predictability and stability testing to assess clinical decision instrument performance for children after blunt torso trauma
title_sort predictability and stability testing to assess clinical decision instrument performance for children after blunt torso trauma
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9931266/
https://www.ncbi.nlm.nih.gov/pubmed/36812570
http://dx.doi.org/10.1371/journal.pdig.0000076
work_keys_str_mv AT kornblithaarone predictabilityandstabilitytestingtoassessclinicaldecisioninstrumentperformanceforchildrenafterblunttorsotrauma
AT singhchandan predictabilityandstabilitytestingtoassessclinicaldecisioninstrumentperformanceforchildrenafterblunttorsotrauma
AT devlingabriel predictabilityandstabilitytestingtoassessclinicaldecisioninstrumentperformanceforchildrenafterblunttorsotrauma
AT addonewton predictabilityandstabilitytestingtoassessclinicaldecisioninstrumentperformanceforchildrenafterblunttorsotrauma
AT streckchristianj predictabilityandstabilitytestingtoassessclinicaldecisioninstrumentperformanceforchildrenafterblunttorsotrauma
AT holmesjamesf predictabilityandstabilitytestingtoassessclinicaldecisioninstrumentperformanceforchildrenafterblunttorsotrauma
AT kuppermannnathan predictabilityandstabilitytestingtoassessclinicaldecisioninstrumentperformanceforchildrenafterblunttorsotrauma
AT gruppphelanjacqueline predictabilityandstabilitytestingtoassessclinicaldecisioninstrumentperformanceforchildrenafterblunttorsotrauma
AT finemanjeffrey predictabilityandstabilitytestingtoassessclinicaldecisioninstrumentperformanceforchildrenafterblunttorsotrauma
AT butteatulj predictabilityandstabilitytestingtoassessclinicaldecisioninstrumentperformanceforchildrenafterblunttorsotrauma
AT yubin predictabilityandstabilitytestingtoassessclinicaldecisioninstrumentperformanceforchildrenafterblunttorsotrauma