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...
Autores principales: | , , , , , , , , , , |
---|---|
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 |