Using the Field Artificial Intelligence Triage (FAIT) tool to predict hospital critical care resource utilization in patients with truncal gunshot wounds.
AI
Gunshot
Machine learning
Resource utilization
Triage
Journal
American journal of surgery
ISSN: 1879-1883
Titre abrégé: Am J Surg
Pays: United States
ID NLM: 0370473
Informations de publication
Date de publication:
08 2023
08 2023
Historique:
received:
25
01
2023
revised:
10
03
2023
accepted:
16
03
2023
medline:
4
9
2023
pubmed:
23
3
2023
entrez:
22
3
2023
Statut:
ppublish
Résumé
Tiered trauma triage systems have resulted in a significant mortality reduction, but models have remained unchanged. The aim of this study was to develop and test an artificial intelligence algorithm to predict critical care resource utilization. We queried the ACS-TQIP 2017-18 database for truncal gunshot wounds(GSW). An information-aware deep neural network (DNN-IAD) model was trained to predict ICU admission and need for mechanical ventilation (MV). Input variables included demographics, comorbidities, vital signs, and external injuries. The model's performance was assessed using the area under receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC). For the ICU admission analysis, we included 39,916 patients. For the MV need analysis, 39,591 patients were included. Median (IQR) age was 27 (22,36). AUROC and AUPRC for predicting ICU need were 84.8 ± 0.5 and 75.4 ± 0.5, and the AUROC and AUPRC for MV need were 86.8 ± 0.5 and 72.5 ± 0.6. Our model predicts hospital utilization outcomes in patients with truncal GSW with high accuracy, allowing early resource mobilization and rapid triage decisions in hospitals with capacity issues and austere environments.
Sections du résumé
BACKGROUND
Tiered trauma triage systems have resulted in a significant mortality reduction, but models have remained unchanged. The aim of this study was to develop and test an artificial intelligence algorithm to predict critical care resource utilization.
METHODS
We queried the ACS-TQIP 2017-18 database for truncal gunshot wounds(GSW). An information-aware deep neural network (DNN-IAD) model was trained to predict ICU admission and need for mechanical ventilation (MV). Input variables included demographics, comorbidities, vital signs, and external injuries. The model's performance was assessed using the area under receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC).
RESULTS
For the ICU admission analysis, we included 39,916 patients. For the MV need analysis, 39,591 patients were included. Median (IQR) age was 27 (22,36). AUROC and AUPRC for predicting ICU need were 84.8 ± 0.5 and 75.4 ± 0.5, and the AUROC and AUPRC for MV need were 86.8 ± 0.5 and 72.5 ± 0.6.
CONCLUSIONS
Our model predicts hospital utilization outcomes in patients with truncal GSW with high accuracy, allowing early resource mobilization and rapid triage decisions in hospitals with capacity issues and austere environments.
Identifiants
pubmed: 36948898
pii: S0002-9610(23)00115-0
doi: 10.1016/j.amjsurg.2023.03.019
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
245-250Informations de copyright
Copyright © 2023 Elsevier Inc. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of competing interest The authors have no conflict of interest to report. This material is based upon work supported by the Under Secretary of Defense for Research and Engineering under Air Force contract number FA8702-15-D-0001. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Under Secretary of Defense for Research and Engineering .