Predicting Unplanned Intensive Care Unit Admission for Trauma Patients: The CRASH Score.


Journal

The Journal of surgical research
ISSN: 1095-8673
Titre abrégé: J Surg Res
Pays: United States
ID NLM: 0376340

Informations de publication

Date de publication:
11 2022
Historique:
received: 27 02 2022
revised: 10 05 2022
accepted: 11 06 2022
pubmed: 18 7 2022
medline: 24 9 2022
entrez: 17 7 2022
Statut: ppublish

Résumé

Unplanned transfer of trauma patients to the intensive care unit (ICU) carries an associated increase in mortality, hospital length of stay, and cost. Trauma teams need to determine which patients necessitate ICU admission on presentation rather than waiting to intervene on deteriorating patients. This study sought to develop a novel Clinical Risk of Acute ICU Status during Hospitalization (CRASH) score to predict the risk of unplanned ICU admission. The 2017 Trauma Quality Improvement Program database was queried for patients admitted to nonICU locations. The group was randomly divided into two equal sets (derivation and validation). Multiple logistic regression models were created to determine the risk of unplanned ICU admission using patient demographics, comorbidities, and injuries. The weighted average and relative impact of each independent predictor were used to derive a CRASH score. The score was validated using area under the curve. A total of 624,786 trauma patients were admitted to nonICU locations. From 312,393 patients in the derivation-set, 3769 (1.2%) had an unplanned ICU admission. A total of 24 independent predictors of unplanned ICU admission were identified and the CRASH score was derived with scores ranging from 0 to 32. The unplanned ICU admission rate increased steadily from 0.1% to 3.9% then 12.9% at scores of 0, 6, and 14, respectively. The area under the curve for was 0.78. The CRASH score is a novel and validated tool to predict unplanned ICU admission for trauma patients. This tool may help providers admit patients to the appropriate level of care or identify patients at-risk for decompensation.

Identifiants

pubmed: 35842975
pii: S0022-4804(22)00403-6
doi: 10.1016/j.jss.2022.06.039
pii:
doi:

Types de publication

Journal Article Randomized Controlled Trial

Langues

eng

Sous-ensembles de citation

IM

Pagination

505-510

Informations de copyright

Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.

Auteurs

Louis Prado (L)

Department of Surgery, University of California, Irvine, Orange, California.

Stephen Stopenski (S)

Department of Surgery, University of California, Irvine, Orange, California.

Areg Grigorian (A)

Department of Surgery, University of Southern California, Los Angeles, California.

Sebastian Schubl (S)

Department of Surgery, University of California, Irvine, Orange, California.

Cristobal Barrios (C)

Department of Surgery, University of California, Irvine, Orange, California.

Catherine Kuza (C)

Department of Anesthesia, University of Southern California, Los Angeles, California.

Kazuhide Matsushima (K)

Department of Surgery, University of Southern California, Los Angeles, California.

Damon Clark (D)

Department of Surgery, University of Southern California, Los Angeles, California.

Jeffry Nahmias (J)

Department of Surgery, University of California, Irvine, Orange, California. Electronic address: jnahmias@hs.uci.edu.

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