PMID- 36115148
OWN - NLM
STAT- MEDLINE
DCOM- 20221123
LR  - 20230130
IS  - 1095-8673 (Electronic)
IS  - 0022-4804 (Linking)
VI  - 281
DP  - 2023 Jan
TI  - Deterioration Index in Critically Injured Patients: A Feasibility Analysis.
PG  - 45-51
LID - S0022-4804(22)00524-8 [pii]
LID - 10.1016/j.jss.2022.08.019 [doi]
AB  - INTRODUCTION: Continuous prediction surveillance modeling is an emerging tool 
      giving dynamic insight into conditions with potential mitigation of adverse 
      events (AEs) and failure to rescue. The Epic electronic medical record contains a 
      Deterioration Index (DI) algorithm that generates a prediction score every 15 min 
      using objective data. Previous validation studies show rapid increases in DI 
      score (>/=14) predict a worse prognosis. The aim of this study was to demonstrate 
      the utility of DI scores in the trauma intensive care unit (ICU) population. 
      METHODS: A prospective, single-center study of trauma ICU patients in a Level 1 
      trauma center was conducted during a 3-mo period. Charts were reviewed every 24 h 
      for minimum and maximum DI score, largest score change (Delta), and AE. Patients were 
      grouped as low risk (DeltaDI <14) or high risk (DeltaDI >/=14). RESULTS: A total of 224 
      patients were evaluated. High-risk patients were more likely to experience AEs 
      (69.0% versus 47.6%, P = 0.002). No patients with DI scores <30 were readmitted 
      to the ICU after being stepped down to the floor. Patients that were readmitted 
      and subsequently died all had DI scores of >/=60 when first stepped down from the 
      ICU. CONCLUSIONS: This study demonstrates DI scores predict decompensation risk 
      in the surgical ICU population, which may otherwise go unnoticed in real time. 
      This can identify patients at risk of AE when transferred to the floor. Using the 
      DI model could alert providers to increase surveillance in high-risk patients to 
      mitigate unplanned returns to the ICU and failure to rescue.
CI  - Copyright (c) 2022 Elsevier Inc. All rights reserved.
FAU - Wu, Rebecca
AU  - Wu R
AD  - Department of Surgery, Houston Methodist Hospital, Houston, Texas. Electronic 
      address: rewu@houstonmethodist.org.
FAU - Smith, Alison
AU  - Smith A
AD  - Department of Surgery, Louisiana State University, New Orleans, Louisiana.
FAU - Brown, Tommy
AU  - Brown T
AD  - Department of Surgery, Louisiana State University, New Orleans, Louisiana.
FAU - Hunt, John P
AU  - Hunt JP
AD  - Department of Surgery, Louisiana State University, New Orleans, Louisiana.
FAU - Greiffenstein, Patrick
AU  - Greiffenstein P
AD  - Department of Surgery, Louisiana State University, New Orleans, Louisiana.
FAU - Taghavi, Sharven
AU  - Taghavi S
AD  - Department of Surgery, Tulane University, New Orleans, Louisiana.
FAU - Tatum, Danielle
AU  - Tatum D
AD  - Department of Surgery, Tulane University, New Orleans, Louisiana.
FAU - Jackson-Weaver, Olan
AU  - Jackson-Weaver O
AD  - Department of Surgery, Tulane University, New Orleans, Louisiana.
FAU - Duchesne, Juan
AU  - Duchesne J
AD  - Department of Surgery, Tulane University, New Orleans, Louisiana.
LA  - eng
PT  - Journal Article
DEP - 20220915
PL  - United States
TA  - J Surg Res
JT  - The Journal of surgical research
JID - 0376340
SB  - IM
MH  - Humans
MH  - Prospective Studies
MH  - Feasibility Studies
MH  - *Intensive Care Units
MH  - *Electronic Health Records
MH  - Retrospective Studies
MH  - Hospital Mortality
OTO - NOTNLM
OT  - Artificial intelligence
OT  - Failure to Rescue
OT  - ICU
OT  - Prediction model
OT  - Trauma
EDAT- 2022/09/18 06:00
MHDA- 2022/11/24 06:00
CRDT- 2022/09/17 18:18
PHST- 2022/02/08 00:00 [received]
PHST- 2022/08/19 00:00 [revised]
PHST- 2022/08/22 00:00 [accepted]
PHST- 2022/09/18 06:00 [pubmed]
PHST- 2022/11/24 06:00 [medline]
PHST- 2022/09/17 18:18 [entrez]
AID - S0022-4804(22)00524-8 [pii]
AID - 10.1016/j.jss.2022.08.019 [doi]
PST - ppublish
SO  - J Surg Res. 2023 Jan;281:45-51. doi: 10.1016/j.jss.2022.08.019. Epub 2022 Sep 15.