Literature DB >> 28279496

Outcomes of nighttime refusal of admission to the intensive care unit: The role of the intensivist in triage.

Nicholas Hinds1, Amit Borah1, Erika J Yoo2.   

Abstract

PURPOSE: To compare outcomes of patients refused medical intensive care unit (MICU) admission overnight to those refused during the day and to examine the impact of the intensivist in triage.
MATERIALS AND METHODS: Retrospective, observational study of patients refused MICU admission at an urban university hospital.
RESULTS: Of 294 patients, 186 (63.3%) were refused admission overnight compared to 108 (36.7%) refused during the day. Severity-of-illness by the Mortality Probability Model was similar between the two groups (P=.20). Daytime triage refusals were more likely to be staffed by an intensivist (P=.01). After risk-adjustment, daytime refusals had a lower odds of subsequent ICU admission (OR 0.46, 95% CI 0.22-0.95, P=.04) than patients triaged at night. There was no evidence for interaction between time of triage and intensivist staffing of the patient (P=.99).
CONCLUSIONS: Patients refused MICU admission overnight are more likely to be later admitted to an ICU than patients refused during the day. However, the mechanism for this observation does not appear to depend on the intensivist's direct evaluation of the patient. Further investigation into the clinician-specific effects of ICU triage and identification of potentially modifiable hospital triage practices will help to improve both ICU utilization and patient safety.
Copyright © 2017 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Intensive care unit; Intensivist; Refusal; Triage

Mesh:

Year:  2017        PMID: 28279496     DOI: 10.1016/j.jcrc.2016.12.024

Source DB:  PubMed          Journal:  J Crit Care        ISSN: 0883-9441            Impact factor:   3.425


  2 in total

1.  [Exclusion of admission to the intensive care unit: survey of 100 Moroccan resuscitators].

Authors:  Boubakar Charra; Amine Raja
Journal:  Pan Afr Med J       Date:  2020-11-16

2.  Assessment of Time-Series Machine Learning Methods for Forecasting Hospital Discharge Volume.

Authors:  Thomas H McCoy; Amelia M Pellegrini; Roy H Perlis
Journal:  JAMA Netw Open       Date:  2018-11-02
  2 in total

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