Literature DB >> 23560963

Common laboratory tests predict imminent medical emergency team calls, intensive care unit admission or death in emergency department patients.

Elsa Loekito1, James Bailey, Rinaldo Bellomo, Graeme K Hart, Colin Hegarty, Peter Davey, Christopher Bain, David Pilcher, Hans Schneider.   

Abstract

OBJECTIVE: To estimate the ability of commonly measured laboratory variables to predict imminent (within the same or next calendar day) medical emergency team (MET) calls, ICU admission or death.
METHODS: We performed a retrospective observational study of ED patients. We estimated the ability of each laboratory variable or combination of variables together with patient age to predict imminent MET calls, ICU admission or death. We externally validated our findings in patients from a different hospital.
RESULTS: We studied 160 341 batches in 71 453 ED patients (average age: 59.9 ± 22.1 years) for a total of 1 million individual measurements. There were 341 MET calls, 160 ICU admissions from the wards and 858 deaths. Multivariable modelling achieved a receiver operating characteristic area under the curve (ROC-AUC) of 0.69 (95% CI 0.63-0.74) for imminent MET call with prediction occurring a mean of 11.9 h before the call. Additionally, it achieved a ROC-AUC of 0.82 (95% CI 0.73-0.87) for imminent ICU admission. Finally, it achieved a ROC-AUC of 0.90 (95% CI 0.87-0.91) for imminent death. When tested using an additional 37 367 batches from a cohort of 21 430 ED patients from a second teaching hospital, the multivariate model achieved a ROC-AUC of 0.70 (95% CI 0.66-0.73) for imminent MET call, a ROC-AUC of 0.84 (95% CI 0.78-0.90) for imminent ICU admission. Finally, it achieved a ROC-AUC of 0.89 (95% CI 0.86-0.91) for imminent death.
CONCLUSIONS: Commonly performed laboratory tests can help predict imminent MET calls, ICU admission or death in ED patients. Prospective investigations of the clinical utility of such predictions appear desirable.
© 2013 The Authors. EMA © 2013 Australasian College for Emergency Medicine and Australasian Society for Emergency Medicine.

Entities:  

Mesh:

Year:  2013        PMID: 23560963     DOI: 10.1111/1742-6723.12040

Source DB:  PubMed          Journal:  Emerg Med Australas        ISSN: 1742-6723            Impact factor:   2.151


  5 in total

1.  EHR in emergency rooms: exploring the effect of key information components on main complaints.

Authors:  Ofir Ben-Assuli; Itamar Shabtai; Moshe Leshno; Shawndra Hill
Journal:  J Med Syst       Date:  2014-04-01       Impact factor: 4.460

2.  Improving performance in the ED through laboratory information exchange systems.

Authors:  Louis Raymond; Guy Paré; Éric Maillet; Ana Ortiz de Guinea; Marie-Claude Trudel; Josianne Marsan
Journal:  Int J Emerg Med       Date:  2018-03-12

3.  Patient centred variables with univariate associations with unplanned ICU admission: a systematic review.

Authors:  James Malycha; Timothy Bonnici; David A Clifton; Guy Ludbrook; J Duncan Young; Peter J Watkinson
Journal:  BMC Med Inform Decis Mak       Date:  2019-05-15       Impact factor: 2.796

4.  A deep learning backcasting approach to the electrolyte, metabolite, and acid-base parameters that predict risk in ICU patients.

Authors:  Albion Dervishi
Journal:  PLoS One       Date:  2020-12-17       Impact factor: 3.240

5.  Predicting in-hospital mortality and unanticipated admissions to the intensive care unit using routinely collected blood tests and vital signs: Development and validation of a multivariable model.

Authors:  Oliver C Redfern; Marco A F Pimentel; David Prytherch; Paul Meredith; David A Clifton; Lionel Tarassenko; Gary B Smith; Peter J Watkinson
Journal:  Resuscitation       Date:  2018-09-22       Impact factor: 5.262

  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.