Model to predict inpatient mortality from information gathered at presentation to an emergency department: The Triage Information Mortality Model (TIMM)

David Jo Teubner, Julie Considine, Paul Hakendorf, Susan Kim, Andrew D. Bersten

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

Abstract

Objectives: To derive and validate a mortality prediction model from information available at ED triage. Methods: Multivariable logistic regression of variables from administrative datasets to predict inpatient mortality of patients admitted through an ED. Accuracy of the model was assessed using the receiver operating characteristic area under the curve (ROC-AUC) and calibration using the Hosmer-Lemeshow goodness of fit test. The model was derived, internally validated and externally validated. Derivation and internal validation were in a tertiary referral hospital and external validation was in an urban community hospital. Results: The ROC-AUC for the derivation set was 0.859 (95% CI 0.856-0.865), for the internal validation set was 0.848 (95% CI 0.840-0.856) and for the external validation set was 0.837 (95% CI 0.823-0.851). Calibration assessed by the Hosmer-Lemeshow goodness of fit test was good. Conclusions: The model successfully predicts inpatient mortality from information available at the point of triage in the ED.

Original languageEnglish
Pages (from-to)300-306
Number of pages7
JournalEMA - Emergency Medicine Australasia
Volume27
Issue number4
DOIs
Publication statusPublished - 1 Aug 2015
Externally publishedYes

Keywords

  • Hospital mortality
  • Mortality prediction
  • Triage

ASJC Scopus subject areas

  • Medicine(all)
  • Emergency Medicine

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