• Ziad Nehme, Malcolm J Boyle, and Ted Brown.
• School of Primary Health Care, Faculty of Medicine, Nursing & Health Sciences, Monash University, Frankston, Victoria, Australia.
• J Emerg Med. 2013 May 1;44(5):946-954.e6.

BackgroundAlthough cardiac risk prediction is widely used in various clinical settings, its potential role in enhancing prehospital triage is yet to be understood.ObjectiveTo systematically review the diagnostic accuracy of short-term clinical prediction models for potential use in a prehospital population with suspected acute coronary syndrome.MethodsEleven electronic medical databases were searched from 1990 to the end of August 2010 for all English-language observational and interventional studies. An online search strategy tool was used to identify grey-literature studies. Eligibility criteria were: 1) an unselected population of adult acute coronary syndrome patients; 2) recruited within the Emergency Department or Emergency Medical Services; 3) reported multivariate analysis encompassing patient history or physical examination; 4) reported short-term outcome measures; 5) were not solely computer protocols; and 6) were not reliant on tests unavailable out of the hospital. Data extraction was conducted by a single reviewer and verified by a second reviewer. Study quality was assessed independently by two reviewers using a validated quality assessment tool.ResultsA total of seven clinical prediction models were identified. Only two models reported were derived from a prehospital study population. Six clinical prediction models described good discriminate abilities (c-statistic) of 0.72 to 0.87. Among the range of independent predictors identified, electrocardiogram abnormalities, age, heart rate, and systolic blood pressure provided the strongest prognostic information.ConclusionThe models identified provided reasonable diagnostic accuracy for determining short-term outcomes. Methodological weaknesses and variability in the populations investigated limit their use in clinical practice.Crown Copyright © 2013. Published by Elsevier Inc. All rights reserved.

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