Diagnostic accuracy of prehospital clinical prediction models to identify short-term outcomes in patients with acute coronary syndromes: A systematic review

Abstract

Background Although cardiac risk prediction is widely used in various clinical settings, its potential role in enhancing prehospital triage is yet to be understood.

Objective To systematically review the diagnostic accuracy of short-term clinical prediction models for potential use in a prehospital population with suspected acute coronary syndrome.

Methods Eleven 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.

Results A 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.

Conclusion The 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.