External validation of an emergency department triage algorithm for chest pain patients

We aimed to externally validate an emergency department triage algorithm including five hierarchical clinical variables developed to identify chest pain patients at low risk of having an acute coronary syndrome justifying delayed rather than immediate evaluation.

In a single-centre cohort enrolling 29,269 consecutive patients presenting with chest pain, the performance of the algorithm was compared against the emergency department discharge diagnosis. In an international multicentre study enrolling 4069 patients, central adjudication by two independent cardiologists using all data derived from cardiac work-up including follow-up served as the reference. Triage towards 'low-risk' required absence of all five clinical 'high-risk' variables: history of coronary artery disease, diabetes, pressure-like chest pain, retrosternal chest pain and age above 40 years. Safety (sensitivity and negative predictive value (NPV)) and efficacy (percentage of patients classified as low risk) was tested in this initial proposal (Model A) and in two additional models: omitting age criteria (Model B) and allowing up to one (any) of the five high-risk variables (Model C).

The prevalence of acute coronary syndrome was 9.4% in the single-centre and 28.4% in the multicentre study. The triage algorithm had very high sensitivity/NPV in both cohorts (99.4%/99.1% and 99.9%/99.1%, respectively), but very low efficacy (6.2% and 2.7%, respectively). Model B resulted in sensitivity/NPV of 97.5%/98.3% and 96.1%/89.4%, while efficacy increased to 14.2% and 10.4%, respectively. Model C resulted in sensitivity/NPV of 96.7%/98.6% and 95.2%/91.3%, with a further increase in efficacy to 23.1% and 15.5%, respectively.

A triage algorithm for the identification of low-risk chest pain patients exclusively based on simple clinical variables provided reasonable performance characteristics possibly justifying delayed rather than immediate evaluation in the emergency department.