Development and validation of a prediction rule for patients suspected of acute coronary syndrome in primary care: a cross-sectional study

Objective To develop and validate a symptom-based prediction rule for early recognition of acute coronary syndrome (ACS) in patients with acute chest discomfort who call out-of-hours services for primary care (OHS-PC). Design Cross-sectional study. A diagnostic prediction rule was developed with multivariable regression analyses. All models were validated with internal-external cross validation within seven OHS-PC locations. Both age and sex were analysed as statistical interaction terms, applying for age non-linear effects. Setting Seven OHS-PC in the Netherlands. Participants 2192 patients who called OHS-PC for acute chest discomfort (pain, pressure, tightness or discomfort) between 2014 and 2017. Backed up recordings of telephone triage conversations were analysed. Primary and secondary outcomes measures Diagnosis of ACS retrieved from the patient's medical records in general practice, including hospital specialists discharge letters. Performance of the prediction rules was calculated with the c-statistic and the final model was chosen based on net benefit analyses. Results Among the 2192 patients who called the OHS-PC with acute chest discomfort, 8.3% females and 15.3% males had an ACS. The final diagnostic model included seven predictors (sex, age, acute onset of chest pain lasting less than 12 hours, a pressing/heavy character of the pain, radiation of the pain, sweating and calling at night). It had an adjusted c-statistic of 0.77 (95% CI 0.74 to 0.79) with good calibration. Conclusion The final prediction model for ACS has good discrimination and calibration and shows promise for replacing the existing telephone triage rules for patients with acute chest discomfort in general practice and OHS-PC.