Beyond chest pain: Incremental value of other variables to identify patients for an early ECG.

Chest pain (CP) is the hallmark symptom for acute coronary syndrome (ACS) but is not reported in 20–30% of patients, especially women, elderly, non-white patients, presenting to the emergency department (ED) with an ST-segment elevation myocardial infarction (STEMI). We used a retrospective 5-year adult ED sample of 279,132 patients to explore using CP alone to predict ACS, then we incrementally added other ACS chief complaints, age, and sex in a series of multivariable logistic regression models. We evaluated each model's identification of ACS and STEMI. Using CP alone would recommend ECGs for 8% of patients (sensitivity, 61%; specificity, 92%) but missed 28.4% of STEMIs. The model with all variables identified ECGs for 22% of patients (sensitivity, 82%; specificity, 78%) but missed 14.7% of STEMIs. The model with CP and other ACS chief complaints had the highest sensitivity (93%) and specificity (55%), identified 45.1% of patients for ECG, and only missed 4.4% of STEMIs. CP alone had highest specificity but lacked sensitivity. Adding other ACS chief complaints increased sensitivity but identified 2.2-fold more patients for ECGs. Achieving an ECG in 10 min for patients with ACS to identify all STEMIs will be challenging without introducing more complex risk calculation into clinical care. • The use of additional variables to identify patients for ECGs in the ED can increase the sensitivity of screening for ACS without increasing the number of ECGs that need to be performed in the ED. • Screening to obtain an ECG in 100% of ACS patients in the ED is challenging without introducing more complex risk calculation into clinical care. • The use of predictive modeling to identify those with high risk for ACS, as well as the subset with STEMI, can improve emergency cardiovascular care delivery. [ABSTRACT FROM AUTHOR]