Atypical symptom presentation in patients with acute myocardial infarction (AMI) is associated with longer delay in care seeking and poorer prognosis. Symptom recognition in these patients is a challenging task.Objectives:
Our purpose in this risk prediction model development study was to develop and validate a risk scoring system for estimating cumulative risk for atypical AMI presentation.Methods:
A consecutive sample was recruited for the developmental (n = 300) and validation (n = 97) cohorts. Symptom experience was measured with the validated Chinese version of the Symptoms of Acute Coronary Syndromes Inventory. Potential predictors were identified from the literature. Multivariable logistic regression was performed to identify significant predictors. A risk scoring system was then constructed by assigning weights to each significant predictor according to their b coefficients.Results:
Five independent predictors for atypical symptom presentation were older age (≥75 years), female gender, diabetes mellitus, history of AMI, and absence of hyperlipidemia. The Hosmer and Lemeshow test (χ26 = 4.47, P = .62) indicated that this predictive model was adequate to predict the outcome. Acceptable discrimination was demonstrated, with area under the receiver operating characteristic curve as 0.74 (95% confidence interval, 0.67–0.82) (P < .001). The predictive power of this risk scoring system was confirmed in the validation cohort.Conclusions:
Atypical AMI presentation is common. A simple risk scoring system developed on the basis of the 5 identified predictors can raise awareness of atypical AMI presentation and promote symptom recognition by estimating the cumulative risk for an individual to present with atypical AMI symptoms.