Abstract 316: A Novel Risk Tool to Predict Chronic Stress in Patients With Cardiovascular Disease
Background: Chronic stress in patients with cardiovascular disease (CVD) is independently associated with higher mortality, increased risk of cardiovascular events, and poorer health status. However, stress is a modifiable risk factor, and interventions to reduce chronic stress have previously shown to improve outcomes in patients with cardiovascular disease. A clinically practical tool to identify patients at risk of developing chronic stress is needed to appropriately select patients for interventions to reduce stress.
Methods: In the prospective US multicenter myocardial infarction (MI) registry (TRIUMPH), we used hierarchical logistic regression on 27 patient demographic, socioeconomic and clinical factors, adjusting for site, to identify predictors of chronic stress over 1 year. Stress at baseline and at 1-, 6- and 12-month follow-up was measured using the 4-point Perceived Stress Scale (PSS-4) [range 0-16, scores ≥ 6 depicting high stress]. Chronic stress was defined as at least 2 follow-up PSS-4 scores ≥ 6. We use multiple imputation to account for patients with missing assessments. We identified a simplified model using backward selection on the R2 of the full-model predictions and validated this final model (recalibrating the intercept) in another prospective US multicenter registry of patients with symptomatic peripheral arterial disease (PAD), the PORTRAIT study.
Results: Our derivation cohort consisted of 3,470 MI patients (mean age 59±12, 33% females, 27% non-white), of whom 30% had chronic stress at follow-up. Of the 27 factors examined, higher baseline PSS-4 score, patient financial distress, lower ENRICHD Social Support Instrument (ESSI) score, younger age and smoking status were associated with higher likelihood of chronic stress (Figure A). The bootstrap-validated c-index was 0.81. In the validation cohort of 696 PAD patients (mean age 69±9, 41% females, 28% non-white, 18% chronic stress) the c-statistic for the model was 0.78 and calibration was excellent (Figure B).
Conclusions: We describe and externally validated a novel risk prediction tool to predict chronic stress in patients presenting with worsening CVD. Our tool is practical and can be used in clinical practice to select patients for interventions to mitigate the adverse cardiovascular effects of chronic stress.
