Abstract 13264: Gender-Based Risk Prediction for 1-year Readmission Among Younger Women Hospitalized for Acute Myocardial Infarction

Abstract

Background: Younger women (≤55 years) are at higher risk for readmission within one-year of hospitalization following acute myocardial infarction (AMI) compared to similarly aged men, yet there are no gender-specific risk prediction models for this population. Prior research suggests that gender-related factors more comprehensively explain sex-based differences in readmission risk. The objective was to develop and validate a risk prediction model of 1-year post-AMI readmission in young women considering demographic, clinical, and gender-related factors. Methods: We used data from women enrolled in the VIRGO study (n =2,007), a prospective observational study of young patients aged ≤55 years hospitalized with AMI in the US. Data were obtained from patient interviews, medical record abstraction, and adjudicated hospitalization records. Bayesian Model Averaging was used for model selection in a derivation cohort of 1338 women and subsequently validated in the remaining 669 women. Results: Within 1-year post-AMI, 684 (34.1%) women were readmitted at least once with a majority of readmissions due to cardiac causes (57.5%). The final model contained 9 predictors: experiencing any in-hospital complications, physical health at baseline (SF-12), disease-specific quality of life (Seattle Angina Questionnaire), diabetes, history of congestive heart failure, low income (≤30,000 USD), depression, length of hospital stay, and employment status (Figure) . Of the 9 predictors, 5 were gender-related. The model was well calibrated (calibration plots) and exhibited modest discrimination (C statistic=0.66 in development and validation cohorts). Conclusions: Younger women with diabetes, depression, history of congestive heart failure, and longer hospital stays were more likely to be readmitted. While clinical factors were the strongest predictors of readmission within 1-year among younger women with AMI, gender-related variables were important complements.