Development and Validation of a Risk Prediction Model for 1-Year Readmission Among Young Adults Hospitalized for Acute Myocardial Infarction

Abstract

Background Readmission over the first year following hospitalization for acute myocardial infarction (AMI) is common among younger adults (≤55 years). Our aim was to develop/validate a risk prediction model that considered a broad range of factors for readmission within 1 year. Methods and Results We used data from the VIRGO (Variation in Recovery: Role of Gender on Outcomes of Young AMI Patients) study, which enrolled young adults aged 18 to 55 years hospitalized with AMI across 103 US hospitals (N=2979). The primary outcome was ≥1 all-cause readmissions within 1 year of hospital discharge. Bayesian model averaging was used to select the risk model. The mean age of participants was 47.1 years, 67.4% were women, and 23.2% were Black. Within 1 year of discharge for AMI, 905 (30.4%) of participants were readmitted and were more likely to be female, Black, and nonmarried. The final risk model consisted of 10 predictors: depressive symptoms (odds ratio [OR], 1.03; 95% CI, 1.01-1.05), better physical health (OR, 0.98; 95% CI, 0.97-0.99), in-hospital complication of heart failure (OR, 1.44; 95% CI, 0.99-2.08), chronic obstructive pulmomary disease (OR, 1.29; 95% CI, 0.96-1.74), diabetes mellitus (OR, 1.23; 95% CI, 1.00-1.52), female sex (OR, 1.31; 95% CI, 1.05-1.65), low income (OR, 1.13; 95% CI, 0.89-1.42), prior AMI (OR, 1.47; 95% CI, 1.15-1.87), in-hospital length of stay (OR, 1.13; 95% CI, 1.04-1.23), and being employed (OR, 0.88; 95% CI, 0.69-1.12). The model had excellent calibration and modest discrimination (C statistic=0.67 in development/validation cohorts). Conclusions Women and those with a prior AMI, increased depressive symptoms, longer inpatient length of stay and diabetes may be more likely to be readmitted. Notably, several predictors of readmission were psychosocial characteristics rather than markers of AMI severity. This finding may inform the development of interventions to reduce readmissions in young patients with AMI.

Keywords: Bayesian model averaging; acute myocardial infarction; psychosocial factors; risk prediction model; young adults.

Figure 1. Stages of selection for the final multivariable risk prediction model.

ACEi indicates angiotensin‐converting enzyme inhibitors; AMI, acute myocardial infarction; ARBs, angiotensin receptor blockers; ASA, aspirin; BMI, body mass index; CABG, coronary artery bypass grafting; CAD, coronary artery disease; CCU/ICU, cardiac or medical intensive care unit; CLOP, clopidegrel; COPD, chronic obstructive pulmonary disease; CVD, cardiovascular disease; ESSI‐7, ENRICHD Social Support Instrument; GRACE, Global Registry of Acute Coronary Events; PAD, peripheral arterial disease; PSS‐14, Perceived Stress Scale‐14; and SAQ, Seattle Angina Score.

Figure 2. Forest plot showing predictors of 1‐year readmission post AMI (odds ratio for readmitted vs not readmitted).

Note that for the purposes of interpretability we have inverted 2 predictors so they align better in the figure (physical health [SF‐12], unemployment status). AMI indicates acute myocardial infarction; COPD, chronic obstructive pulmonary disease; LCL, lower control limit; OR, odds ratio; PHQ‐9, Patient Health Questionnaire‐9; SF‐12, Short Form‐12; and UCL, upper control limit.

Figure 3. Calibration plots of observed vs predicted risk from the 10‐predictor risk model of all‐cause readmission within 1‐year of hospitalization for AMI among younger adults.

A, Calibration plot from the development sample (N=1986) used to create the 10‐predictor risk model that demonstrates how well the deciles of observed and predicted probabilities of 1‐year readmission agree over the entire range of predicted risk, where the diagonal line represents perfect agreement. B, Calibration plot from the validation sample (N=993) used to exhibit successful application of the 10‐predictor model by demonstrating how well the deciles of observed and predicted probabilities of 1‐year readmission agree over the entire range of predicted risk, where the diagonal line represents perfect agreement. AMI indicates acute myocardial infarction; and AUC, area under the curve.