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Background: Studies have shown that women with acute myocardial infarction (AMI) have a higher prevalence of unfavorable social variables then men and have a worse outcome. Less is known regarding the impact of these social variables on 30-day readmission after AMI. Materials and Methods: We analyzed adult patients with AMI enrolled in a Quality Improvement Program intended to improve the peri-discharge care of patients with an AMI, and decrease all-cause 30-day unplanned readmissions. We compared clinical and social variables by gender. Multivariate logistic regression, with separate adjustment for clinical and for social variable, was used to measure adjusted odds for readmission by gender. Results: Among 208 patients included in our project 68 (32.7%) were women. Only 30.9% of women were married or had domestic partner at the time of the interview and only 16.2% were employed. Nearly half of women (48.5%) needed help with medical care, and 39.7% of women did not speak English as their first language. These variables were significantly different by gender. Rates of 30-day readmissions were higher in women than men (22.1% vs. 7.8%, p = 0.024). After adjusting for clinical variables this difference by gender in 30-day readmissions remained significant (odds ratio [OR] 3.34 95% confidence interval [CI] 1.1-11.1, p = 0.049). However, when adjusting for social variables, this difference was no longer noted (OR 0.87 95% CI 0.27-2.78, p = 0.822). Conclusion: Women with AMI are more likely than men to have unfavorable social factors that can impact recovery from AMI and women have a higher 30-day readmission rate. The higher 30-day readmissions in women appears to be influenced by these social factors. Health care interventions aimed at reducing 30-day readmission after AMI should focus on eliciting a detailed social history and providing aid for those requiring additional social support at home.
RESUMEN
INTRODUCTION AND AIM: Patients undergoing exercise echocardiography with no evidence of myocardial ischemia are considered a low-risk group; however, this group is likely heterogeneous in terms of short-term adverse events and subsequent cardiac testing. We hypothesized that unsupervised cluster modeling using clinical and stress characteristics can detect heterogeneity in cardiovascular risk and need for subsequent cardiac testing among these patients. METHODS: We retrospectively studied 445 patients who had exercise echocardiography results negative for myocardial ischemia. All patients were followed for adverse cardiovascular events, subsequent cardiac testing, and nonacute coronary syndrome (ACS) revascularization. The heterogeneity of the study subjects was tested using computational clustering, an exploratory statistical method designed to uncover invisible natural groups within data. Clinical and stress predictors of adverse events were extracted and used to construct 3 unsupervised cluster models: clinical, stress, and combined. The study population was split into training (357 patients) and testing sets (88 patients). RESULTS: In the training set, the clinical, stress, and combined cluster models yielded 5, 4, and 3 clusters, respectively. Each model had 1 high-risk and 1 low-risk cluster while other clusters were intermediate. The combined model showed a better predictive ability compared to the clinical or stress models alone. The need for future testing mirrored the pattern of adverse cardiovascular events. A risk score derived from the combined cluster model predicted end points with acceptable accuracy. The patterns of risk and the calculated risk scores were preserved in the testing set. CONCLUSIONS: Patients with no evidence of ischemia on exercise stress echocardiography represent a heterogeneous group. Cluster-based modeling using combined clinical and stress characteristics can expose this heterogeneity. The findings can help better risk-stratify this group of patients and aid cost-effective healthcare utilization toward better diagnostics and therapeutics.
