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• W2999672542 abstract "Background Unplanned hospital readmissions are an important quality metric for benchmarking, but there are limited data following an acute myocardial infarction (AMI). This study aims to examine the 30-day unplanned readmission rate, predictors, causes and outcomes after hospitalization for AMI. Methods The USA Nationwide Readmission Database was utilized to analyze patients with a primary diagnosis of AMI between 2010 and 2014. Rates of readmissions, causes and costs were determined and multiple logistic regressions were used to identify predictors of readmissions. Results Of 2 204 104 patients with AMI, the 30-day unplanned readmission rate was 12.3% (n = 270 510), which changed from 13.0 to 11.5% between 2010 and 2014. The estimated impact of readmissions in AMI was ~718 million USD and ~281000 additional bed days per year. Comorbidities such as diabetes [odds ratio (OR) 1.27, 95% confidence interval (CI) 1.25-1.29], chronic lung disease (OR 1.29, 95% CI 1.26-1.31), renal failure (OR 1.38, 95% CI 1.35-1.40) and cancer (OR 1.35, 95% CI 1.30-1.41) were independently associated with unplanned readmission. Discharge against medical advice was the variable most strongly associated with unplanned readmission (OR 2.40, 95% CI 2.27-2.54). Noncardiac causes for readmissions accounted for 52.9% of all readmissions. The most common cause of cardiac readmission was heart failure (14.3%) and for noncardiac readmissions was infections (8.8%). Conclusion Readmissions during the first month after AMI occur in more than one in 10 patients resulting in a healthcare cost of ~718 million USD per year and ~281000 additional bed days per year. These findings have important public health implications. Strategies to identify and reduce readmissions in AMI will dramatically reduce healthcare costs for society." @default.
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• W2999672542 date "2020-06-01" @default.
• W2999672542 modified "2023-10-18" @default.
• W2999672542 title "Unplanned hospital readmissions after acute myocardial infarction: a nationwide analysis of rates, trends, predictors and causes in the United States between 2010 and 2014" @default.
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• W2999672542 doi "https://doi.org/10.1097/mca.0000000000000844" @default.
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