Abstract
BACKGROUND: Readmissions following acute type A aortic dissections (ATAAD) are associated with potentially worse clinical outcomes and increased hospital costs. Predicting which patients are at risk for readmission may guide patient management prior to discharge. METHODS: The National Readmissions Database was utilized to identify patients treated for ATAAD between 2010 and 2018. Univariate mixed effects logistic regression was used to assess each variable. Variables were assigned risk points based off the bootstrapped (bias-corrected) odds ratio of the final variable model according to the Johnson's scoring system. A mixed effect logistic regression was run on the risk score (sum of risk points) and 30-day readmission. Calibration plots and predicted readmission curves were generated for model assessment. RESULTS: A total of 30,727 type A aortic dissections were identified. The majority of ATAAD (66%) were in men with a median age of 61 years and 30-day readmission rate of 19.4%. The risk scores ranging from -1 to 14 mapped to readmission probabilities between 3.5% and 29% for ATAAD. The predictive model showed good calibration and receiver operator characteristics with an area under the curve (AUC) of 0.81. Being a resident of the hospital state (OR: 2.01 [1.64, 2.47], p < 0.001) was the highest contributor to readmissions followed by chronic kidney disease (1.35 [1.16, 1.56], p = 0), discharge to a short-term facility (1.31 [1.09, 1.57], p = 0.003), and developing a myocardial infarction (1.20 [1.00, 1.45], p = 0.048). CONCLUSIONS: The readmission model had good predictive capability given by the large AUC. Being a resident in the State of the index admission was the most significant contributor to readmission.