A Novel Risk Score to Predict Thirty-Day Readmissions after Acute Type A Aortic Dissections.

Area of Science:

• Cardiovascular Surgery
• Thoracic Aortic Disease
• Health Services Research

Background:

• Acute type A aortic dissection (ATAAD) readmissions are linked to adverse outcomes and increased healthcare costs.
• Identifying patients at high risk for readmission is essential for optimizing pre-discharge management strategies.
• Developing predictive models can improve patient care and resource allocation following ATAAD.

Purpose of the Study:

• To develop and validate a predictive model for 30-day readmissions in patients treated for ATAAD.
• To identify key risk factors associated with hospital readmission after ATAAD.
• To assess the predictive performance of the developed risk score model.

Main Methods:

• Utilized the National Readmissions Database (NRD) to identify ATAAD patients from 2010-2018.
• Employed mixed-effects logistic regression to identify predictors and assign risk points using Johnson's scoring system.
• Validated the model using calibration plots and receiver operating characteristic (ROC) curves.

Main Results:

• A total of 30,727 ATAAD cases were analyzed, with a 19.4% 30-day readmission rate.
• The predictive model demonstrated good calibration and discrimination (AUC = 0.81).
• Key predictors of readmission included residing in the hospital's state (OR: 2.01), chronic kidney disease (OR: 1.35), discharge to a short-term facility (OR: 1.31), and myocardial infarction (OR: 1.20).

Conclusions:

• The developed risk score model effectively predicts 30-day readmissions in ATAAD patients.
• Patient residence within the state of the index admission was the strongest predictor of readmission.
• The findings can inform targeted interventions to reduce readmission rates and improve outcomes for ATAAD patients.