Risk of Readmission After Acute Myocardial Infarction: Insights From the CADOSA Registry.

Area of Science:

• Cardiology
• Health Services Research
• Predictive Analytics

Background:

• One in five patients experience 30-day readmission after acute myocardial infarction (AMI).
• Early identification allows for targeted interventions to reduce readmission risk.
• Developing a predictive model is essential for identifying high-risk individuals.

Purpose of the Study:

• To develop and validate a model predicting 30-day, all-cause, unplanned readmission after hospitalization for AMI.
• To identify key clinical predictors of readmission in AMI patients.
• To assess the model's performance in an Australian cohort.

Main Methods:

• Utilized data from the Coronary Angiogram Database of South Australia (CADOSA) Registry (2012-2022).
• Employed a 70% derivation and 30% validation split sample.
• Developed a parsimonious clinical model using logistic regression with combination elimination.

Main Results:

• 14.2% of 13,289 patients were readmitted within 30 days.
• Predictors of readmission included abnormal estimated glomerular filtration rate, cardiogenic shock, increased heart rate, new heart failure diagnosis, older age, female sex, and depression history.
• The model demonstrated moderate discrimination (C-statistic=0.63).

Conclusions:

• An Australian model for predicting 30-day AMI readmissions shows comparable performance to US models.
• High-risk patients require enhanced support upon hospital arrival to mitigate readmission.
• Further research should focus on refining interventions for high-risk AMI populations.