Risk prediction in people with acute myocardial infarction in England: a cohort study using data from 1521 general...

Area of Science:

• Cardiology
• Public Health
• Health Informatics

Background:

• Acute myocardial infarction (AMI) is a leading cause of cardiovascular mortality and morbidity.
• Accurate risk prediction is crucial for effective secondary prevention and patient management.
• Existing models may not fully capture short-term risks in diverse primary care populations.

Purpose of the Study:

• To develop and validate prediction models for short-term outcomes (1-year and 5-year) following a first (index) or previous (prevalent) AMI event.
• To assess the models' performance in predicting all-cause mortality and composite cardiovascular outcomes.

Main Methods:

• Retrospective cohort study utilizing data from the Clinical Practice Research Datalink (CPRD) Aurum and GOLD databases (2006-2019).
• Logistic regression models were developed and internally validated on CPRD Aurum data, then externally validated on CPRD GOLD.
• Model performance was evaluated using discrimination (AUC) and calibration metrics.

Main Results:

• Models demonstrated good discrimination for 1-year and 5-year all-cause mortality and composite cardiovascular outcomes (stroke, heart failure, death) in both index and prevalent AMI cohorts.
• Area Under the Curve (AUC) values for 1-year mortality in the index cohort were 0.802 (internal) and 0.800 (external).
• Higher discrimination was observed in the prevalent AMI cohort, particularly for 1-year mortality (AUC: 0.896).

Conclusions:

• Developed prediction models effectively identify patients at increased short-term risk of adverse outcomes post-AMI.
• These models can support clinicians in tailoring monitoring strategies and secondary prevention efforts.
• The findings aid in guiding patient counseling regarding modifiable risk factors.