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A simplified approach to identification of risk status in patients with atherosclerotic cardiovascular disease
Aparna Sajja1, Hsin-Fang Li2, Kateri J Spinelli2
1Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, 1800 Orleans Street, Baltimore, MD 21287, United States.
Insights
A new study found that 58% of atherosclerotic cardiovascular disease (ASCVD) patients are very high-risk (VHR). Classification and regression tree (CART) analysis simplifies identifying these patients, improving clinical decision-making for ASCVD risk assessment.
Area of Science:
- Cardiology
- Medical Informatics
- Public Health
Background:
- The 2018 AHA/ACC Blood Cholesterol Guideline stratifies atherosclerotic cardiovascular disease (ASCVD) patients into very high-risk (VHR) and not very high-risk (NVHR) categories.
- This classification significantly impacts medical therapy escalation strategies.
- Accurate identification of VHR patients is crucial for effective cardiovascular disease management.
Purpose of the Study:
- To determine the prevalence and clinical characteristics of VHR and NVHR patients within a large multi-state healthcare system.
- To develop a simplified method for identifying VHR patients using classification and regression tree (CART) analysis.
Main Methods:
- Retrospective analysis of 180,669 ASCVD patients from a 28-hospital US healthcare system in 2018.
- ASCVD population defined using ICD-10 codes.
- VHR status determined by ≥2 major ASCVD events or 1 major ASCVD event and ≥2 high-risk conditions, per AHA/ACC Guideline.
- CART analysis performed on training and validation datasets, with random forest model verification.
Main Results:
- 58% of identified ASCVD patients were classified as VHR.
- Patients with a history of myocardial infarction (MI) or recent acute coronary syndrome (ACS) had a VHR classification of 99% and 96%, respectively.
- CART and random forest models identified recent ACS, ischemic stroke, hypertension, peripheral artery disease, history of MI, and age as key VHR predictors.
- A simplified five-rule CART model required fewer than 50% of risk factors for VHR assignment.
Conclusions:
- CART analysis effectively streamlines the identification of VHR patients using a limited set of rules and risk factors.
- This simplified approach can enhance clinical decision-making by facilitating ASCVD risk assessment at the point of care.
- Further validation in diverse populations is recommended to confirm the generalizability of these findings.
Objective:
The 2018 American Heart Association/American College of Cardiology (AHA/ACC) Blood Cholesterol Guideline recommendation to classify patients with atherosclerotic cardiovascular disease (ASCVD) as very high-risk (VHR) vs not-VHR (NVHR) has important implications for escalation of medical therapy. We aimed to define the prevalence and clinical characteristics of these two groups within a large multi-state healthcare system and develop a simpler means to assist clinicians in identifying VHR patients using classification and regression tree (CART) analysis.
Methods:
We performed a retrospective analysis of all patients in a 28-hospital US healthcare system in 2018. ICD-10 codes were used to define the ASCVD population. Per the AHA/ACC Guideline, VHR status was defined by ≥2 major ASCVD events or 1 major ASCVD event and ≥2 high-risk conditions. CART analysis was performed on training and validation datasets. A random forest model was used to verify results.
Results:
Of 180,669 ASCVD patients identified, 58% were VHR. Among patients with a history of myocardial infarction (MI) or recent acute coronary syndrome (ACS), 99% and 96% were classified as VHR, respectively. Both CART and random forest models identified recent ACS, ischemic stroke, hypertension, peripheral artery disease, history of MI, and age as the most important predictors of VHR status. Using five rules identified by CART analysis, fewer than 50% of risk factors were required to assign VHR status.
Conclusion:
CART analysis helped to streamline the identification of VHR patients based on a limited number of rules and risk factors. This approach may help improve clinical decision making by simplifying ASCVD risk assessment at the point of care. Further validation is needed, however, in more diverse populations.
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