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Cardiovascular risk prediction in healthy older people
Johannes T Neumann1,2,3, Le T P Thao4, Emily Callander4
1Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, 99 Commercial Road, Melbourne, Victoria, 3004, Australia. j.neumann@uke.de.
Insights
A new model identifies major adverse cardiovascular events (MACE) risk in healthy older adults using factors like age, sex, and kidney function. This algorithm aids in predicting cardiovascular events in the elderly population.
Area of Science:
- Cardiology
- Geriatrics
- Preventive Medicine
Background:
- Identifying individuals at high risk for major adverse cardiovascular events (MACE) is crucial for preventive strategies.
- Existing risk prediction algorithms often lack specific validation for the elderly population.
- There is a need for accurate tools to assess cardiovascular risk in healthy older adults.
Purpose of the Study:
- To develop and validate a predictive model for 5-year incident MACE in healthy individuals aged 70 years and older.
- To identify key predictors of MACE in this specific demographic using both prior evidence and machine learning.
- To assess the model's performance through internal and external validation.
Main Methods:
- Analysis of data from a randomized trial of 18,548 participants (≥70 years) and external validation in 25,138 primary care individuals.
- MACE definition included coronary heart disease death, ischemic stroke, or myocardial infarction.
- Cox regression and machine learning were employed to identify predictors and build the risk model, with performance evaluated using Area Under the Curve (AUC).
Main Results:
- The final model incorporated age, sex, smoking, systolic blood pressure, HDL-c, non-HDL-c, serum creatinine, diabetes, and antihypertensive use.
- Machine learning identified age, sex, and creatinine as the most significant predictors.
- The model achieved an AUC of 68.1% in the primary cohort, 67.5% in internal validation, and 64.2% in external validation, demonstrating good risk discrimination but underestimating absolute risk externally.
Conclusions:
- A predictive model incorporating established and novel risk factors, notably renal function, can identify incident MACE risk in healthy elderly individuals.
- The model effectively ranks risk but requires calibration for accurate absolute risk prediction in diverse populations.
- The findings highlight the importance of considering renal function in cardiovascular risk assessment for older adults.
Abstract:
Identification of individuals with increased risk of major adverse cardiovascular events (MACE) is important. However, algorithms specific to the elderly are lacking. Data were analysed from a randomised trial involving 18,548 participants ≥ 70 years old (mean age 75.4 years), without prior cardiovascular disease events, dementia or physical disability. MACE included coronary heart disease death, fatal or nonfatal ischaemic stroke or myocardial infarction. Potential predictors tested were based on prior evidence and using a machine-learning approach. Cox regression analyses were used to calculate 5-year predicted risk, and discrimination evaluated from receiver operating characteristic curves. Calibration was also assessed, and the findings internally validated using bootstrapping. External validation was performed in 25,138 healthy, elderly individuals in the primary care environment. During median follow-up of 4.7 years, 594 MACE occurred. Predictors in the final model included age, sex, smoking, systolic blood pressure, high-density lipoprotein cholesterol (HDL-c), non-HDL-c, serum creatinine, diabetes and intake of antihypertensive agents. With variable selection based on machine-learning, age, sex and creatinine were the most important predictors. The final model resulted in an area under the curve (AUC) of 68.1 (95% confidence intervals 65.9; 70.4). The model had an AUC of 67.5 in internal and 64.2 in external validation. The model rank-ordered risk well but underestimated absolute risk in the external validation cohort. A model predicting incident MACE in healthy, elderly individuals includes well-recognised, potentially reversible risk factors and notably, renal function. Calibration would be necessary when used in other populations.
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