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Published on: February 23, 2020
Prediction model for aortic dissection, aortic aneurysm, and peripheral artery disease
Yuta Suzuki1,2, Hidehiro Kaneko3,4, Akira Okada5
1Department of Cardiovascular Medicine, The University of Tokyo, Tokyo, Japan.
Summary
Validated risk prediction models for aortic dissection (AD), aortic aneurysm (AA), and peripheral artery disease (PAD) were developed using routine health check-up data. These models can aid in early diagnosis and intervention for these life-threatening vascular conditions.
Area of Science:
- Cardiovascular Medicine
- Epidemiology
- Biostatistics
Background:
- Aortic dissection (AD), aortic aneurysm (AA), and peripheral artery disease (PAD) are significant causes of mortality.
- Validated prediction models for these vascular conditions are currently scarce.
- Routine health check-up data offers a potential resource for risk stratification.
Purpose of the Study:
- To develop and validate risk prediction models for AD, AA, and PAD.
- To utilize routine health check-up data for estimating 5-year risk of these diseases.
- To enable earlier diagnosis and intervention for vascular diseases through risk stratification.
Main Methods:
- Development of flexible parametric survival models using a large dataset (1,082,369 participants).
- Random split of participants into derivation (50%) and internal validation (50%) cohorts.
- Inclusion of variables such as age, sex, BMI, blood pressure, lipids, glucose, smoking, physical activity, and medication use.
Main Results:
- Models demonstrated good predictive performance with Harrell's C-index: 0.781 for AD, 0.812 for AA, and 0.701 for PAD.
- Royston D statistics indicated strong discriminative ability for all outcomes.
- The developed models showed good calibration across the prediction ranges.
Conclusions:
- Validated risk prediction models for AD, AA, and PAD have been successfully developed using routine health check-up data.
- These models can facilitate risk stratification, potentially leading to earlier diagnosis and intervention.
- External validation in diverse populations and settings is recommended to confirm generalizability.
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