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Published on: September 27, 2024
Prediction of Cardiovascular Events by Pulse Waveform Parameters: Analysis of CARTaGENE
Louis-Charles Desbiens1, Catherine Fortier1,2, Annie-Claire Nadeau-Fredette1,3
1Department of Medicine Université de Montréal Montreal Canada.
Insights
Aortic waveform parameters like augmentation index offer slight improvements in predicting major adverse cardiovascular events (MACE), particularly for individuals at low cardiovascular risk. Some parameters are independently associated with MACEs.
Area of Science:
- Cardiovascular Physiology
- Biomedical Engineering
- Epidemiology
Background:
- Aortic wave reflection parameters offer insights into cardiovascular health.
- Their predictive value for cardiovascular events is not well established.
- This study investigates their role in predicting major adverse atherosclerotic events (MACE).
Purpose of the Study:
- To assess the association between aortic waveform parameters and MACE.
- To evaluate the incremental predictive value of these parameters over the atherosclerotic cardiovascular disease (ASCVD) score.
- To identify specific parameters that enhance cardiovascular risk prediction.
Main Methods:
- Analysis of participants aged 40-69 from the CARTaGENE cohort.
- Derivation of pulse wave analysis and wave separation analysis parameters from radial artery tonometry.
- Cox models and reclassification metrics used to assess MACE prediction.
Main Results:
- Central pulse pressure, forward pressure, and backward pressure were associated with MACE.
- Augmentation index and reflection magnitude showed minimal but statistically significant improvement in MACE prediction when added to the ASCVD score.
- Prediction enhancement was most notable in individuals with low baseline ASCVD scores.
Conclusions:
- Certain aortic waveform parameters are independently linked to MACE in a population-based setting.
- Augmentation index and reflection magnitude offer a slight enhancement to cardiovascular risk prediction, especially for low-risk individuals.
- These findings suggest a potential role for advanced waveform analysis in refining cardiovascular risk stratification.
Abstract:
Background Waveform parameters provide approximate data about aortic wave reflection. However, their association with cardiovascular events remains controversial and their role in cardiovascular prediction is unknown. Methods and Results We analyzed participants aged between 40 and 69 from the population-based CARTaGENE cohort. Baseline pulse wave analysis (central pulse pressure, augmentation index) and wave separation analysis (forward pressure, backward pressure, reflection magnitude) parameters were derived from radial artery tonometry. Associations between each parameter and major adverse atherosclerotic events (MACE; cardiovascular death, stroke, myocardial infarction) were obtained using adjusted Cox models. The incremental predictive value of each parameter compared with the 10-year atherosclerotic cardiovascular disease score alone was assessed using hazard ratios, c-index differences, continuous net reclassification indexes, and integrated discrimination indexes. From 17 561 eligible patients, 2315 patients had a MACE during a median follow-up of 10.1 years. Central pulse pressure, forward pressure, and backward pressure, but not augmentation index and reflection magnitude, were significantly associated with MACE after full adjustment. All parameters except forward pressure statistically improved MACE prediction compared with the atherosclerotic cardiovascular disease score alone. The greatest prediction improvement was seen with augmentation index and reflection magnitude but remained small in magnitude. These 2 parameters enhanced predictive performance more strongly in patients with low baseline atherosclerotic cardiovascular disease scores. Up to 5.7% of individuals were reclassified into a different risk stratum by adding waveform parameters to atherosclerotic cardiovascular disease scores. Conclusions Some waveform parameters are independently associated with MACEs in a population-based cohort. Augmentation index and reflection magnitude slightly improve risk prediction, especially in patients at low cardiovascular risk.
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