Influence of cardiovascular risk factors and treatment exposure on cardiovascular event incidence: Assessment using
Sara Castel-Feced1,2,3, Sara Malo1,2,3, Isabel Aguilar-Palacio1,2,3
1Microbiology, Pediatrics, Radiology, and Public Health, University of Zaragoza, Zaragoza, Spain.
Insights
Machine learning models accurately predict cardiovascular events (CVEs), highlighting age as a key risk factor. Treatment adherence significantly influences CVE risk, enabling personalized cardiovascular prevention strategies.
Area of Science:
- Cardiology
- Biostatistics
- Computational Medicine
Background:
- Traditional cardiovascular risk scoring systems have limitations in personalized medicine.
- Machine learning (ML) offers advanced capabilities for predicting cardiovascular events (CVEs).
- Understanding the influence of cardiovascular risk factors (CVRF) is crucial for effective prevention.
Purpose of the Study:
- To evaluate ML algorithms for CVE prediction.
- To analyze the influence of CVRF and treatment adherence on CVE prediction.
- To compare the performance of different ML models in predicting CVEs.
Main Methods:
- A cohort study of 3746 male workers using populational data.
- Application of XGBoost, Random Forest, and Naïve Bayes (NB) ML algorithms.
- Analysis of CVRF (age, physical status, Hypercholesterolemia, Hypertension, Diabetes Mellitus) with and without treatment exposure variables.
Main Results:
- Age was consistently identified as the most influential variable for CVE incidence across all models.
- Treatment exposure (adherence) emerged as more influential than other CVRF, varying by model.
- Random Forest demonstrated the highest accuracy (F1 score 0.84) when treatment exposure was included.
Conclusions:
- ML algorithms can enhance cardiovascular risk prediction beyond existing systems.
- Adherence to treatment is a critical, often underestimated, factor in CVE risk.
- Personalized cardiovascular prevention models can be developed for specific populations using these ML approaches in primary care.
Abstract:
Assessment of the influence of cardiovascular risk factors (CVRF) on cardiovascular event (CVE) using machine learning algorithms offers some advantages over preexisting scoring systems, and better enables personalized medicine approaches to cardiovascular prevention. Using data from four different sources, we evaluated the outcomes of three machine learning algorithms for CVE prediction using different combinations of predictive variables and analysed the influence of different CVRF-related variables on CVE prediction when included in these algorithms. A cohort study based on a male cohort of workers applying populational data was conducted. The population of the study consisted of 3746 males. For descriptive analyses, mean and standard deviation were used for quantitative variables, and percentages for categorical ones. Machine learning algorithms used were XGBoost, Random Forest and Naïve Bayes (NB). They were applied to two groups of variables: i) age, physical status, Hypercholesterolemia (HC), Hypertension, and Diabetes Mellitus (DM) and ii) these variables plus treatment exposure, based on the adherence to the treatment for DM, hypertension and HC. All methods point out to the age as the most influential variable in the incidence of a CVE. When considering treatment exposure, it was more influential than any other CVRF, which changed its influence depending on the model and algorithm applied. According to the performance of the algorithms, the most accurate was Random Forest when treatment exposure was considered (F1 score 0.84), followed by XGBoost. Adherence to treatment showed to be an important variable in the risk of having a CVE. These algorithms could be applied to create models for every population, and they can be used in primary care to manage interventions personalized for every subject.
More Related Videos
07:51Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis
Published on: September 26, 2018
08:51Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
Published on: September 20, 2024
Related Concept Videos
01:23Assessment of the Cardiovascular System I: Subjective Data
Initial Enquiry
Ask the patient about their primary concern and thoroughly explore all reported symptoms.
Medical History
Investigate past illnesses affecting the cardiovascular system, such as angina, anemia, rheumatic fever, congenital heart disease, stroke, thrombophlebitis, dysrhythmias, varicosities
Inquire about symptoms...
01:30Coronary Artery Disease I: Introduction
01:26Coronary Artery Disease IV: Preventive Measures
01:25Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT
01:20Exercise and Cardiovascular Response
Light to moderate physical activity initiates a series of interconnected responses in the body. The heart rate modestly increases in anticipation of the workout, followed by widespread vasodilation as oxygen consumption by skeletal muscles increases. This results in decreased peripheral resistance, increased capillary blood flow, and accelerated...
01:11Genome-wide Association Studies-GWAS
GWAS does not require the identification of the target gene involved in...