Optimizing Cardiovascular Risk Management in Primary Care Using a Personalized eCoach Solution Enhanced by an Artificial Intelligence–Driven Clinical Prediction Model: Protocol from the Coronary Artery Disease Risk Estimation and Early Detection Consortium
BackgroundAtherosclerotic cardiovascular disease poses a heavy burden on the population’s health and health care costs. Identifying apparently healthy individuals at risk of developing cardiovascular diseases using clinical prediction models raises awareness, facilitates shar...
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JMIR Publications
2025-08-01
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| Series: | JMIR Research Protocols |
| Online Access: | https://www.researchprotocols.org/2025/1/e66068 |
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| author | Rutger van Mierlo Bart Scheenstra Joost Verbeek Anke Bruninx Petros Kalendralis Inigo Bermejo Andre Dekker Arnoud van 't Hof Marieke Spreeuwenberg Laura Hochstenbach |
| author_facet | Rutger van Mierlo Bart Scheenstra Joost Verbeek Anke Bruninx Petros Kalendralis Inigo Bermejo Andre Dekker Arnoud van 't Hof Marieke Spreeuwenberg Laura Hochstenbach |
| author_sort | Rutger van Mierlo |
| collection | DOAJ |
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BackgroundAtherosclerotic cardiovascular disease poses a heavy burden on the population’s health and health care costs. Identifying apparently healthy individuals at risk of developing cardiovascular diseases using clinical prediction models raises awareness, facilitates shared decision-making, and supports tailored management of disease prevention. In the CARRIER project, a personalized cardiovascular risk management (CVRM) eCoach approach is cocreated, in which identified individuals receive education, guidance, and monitoring to prevent atherosclerotic cardiovascular disease through existing interventions. In this approach, an artificial intelligence–driven clinical prediction model calculates the 10-year risk for atherosclerotic cardiovascular disease, which supports informed decision-making.
ObjectiveThis study aims to assess the effectiveness of our CVRM eCoach approach through a 10-year risk calculation of atherosclerotic cardiovascular disease, including risk factors contributing to this risk.
MethodsThis pretest-posttest interventional study provides the CVRM eCoach approach for 6 months to 100 apparently healthy individuals eligible for CVRM. The CVRM eCoach approach is a multicomponent eHealth solution, including a clinical prediction under intervention model that not only calculates the 10-year risk of cardiovascular disease through conventional risk factors (smoking, blood pressure, and lipid profile) and individual characteristics (age, gender, socioeconomic status, physical activity, and diet) but also calculates how the risk changes after hypothetical lifestyle or medical interventions. The CVRM eCoach approach includes features that encourage behavior change. Most of these features include goal setting, decision cards to help decide on an intervention, intervention monitoring, remote communication, and education, all accessible from one dashboard. A practice nurse or physician consults the individuals after risk calculation with the clinical prediction model and uses behavior change features, such as the decision cards, to support shared decision-making. Data are primarily collected via the eCoach, after which the 10-year risk for atherosclerotic cardiovascular disease and its components are analyzed using paired-sample analyses.
ResultsRecruitment began in March 2024 and will continue until 100 participants have been recruited, which is expected in 2025.
ConclusionsWe anticipate that our CVRM eCoach approach will be valuable in the primary prevention setting. During the crucial initial first months of habit formation, factors such as education, regular check-ups via the eCoach, and clear risk communication could support individuals in sustaining their medical or lifestyle interventions. We hypothesize that there will be a slight to moderate reduction in the 10-year risk of atherosclerotic cardiovascular disease, which over time will lead to significant health improvements on a larger scale.
Trial RegistrationCCMO NL84584.096.23; https://onderzoekmetmensen.nl/nl/trial/56578 |
| format | Article |
| id | doaj-art-8f5be51bc036435885476caa313a58cf |
| institution | DOAJ |
| issn | 1929-0748 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | JMIR Research Protocols |
| spelling | doaj-art-8f5be51bc036435885476caa313a58cf2025-08-20T02:55:09ZengJMIR PublicationsJMIR Research Protocols1929-07482025-08-0114e6606810.2196/66068Optimizing Cardiovascular Risk Management in Primary Care Using a Personalized eCoach Solution Enhanced by an Artificial Intelligence–Driven Clinical Prediction Model: Protocol from the Coronary Artery Disease Risk Estimation and Early Detection ConsortiumRutger van Mierlohttps://orcid.org/0009-0009-4156-5669Bart Scheenstrahttps://orcid.org/0000-0001-5778-2574Joost Verbeekhttps://orcid.org/0000-0001-6204-1877Anke Bruninxhttps://orcid.org/0000-0002-5916-8735Petros Kalendralishttps://orcid.org/0000-0002-4471-2021Inigo Bermejohttps://orcid.org/0000-0001-9105-8088Andre Dekkerhttps://orcid.org/0000-0002-0422-7996Arnoud van 't Hofhttps://orcid.org/0000-0002-2344-7564Marieke Spreeuwenberghttps://orcid.org/0000-0002-5798-0041Laura Hochstenbachhttps://orcid.org/0000-0003-2854-1055 BackgroundAtherosclerotic cardiovascular disease poses a heavy burden on the population’s health and health care costs. Identifying apparently healthy individuals at risk of developing cardiovascular diseases using clinical prediction models raises awareness, facilitates shared decision-making, and supports tailored management of disease prevention. In the CARRIER project, a personalized cardiovascular risk management (CVRM) eCoach approach is cocreated, in which identified individuals receive education, guidance, and monitoring to prevent atherosclerotic cardiovascular disease through existing interventions. In this approach, an artificial intelligence–driven clinical prediction model calculates the 10-year risk for atherosclerotic cardiovascular disease, which supports informed decision-making. ObjectiveThis study aims to assess the effectiveness of our CVRM eCoach approach through a 10-year risk calculation of atherosclerotic cardiovascular disease, including risk factors contributing to this risk. MethodsThis pretest-posttest interventional study provides the CVRM eCoach approach for 6 months to 100 apparently healthy individuals eligible for CVRM. The CVRM eCoach approach is a multicomponent eHealth solution, including a clinical prediction under intervention model that not only calculates the 10-year risk of cardiovascular disease through conventional risk factors (smoking, blood pressure, and lipid profile) and individual characteristics (age, gender, socioeconomic status, physical activity, and diet) but also calculates how the risk changes after hypothetical lifestyle or medical interventions. The CVRM eCoach approach includes features that encourage behavior change. Most of these features include goal setting, decision cards to help decide on an intervention, intervention monitoring, remote communication, and education, all accessible from one dashboard. A practice nurse or physician consults the individuals after risk calculation with the clinical prediction model and uses behavior change features, such as the decision cards, to support shared decision-making. Data are primarily collected via the eCoach, after which the 10-year risk for atherosclerotic cardiovascular disease and its components are analyzed using paired-sample analyses. ResultsRecruitment began in March 2024 and will continue until 100 participants have been recruited, which is expected in 2025. ConclusionsWe anticipate that our CVRM eCoach approach will be valuable in the primary prevention setting. During the crucial initial first months of habit formation, factors such as education, regular check-ups via the eCoach, and clear risk communication could support individuals in sustaining their medical or lifestyle interventions. We hypothesize that there will be a slight to moderate reduction in the 10-year risk of atherosclerotic cardiovascular disease, which over time will lead to significant health improvements on a larger scale. Trial RegistrationCCMO NL84584.096.23; https://onderzoekmetmensen.nl/nl/trial/56578https://www.researchprotocols.org/2025/1/e66068 |
| spellingShingle | Rutger van Mierlo Bart Scheenstra Joost Verbeek Anke Bruninx Petros Kalendralis Inigo Bermejo Andre Dekker Arnoud van 't Hof Marieke Spreeuwenberg Laura Hochstenbach Optimizing Cardiovascular Risk Management in Primary Care Using a Personalized eCoach Solution Enhanced by an Artificial Intelligence–Driven Clinical Prediction Model: Protocol from the Coronary Artery Disease Risk Estimation and Early Detection Consortium JMIR Research Protocols |
| title | Optimizing Cardiovascular Risk Management in Primary Care Using a Personalized eCoach Solution Enhanced by an Artificial Intelligence–Driven Clinical Prediction Model: Protocol from the Coronary Artery Disease Risk Estimation and Early Detection Consortium |
| title_full | Optimizing Cardiovascular Risk Management in Primary Care Using a Personalized eCoach Solution Enhanced by an Artificial Intelligence–Driven Clinical Prediction Model: Protocol from the Coronary Artery Disease Risk Estimation and Early Detection Consortium |
| title_fullStr | Optimizing Cardiovascular Risk Management in Primary Care Using a Personalized eCoach Solution Enhanced by an Artificial Intelligence–Driven Clinical Prediction Model: Protocol from the Coronary Artery Disease Risk Estimation and Early Detection Consortium |
| title_full_unstemmed | Optimizing Cardiovascular Risk Management in Primary Care Using a Personalized eCoach Solution Enhanced by an Artificial Intelligence–Driven Clinical Prediction Model: Protocol from the Coronary Artery Disease Risk Estimation and Early Detection Consortium |
| title_short | Optimizing Cardiovascular Risk Management in Primary Care Using a Personalized eCoach Solution Enhanced by an Artificial Intelligence–Driven Clinical Prediction Model: Protocol from the Coronary Artery Disease Risk Estimation and Early Detection Consortium |
| title_sort | optimizing cardiovascular risk management in primary care using a personalized ecoach solution enhanced by an artificial intelligence driven clinical prediction model protocol from the coronary artery disease risk estimation and early detection consortium |
| url | https://www.researchprotocols.org/2025/1/e66068 |
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