Antihypertensive drug recommendations for reducing arterial stiffness in patients with hypertension: machine learning–based multicohort (RIGIPREV) study
[EN]High systolic blood pressure is one of the leading global risk factors for mortality, contributing significantly to cardiovascular diseases. Despite advances in treatment, a large proportion of patients with hypertension do not achieve optimal blood pressure control. Arterial stiffness (AS), mea...
| Autores: | , , , , , , , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Universidad de Salamanca (USAL) |
| Repositorio: | GREDOS. Repositorio Institucional de la Universidad de Salamanca |
| OAI Identifier: | oai:gredos.usal.es:10366/168913 |
| Acceso en línea: | http://hdl.handle.net/10366/168913 |
| Access Level: | acceso abierto |
| Palabra clave: | Antihypertensive Drugs Models Patients Pulse wave velocity Recommendations Hypertension Machine learning Drug recommendations Arterial stiffness RIGIPREV Aged Vascular Stiffness Humans Pulse Wave Analysis Antihypertensive Agents Cohort Studies Middle Aged antihipertensivos humanos análisis de la onda del pulso estudios de cohortes anciano rigidez vascular mediana edad hipertensión |
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Antihypertensive drug recommendations for reducing arterial stiffness in patients with hypertension: machine learning–based multicohort (RIGIPREV) study |
| title |
Antihypertensive drug recommendations for reducing arterial stiffness in patients with hypertension: machine learning–based multicohort (RIGIPREV) study |
| spellingShingle |
Antihypertensive drug recommendations for reducing arterial stiffness in patients with hypertension: machine learning–based multicohort (RIGIPREV) study Cavero-Redondo, Ivan Antihypertensive Drugs Models Patients Pulse wave velocity Recommendations Hypertension Machine learning Drug recommendations Arterial stiffness RIGIPREV Aged Vascular Stiffness Humans Pulse Wave Analysis Antihypertensive Agents Cohort Studies Hypertension Middle Aged antihipertensivos humanos análisis de la onda del pulso estudios de cohortes anciano rigidez vascular mediana edad hipertensión |
| title_short |
Antihypertensive drug recommendations for reducing arterial stiffness in patients with hypertension: machine learning–based multicohort (RIGIPREV) study |
| title_full |
Antihypertensive drug recommendations for reducing arterial stiffness in patients with hypertension: machine learning–based multicohort (RIGIPREV) study |
| title_fullStr |
Antihypertensive drug recommendations for reducing arterial stiffness in patients with hypertension: machine learning–based multicohort (RIGIPREV) study |
| title_full_unstemmed |
Antihypertensive drug recommendations for reducing arterial stiffness in patients with hypertension: machine learning–based multicohort (RIGIPREV) study |
| title_sort |
Antihypertensive drug recommendations for reducing arterial stiffness in patients with hypertension: machine learning–based multicohort (RIGIPREV) study |
| dc.creator.none.fl_str_mv |
Cavero-Redondo, Ivan Martínez Rodrigo, Arturo Saz Lara, Alicia Moreno Herraiz, Nerea Casado Vicente, Verónica Gómez Sánchez, Leticia García Ortiz, Luis Gómez Marcos, Manuel Ángel |
| author |
Cavero-Redondo, Ivan |
| author_facet |
Cavero-Redondo, Ivan Martínez Rodrigo, Arturo Saz Lara, Alicia Moreno Herraiz, Nerea Casado Vicente, Verónica Gómez Sánchez, Leticia García Ortiz, Luis Gómez Marcos, Manuel Ángel |
| author_role |
author |
| author2 |
Martínez Rodrigo, Arturo Saz Lara, Alicia Moreno Herraiz, Nerea Casado Vicente, Verónica Gómez Sánchez, Leticia García Ortiz, Luis Gómez Marcos, Manuel Ángel |
| author2_role |
author author author author author author author |
| dc.subject.none.fl_str_mv |
Antihypertensive Drugs Models Patients Pulse wave velocity Recommendations Hypertension Machine learning Drug recommendations Arterial stiffness RIGIPREV Aged Vascular Stiffness Humans Pulse Wave Analysis Antihypertensive Agents Cohort Studies Hypertension Middle Aged antihipertensivos humanos análisis de la onda del pulso estudios de cohortes anciano rigidez vascular mediana edad hipertensión |
| topic |
Antihypertensive Drugs Models Patients Pulse wave velocity Recommendations Hypertension Machine learning Drug recommendations Arterial stiffness RIGIPREV Aged Vascular Stiffness Humans Pulse Wave Analysis Antihypertensive Agents Cohort Studies Hypertension Middle Aged antihipertensivos humanos análisis de la onda del pulso estudios de cohortes anciano rigidez vascular mediana edad hipertensión |
| description |
[EN]High systolic blood pressure is one of the leading global risk factors for mortality, contributing significantly to cardiovascular diseases. Despite advances in treatment, a large proportion of patients with hypertension do not achieve optimal blood pressure control. Arterial stiffness (AS), measured by pulse wave velocity (PWV), is an independent predictor of cardiovascular events and overall mortality. Various antihypertensive drugs exhibit differential effects on PWV, but the extent to which these effects vary depending on individual patient characteristics is not well understood. Given the complexity of selecting the most appropriate antihypertensive medication for reducing PWV, machine learning (ML) techniques offer an opportunity to improve personalized treatment recommendations. This study aims to develop an ML model that provides personalized recommendations for antihypertensive medications aimed at reducing PWV. The model considers individual patient characteristics, such as demographic factors, clinical data, and cardiovascular measurements, to identify the most suitable antihypertensive agent for improving AS. This study, known as the RIGIPREV study, used data from the EVA, LOD-DIABETES, and EVIDENT studies involving individuals with hypertension with baseline and follow-up measurements. Antihypertensive drugs were grouped into classes such as angiotensin-converting enzyme inhibitors (ACEIs), angiotensin receptor blockers (ARBs), β-blockers, diuretics, and combinations of diuretics with ACEIs or ARBs. The primary outcomes were carotid-femoral and brachial-ankle PWV, while the secondary outcomes included various cardiovascular, anthropometric, and biochemical parameters. A multioutput regressor using 6 random forest models was used to predict the impact of each antihypertensive class on PWV reduction. Model performance was evaluated using the coefficient of determination (R2) and mean squared error. The random forest models exhibited strong predictive capabilities, with internal validation yielding R2 values between 0.61 and 0.74, while external validation showed a range of 0.26 to 0.46. The mean squared values ranged from 0.08 to 0.22 for internal validation and from 0.29 to 0.45 for external validation. Variable importance analysis revealed that glycated hemoglobin and weight were the most critical predictors for ACEIs, while carotid-femoral PWV and total cholesterol were key variables for ARBs. The decision tree model achieved an accuracy of 84.02% in identifying the most suitable antihypertensive drug based on individual patient characteristics. Furthermore, the system's recommendations for ARBs matched 55.3% of patients' original prescriptions. This study demonstrates the utility of ML techniques in providing personalized treatment recommendations for antihypertensive therapy. By accounting for individual patient characteristics, the model improves the selection of drugs that control blood pressure and reduce AS. These findings could significantly aid clinicians in optimizing hypertension management and reducing cardiovascular risk. However, further studies with larger and more diverse populations are necessary to validate these results and extend the model's applicability. |
| publishDate |
2024 |
| dc.date.none.fl_str_mv |
2024 2026 2026 |
| dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
| format |
article |
| status_str |
publishedVersion |
| dc.identifier.none.fl_str_mv |
http://hdl.handle.net/10366/168913 |
| url |
http://hdl.handle.net/10366/168913 |
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Inglés |
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Inglés |
| dc.rights.none.fl_str_mv |
Attribution-4.0 Internacional http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess |
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Attribution-4.0 Internacional http://creativecommons.org/licenses/by/4.0/ |
| eu_rights_str_mv |
openAccess |
| dc.publisher.none.fl_str_mv |
JMIR Publications |
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JMIR Publications |
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reponame:GREDOS. Repositorio Institucional de la Universidad de Salamanca instname:Universidad de Salamanca (USAL) |
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Universidad de Salamanca (USAL) |
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GREDOS. Repositorio Institucional de la Universidad de Salamanca |
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GREDOS. Repositorio Institucional de la Universidad de Salamanca |
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1869421773897859072 |
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Antihypertensive drug recommendations for reducing arterial stiffness in patients with hypertension: machine learning–based multicohort (RIGIPREV) studyCavero-Redondo, IvanMartínez Rodrigo, ArturoSaz Lara, AliciaMoreno Herraiz, NereaCasado Vicente, VerónicaGómez Sánchez, LeticiaGarcía Ortiz, LuisGómez Marcos, Manuel ÁngelAntihypertensiveDrugsModelsPatientsPulse wave velocityRecommendationsHypertensionMachine learningDrug recommendationsArterial stiffnessRIGIPREVAgedVascular StiffnessHumansPulse Wave AnalysisAntihypertensive AgentsCohort StudiesHypertensionMiddle Agedantihipertensivoshumanosanálisis de la onda del pulsoestudios de cohortesancianorigidez vascularmediana edadhipertensión[EN]High systolic blood pressure is one of the leading global risk factors for mortality, contributing significantly to cardiovascular diseases. Despite advances in treatment, a large proportion of patients with hypertension do not achieve optimal blood pressure control. Arterial stiffness (AS), measured by pulse wave velocity (PWV), is an independent predictor of cardiovascular events and overall mortality. Various antihypertensive drugs exhibit differential effects on PWV, but the extent to which these effects vary depending on individual patient characteristics is not well understood. Given the complexity of selecting the most appropriate antihypertensive medication for reducing PWV, machine learning (ML) techniques offer an opportunity to improve personalized treatment recommendations. This study aims to develop an ML model that provides personalized recommendations for antihypertensive medications aimed at reducing PWV. The model considers individual patient characteristics, such as demographic factors, clinical data, and cardiovascular measurements, to identify the most suitable antihypertensive agent for improving AS. This study, known as the RIGIPREV study, used data from the EVA, LOD-DIABETES, and EVIDENT studies involving individuals with hypertension with baseline and follow-up measurements. Antihypertensive drugs were grouped into classes such as angiotensin-converting enzyme inhibitors (ACEIs), angiotensin receptor blockers (ARBs), β-blockers, diuretics, and combinations of diuretics with ACEIs or ARBs. The primary outcomes were carotid-femoral and brachial-ankle PWV, while the secondary outcomes included various cardiovascular, anthropometric, and biochemical parameters. A multioutput regressor using 6 random forest models was used to predict the impact of each antihypertensive class on PWV reduction. Model performance was evaluated using the coefficient of determination (R2) and mean squared error. The random forest models exhibited strong predictive capabilities, with internal validation yielding R2 values between 0.61 and 0.74, while external validation showed a range of 0.26 to 0.46. The mean squared values ranged from 0.08 to 0.22 for internal validation and from 0.29 to 0.45 for external validation. Variable importance analysis revealed that glycated hemoglobin and weight were the most critical predictors for ACEIs, while carotid-femoral PWV and total cholesterol were key variables for ARBs. The decision tree model achieved an accuracy of 84.02% in identifying the most suitable antihypertensive drug based on individual patient characteristics. Furthermore, the system's recommendations for ARBs matched 55.3% of patients' original prescriptions. This study demonstrates the utility of ML techniques in providing personalized treatment recommendations for antihypertensive therapy. By accounting for individual patient characteristics, the model improves the selection of drugs that control blood pressure and reduce AS. These findings could significantly aid clinicians in optimizing hypertension management and reducing cardiovascular risk. However, further studies with larger and more diverse populations are necessary to validate these results and extend the model's applicability.JMIR Publications202620262024info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10366/168913reponame:GREDOS. Repositorio Institucional de la Universidad de Salamancainstname:Universidad de Salamanca (USAL)InglésAttribution-4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:gredos.usal.es:10366/1689132026-06-07T06:28:51Z |
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15,81155 |