ArticleJournal of medical systems2019
Pulse Wave Velocity and Machine Learning to Predict Cardiovascular Outcomes in Prediabetic and Diabetic Populations.
Article in Journal of medical systems, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 37 citations in OpenAlex.
- Developing technologies to assess vascular ageing: a roadmap from VascAgeNet.Physiological measurement · 2024Review
- Development of a recommendation system and data analysis in personalized medicine: an approach towards healthy vascular ageing.Health information science and systems · 2024Article
- Chronic Kidney Disease as a Cardiovascular Disorder-Tonometry Data Analyses.International journal of environmental research and public health · 2022Observational
- Machine learning models for prediction of co-occurrence of diabetes and cardiovascular diseases: a retrospective cohort study.Journal of diabetes and metabolic disorders · 2022Article
- Identification of Prediabetes Discussions in Unstructured Clinical Documentation: Validation of a Natural Language Processing Algorithm.JMIR medical informatics · 2022Article
- Assessment Parameters for Arrayed Pulse Wave Analysis and Application in Hypertensive Disorders.Evidence-based complementary and alternative medicine : eCAM · 2022Article
- Leveraging the potential of machine learning for assessing vascular ageing: state-of-the-art and future research.European heart journal. Digital health · 2021Article
- Predictive modeling of hypophosphatasia based on a case series of adult patients with persistent hypophosphatasemia.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2021Article
- Development and Validation of a Machine Learning Model Using Administrative Health Data to Predict Onset of Type 2 Diabetes.JAMA network open · 2021Article
- Predicting adverse outcomes due to diabetes complications with machine learning using administrative health data.NPJ digital medicine · 2021Article
- Use of Machine Learning Approaches in Clinical Epidemiological Research of Diabetes.Current diabetes reports · 2020Review
- Cardiac magnetic resonance assessment of aortic distensibility in prediabetic patients.The Egyptian heart journal : (EHJ) : official bulletin of the Egyptian Society of Cardiology · 2020Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Few studies have addressed the predictive value of arterial stiffness determined by pulse wave velocity (PWV) in a high-risk population with no prevalent cardiovascular disease and with obesity, hypertension, hyperglycemia, and preserved kidney function. This longitudinal, retrospective study enrolled 88 high-risk patients and had a follow-up time of 12.4 years. We collected clinical and laboratory data, as well as information on arterial stiffness parameters using arterial tonometry and measurements from ambulatory blood pressure monitoring. We considered nonfatal, incident cardiovascular events as the primary outcome. Given the small size of our dataset, we used survival analysis (i.e., Cox proportional hazards model) combined with a machine learning-based algorithm/penalization method to evaluate the data. Our predictive model, calculated with Cox regression and least absolute shrinkage and selection operator (LASSO), included body mass index, diabetes mellitus, gender (male), and PWV. We recorded 16 nonfatal cardiovascular events (5 myocardial infarctions, 5 episodes of heart failure, and 6 strokes). The adjusted hazard ratio for PWV was 1.199 (95% confidence interval: 1.09-1.37, p < 0.001). Arterial stiffness was a predictor of cardiovascular disease development, as determined by PWV in a high-risk population. Thus, in obese, hypertensive, hyperglycemic patients with preserved kidney function, PWV can serve as a prognostic factor for major adverse cardiac events.
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