ArticleClinical kidney journal2019
Use of machine-learning algorithms to determine features of systolic blood pressure variability that predict poor outcomes in hypertensive patients.
Article in Clinical kidney journal, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it, 50 citations in OpenAlex.
- Artificial intelligence in the risk prediction models of cardiovascular disease and development of an independent validation screening tool: a systematic review.BMC medicine · 2024Pooled it
- A Systematic Review of Time Series Classification Techniques Used in Biomedical Applications.Sensors (Basel, Switzerland) · 2022Pooled it
- Physiological measures variability and risks of heart disease and stroke: evidence from three cohort studies.BMC medicine · 2024Article
- Application of artificial intelligence in hypertension.Clinical hypertension · 2024Review
- Longitudinal machine learning model for predicting systolic blood pressure in patients with heart failure.Journal of preventive medicine and hygiene · 2023Article
- Survey and Evaluation of Hypertension Machine Learning Research.Journal of the American Heart Association · 2023Article
- Workplace Predictors of Violence against Nurses Using Machine Learning Techniques: A Cross-Sectional Study Utilizing the National Standard of Psychological Workplace Health and Safety.Healthcare (Basel, Switzerland) · 2023Article
- Combating hypertension beyond genome-wide association studies: Microbiome and artificial intelligence as opportunities for precision medicine.Cambridge prisms. Precision medicine · 2023Review
- Interpretable classifiers for prediction of disability trajectories using a nationwide longitudinal database.BMC geriatrics · 2022Article
- Applications of artificial intelligence for hypertension management.Journal of clinical hypertension (Greenwich, Conn.) · 2021Review
- Prediction of Masked Hypertension and Masked Uncontrolled Hypertension Using Machine Learning.Frontiers in cardiovascular medicine · 2021Article
- Potential predictors of type-2 diabetes risk: machine learning, synthetic data and wearable health devices.BMC bioinformatics · 2020Article
- Artificial Intelligence and Hypertension: Recent Advances and Future Outlook.American journal of hypertension · 2020Review
- Article
- Wearable health devices and personal area networks: can they improve outcomes in haemodialysis patients?Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association · 2020Review
- The Population Health OutcomEs aNd Information EXchange (PHOENIX) Program - A Transformative Approach to Reduce the Burden of Chronic Disease.Online journal of public health informatics · 2020Article
- Machine learning and blood pressure.Journal of clinical hypertension (Greenwich, Conn.) · 2019Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWe re-analyzed data from the Systolic Blood Pressure Intervention Trial (SPRINT) trial to identify features of systolic blood pressure (SBP) variability that portend poor cardiovascular outcomes using a nonlinear machine-learning algorithm.
methodsWe included all patients who completed 1 year of the study without reaching any primary endpoint during the first year, specifically: myocardial infarction, other acute coronary syndromes, stroke, heart failure or death from a cardiovascular event (
resultsA total of 365 patients (4.1%) reached the combined primary outcome over 37 months of follow-up. The random forest classifier had an AUC of 0.71 on the testing set. The 10 most significant features selected in order of importance by the automated algorithm included the urine albumin/creatinine (CR) ratio, estimated glomerular filtration rate, age, serum CR, history of subclinical cardiovascular disease (CVD), cholesterol, a variable representing SBP signals using wavelet transformation, high-density lipoprotein, the 90th percentile of SBP and triglyceride level.
conclusionsWe successfully demonstrated use of random forest algorithm to define best prognostic longitudinal SBP representations. In addition to known risk factors for CVD, transformed variables for time series SBP measurements were found to be important in predicting poor cardiovascular outcomes and require further evaluation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.