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Value of a Machine Learning Approach for Predicting Clinical Outcomes in Young Patients With Hypertension
- Source :
- Hypertension. 75:1271-1278
- Publication Year :
- 2020
- Publisher :
- Ovid Technologies (Wolters Kluwer Health), 2020.
-
Abstract
- Risk stratification of young patients with hypertension remains challenging. Generally, machine learning (ML) is considered a promising alternative to traditional methods for clinical predictions because it is capable of processing large amounts of complex data. We, therefore, explored the feasibility of an ML approach for predicting outcomes in young patients with hypertension and compared its performance with that of approaches now commonly used in clinical practice. Baseline clinical data and a composite end point—comprising all-cause death, acute myocardial infarction, coronary artery revascularization, new-onset heart failure, new-onset atrial fibrillation/atrial flutter, sustained ventricular tachycardia/ventricular fibrillation, peripheral artery revascularization, new-onset stroke, end-stage renal disease—were evaluated in 508 young patients with hypertension (30.83±6.17 years) who had been treated at a tertiary hospital. Construction of the ML model, which consisted of recursive feature elimination, extreme gradient boosting, and 10-fold cross-validation, was performed at the 33-month follow-up evaluation, and the model’s performance was compared with that of the Cox regression and recalibrated Framingham Risk Score models. An 11-variable combination was considered most valuable for predicting outcomes using the ML approach. The C statistic for identifying patients with composite end points was 0.757 (95% CI, 0.660–0.854) for the ML model, whereas for Cox regression model and the recalibrated Framingham Risk Score model it was 0.723 (95% CI, 0.636–0.810) and 0.529 (95% CI, 0.403–0.655). The ML approach was comparable with Cox regression for determining the clinical prognosis of young patients with hypertension and was better than that of the recalibrated Framingham Risk Score model.
- Subjects :
- Adult
Adolescent
Heart Diseases
medicine.medical_treatment
030204 cardiovascular system & hematology
Revascularization
Machine learning
computer.software_genre
Models, Biological
Machine Learning
Young Adult
03 medical and health sciences
0302 clinical medicine
Internal Medicine
medicine
Humans
Prospective Studies
030212 general & internal medicine
Myocardial infarction
Stroke
Antihypertensive Agents
Proportional Hazards Models
Framingham Risk Score
business.industry
Proportional hazards model
Atrial fibrillation
Prognosis
medicine.disease
Hospitalization
Treatment Outcome
Heart failure
Hypertension
Kidney Failure, Chronic
Artificial intelligence
business
computer
Atrial flutter
Follow-Up Studies
Forecasting
Subjects
Details
- ISSN :
- 15244563 and 0194911X
- Volume :
- 75
- Database :
- OpenAIRE
- Journal :
- Hypertension
- Accession number :
- edsair.doi.dedup.....99af198e3d093d991ea0ea3a004cbf77