Tran, T. Q. B. et al. (2024) Assessing machine learning for diagnostic classification of hypertension types identified by ambulatory blood pressure monitoring. CJC Open, (doi: 10.1016/j.cjco.2024.03.005) (Early Online Publication)
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Abstract
Background: Inaccurate blood pressure classification results in inappropriate treatment. We tested if machine learning (ML), using routine clinical data, can serve as a reliable alternative to Ambulatory Blood Pressure Monitoring (ABPM) in classifying blood pressure status. Methods: This study employed a multi-centre approach involving three derivation cohorts from Glasgow, Gdańsk, and Birmingham, and a fourth independent evaluation cohort. ML models were trained using office BP, ABPM, and clinical, laboratory, and demographic data, collected from patients referred for hypertension assessment. Seven ML algorithms were trained to classify patients into five groups: Normal/Target, Hypertension-Masked, Normal/Target-White-Coat, Hypertension-White-Coat, and Hypertension. The 10-year cardiovascular outcomes and 27-year all-cause mortality risks were calculated for the ML-derived groups using the Cox proportional hazards model. Results: Overall XGBoost showed the highest AUROC of 0.85-0.88 across derivation cohorts, Glasgow (n=923; 43% females; age 50.7±16.3 years), Gdańsk (n=709; 46% females; age 54.4±13 years), and Birmingham (n=1,222; 56% females; age 55.7±14 years). But accuracy (0·57-0·72) and F1 scores (0·57-0·69) were low across the three patient cohorts. The evaluation cohort (n=6213, 51% females; age 51.2±10.8 years) indicated elevated 10-year risks of composite cardiovascular events in the Normal/Target-White-Coat and Hypertension-White-Coat groups, with heightened 27-year all-cause mortality observed in all groups except Hypertension-Masked, compared to the Normal/Target group. Conclusions: Machine learning has limited potential in accurate blood pressure classification when ABPM is unavailable. Larger studies including diverse patient groups and different resource settings are warranted.
| Item Type: | Articles |
|---|---|
| Additional Information: | SP and AFD are supported by the British Heart Foundation Centre of Excellence Award (RE/18/6/34217) and the UKRI Strength in Places Fund (SIPF00007/1). |
| Status: | Early Online Publication |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Lip, Dr Stefanie and Mccallum, Dr Linsay and Padmanabhan, Professor Sandosh and Dominiczak, Professor Anna and Du Toit, Ms Clea |
| Authors: | Tran, T. Q. B., Lip, S., du Toit, C., Kalaria, T. K., Bhaskar, R. K., O'Neil, A. Q., Graff, B., Hoffmann, M., Szyndler, A., Polonis, K., Wolf, J., Reddy, S., Narkiewicz, K., Dasgupta, I., Dominiczak, A. F., Visweswaran, S., McCallum, L., and Padmanabhan, S. |
| College/School: | College of Medical Veterinary and Life Sciences > School of Cardiovascular & Metabolic Health |
| Journal Name: | CJC Open |
| Publisher: | Elsevier |
| ISSN: | 2589-790X |
| ISSN (Online): | 2589-790X |
| Published Online: | 15 March 2024 |
| Copyright Holders: | Copyright © 2024 Published by Elsevier Inc. on behalf of the Canadian Cardiovascular Society |
| First Published: | First published in CJC Open 2024 |
| Publisher Policy: | Reproduced under a Creative Commons license |
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