Key result
A CatBoost machine learning model accurately predicted post-treatment 24-hour systolic and diastolic blood pressure changes with significant correlations to measured values (r=0.74 and r=0.68).
Why the study?
Pre-treatment prediction of individual blood pressure response to anti-hypertensive medication is important to determine specific regimens for promptly and safely achieving target blood pressure.
Do machine learning models accurately predict post-treatment ambulatory blood pressure in patients with hypertension?
Observational (n=1,129)
Do machine learning models accurately predict post-treatment ambulatory blood pressure in patients with hypertension?
Effect estimate: r = 0.74 (systolic), r = 0.68 (diastolic)
Machine learning algorithms can accurately predict post-treatment ambulatory blood pressure levels, potentially aiding in personalized anti-hypertensive therapy.
Purpose. Pre-treatment prediction of individual blood pressure (BP) response to anti-hypertensive medication is important to determine the specific regimen for promptly and safely achieving a target BP. This study aimed to develop supervised machine learning (ML) models for predicting patient-specific treatment effects using 24-hour ambulatory BP monitoring (ABPM) data.Materials and Methods. A total of 1,129 patients who had both baseline and follow-up ABPM data were randomly assigned into training, validation and test sets in a 3:1:1 ratio. Utilising the features including clinical and laboratory findings, initial ABPM data, and anti-hypertensive medication at baseline and at follow-up, ML models were developed to predict post-treatment individual BP response. Each case was labelled by the mean 24-hour and daytime BPs derived from the follow-up ABPM.Results. At baseline, 616 (55%) patients had been treated using mono or combination therapy with 45 anti-hypertensive drugs and the remaining 513 (45%) patients had been untreated (drug-naïve). By using CatBoost, the difference between predicted vs. measured mean 24-hour systolic BP at follow-up was 8.4 ± 7.0 mm Hg (% difference of 6.6% ± 5.7%). The difference between predicted vs. measured mean 24-hour diastolic BP was 5.3 ± 4.3 mm Hg (% difference of 6.8% ± 5.5%). There were significant correlations between the CatBoost-predicted vs. the ABPM-measured changes in the mean 24-hour Systolic (r = 0.74) and diastolic (r = 0.68) BPs from baseline to follow-up. Even in the patients with renal insufficiency or diabetes, the correlations between CatBoost-predicted vs. ABPM-measured BP changes were significant.Conclusion. ML algorithms accurately predict the post-treatment ambulatory BP levels, which may assist clinicians in personalising anti-hypertensive treatment.
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Hae et al. (2023) conducted an observational in Hypertension (n=1,129). Machine learning models (CatBoost) vs. Measured ABPM was evaluated on Post-treatment individual blood pressure response (mean 24-hour and daytime BPs) (r = 0.74 (systolic), r = 0.68 (diastolic)). A CatBoost machine learning model accurately predicted post-treatment 24-hour systolic and diastolic blood pressure changes with significant correlations to measured values (r=0.74 and r=0.68).
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