BACKGROUND
Pre-treatment prediction of individual blood pressure (BP) response to anti-hypertensive drugs is important to select and optimize medication for promptly and safely achieving a target BP. However, it is challenging to predict individually variable BP responses to a specific regimen.
OBJECTIVE
This study aimed to develop supervised machine learning (ML) models for predicting patient-specific treatment effects using 24-hour ambulatory BP monitoring (ABPM) data.
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. Utilizing 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 labeled 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. 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.
CONCLUSIONS
ML algorithms accurately predict the post-treatment ambulatory BP levels, which may assist clinicians in personalizing anti-hypertensive treatment.
CLINICALTRIAL
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