Machine-learning-based high-benefit approach versus conventional high-risk approach in blood pressure management.

ABSTRACT:

Background

In medicine, clinicians treat individuals under an implicit assumption that high-risk patients would benefit most from the treatment ('high-risk approach'). However, treating individuals with the highest estimated benefit using a novel machine-learning method ('high-benefit approach') may improve population health outcomes.

Methods

This study included 10 672 participants who were randomized to systolic blood pressure (SBP) target of either <120 mmHg (intensive treatment) or <140 mmHg (standard treatment) from two randomized controlled trials (Systolic Blood Pressure Intervention Trial, and Action to Control Cardiovascular Risk in Diabetes Blood Pressure). We applied the machine-learning causal forest to develop a prediction model of individualized treatment effect

SUBMITTER: Inoue K 

PROVIDER: S-EPMC10396418 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature