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Machine-learning-based high-benefit approach versus conventional high-risk approach in blood pressure management
Kosuke Inoue1, Susan Athey2, Yusuke Tsugawa3,4
1Department of Social Epidemiology, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
Insights
A new machine-learning approach focusing on highest individual benefit, rather than just high-risk factors, significantly improved treatment effectiveness for systolic blood pressure control, offering better population health outcomes.
Area of Science:
- Cardiovascular Medicine
- Machine Learning in Healthcare
- Clinical Trial Analysis
Background:
- Conventional medical practice often assumes high-risk patients benefit most from treatment.
- This 'high-risk approach' may not optimize population health outcomes.
- An alternative 'high-benefit approach' using machine learning could identify individuals with the greatest potential treatment benefit.
Purpose of the Study:
- To compare the effectiveness of a novel machine-learning-based 'high-benefit approach' against the traditional 'high-risk approach' for managing systolic blood pressure.
- To evaluate the potential of the high-benefit approach in improving overall population health outcomes.
Main Methods:
- Utilized data from 10,672 participants in two randomized controlled trials on systolic blood pressure targets (<120 mmHg vs. <140 mmHg).
- Applied machine-learning causal forest to predict individualized treatment effect (ITE) of intensive systolic blood pressure control.
- Compared outcomes between the high-benefit approach (ITE > 0) and the high-risk approach (systolic blood pressure ≥ 130 mmHg).
- Estimated approach effects in 14,575 US adults using NHANES data via transportability formulas.
Main Results:
- The high-benefit approach demonstrated superior performance compared to the high-risk approach.
- Average treatment effect was +9.36 percentage points for high-benefit vs. +1.65 for high-risk.
- The difference between the two approaches was +7.71 percentage points (P < 0.001).
- Findings were consistent when applied to the NHANES dataset.
Conclusions:
- A machine-learning-driven 'high-benefit approach' significantly enhances treatment effectiveness over the conventional 'high-risk approach'.
- This strategy holds potential for maximizing treatment efficacy and improving population health.
- Further validation in future research is warranted.
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 (ITE) of intensive SBP control on the reduction in cardiovascular outcomes at 3 years. We then compared the performance of high-benefit approach (treating individuals with ITE >0) versus the high-risk approach (treating individuals with SBP ≥130 mmHg). Using transportability formula, we also estimated the effect of these approaches among 14 575 US adults from National Health and Nutrition Examination Surveys (NHANES) 1999-2018.
Results:
We found that 78.9% of individuals with SBP ≥130 mmHg benefited from the intensive SBP control. The high-benefit approach outperformed the high-risk approach [average treatment effect (95% CI), +9.36 (8.33-10.44) vs +1.65 (0.36-2.84) percentage point; difference between these two approaches, +7.71 (6.79-8.67) percentage points, P-value <0.001]. The results were consistent when we transported the results to the NHANES data.
Conclusions:
The machine-learning-based high-benefit approach outperformed the high-risk approach with a larger treatment effect. These findings indicate that the high-benefit approach has the potential to maximize the effectiveness of treatment rather than the conventional high-risk approach, which needs to be validated in future research.
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