ArticleInternational journal of epidemiology2023
Machine-learning-based high-benefit approach versus conventional high-risk approach in blood pressure management.
Article in International journal of epidemiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 1 of them a synthesis that pooled it.
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Who cites it
34 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Does clinical practice supported by artificial intelligence improve hypertension care management? A pilot systematic review.Hypertension research : official journal of the Japanese Society of Hypertension · 2024Pooled it
- Heterogeneous effects of Medicaid coverage on cardiovascular risk factors: secondary analysis of randomized controlled trial.BMJ (Clinical research ed.) · 2024Trial
- Estimating and discovering heterogeneous treatment effects using machine learning in epidemiological studies: a practical guide.International journal of epidemiology · 2026Review
- Health Equity Perspective on Data-Driven Treatment Decisions in Cardiovascular Care: Risk Assessments Versus Individualized Treatment Rules.Journal of the American Heart Association · 2026Observational
- Heterogeneous cardiovascular effects of sodium-glucose cotransporter 2 inhibitors in type 2 diabetes: a causal forest and target trial emulation study.European journal of preventive cardiology · 2026Article
- Who does tracing work for? Characteristics of clients successfully re-engaged in ART care in sub-Saharan Africa after a tracing intervention: A systematic review.PLOS global public health · 2026Article
- Therapeutic target of high fresh frozen plasma to red blood cell ratio in severe blunt trauma.Critical care (London, England) · 2025Article
- Review
- Sociodemographic heterogeneity in the association between social isolation and all-cause mortality among Japanese older adults: JAGES longitudinal panel study.The Lancet regional health. Western Pacific · 2025Article
- Artificial intelligence in cardiovascular pharmacotherapy: applications and perspectives.European heart journal · 2025Review
- Social Participation and Depressive Symptoms Among Older Adults.JAMA network open · 2025Article
- Machine learning-based high-benefit approach versus traditional high-risk approach in statin therapy: the Shizuoka Kokuho database study.Scientific reports · 2025Article
- Predictive Modeling of Heterogeneous Treatment Effects in RCTs: A Scoping Review.JAMA network open · 2025Article
- What Epidemiologists Can Do in the Era of Machine Learning and Artificial Intelligence.Journal of epidemiology · 2025Article
- Controversy in Hypertension: Pro-Side of the Argument Using Artificial Intelligence for Hypertension Diagnosis and Management.Hypertension (Dallas, Tex. : 1979) · 2025Review
- Artificial intelligence to improve cardiovascular population health.European heart journal · 2025Review
- Reformulating patient stratification for targeting interventions by accounting for severity of downstream outcomes resulting from disease onset: a case study in sepsis.Journal of the American Medical Informatics Association : JAMIA · 2025Observational
- Poverty and Childhood Obesity: Current Evidence and Methodologies for Future Research.Current obesity reports · 2025Review
- Generalized framework for identifying meaningful heterogenous treatment effects in observational studies: A parametric data-adaptive G-computation approach.Statistical methods in medical research · 2025Article
- When, why and how are estimated effects transported between populations? A scoping review of studies applying transportability methods.European journal of epidemiology · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
backgroundIn 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.
methodsThis 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.
resultsWe 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.
conclusionsThe 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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.