Machine-learning-based high-benefit approach versus conventional high-risk approach in blood pressure management.
Machine-learning-based high-benefit approach versus conventional high-risk approach in blood pressure management.
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DOI：
10.1093/ije/dyad037
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发表时间：
2023-08-02
影响因子：
7.7
通讯作者：
中科院分区：
文献类型：
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作者：
在医学上，临床医生在一个隐含的假设下治疗个体，即高风险患者将从治疗中获益最多（“高风险方法”）。然而，使用一种新的机器学习方法（“高效益方法”）治疗具有最高估计效益的个体可能会改善人口健康结果。本研究纳入了10672名参与者，他们被随机分为收缩压（SBP）目标<120 mmHg（强化治疗）或<140 mmHg（标准治疗），来自两个随机对照试验（收缩压干预试验和控制糖尿病血压心血管风险的行动）。我们应用机器学习因果林建立了强化收缩压控制的个体化治疗效果（ITE）对3年心血管结局降低的预测模型。然后，我们比较了高效益方法（治疗ITE患者）和高风险方法（治疗收缩压≥130 mmHg的患者）的性能。使用可运输性公式，我们还估计了1999-2018年国家健康与营养检查调查（NHANES）中14,575名美国成年人中这些方法的效果。我们发现78.9%收缩压≥130 mmHg的个体受益于强化收缩压控制。高效益方法优于高风险方法[平均治疗效果（95% CI）， +9.36 (8.33-10.44) vs +1.65（0.36-2.84）个百分点；两种方法差异+7.71（6.79 ~ 8.67）个百分点，p值<0.001]。当我们将结果传输到NHANES数据时，结果是一致的。基于机器学习的高效益方法以更大的治疗效果优于高风险方法。这些发现表明，与传统的高风险方法相比，高效益方法具有最大治疗效果的潜力，这需要在未来的研究中进行验证。
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. 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. 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. 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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影响因子：
39.2
作者：
Basu S;Sussman JB;Hayward RA
通讯作者：
Hayward RA
DOI：
10.1136/bmj.k2357
发表时间：
2018-07-11
期刊：
BMJ (Clinical research ed.)
影响因子：
--
作者：
Khera R;Lu Y;Lu J;Saxena A;Nasir K;Jiang L;Krumholz HM
通讯作者：
Krumholz HM
DOI：
10.1177/1740774514537404
发表时间：
2014-10
期刊：
Clinical trials (London, England)
影响因子：
--
作者：
Ambrosius WT;Sink KM;Foy CG;Berlowitz DR;Cheung AK;Cushman WC;Fine LJ;Goff DC Jr;Johnson KC;Killeen AA;Lewis CE;Oparil S;Reboussin DM;Rocco MV;Snyder JK;Williamson JD;Wright JT Jr;Whelton PK;SPRINT Study Research Group
通讯作者：
SPRINT Study Research Group
影响因子：
158.5
作者：
Flint, Alexander C.;Conell, Carol;Bhatt, Deepak L.
通讯作者：
Bhatt, Deepak L.
影响因子：
37.8
作者：
Inoue, Kosuke;Seeman, Teresa E.;Horwich, Tamara;Budoff, Matthew J.;Watson, Karol E.
通讯作者：
Watson, Karol E.