[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124693-en":3,"doc-seo-124693-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124693,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",7,"Healthcare","Machine-learning-based high-benefit approach versus conventional high-risk approach in blood pressure management","The study challenges the “high-risk approach” in blood pressure care by evaluating whether a machine-learning “high-benefit approach” better identifies who benefits from intensive systolic blood pressure control. Using randomized trial data, an individualized treatment effect model from a machine-learning causal forest was built to target participants with ITE > 0, then compared with targeting based on estimated high risk. Transportability was assessed using NHANES adults from 1999–2018, with results consistent across datasets.","TITLE:  \nMachine-learning-based highbenefit approach versus conventional high-risk approach in blood pressure management  \nAUTHOR(S):  \nInoue, Kosuke; Athey, Susan; Tsugawa, Yusuke  \nCITATION:  \nInoue, Kosuke ... [et al] . Machine-learning-based high-benefit approach versus conventional high-risk approach in blood pressure management. International Journal of Epidemiology 2023, 52(4): 1243-1256  \nISSUE DATE:  \n2023-08  \nURL:  \n[http://hdl.handle.net/2433/284582](http://hdl.handle.net/2433/284582)  \nRIGHT:  \n© The Author(s) 2023. Published by Oxford University Press on behalf of the International Epidemiological Association. ; This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercialNoDerivs licence, which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited.  \nA Self-archived copy in  \nKyoto University Research Information Repository [https://repository.kulib.kyoto-u.ac.jp](https://repository.kulib.kyoto-u.ac.jp)  \nIEA  \nInternational Epidemiological Association  \nInternational Journal of Epidemiology, 2023, 1243–1256 [https://doi.org/10.1093/ije/dyad037](https://doi.org/10.1093/ije/dyad037)  \nAdvance Access Publication Date: 4 April 2023 Original article  \nMiscellaneous  \nMachine-learning-based high-benefit approach versus conventional high-risk approach in blood pressure management  \nKosuke Inoue  , 1 * Susan Athey2 and Yusuke Tsugawa3,4  \n1 Department of Social Epidemiology, Graduate School of Medicine, Kyoto University, Kyoto, Japan, 2Graduate School of Business, Stanford University, Stanford, CA, USA, 3 Division of General Internal Medicine and Health Services Research, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA and 4 Department of Health Policy and Management, UCLA Fielding School of Public Health, Los Angeles, CA, USA  \n*Corresponding author. Department of Social Epidemiology, Graduate School of Medicine, Kyoto University, Floor 2, Science  \nFrontier Laboratory, Yoshida-konoe-cho, Sakyo-ku Kyoto, Kyoto 604-8146, Japan. E-mail: [inoue.kosuke.2j@kyoto-u.ac.jp](inoue.kosuke.2j@kyoto-u.ac.jp)  \nReceived 12 May 2022; Editorial decision 15 February 2023; Accepted 10 March 2023  \nAbstract  \nBackground: 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 machinelearning method (‘high-benefit approach’) may improve population health outcomes. Methods: This study included 10672 participants who were randomized to systolic blood pressure (SBP) target of either \u003C120 mmHg (intensive treatment) or \u003C140 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􀀂130mmHg) . Using transportability formula, we also estimated the effect of these approaches among 14575 US adults from National Health and Nutrition Examination Surveys (NHANES) 1999–2018 .  \nResults: We found that 78.9% of individuals with SBP 􀀂130mmHg benefited from the intensive SBP control. The high-benefit approach outperformed the high-risk approach [average treatment effect (95% CI), þ9.36 (8.33–10.44) vs þ1.65 (0.36–2.84) percentage point; difference between these two approaches, þ7.71 (6.79–8.67) percentage points, P-value \u003C0.001] .  \nThe results were consistent when we transported the results to the NHANES data. Conclusions: The","cbCaicI6R2Zfhwss","https://ap.wps.com/l/cbCaicI6R2Zfhwss","pdf",4446640,1,15,"English","en",105,"# Abstract\n## Background\n## Methods\n## Results\n## Conclusions\n# Key Messages","[{\"question\":\"What problem does the study address in blood pressure management?\",\"answer\":\"Clinicians often assume that patients with the highest risk gain the most from intensive treatment. The study tests whether a machine-learning approach can better target those who benefit most.\"},{\"question\":\"How is the “high-benefit approach” different from the “high-risk approach”?\",\"answer\":\"The high-benefit approach treats based on individualized treatment effect estimates (ITE \\u003e 0), while the high-risk approach treats based on estimated high risk (e.g., SBP threshold).\"},{\"question\":\"What do the results show about which patients benefit from intensive SBP control?\",\"answer\":\"The high-benefit approach outperformed the high-risk approach with a larger treatment effect, and results were consistent when transported to NHANES data.\"}]","Machine-learning-based high-benefit approach versus conventional high-risk approach in blood pressure management | PDF",1785893948,38,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-based-high-benefit-approach-versus-conventional-high-risk-approach-in-blood-pressure-management","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-based-high-benefit-approach-versus-conventional-high-risk-approach-in-blood-pressure-management/124693/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the study address in blood pressure management?","Question",{"text":75,"@type":76},"Clinicians often assume that patients with the highest risk gain the most from intensive treatment. The study tests whether a machine-learning approach can better target those who benefit most.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is the “high-benefit approach” different from the “high-risk approach”?",{"text":80,"@type":76},"The high-benefit approach treats based on individualized treatment effect estimates (ITE > 0), while the high-risk approach treats based on estimated high risk (e.g., SBP threshold).",{"name":82,"@type":73,"acceptedAnswer":83},"What do the results show about which patients benefit from intensive SBP control?",{"text":84,"@type":76},"The high-benefit approach outperformed the high-risk approach with a larger treatment effect, and results were consistent when transported to NHANES data.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,118,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":116,"slug":117},40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]