{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T12:30:34Z","timestamp":1784205034435,"version":"3.55.0"},"reference-count":22,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T00:00:00Z","timestamp":1754092800000},"content-version":"vor","delay-in-days":1,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100018684","name":"National Health Commission of the People's Republic of China","doi-asserted-by":"publisher","award":["WKZX2023JG0128"],"award-info":[{"award-number":["WKZX2023JG0128"]}],"id":[{"id":"10.13039\/501100018684","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J of Clinical Hypertension"],"published-print":{"date-parts":[[2025,8]]},"abstract":"<jats:title>ABSTRACT<\/jats:title><jats:p>Current hypertension guidelines lack personalized strategies for blood pressure control. While the Systolic Blood Pressure Intervention Trial (SPRINT) demonstrated benefits of intensive blood pressure lowering, identifying optimal candidates for such treatment remains challenging. We developed and validated a risk stratification model using data from 9139 SPRINT participants. The model incorporated 11 clinical variables through multivariable Cox regression analysis. Patients were stratified into low\u2010, medium\u2010, and high\u2010risk groups. The study protocol was registered at ClinicalTrials.gov (NCT01206062). The model showed good discrimination with C\u2010indices of 0.7354 (95% CI: 0.7065\u20130.7710) and 0.6894 (95% CI: 0.6545\u20130.7266) at 3 years for training and validation sets, respectively. Intensive treatment significantly reduced cardiovascular events in medium\u2010risk (3.17%\u00a0vs. 5.11%, <jats:italic>p<\/jats:italic> = 0.0376) and high\u2010risk groups (9.34%\u00a0vs. 11.86%, <jats:italic>p<\/jats:italic> = 0.0269), while showing a nonsignificant trend in the low\u2010risk group (2.87%\u00a0vs. 3.34%, <jats:italic>p<\/jats:italic> = 0.0870). The Rank\u2010Weighted Average Treatment Effect analysis (16.06) supported potential benefits from individualized treatment allocation. No increased risk of severe adverse events was observed across risk groups. Our risk stratification model effectively identifies patients who derive significant cardiovascular benefits from intensive blood pressure lowering, particularly in medium\u2010 and high\u2010risk groups. This approach could guide more personalized hypertension management strategies.<\/jats:p>","DOI":"10.1111\/jch.70098","type":"journal-article","created":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T08:59:36Z","timestamp":1754125176000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Risk Stratification Model for Predicting Benefits of Intensive Blood Pressure Treatment: Analysis of the Systolic Blood Pressure Intervention Trial"],"prefix":"10.1111","volume":"27","author":[{"given":"Na","family":"Li","sequence":"first","affiliation":[{"name":"Department of Health Care (Geriatrics) China\u2010Japan Friendship Hospital Ministry of Health  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhixin","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Mathematical Sciences Ball State University  Muncie Indiana USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Quan","family":"Yuan","sequence":"additional","affiliation":[{"name":"Department of Information System and Operations Management Ball State University  Muncie Indiana USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-4356-5833","authenticated-orcid":false,"given":"Wenli","family":"Cheng","sequence":"additional","affiliation":[{"name":"Centre of Hypertension, Beijing Anzhen Hospital, Capital Medical University  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,8,2]]},"reference":[{"key":"e_1_2_12_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140\u20106736(15)00128\u20102"},{"key":"e_1_2_12_3_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa1001286"},{"key":"e_1_2_12_4_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa1511939"},{"key":"e_1_2_12_5_1","doi-asserted-by":"publisher","DOI":"10.7326\/M17\u20103203"},{"key":"e_1_2_12_6_1","doi-asserted-by":"publisher","DOI":"10.1177\/1740774514537404"},{"key":"e_1_2_12_7_1","doi-asserted-by":"publisher","DOI":"10.1093\/gerona\/glv228"},{"key":"e_1_2_12_8_1","unstructured":"S.Yadlowsky S.Fleming N.Shah et\u00a0al. \u201cEvaluating Treatment Prioritization Rules via Rank\u2010Weighted Average Treatment Effects \u201d arXiv:2111.07966 [stat.ME]. 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