{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,13]],"date-time":"2026-11-13T22:55:25Z","timestamp":1794610525684,"version":"4.9.1"},"reference-count":38,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"6","content-domain":{"domain":["www.ahajournals.org"],"crossmark-restriction":true},"short-container-title":["Hypertension"],"published-print":{"date-parts":[[2026,6]]},"abstract":"<jats:sec>\n                    <jats:title>BACKGROUND:<\/jats:title>\n                    <jats:p>Contemporary hypertension guidelines emphasize individualized blood pressure (BP) management, often incorporating age; yet chronological age alone may be insufficient to guide optimal treatment. The frailty index offers a multidimensional measure of biological aging and may better guide BP management.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>METHODS:<\/jats:title>\n                    <jats:p>We pooled participant-level data from SPRINT (Systolic Blood Pressure Intervention Trial) and ACCORD (Action to Control Cardiovascular Risk in Diabetes). The frailty index was calculated using a 31-item Rockwood cumulative-deficit model, with frailty defined as a frailty index &gt;0.21. Participants were also categorized by age (&lt;65 versus \u226565 years). Systolic BP (SBP) time in target range (TTR) was calculated using linear interpolation across 10 mm\u2009Hg intervals. Restricted cubic splines and stratified Cox models were used to assess the association between TTR within predefined SBP targets and major adverse cardiovascular events.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS:<\/jats:title>\n                    <jats:p>A total of 19\u2009230 participants were included in the analysis (mean age, 65.2 years; 49.0% women; 68.2% classified as frail). Restricted cubic spline analyses showed a J-shaped relationship between average SBP and major adverse cardiovascular events, with clearer separation by frailty than by age. Among frail individuals, greater time spent within SBP intervals between 110 and 140 mm\u2009Hg was associated with lower major adverse cardiovascular event risk (hazard ratios per 10% increase in TTR, 0.92\u20130.94), whereas among nonfrail individuals, greater time spent below 130 mm\u2009Hg was associated with lower risk (hazard ratios per 10% increase in TTR, 0.89\u20130.98). Age demonstrated limited discrimination. Findings were consistent in separate analyses of SPRINT and ACCORD.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>CONCLUSIONS:<\/jats:title>\n                    <jats:p>The frailty index, rather than chronological age, more accurately discriminates optimal SBP targets in hypertensive patients, whereas chronological age may remain a more practical tool in resource-limited settings.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1161\/hypertensionaha.125.26397","type":"journal-article","created":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T10:01:31Z","timestamp":1772791291000},"update-policy":"https:\/\/doi.org\/10.1161\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":4,"title":["Frailty Index Offers Greater Discrimination Than Age for Optimal Blood Pressure Targets: A Pooled Analysis of Two Randomized 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C.L.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9639-7732","authenticated-orcid":true,"given":"Yezhou","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Epidemiology and Health Statistics, School of Public Health, Xi\u2019an Jiaotong University Health Science Center, Xi\u2019an, Shaanxi, China (Yizhen Lyu, W.B., Yezhou Liu, Yetong Liu, J.Z., Z.S., Yamei Liu, T.C., C.L.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-7628-286X","authenticated-orcid":true,"given":"Yetong","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Epidemiology and Health Statistics, School of Public Health, Xi\u2019an Jiaotong University Health Science Center, Xi\u2019an, Shaanxi, China (Yizhen Lyu, W.B., Yezhou Liu, Yetong Liu, J.Z., Z.S., Yamei Liu, T.C., 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United Kingdom (T.C.)."},{"name":"Key Laboratory of Environment and Genes Related to Diseases (Xi\u2019an Jiaotong University), Ministry of Education, Xi\u2019an, Shaanxi, China (T.C., C.L.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4417-1739","authenticated-orcid":true,"given":"Chao","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Epidemiology and Health Statistics, School of Public Health, Xi\u2019an Jiaotong University Health Science Center, Xi\u2019an, Shaanxi, China (Yizhen Lyu, W.B., Yezhou Liu, Yetong Liu, J.Z., Z.S., Yamei Liu, T.C., C.L.)."},{"name":"Key Laboratory of Environment and Genes Related to Diseases (Xi\u2019an Jiaotong University), Ministry of Education, Xi\u2019an, Shaanxi, China (T.C., 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