Section of Cardiology, Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA.
Division of Epidemiology, Department of Public Health Sciences, University of Miami, Miami, Florida, USA.
Am J Hypertens. 2021 Sep 22;34(9):973-980. doi: 10.1093/ajh/hpab059.
While studies have assessed the association between blood pressure trajectories and cardiovascular disease (CVD) outcomes using observational data, few have assessed these associations using clinical trial data. We sought to identify systolic blood pressure (SBP) trajectories and to determine if these trajectory patterns carry inherent CVD risk, irrespective of baseline blood pressure.
SBP trajectories were identified using latent class group-based modeling among a cohort of Systolic Blood Pressure Intervention Trial (SPRINT) participants by incorporating SBP measures during the first 12 months of the trial postrandomization. Cox models were used to evaluate the association between SBP trajectory with CVD and all-cause mortality.
Four distinct SBP trajectories were identified: "low decline" (41%), "high decline" (6%), "low stable" (48%), and "high stable" (5%). Relative to the "low decline" group, the "low stable" group was associated with a 29% increased risk of CVD (hazard ratio [HR]: 1.29, 95% confidence interval [CI]: 1.06-1.57) and the "high stable" group was associated with a 76% increased risk of all-cause mortality (HR: 1.76, 95% CI: 1.15-2.68). Relative to the "low stable" group, the "high stable" group was associated with a 54% increased risk of all-cause mortality (HR: 1.54, 95% CI: 1.05-2.28).
Our results demonstrate that SBP trajectory patterns are associated with important cardiovascular outcomes, irrespective of baseline blood pressure, which may help better identify individuals at risk and assist with accurate adjudication of antihypertensive therapy to reduce future events.
虽然有研究使用观察性数据评估血压轨迹与心血管疾病 (CVD) 结局之间的关系,但很少有研究使用临床试验数据评估这些关系。我们试图确定收缩压 (SBP) 轨迹,并确定无论基线血压如何,这些轨迹模式是否具有内在的 CVD 风险。
通过在随机分组后试验的前 12 个月内纳入 SBP 测量值,使用基于潜在类别群组的建模在 Systolic Blood Pressure Intervention Trial (SPRINT) 参与者队列中确定 SBP 轨迹。使用 Cox 模型评估 SBP 轨迹与 CVD 和全因死亡率之间的关联。
确定了四种不同的 SBP 轨迹:“低下降”(41%)、“高下降”(6%)、“低稳定”(48%)和“高稳定”(5%)。与“低下降”组相比,“低稳定”组 CVD 风险增加 29%(风险比 [HR]:1.29,95%置信区间 [CI]:1.06-1.57),“高稳定”组全因死亡率风险增加 76%(HR:1.76,95% CI:1.15-2.68)。与“低稳定”组相比,“高稳定”组全因死亡率风险增加 54%(HR:1.54,95% CI:1.05-2.28)。
我们的研究结果表明,SBP 轨迹模式与重要的心血管结局相关,无论基线血压如何,这可能有助于更好地识别风险人群,并协助准确判断降压治疗以减少未来事件。