Impact of Cardiovascular Risk on the Relative Benefit and Harm of Intensive Treatment of Hypertension
Impact of Cardiovascular Risk on the Relative Benefit and Harm of Intensive Treatment of Hypertension
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DOI：
10.1016/j.jacc.2018.01.074
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发表时间：
2018-04-17
影响因子：
24
通讯作者：
Schussheim, Adam E.
中科院分区：
文献类型：
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作者：
Phillips, Robert A.;Xu, Jiaqiong;Schussheim, Adam E.
SPRINT（收缩压干预试验）中强化治疗组的主要结局事件发生率较低，与临床显著严重不良事件（SAE）增加相关。2017年，美国心脏病学会/美国心脏协会发布了基于风险的血压治疗指南。作者假设，根据未来心血管疾病（CVD）风险程度对SPRINT人群进行分层可能会确定一组从强化治疗中获益最多的人群。使用考克斯比例风险模型检查治疗组与主要结局事件和SAE的相关性。使用乘法泊松回归，预测模型被开发出来，以确定作为一个功能的CVD risk.RESULTS的利益-危害比，在每个四分位数，有一个较低的主要结局事件在强化治疗组，与所有原因的严重不良事件没有差异。从第一四分位数到第一四分位数，需要治疗以预防主要结局的人数从91人减少到38人。全因SAE所需的伤害数量从62例增加至250例。预测模型显示，第一、第二、第三和第四四个四分位数的获益-损害比（+/- SE）分别显著增加，为0.50 +/- 0.15、0.78 +/- 0.26、2.13 +/- 0.73和4.80 +/- 1.86（趋势p
BACKGROUND The lower rate of primary outcome events in the intensive treatment group in SPRINT (Systolic Pressure Intervention Trial) was associated with increased clinically significant serious adverse events (SAEs). In 2017, the American College of Cardiology/American Heart Association issued risk-based blood pressure treatment guidelines. The authors hypothesized that stratification of the SPRINT population by degree of future cardiovascular disease (CVD) risk might identify a group which could benefit the most from intensive treatment.OBJECTIVES This study investigated the effect of baseline 10-year CVD risk on primary outcome events and all-cause SAEs in SPRINT.METHODS Stratifying by quartiles of baseline 10-year CVD risk, Cox proportional hazards models were used to examine the associations of treatment group with the primary outcome events and SAEs. Using multiplicative Poisson regression, a predictive model was developed to determine the benefit-to-harm ratio as a function of CVD risk.RESULTS Within each quartile, there was a lower rate of primary outcome events in the intensive treatment group, with no differences in all-cause SAEs. From the first to fourth quartiles, the number needed to treat to prevent primary outcomes decreased from 91 to 38. The number needed to harm for all-cause SAEs increased from 62 to 250. The predictive model demonstrated significantly increasing benefit-to-harm ratios (+/- SE) of 0.50 +/- 0.15, 0.78 +/- 0.26, 2.13 +/- 0.73, and 4.80 +/- 1.86, for the first, second, third, and fourth quartile, respectively (p for trend