Individualising the risks of statins in men and women in England and Wales: population-based cohort study
Individualising the risks of statins in men and women in England and Wales: population-based cohort study
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DOI：
10.1136/hrt.2010.199034
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发表时间：
2010-06-01
期刊：
影响因子：
5.7
通讯作者：
Coupland, Carol
中科院分区：
文献类型：
--
作者：
Hippisley-Cox, Julia;Coupland, Carol
目的推导并验证风险算法，以估计与他汀类药物使用相关的四种临床结果的风险。设计前瞻性开放队列研究，使用常规收集的来自英格兰和威尔士的368个QResearch全科实践的数据来编制评分。使用两组独立的实践来验证分数——188个独立的QResearch实践和364个贡献给THIN数据库的实践。在QResearch衍生队列中，研究对象为225922名他汀类药物新使用者和1778770名非他汀类药物使用者。在QResearch验证队列中，研究了118372名他汀类药物使用者和877812名非他汀类药物使用者。在THIN验证队列中，我们研究了282856名他汀类药物使用者和1923840名非他汀类药物使用者。方法在衍生队列中建立Cox比例风险模型，推导风险方程。两个验证队列的校准和鉴别措施。中度/重度肌病事件的5年风险；中度/重度肝功能障碍；急性肾衰竭和白内障。结果三种风险预测算法在THIN队列中的表现都很好。例如，在女性中，中度/严重肌病的算法解释了42.15%的变异。相应的D统计量为1.75。急性肾功能衰竭算法解释了59.62%的变异（D统计量=2.49）。白内障算法解释了59.14%的变异（D统计量=2.46）。预测中度/重度肝功能障碍的算法仅解释了15.55%的变异（D统计量=0.89）。在QResearch验证队列中测试时，每种算法的性能在两性中都是相似的。结论预测急性肾功能衰竭、中度/重度肌病和白内障的算法可用于识别这些不良反应风险增加的患者，使患者能够更密切地监测。需要进一步的研究来开发更好的算法来预测肝功能障碍。
Objective To derive and validate risk algorithms so that the risks of four clinical outcomes associated with statin use can be estimated for individual patients.Design Prospective open cohort study using routinely collected data from 368 QResearch general practices in England and Wales to develop the scores. The scores were validated using two separate sets of practices-188 separate QResearch practices and 364 practices contributing to the THIN database.Subjects In the QResearch derivation cohort 225 922 new users of statins and 1 778 770 non-users of statins were studied. In the QResearch validation cohort 118 372 statin users and 877 812 non-users of statins were studied. In the THIN validation cohort, we studied 282 056 statin users and 1 923 840 non-users of statins were studied.Methods Cox proportional hazards models in the derivation cohort to derive risk equations. Measures of calibration and discrimination in both validation cohorts.Outcomes 5-Year risk of moderate/serious myopathic events; moderate/serious liver dysfunction; acute renal failure and cataract.Results The performance of three of the risk prediction algorithms in the THIN cohort was very good. For example, in women, the algorithm for moderate/serious myopathy explained 42.15% of the variation. The corresponding D statistics was 1.75. The acute renal failure algorithm explained 59.62% of the variation (D statistic=2.49). The cataract algorithm explained 59.14% of the variation (D statistic=2.46). The algorithms to predict moderate/severe liver dysfunction only explained 15.55% of the variation (D statistics=0.89). The performance of each algorithm was similar for both sexes when tested on the QResearch validation cohort.Conclusions The algorithms to predict acute renal failure, moderate/serious myopathy and cataract could be used to identify patients at increased risk of these adverse effects enabling patients to be monitored more closely. Further research is needed to develop a better algorithm to predict liver dysfunction.