METHODS: A retrospective observational cohort was assembled from primary care patients in Ontario, Canada between January 1st, 2010, to December 31st, 2014 and followed for up to 5 years. Risk estimation was undertaken in patients 40-75 years of age, without CVD, diabetes, or chronic kidney disease using the Framingham Risk Score (FRS), Pooled Cohort Equations (PCEs), a recalibrated FRS (R-FRS), Systematic Coronary Risk Evaluation 2 (SCORE2), and the low-risk region recalibrated SCORE2 (LR-SCORE2).
RESULTS: The cohort consisted of 47,399 patients (59% women, mean age 54). The NNT with statins was lowest for SCORE2 at 40, followed by LR-SCORE2 at 41, R-FRS at 43, PCEs at 55, and FRS at 65. Models that selected for individuals with a lower NNT recommended statins to fewer, but higher risk patients. For instance, SCORE2 recommended statins to 7.9% of patients (5-year CVD incidence 5.92%). The FRS, however, recommended statins to 34.6% of patients (5-year CVD incidence 4.01%). Accordingly, the NEP was highest for the FRS at 406 and lowest for SCORE2 at 156.
CONCLUSIONS: Newer models such as SCORE2 may improve statin allocation to higher risk groups with a lower NNT but prevent fewer events at the population level.
方法:从安大略省的初级保健患者中收集了一个回顾性观察队列,加拿大1月1日之间,2010年至12月31日,2014年,随访长达5年。对40-75岁的患者进行了风险评估,没有CVD,糖尿病,或使用弗雷明汉风险评分(FRS)的慢性肾脏疾病,集合队列方程(PCE),重新校准的FRS(R-FRS),系统冠状动脉风险评估2(SCORE2),和低风险区域重新校准SCORE2(LR-SCORE2)。
结果:该队列包括47,399名患者(59%为女性,平均年龄54岁)。他汀类药物的NNT最低,SCORE2为40,其次是LR-SCORE2为41,R-FRS为43,PCE为55,FRS为65。为NNT较低的个体选择的模型推荐他汀类药物较少,但风险较高的患者。例如,SCORE2对7.9%的患者推荐他汀类药物(5年CVD发生率5.92%)。FRS,然而,34.6%的患者推荐他汀类药物(5年CVD发生率4.01%).因此,FRS的NEP最高,为406,SCORE2最低,为156。
结论:新的模型如SCORE2可以改善他汀类药物在NNT较低的高风险人群中的分配,但在人群水平上预防较少事件。