Abstract：
BACKGROUND: A lack of consensus exists across guidelines as to which risk model should be used for the primary prevention of cardiovascular disease (CVD). Our objective was to determine potential improvements in the number needed to treat (NNT) and number of events prevented (NEP) using different risk models in patients eligible for risk stratification.
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.
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.
摘要：
背景：关于哪种风险模型应用于心血管疾病（CVD）的一级预防，指南之间缺乏共识。我们的目标是在符合风险分层的患者中使用不同的风险模型来确定所需治疗数量（NNT）和预防事件数量（NEP）的潜在改善。
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。
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。
