Interpretation of CVD risk predictions in clinical practice: Mission impossible?

Background
Cardiovascular disease (CVD) risk prediction models are often used to identify individuals at high risk of CVD events. Providing preventive treatment to these individuals may then reduce the CVD burden at population level. However, different prediction models may predict different (sets of) CVD outcomes which may lead to variation in selection of high risk individuals. Here, it is investigated if the use of different prediction models may actually lead to different treatment recommendations in clinical practice.
Method
The exact definition of and the event types included in the predicted outcomes of four widely used CVD risk prediction models (ATP-III, Framingham (FRS), Pooled Cohort Equations (PCE) and SCORE) was determined according to ICD-10 codes. The models were applied to a Dutch population cohort (n = 18,137) to predict the 10-year CVD risks. Finally, treatment recommendations, based on predicted risks and the treatment threshold associated with each model, were investigated and compared across models.
Results
Due to the different definitions of predicted outcomes, the predicted risks varied widely, with an average 10-year CVD risk of 1.2% (ATP), 5.2% (FRS), 1.9% (PCE), and 0.7% (SCORE). Given the variation in predicted risks and recommended treatment thresholds, preventive drugs would be prescribed for 0.2%, 14.9%, 4.4%, and 2.0% of all individuals when using ATP, FRS, PCE and SCORE, respectively.
Conclusion
Widely used CVD prediction models vary substantially regarding their outcomes and associated absolute risk estimates. Consequently, absolute predicted 10-year risks from different prediction models cannot be compared directly. Furthermore, treatment decisions often depend on which prediction model is applied and its recommended risk threshold, introducing unwanted practice variation into risk-based preventive strategies for CVD.

### 背景
心血管疾病（Cardiovascular disease, CVD）风险预测模型常被用于识别存在心血管疾病事件高风险的个体。对此类个体实施预防性治疗，可在人群层面降低心血管疾病的疾病负担。然而，不同的预测模型可能会预测不同的（一组）心血管疾病结局，这可能导致高风险个体筛选结果出现差异。本研究旨在探讨，临床实践中使用不同的预测模型是否会实际导致治疗建议出现差异。

### 方法
本研究依据国际疾病分类第10版（ICD-10）编码，明确了4种广泛使用的心血管疾病风险预测模型（ATP-III、弗雷明汉风险评分（Framingham Risk Score, FRS）、合并队列方程（Pooled Cohort Equations, PCE）以及SCORE）的预测结局的准确定义及其所纳入的事件类型。将这些模型应用于荷兰人群队列（n=18137），以预测10年心血管疾病风险。最后，基于预测风险与各模型对应的治疗阈值，本研究对不同模型的治疗建议进行了分析与比较。

### 结果
由于预测结局的定义不同，各模型的预测风险差异显著：ATP-III的平均10年心血管疾病风险为1.2%，FRS为5.2%，PCE为1.9%，SCORE为0.7%。结合预测风险与推荐治疗阈值的差异，使用ATP-III、FRS、PCE和SCORE模型时，分别会有0.2%、14.9%、4.4%和2.0%的全体个体被开具预防性药物处方。

### 结论
广泛使用的心血管疾病预测模型在其结局定义及对应的绝对风险评估方面存在显著差异。因此，不同预测模型得出的10年绝对预测风险无法直接进行比较。此外，治疗决策通常取决于所使用的预测模型及其推荐的风险阈值，这会为心血管疾病基于风险的预防策略引入不必要的临床实践差异。