Division of Imaging, Informatics and Data Science, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Oxford Road, Manchester, M13 9PL, UK.
Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crispigny Park, London, SE5 8AF, UK.
BMC Med Res Methodol. 2020 Nov 30;20(1):289. doi: 10.1186/s12874-020-01173-x.
A downwards secular trend in the incidence of cardiovascular disease (CVD) in England was identified through previous work and the literature. Risk prediction models for primary prevention of CVD do not model this secular trend, this could result in over prediction of risk for individuals in the present day. We evaluate the effects of modelling this secular trend, and also assess whether it is driven by an increase in statin use during follow up.
We derived a cohort of patients (1998-2015) eligible for cardiovascular risk prediction from the Clinical Practice Research Datalink with linked hospitalisation and mortality records (N = 3,855,660). Patients were split into development and validation cohort based on their cohort entry date (before/after 2010). The calibration of a CVD risk prediction model developed in the development cohort was tested in the validation cohort. The calibration was also assessed after modelling the secular trend. Finally, the presence of the secular trend was evaluated under a marginal structural model framework, where the effect of statin treatment during follow up is adjusted for.
Substantial over prediction of risks in the validation cohort was found when not modelling the secular trend. This miscalibration could be minimised if one was to explicitly model the secular trend. The reduction in risk in the validation cohort when introducing the secular trend was 35.68 and 33.24% in the female and male cohorts respectively. Under the marginal structural model framework, the reductions were 33.31 and 32.67% respectively, indicating increasing statin use during follow up is not the only the cause of the secular trend.
Inclusion of the secular trend into the model substantially changed the CVD risk predictions. Models that are being used in clinical practice in the UK do not model secular trend and may thus overestimate the risks, possibly leading to patients being treated unnecessarily. Wider discussion around the modelling of secular trends in a risk prediction framework is needed.
通过之前的工作和文献研究，发现英格兰心血管疾病（CVD）的发病率呈下降趋势。心血管疾病一级预防的风险预测模型并未对这种长期趋势进行建模，这可能导致对当今个体风险的过高预测。我们评估了对这种长期趋势进行建模的效果，同时还评估了这种长期趋势是否是由于在随访期间他汀类药物使用率的增加而导致的。
我们从临床实践研究数据链中提取了一个符合心血管风险预测条件的患者队列（1998-2015 年），并与住院和死亡记录相关联（N=3855660）。根据患者入组日期（2010 年之前/之后），将患者分为开发和验证队列。在验证队列中检验了在开发队列中开发的 CVD 风险预测模型的校准情况。在校准评估后，还对长期趋势进行了建模。最后，在边际结构模型框架下评估了长期趋势的存在，该框架中调整了随访期间他汀类药物治疗的效果。
如果不建模长期趋势，在验证队列中会发现风险的严重过高预测。如果明确建模长期趋势，则可以最小化这种校准错误。在引入长期趋势后，验证队列中的风险降低分别为女性队列和男性队列的 35.68%和 33.24%。在边际结构模型框架下，降幅分别为 33.31%和 32.67%，表明随访期间他汀类药物使用的增加并不是长期趋势的唯一原因。
将长期趋势纳入模型后，心血管疾病风险预测发生了重大变化。英国临床实践中使用的模型并未对长期趋势进行建模，因此可能过高估计了风险，可能导致不必要的患者治疗。需要更广泛地讨论在风险预测框架中对长期趋势进行建模的问题。
