Non-Communicable Disease Research Unit, South African Medical Research Council and University of Cape Town , Cape Town , South Africa .
Crit Rev Clin Lab Sci. 2014 Feb;51(1):1-12. doi: 10.3109/10408363.2013.853025. Epub 2013 Dec 4.
Absolute risk models or clinical prediction models have been incorporated in guidelines, and are increasingly advocated as tools to assist risk stratification and guide prevention and treatments decisions relating to common health conditions such as cardiovascular disease (CVD) and diabetes mellitus. We have reviewed the historical development and principles of prediction research, including their statistical underpinning, as well as implications for routine practice, with a focus on predictive modelling for CVD and diabetes. Predictive modelling for CVD risk, which has developed over the last five decades, has been largely influenced by the Framingham Heart Study investigators, while it is only ∼20 years ago that similar efforts were started in the field of diabetes. Identification of predictive factors is an important preliminary step which provides the knowledge base on potential predictors to be tested for inclusion during the statistical derivation of the final model. The derived models must then be tested both on the development sample (internal validation) and on other populations in different settings (external validation). Updating procedures (e.g. recalibration) should be used to improve the performance of models that fail the tests of external validation. Ultimately, the effect of introducing validated models in routine practice on the process and outcomes of care as well as its cost-effectiveness should be tested in impact studies before wide dissemination of models beyond the research context. Several predictions models have been developed for CVD or diabetes, but very few have been externally validated or tested in impact studies, and their comparative performance has yet to be fully assessed. A shift of focus from developing new CVD or diabetes prediction models to validating the existing ones will improve their adoption in routine practice.
绝对风险模型或临床预测模型已被纳入指南，并越来越多地被倡导作为辅助风险分层和指导常见健康状况（如心血管疾病 (CVD) 和糖尿病）的预防和治疗决策的工具。我们回顾了预测研究的历史发展和原则，包括其统计学基础，以及对常规实践的影响，重点是 CVD 和糖尿病的预测模型。过去五十年发展起来的 CVD 风险预测模型在很大程度上受到了弗雷明汉心脏研究调查人员的影响，而在糖尿病领域类似的努力才刚刚开始。预测因素的识别是一个重要的初步步骤，它为最终模型的统计推导中要测试的潜在预测因素提供了知识库。然后，必须在开发样本（内部验证）和不同环境下的其他人群（外部验证）上测试得出的模型。更新程序（例如重新校准）应用于改进未能通过外部验证测试的模型的性能。最终，在广泛传播模型之前，应该在影响研究中测试将经过验证的模型引入常规实践对护理过程和结果以及其成本效益的影响，然后才能在研究范围之外广泛传播模型。已经为 CVD 或糖尿病开发了几种预测模型，但只有很少的模型经过外部验证或影响研究测试，其比较性能尚未得到充分评估。将重点从开发新的 CVD 或糖尿病预测模型转移到验证现有的模型上，将提高它们在常规实践中的应用。
