Prevention of Metabolic Disorders Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Prevention of Metabolic Disorders Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran; Department of Biostatistics and Epidemiology, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
J Clin Epidemiol. 2021 Dec;140:33-43. doi: 10.1016/j.jclinepi.2021.08.026. Epub 2021 Aug 27.
Dynamic prediction models use the repeated measurements of predictors to estimate coefficients that link the longitudinal predictors to a static model (i.e. Cox regression). This study aims to develop and validate a dynamic prediction for incident type 2 diabetes (T2DM) as the outcome.
Data from the Tehran lipid and glucose study was used to develop (n = 5291 individuals; phases 1 to 3) and validate (n = 3147 individuals; phases 3 to 6) the dynamic prediction model among individuals aged ≥ 20 years. We used repeated measurements of fasting plasma glucose (FPG) or waist circumference (WC) in the framework of the joint modeling (JM) of longitudinal and time-to-event analysis.
Compared with the Cox which used just baseline data, JM showed the same discrimination, better calibration, and higher clinical usefulness (i.e. with a net benefit considering both true and false positive decisions); all were shown with repeated measurements of FPG/WC. Additionally, in our study, the dynamic models improve the risk reclassification (net reclassification index 33% for FPG and 24% for WC model).
Dynamic prediction models, compared with the static one could yield significant improvements in the prediction of T2DM. The complexity of the dynamic models could be addressed by using decision support systems.
动态预测模型使用预测指标的重复测量值来估计将纵向预测指标与静态模型（即 Cox 回归）联系起来的系数。本研究旨在开发和验证用于预测 2 型糖尿病（T2DM）事件的动态预测模型。
使用德黑兰血脂和血糖研究的数据，在年龄≥20 岁的人群中开发（n=5291 人；第 1 至 3 阶段）和验证（n=3147 人；第 3 至 6 阶段）动态预测模型。我们使用空腹血糖（FPG）或腰围（WC）的重复测量值，在纵向和时间事件分析的联合建模（JM）框架中进行分析。
与仅使用基线数据的 Cox 相比，JM 显示出相同的区分度、更好的校准度和更高的临床实用性（即考虑真阳性和假阳性决策的净获益）；所有这些都显示了 FPG/WC 的重复测量。此外，在我们的研究中，动态模型改善了风险重新分类（FPG 模型的净重新分类指数为 33%，WC 模型为 24%）。
与静态模型相比，动态预测模型可以显著改善 T2DM 的预测。可以通过使用决策支持系统来解决动态模型的复杂性。
