The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, Department of Pharmacy and the Departments of Health Services and Economics, University of Washington, Seattle, Washington, USA.
Section of General Internal Medicine, The University of Chicago, Chicago, Illinois, USA.
Diabetes Obes Metab. 2024 Nov;26(11):5261-5271. doi: 10.1111/dom.15878. Epub 2024 Sep 2.
To present the Real-World Progression In Diabetes (RAPIDS) 2.0 Risk Engine, the only simulation model to study the long-term trajectories of outcomes arising from dynamic sequences of glucose-lowering treatments in type 2 diabetes (T2DM).
The RAPIDS model's risk equations were re-estimated using a Least Absolute Shrinkage and Selection Operator (LASSO)-based regularization of features that spanned baseline data from the last two quarters of current time and interactions with age. These equations were supplemented with estimates for the impact of dipeptidyl peptidase-4 inhibitors, glucagon-like peptide-1 receptor agonists, and sodium-glucose cotransporter-2 inhibitor classes of drugs as monotherapies and their combinations with metformin based on newer trial data and comprehensive meta-analyses. The probabilistic RAPIDS 2.0 model was calibrated (N = 25 000) and validated (N = 263 816) using electronic medical records (EMR) data between 2008 and 2021 from a national network of US healthcare organizations.
The EMR-based cohort had a mean age of 61 years at baseline, with 50% women, 70% non-Hispanic White individuals and 20% non-Hispanic Black individuals, and was followed for 17.5 quarters (range: 3-50). The final RAPIDS 2.0 risk engine accurately predicted the long-term trajectories of all nine biomarkers and nine outcomes in the hold-out validation sample. Similar accuracies in predictions were observed in each of the 14 subgroups studied.
The RAPIDS 2.0 model demonstrated valid long-term predictions of outcomes in individuals with T2DM in the United States as a function of dynamic sequences of treatment use patterns. This highlights its potential to project long-term comparative effectiveness between alternative sequences of glucose-lowering treatment uses in the United States.
介绍真实世界糖尿病进展（RAPIDS）2.0 风险引擎，这是唯一一个模拟模型，可研究 2 型糖尿病（T2DM）中动态降糖治疗序列产生的结果的长期轨迹。
使用基于最小绝对收缩和选择算子（LASSO）的特征正则化方法重新估计 RAPIDS 模型的风险方程，这些特征跨越了当前时间最后两个季度的基线数据以及与年龄的交互作用。这些方程补充了基于较新试验数据和综合荟萃分析的二肽基肽酶-4 抑制剂、胰高血糖素样肽-1 受体激动剂和钠-葡萄糖共转运蛋白-2 抑制剂类药物作为单药治疗及其与二甲双胍联合治疗的影响估计。使用来自美国医疗保健组织国家网络的电子病历（EMR）数据（2008 年至 2021 年），对概率 RAPIDS 2.0 模型进行校准（N=25000）和验证（N=263816）。
基于 EMR 的队列在基线时的平均年龄为 61 岁，女性占 50%，非西班牙裔白人占 70%，非西班牙裔黑人占 20%，随访 17.5 个季度（范围：3-50）。最终的 RAPIDS 2.0 风险引擎准确预测了验证样本中所有 9 个生物标志物和 9 个结局的长期轨迹。在研究的 14 个亚组中，均观察到类似的预测准确性。
RAPIDS 2.0 模型在美国 T2DM 个体中表现出对治疗使用模式的动态序列的长期结果的准确预测。这突出了其在预测美国不同降糖治疗使用序列之间的长期比较效果方面的潜力。
