Updating and calibrating the Real‐World Progression In Diabetes (RAPIDS) model in a non‐Veterans Affairs population
Authors' conclusion
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.
Study context
- Population
- Individuals with T2D, electronic medical records (EMR) data 2008–2021 from a national network of US healthcare organizations; mean age 61 years, 50% women
- Outcome
- Prediction of long-term trajectories in hold-out validation sample
- Follow-up
- Followed for 17.5 quarters (range: 3-50)
Methodological detailsprobabilistic simulation model, Least Absolute Shrinkage and Selection Operator (LASSO)-based regularization, EMR-based calibration and hold-out validation
- Probabilistic simulation model
- LASSO-based regularization
- EMR-based calibration and hold-out validation
Performance
As reported in the abstract
- RAPIDS 2.0 accurately predicted long-term trajectories of all nine biomarkers and nine outcomes
Further findings
Prediction accuracy in subgroups
- Similar accuracies in each of the 14 subgroups studied
Parts of this Brief are withheld: the automatic checks could not confirm them against the abstract.
Original publication
Updating and calibrating the Real‐World Progression In Diabetes (RAPIDS) model in a non‐Veterans Affairs population. Diabetes Obes Metab. Published 2 September 2024. doi:10.
Composed by AI from the published abstract and checked against it. How Briefs are made →