Prediction of Six-Month Glycemic Status in Type 2 Diabetes: Development and Internal Validation of a Multivariable...

Background: Baseline HbA1c is expected to dominate prediction of subsequent HbA1c status; therefore, the clinically relevant question is whether additional routinely available variables provide reproducible incremental value. Methods: This retrospective longitudinal study used 809 records from adults with type 2 diabetes receiving routine outpatient care. Six-month HbA1c was available for 808 participants. The primary outcome was six-month glycemic status (HbA1c < 7.0%). An HbA1c-only benchmark and three fixed blockwise logistic models were compared in the same complete-case cohort (n = 806). Internal validation used 1000 bootstrap resamples and reported optimism-corrected AUC, Brier score, calibration intercept, and calibration slope. Correlations, variance inflation factors (VIFs), nonlinear effects, decision-curve analysis, and a sensitivity analysis restricted to baseline HbA1c ≥ 7.0% were evaluated. Results: At baseline, 215 participants had HbA1c < 7.0%; 179 maintained this status, 206 newly reached <7.0%, 36 moved above the threshold, and 387 remained ≥7.0%. In complete cases, the optimism-corrected AUC was 0.868 (95% CI 0.844-0.892) for HbA1c alone and 0.870 (95% CI 0.848-0.893) for the full model. The corrected AUC difference between the full and HbA1c-only models was 0.001 (95% CI of -0.011 to 0.011). For the full model, the corrected Brier score was 0.146, the calibration intercept was 0.001, and the calibration slope was 0.925. VIFs were low (maximum of 1.33), although the TyG coefficient reversed direction after adjustment and was not interpreted causally. In participants with baseline HbA1c ≥ 7.0% (n = 592 complete cases), corrected AUCs were 0.850 for HbA1c alone and 0.849 for the full model. Conclusions: Baseline HbA1c accounts for nearly all discriminative performance. Added biomarkers and baseline therapeutic classes provide little internally validated incremental value, and the model should not be used for treatment decisions before external validation and prospective evaluation of clinical impact.