Data Sheet 1_Outcome-specific value of longitudinal clinical information and model complexity for dynamic prediction of type 2 diabetes-related outcomes: a pooled landmarking study.pdf

We also evaluated fully nested model selection and model-development stability using 500 patient-level bootstrap replicates with out-of-bag evaluation.</p>Results<p>Pooled out-of-fold area under the receiver operating characteristic curve ranged from 0.722 to 0.952 across the five representative models. The paired history-current comparison showed higher AUC for hypertension with elastic-net (ΔAUC 0.097, 95% confidence interval 0.057 to 0.136) and LightGBM (0.118, 0.081 to 0.158), and for metabolic dysfunction-associated steatotic liver disease with elastic-net (0.022, 0.002 to 0.042) and LightGBM (0.025, 0.009 to 0.041).

The other three outcomes showed no comparable improvement. Algorithm differences were modest and outcome dependent.</p>Conclusion<p>In this single-center cohort with complete annual T0–T3 follow-up, the incremental value of longitudinal information and model complexity was outcome specific. Longitudinal history showed greater benefit for selected outcomes, whereas algorithm differences were modest and outcome dependent.

The smaller MASLD signal was supported primarily by the pooled L1–L2 analysis. Independent external validation is required before clinical application.</p>