Frontiers in endocrinology
Outcome-specific value of longitudinal clinical information and model complexity for dynamic prediction of type 2 diabetes-related outcomes: a pooled landmarking study
Door Fu M, Liu X, Shi T en 6 anderen.
Inhoud in het Engels
Abstract
Objective
To evaluate the outcome-specific incremental value of longitudinal clinical history and model complexity for annual dynamic prediction of multiple type 2 diabetes-related outcomes using pooled landmarking.
Methods
We included 646 patients with complete annual records from baseline through year 3. Landmark times at years 1 and 2 were used to predict the first recorded occurrence of hypertension, coronary heart disease, diabetic kidney disease, diabetic retinopathy, or metabolic dysfunction-associated steatotic liver disease during the subsequent year. Four progressively expanded feature sets were compared using elastic-net logistic regression and Light Gradient Boosting Machine. The primary history-current estimand was a paired within-algorithm contrast with the risk set, validation splits, preprocessing, tuning, calibration, and evaluation records held constant. Internal validation used patient-level repeated nested cross-validation. We also evaluated fully nested model selection and model-development stability using 500 patient-level bootstrap replicates with out-of-bag evaluation.