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oai:figshare.com:article/34053090 2026-10-02T04:30:10Z category_658 portal_316 item_type_3 month_year_10_2026

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 Meiling Fu (12077649) Xiaohong Liu (50816) Ting Shi (3955) Yuxuan Lu (11493250) Jie Zou (119503) Xiaowei Huang (111125) Qingmei Pan (25161021) Yun Chen (279569) Yinqin Zhong (24172359) Cell Metabolism dynamic prediction internal validation landmark analysis longitudinal information machine learning type 2 diabetes Objective<p>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.</p>Methods<p>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.</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> 2026-10-02T04:30:10Z Dataset Dataset 10.3389/fendo.2026.1964592.s001 https://figshare.com/articles/dataset/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/34053090 CC BY 4.0