Benchmarking parametric models of disease progression for early detection of cognitive decline

Resumen

Background and Objective: Disease progression models (DPMs) are valuable tools for characterizing early cognitive decline in Alzheimer’s Disease (AD) and supporting clinical decision-making. This study aimed to (1) evaluate the diagnostic and prognostic performance of parametric DPMs, (2) identify optimal subsets of neuropsychological markers for DPM construction, and (3) benchmark three parametric DPM frameworks in early detection tasks.
Methods: We analyzed longitudinal neuropsychological data from 1163 participants classified as cognitively unimpaired (CU) or with mild cognitive impairment (MCI). Three DPM approaches (Leaspy, RPDPM, and GRACE) were trained on selected marker subsets and evaluated using metrics related to diagnostic accuracy, time to conversion estimation, and robustness to missing data. Model performance was assessed via detection rates, area under the curve (AUC), mean absolute error (MAE), and Pearson correlation between estimated/observed onset ages.
Results: Leaspy achieved the highest diagnostic accuracy with an AUC of 0.96 and strong correlation with observed conversion time (r = 0.78). RPDPM showed superior robustness to missing data and maintained accurate predictions even with up to 40% data loss. GRACE offered the best trajectory fit (lowest error) but lower sensitivity to clinical transitions. A compact combination of neuropsychological tests, particularly CDRSB, ADAS13, and MMSE, was sufficient for reliable model training. Prognostic evaluation demonstrated that Leaspy provided the most consistent identification of individuals who converted to mild cognitive impairment within five years.
Conclusions: Parametric DPMs based solely on neuropsychological measures can effectively support early detection and prognosis of cognitive decline. Leaspy showed the best overall performance, while RPDPM proved more resilient to missing data. These models enable individualized disease timelines and can inform clinical decision-making and patient stratification. All code and data used are publicly available, facilitating reproducibility and clinical translation.