Baseline clinical features outperform structural MRI in predicting rapid cognitive and motor decline in Parkinson’s disease

Vollständiger Abstract

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Abstract Cognitive and motor impairments are common in Parkinson’s disease (PD), but rapid decline trajectories remain difficult to predict at the individual level. Identifying reliable early-stage prognostic markers could define disease-modification windows and improve trial enrichment. Classification models were developed to predict rapid decline, defined as a decrease of ≥5 points on the Montreal Cognitive Assessment (MoCA) and an increase of ≥10 points on the MDS-UPDRS3 from baseline to any timepoint 3–5 years post-baseline. Models were trained using longitudinal MRI-derived regional atrophy rates and baseline clinical features. Model performance was evaluated using AUROC and complementary classification metrics; calibration, performance ceilings, and clinical utility were further assessed using calibration curves and decision curve analysis (DCA). Feature analysis was performed to identify clinically informative predictors. Structural MRI (e.g., annualized atrophy rate) demonstrated limited prognostic utility for individual-level prediction; this remained true with finer-grained atlases. In contrast, baseline clinical features yielded substantially stronger discrimination for both outcomes. Notably, cognitive prediction remained robust after removal of baseline MoCA, whereas motor prediction was strongly dependent on baseline UPDRS3. External validation preserved high NPVs (cognitive: 0.908 [0.872–0.940]; motor: 0.863 [0.818–0.901]). Incorporating first-year trajectory slopes improved AUROC by approximately 5%, supporting a single follow-up visit as a practical refinement timepoint. Performance gains rapidly plateaued after inclusion of a small number of high-value features, indicating an early performance ceiling. Decision curve analysis suggested potential net benefit across selected threshold probabilities, but prospective evaluation in broader and more heterogeneous populations is required before clinical implementation.