A Multimodal Nomogram Predicting the Risk of Cognitive Impairment in Parkinson Disease

BACKGROUND: Cognitive impairment is the most consequential non-motor complication of Parkinson's disease (PD), yet no validated tool integrates multimodal data to predict individualized risk trajectories. OBJECTIVES: The aim is to develop and internally validate a time-to-event nomogram for predicting 5-year risk of cognitive impairment in PD using the Parkinson's Precision Markers Initiative (PPMI). METHODS: We analyzed 1188 PD patients with normal cognition at their first cognitive assessment and up to 13 years of follow-up. Twenty-seven candidate predictors across seven domains were evaluated. Least absolute shrinkage and selection operator (LASSO)-penalized Cox regression performed variable selection. Performance was assessed via nested cross-validation, bootstrap optimism correction, calibration, and time-dependent area under the curve (AUC). RESULTS: During median follow-up of 3.0 years (interquartile range [IQR], 2.0-5.0), 457 patients (38.5%) developed cognitive impairment. LASSO selected 14 predictors. The Cox model achieved a concordance index (C-index) of 0.747 (optimism-corrected: 0.733; nested CV: 0.736 ± 0.042), with apparent time-dependent AUC of 0.77, 0.79, and 0.83 at 3, 5, and 8 years (held-out: 0.75, 0.78, and 0.81). A companion model without genetic predictors, for unknown GBA status, performed similarly (optimism-corrected C-index, 0.729). The strongest predictors were age (hazard ratio [HR], 1.05/year; P < 0.001), GBA mutation (HR, 2.05; P < 0.001), cognitive complaints (HR, 1.67; P < 0.001), and Symbol Digit Modalities Test (HR, 0.98/point; P = 0.001). CONCLUSIONS: A multimodal nomogram integrating clinical, cognitive, genetic, and neuropsychiatric predictors provides individualized estimates of 5-year risk of cognitive impairment in PD; after external validation, it may support prognostic counseling and trial enrichment.