Machine Learning Prediction of Hoehn and Yahr Scores at 5-Years Post-¹²³I-Ioflupane SPECT Imaging in a Real-World Parkinson's Disease Dataset.

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Abstract

Parkinson's disease (PD) progression is highly heterogeneous, complicating clinical management and prognostication. While machine learning models have been developed using research datasets such as Parkinson's Precision Medicine Initiative (PPMI) and Parkinson's Disease Biomarkers Program (PDBP), their clinical translatability is limited due to differences in routinely collected data. The Hoehn and Yahr (H&Y) scale is commonly used in clinical practice to stage PD, yet most predictive models focus on less practical measures. This study developed and validated machine learning models to predict H&Y scores at 5 years post-123I-ioflupane single-photon emission computed tomography (SPECT) imaging, leveraging both a real-world dataset and a subset of the PPMI cohort. The goal was to assess the utility of routinely collected clinical and imaging data for prognostic modeling. Data from medical records and imaging were harmonized from 343 real-world patients and 134 PPMI patients, resulting in a merged dataset with 83 overlapping features. Random Forest and Gradient Boosting models were trained to predict 5-year H&Y scores using varying amounts of longitudinal data and imaging features. Models using 2 years of clinical follow-up data achieved the highest predictive accuracy. The most important predictors were early H&Y scores, gait symptom severity, and select imaging features. Machine learning models can predict 5-year H&Y scores in PD using real-world clinical data, but imaging features add limited prognostic value. This study demonstrated that implementing machine learning models, when using real-world data, did not significantly improve the already known gap between prognostic modeling and real-world implementation. Improvement of models is, however, a promising prospect and further studies are encouraged.

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