Klinik belirteçler ve makine öğrenimi kullanarak prodromal sinükleinopatilerde bilişsel düşüşün tahmin edilmesi

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Abstract Neuroprotective interventions for dementia with Lewy bodies (DLB) and Parkinson’s disease (PD) are still in their early days. Clinical trials are expected to target idiopathic rapid eye movement sleep behavior disorder (iRBD), their strongest predictor. However, the presentation and progression of symptoms within this population show significant heterogeneity. We used machine learning (ML) to identify the clinical markers that are best at distinguishing iRBD patients (n=156) who developed DLB (n=26) from those who developed PD (n=34) at a mean follow-up of 4.37 years. Our model classified subsequent conversion to DLB versus PD with 0.80 accuracy, with mild cognitive impairment as best predictive feature. Cognitive tests of executive functions and verbal learning also played a major role in classifying other related pathological trajectories. These findings support the use of ML with clinical markers in iRBD, paving the way for a more targeted selection of participants in future neuroprotective trials of synucleinopathies.

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