Novel decision algorithm to discriminate parkinsonism with combined blood and imaging biomarkers.

Introduction: To determine an exploratory multimodal approach including serum NFL and MR planimetric measures to discriminate Parkinson's disease (PD), multiple system atrophy (MSA) and progressive supranuclear palsy (PSP).
Methods: MR planimetric measurements and NFL serum levels, with a mean time interval of 60 months relative to symptom onset, were assessed in a retrospective cohort of 11 progressive supranuclear palsy (PSP), 22 Parkinson's disease (PD), 16 multiple system atrophy (MSA) patients and 42 healthy controls (HC). A decision tree model to discriminate PD, PSP, and MSA was constructed using receiver operating characteristic curve analysis and Classification and Regression Trees algorithm.
Results: Our multimodal decision tree provided accurate differentiation of PD versus MSA and PSP patients using a serum NFL cut-off of 14.66 ng/L. The pontine-to-midbrain-diameter-ratio (P _d /M _d ) discriminated MSA from PSP at a cut-off value of 2.06. The combined overall diagnostic yield was an accuracy of 83.7% (95% CI 69.8-90.8%).
Conclusion: We provide a clinically feasible decision algorithm which combines serum NFL levels and a planimetric MRI marker to differentiate PD, MSA and PSP with high diagnostic accuracy.
Classification of Evidence: This study provides Class III evidence that the combination of serum NFL levels und MR planimetric measurements discriminates between PD, PSP and MSA.
(Copyright © 2020 The Authors. Published by Elsevier Ltd.. All rights reserved.)