Prodromal features and risk of neurodegenerative disorders diagnosis in outpatients with REM sleep behavior disorder.

Study objectives: Individuals with isolated REM sleep behavior disorder (iRBD) are at high risk of neurodegenerative parkinsonian disorders or dementia (NPD). Determining which characteristics predict greatest risk could improve clinical care. Our objectives were to utilize electronic health record (EHR) data to apply prodromal PD research diagnostic criteria to iRBD outpatients and determine their utility for identifying iRBD cases at high vs low risk for NPD diagnosis.

Methods: This was a retrospective cohort study at a tertiary care center in Western Pennsylvania. Diagnosis of iRBD was confirmed with expert manual chart review. Prodromal risk markers and signs/symptoms were determined with diagnostic codes. Multivariable Cox proportional hazards models examined a range of covariates as predictors of time to NPD diagnosis.

Results: Of 448 iRBD cases, 82 (18.30%) were diagnosed with NPD. Forty-nine (10.93%) had >80% prodromal PD probability. There was no difference in time to NPD among those who met vs did not meet >80% probability (log rank p=0.49). In a Cox model that included all assessed criteria features, risk of diagnosis was associated with male sex (HR=2.06, 95%CI 1.04-1.10), older baseline age (HR=1.07; 95%CI 1.05-1.10), and cognitive dysfunction diagnostic code (HR= 2.83, 95%CI 1.79-4.46). Time to NPD diagnosis among predicted high- vs low- risk cases was significantly different (Log-rank test p=0.012).

Conclusions: In outpatients with iRBD, a model combining individual PD risk factors and prodromal features accurately identifies individuals at high risk for NPD diagnosis. Results demonstrate the potential of EHR data to translate research on prodromal PD to the clinic.