REM Sleep Behavior Disorder Diagnostic Code Accuracy and Implications in the Real-World Setting.

Background and Objectives: Isolated REM sleep behavior disorder (iRBD) carries increased risk of neurodegenerative parkinsonian disorder or dementia (NPD) but is difficult to accurately screen for in the community. Health care data offer the opportunity to identify large numbers of iRBD cases among outpatients. We aimed to determine the positive predictive value (PPV) of an RBD Methods: In this retrospective cohort study, a search of the EHR at a single health care system was conducted to identify outpatients who received the ICD9 or ICD10 RBD code in 2011-2021. The EHR for each case was manually reviewed. Secondary RBD cases were excluded. Remaining cases were classified as no iRBD or actual iRBD (possible, probable, or definite). Incident cases of NPD were identified. PPV of presence of the RBD ICD code for actual iRBD was calculated. Cumulative incidence of NPD with death as a competing event was compared in those with vs without iRBD. Least absolute shrinkage and selection operator (LASSO) regression was used to build a prediction model for iRBD, and the model was validated in an independent data set. Results: Among 1,130 cases with the RBD ICD code, 499 had secondary causes of RBD. For the remaining 628 cases, EHR review indicated no iRBD in 168 (26.8%). PPV of the RBD ICD code was 73.25%. Over a median follow-up of 4.7 years, compared with the no iRBD group, the iRBD group had a higher risk of NPD (subdistribution hazard ratio = 10.4 [95% CI 2.5-43.1]). The LASSO prediction model for iRBD had an area under the receiver operating characteristic curve of 0.844 (95% CI 0.806-0.880). Discussion: PPV of an RBD ICD code is moderate. In the real-world setting, patients with iRBD had a high risk of incident diagnosis of NPD over 4.7 years. Results indicate feasibility of using statistical models developed using EHR data to accurately predict iRBD.