Evaluation of a Structured Screening Assessment to Detect Isolated Rapid Eye Movement Sleep Behavior Disorder

Author:

Seger Aline12ORCID,Ophey Anja3ORCID,Heitzmann Wiebke1,Doppler Christopher E.J.12ORCID,Lindner Marie‐Sophie1,Brune Corinna1,Kickartz Johanna1,Dafsari Haidar S.1,Oertel Wolfgang H.4,Fink Gereon R.12,Jost Stefanie T.1ORCID,Sommerauer Michael12ORCID

Affiliation:

1. Department of Neurology Faculty of Medicine and University Hospital Cologne, University of Cologne Cologne Germany

2. Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM‐3), Research Centre Jülich Jülich Germany

3. Faculty of Medicine and University Hospital Cologne, Medical Psychology, Neuropsychology and Gender Studies and Center for Neuropsychological Diagnostics and Interventions (CeNDI) University of Cologne Cologne Germany

4. Department of Neurology Philipps‐University Marburg Marburg Germany

Abstract

AbstractBackgroundIsolated rapid eye movement (REM) sleep behavior disorder (iRBD) cohorts have provided insights into the earliest neurodegenerative processes in α‐synucleinopathies. Even though polysomnography (PSG) remains the gold standard for diagnosis, an accurate questionnaire‐based algorithm to identify eligible subjects could facilitate efficient recruitment in research.ObjectiveThis study aimed to optimize the identification of subjects with iRBD from the general population.MethodsBetween June 2020 and July 2021, we placed newspaper advertisements, including the single‐question screen for RBD (RBD1Q). Participants' evaluations included a structured telephone screening consisting of the RBD screening questionnaire (RBDSQ) and additional sleep‐related questionnaires. We examined anamnestic information predicting PSG‐proven iRBD using logistic regressions and receiver operating characteristic curves.ResultsFive hundred forty‐three participants answered the advertisements, and 185 subjects fulfilling inclusion and exclusion criteria were screened. Of these, 124 received PSG after expert selection, and 78 (62.9%) were diagnosed with iRBD. Selected items of the RBDSQ, the Pittsburgh Sleep Quality Index, the STOP‐Bang questionnaire, and age predicted iRBD with high accuracy in a multiple logistic regression model (area under the curve >80%). When comparing the algorithm to the sleep expert decision, 77 instead of 124 polysomnographies (62.1%) would have been carried out, and 63 (80.8%) iRBD patients would have been identified; 32 of 46 (69.6%) unnecessary PSG examinations could have been avoided.ConclusionsOur proposed algorithm displayed high diagnostic accuracy for PSG‐proven iRBD cost‐effectively and may be a convenient tool for research and clinical settings. External validation sets are warranted to prove reliability. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Funder

Deutsche Forschungsgemeinschaft

Publisher

Wiley

Subject

Neurology (clinical),Neurology

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