Comparison of rapid eye movement without atonia quantification methods to diagnose rapid eye movement sleep behavior disorder: a systematic review

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Abstract
Study Objectives: Rapid eye movement (REM) sleep without atonia (RWA) is essential for diagnosing REM sleep behavior disorder (RBD). Manual and automatic quantifications of RWA that use different criteria have been validated. This study compared the RWA quantification methods for diagnosing RBD. Methods: The PubMed, EMBASE, Web of Science, and Cochrane Library databases were systemically searched for studies published from inception to December 2021. The inclusion criteria were cohort, cross-sectional, and case-control studies assessing the sensitivity and specificity of RWA quantification methods. Pooled estimates of the sensitivity, specificity, diagnostic odds ratio (DOR), and area under the curve (AUC) were determined. Risk of bias and certainty of evidence was assessed using the Quality Assessment of Diagnostic Accuracy Studies tool and the Grading of Recommendations, Assessment, Development, and Evaluations framework, respectively. Results: Fourteen articles including 402 patients with RBD met the inclusion criteria. Manual methods evaluating any chin and phasic flexor digitorum superficialis (FDS) activity had the highest DOR (138.8, 95% CI = 21.8% to 881.7%) and AUC (0.9686). The automatic REM atonia index (RAI) showed similar or higher sensitivity (89.1%, 95% CI = 84.6% to 92.7%) but a lower specificity (73.5%), DOR (43.1), and AUC (0.9369) than the manual techniques. Conclusions: In this meta-analysis, manual RWA quantification that employed chin or phasic FDS activity had the best RBD diagnostic performance. The automatic RAI method may be useful for screening patients with RBD. The results should be interpreted carefully because of the high risk of bias in patient selection and significant heterogeneity among the studies. PROSPERO Registration number: CRD42021276445.
Funding Information
  • National Research Foundation of Korea (NRF-2020R1C1C1013160)
  • Ministry of Science, ICT & Future Planning (2017M3C7A1029688)
  • National Research Foundation of Korea (2017R1A2B2012280)

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