Cost-effective and efficient screening algorithm for rapid eye movement sleep behavior disorder

Aline Seger, MD

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According to a recent study, an algorithm could reduce the number of unnecessary polysomnography (PSG) examinations. Investigators designed an algorithm that demonstrated high diagnostic accuracy of isolated rapid eye movement (REM) experienced by PSG sleep behavior disorder (iRBD).

This approach can also facilitate more efficient recruitment for research studies by avoiding unnecessary examinations. The study aimed to optimize the identification of participants with iRBD in the general population.

Aline Seger, MD, Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, and the team of investigators compared the performance of the commonly used RBD screening questionnaire (RBDSQ) with the assessments sleep experts. To improve classification accuracy, they selected the most specific items from the RBDSQ and included information from additional sleep-related questionnaires.

A total of 185 participants were screened, of whom 124 received PSG after expert screening, and 78 (62.9%) were diagnosed with iRBD. The results revealed that the RBDSQ total score was a significant predictor of being chosen for PSG by the sleep expert. However, the total RBDSQ score had low specificity.

The RBDSQ total score showed a sensitivity of 95.2%, a specificity of 26.2%, an accuracy of 72.4% and an area under the curve (AUC) of 0.68. Among participants receiving PSG, the RBDSQ total score showed a sensitivity of 96.2% and a specificity of 6.5% at a cutoff score of >5 points and an AUC of 0.64 (accuracy of 62.9 %).

By comparing the algorithm to the sleep expert’s decision, 77 instead of 124 PSG (62.1%) would have been performed, and 63 (80.8%) iRBD patients would have been identified. 32 unnecessary PSG examinations out of 46 (69.6%) could have been avoided.

In the stepwise multiple regression analysis of step 1 (participant selection based on expert assessment), the final model consisted of an RBDSQ subscore of items 6.1+6.2+6.3- 10, the Pittsburgh Sleep Quality Index (PSQI) daytime dysfunction component score, and the STOP-Bang questionnaire.

The final stage 1 model achieved a classification accuracy of 81.1% (sensitivity, 91.9%; specificity, 59%; AUC, 0.84; P < 0.001).

In step 2 (identification of iRBD participants during PSG), the final model consists of the RBDSQ subscore based on items 6.1 + 6.2 + 6.4-9 (notably, items partially different from those in step 1), age and PSQI sleep disturbance component score. With this second step, classification accuracy reached 76.6% (sensitivity, 83.3%; specificity, 65.2%; AUC, 0.82; P < 0.001).

Overall, the researchers reported that the proposed algorithm exhibited high diagnostic accuracy for PSG-proven iRBD in a cost-effective manner and could be a practical tool for research and clinical settings. ROC curves for Stage 1 and Stage 2 models are provided in the supporting information.

The AUCs of the two final models differed significantly from the AUC using the RBDSQ total score alone, indicating significantly better classification accuracy of the resulting step 1 and step 2 algorithms.

“This study presents a new screening algorithm to optimize the identification of participants with iRBD in the general population,” the researchers wrote. “Our approach does not require a PSG selection step by a sleep expert. The algorithm allowed us to identify 80% of iRBD patients who were identified by a sleep expert with a 40% reduction in the number of PSGs.

The references:

Seger, A., Ophey, A., Heitzmann, W., Doppler, CEJ, Lindner, M.-S., Brune, C., Kickartz, J., Dafsari, HS, Oertel, WH, Fink, GR, Jost , ST and Sommerauer, M. (2023), Evaluation of a structured screening assessment to detect isolated rapid eye movement sleep behavior disorder. Disorder Mov. https://doi.org/10.1002/mds.29389