A Cost-Effective and Efficient Screening Algorithm for Rapid Eye Movement Sleep Behavior Disorder

Aline Seeger, M.D.

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Recent research suggests that algorithms may be able to reduce the amount of unnecessary polysomnography (PSG) examinations. Researchers designed an algorithm that demonstrated high diagnostic accuracy for PSG-proven isolated rapid eye movement (REM). sleep behavior disorder (iRBD).

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

Aline Seger, M.D., Ph.D., Department of Neurology, University of Cologne Medical School and Cologne University Hospital, and a research team compared the performance of the commonly used RBD Screening Questionnaire (RBDSQ) with ratings from sleep experts. To improve classification accuracy, they selected the most specific items from her RBDSQ and included information from additional sleep-related questionnaires.

A total of 185 participants were screened, of whom 124 underwent PSG after expert selection and 78 (62.9%) were diagnosed with iRBD. Findings revealed that the RBDSQ total score was a significant predictor selected for her PSG by sleep professionals. However, the specificity of the RBDSQ total score was low.

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

Comparing the algorithm to sleep expert determinations, 77 PSGs instead of 124 (62.1%) should have been performed and identified 63 (80.8%) iRBD patients. 32 out of 46 (69.6%) unnecessary PSG tests could have been avoided.

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

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

In step 2 (identification of iRBD participants on PSG), the final model uses items 6.1+6.2+6.4-9 (particularly items that are partially different from step 1), age, and RBDSQ subscores based on PSQI component scores sleep consists of obstruction. This second step achieved a classification accuracy of 76.6% (sensitivity 83.3%, specificity 65.2%, AUC 0.82, P < 0.001).

Overall, the researchers reported that the proposed algorithm showed high diagnostic accuracy of PSG-proven iRBD in a cost-effective manner and may be a useful tool for research and clinical settings. bottom. ROC curves for both Step 1 and Step 2 models are available in Supporting Information.

The AUCs of both final models were significantly different from the AUCs using only the RBDSQ total score, indicating a significant improvement in the classification accuracy of the resulting Step 1 and Step 2 algorithms.

“This study presents a novel screening algorithm to optimize the identification of participants with iRBD from the general population,” the researchers wrote. It does not require a PSG selection step.This algorithm was able to identify 80% of iRBD patients identified by sleep experts as having a 40% reduction in PSG count.”

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. Move discord. https://doi.org/10.1002/mds.29389