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Development and Validation of a Predictive Nomogram for Possible REM Sleep Behavior Disorders.

Hong Lai1, Xu-Ying Li1,2, Junya Hu3

  • 1Department of Neurology, National Clinical Research Center for Geriatric Diseases, Xuanwu Hospital of Capital Medical University, Beijing, China.

Frontiers in Neurology
|July 18, 2022
PubMed
Summary

Researchers developed a nomogram to predict idiopathic REM sleep behavior disorder (RBD) in Beijing. This tool identifies key risk factors like family history, smoking, and constipation for early detection in the community.

Keywords:
LASSOREM sleep behavior disorder (RBD)decision curve analysis (DCA)nomogrampredictive model

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Area of Science:

  • Neurology
  • Sleep Medicine
  • Epidemiology

Background:

  • Idiopathic REM sleep behavior disorder (RBD) is a neurodegenerative disease precursor.
  • Early detection is crucial for timely intervention and management.
  • Community-based studies are needed to understand RBD prevalence and risk factors.

Purpose of the Study:

  • To develop and validate a predictive nomogram for idiopathic RBD in a Beijing community population.
  • To identify independent predictors of possible RBD (pRBD).
  • To assess the clinical utility of the predictive model.

Main Methods:

  • A cross-sectional study of 1,030 community residents in Beijing.
  • Utilized the RBD questionnaire-Hong Kong (RBDQ-HK) to screen for pRBD.
  • Applied LASSO regression for feature selection and nomogram development.
  • Internal validation using bootstrap resampling, ROC curves, calibration curves, and DCA.

Main Results:

  • Seven independent predictors identified: family history of PD/dementia, smoking, physical activity, pesticide exposure, constipation, depression, and daytime somnolence.
  • The nomogram demonstrated good discrimination with an AUC of 0.885.
  • Internal validation confirmed good predictive accuracy and clinical usefulness (C-index: 0.876).

Conclusions:

  • An effective nomogram was developed for individualized prediction of pRBD.
  • The nomogram shows potential for early identification of individuals at risk for RBD in community settings.
  • This tool can aid in clinical decision-making and facilitate further research.