Periodic limb movements of sleep: empirical and theoretical evidence supporting objective at-home monitoring

Abstract

Introduction

Periodic limb movements of sleep (PLMS) may increase cardiovascular and cerebrovascular morbidity. However, most people with PLMS are either asymptomatic or have nonspecific symptoms. Therefore, predicting elevated PLMS in the absence of restless legs syndrome remains an important clinical challenge.

Methods

We undertook a retrospective analysis of demographic data, subjective symptoms, and objective polysomnography (PSG) findings in a clinical cohort with or without obstructive sleep apnea (OSA) from our laboratory (n=443 with OSA, n=209 without OSA). Correlation analysis and regression modeling were performed to determine predictors of periodic limb movement index (PLMI). Markov decision analysis with TreeAge software compared strategies to detect PLMS: in-laboratory PSG, at-home testing, and a clinical prediction tool based on the regression analysis.

Results

Elevated PLMI values (>15 per hour) were observed in >25% of patients. PLMI values in No-OSA patients correlated with age, sex, self-reported nocturnal leg jerks, restless legs syndrome symptoms, and hypertension. In OSA patients, PLMI correlated only with age and self-reported psychiatric medications. Regression models indicated only a modest predictive value of demographics, symptoms, and clinical history. Decision modeling suggests that at-home testing is favored as the pretest probability of PLMS increases, given plausible assumptions regarding PLMS morbidity, costs, and assumed benefits of pharmacological therapy.

Conclusion

Although elevated PLMI values were commonly observed, routinely acquired clinical information had only weak predictive utility. As the clinical importance of elevated PLMI continues to evolve, it is likely that objective measures such as PSG or at-home PLMS monitors will prove increasingly important for clinical and research endeavors.

Keywords:

• periodic limb movements
• polysomnography
• predictors
• sleep
• decision analysis
• cost-effectiveness
• diagnostic

Supplementary materials

Table S1 Assessment of PLMI and LMAI per categorical variable

Table S2 Spearman’s correlation analysis for PLMI and LMAI per OSA status

Table S3 Spearman’s correlation analyses for age with multiple covariates

Table S4 Spearman’s correlation analyses for sex with multiple covariates (M=1, F=0)

Table S5 Spearman’s correlation analyses for BMI with multiple covariates

Disclosure

Dr Bianchi received funding from the Department of Neurology, Massachusetts General Hospital; the Center for Integration of Medicine and Innovative Technology; the Department of Defense; the Milton Family Foundation; MC10, Inc.; Insomnisolv, Inc.; and the American Sleep Medicine Foundation. He has a patent pending on a home sleep monitoring device. He has consulting agreements with Grand Rounds and International Flavors & Fragrances, has received travel funding from Servier, serves on the advisory board of Foramis, and has provided expert testimony in sleep medicine. The other authors report no conflicts of interest in this work.