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

Authors Moro M, Goparaju B, Castillo J, Alameddine Y, Bianchi M

Received 3 December 2015

Accepted for publication 26 February 2016

Published 8 August 2016 Volume 2016:8 Pages 277—289


Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 2

Editor who approved publication: Professor Steven A Shea

Marilyn Moro,1 Balaji Goparaju,1 Jelina Castillo,1 Yvonne Alameddine,1 Matt T Bianchi1,2

1Neurology Department, Massachusetts General Hospital, 2Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA

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

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