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Original Research

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

, , , &
Pages 277-289 | Published online: 08 Aug 2016

Figures & data

Table 1 Costs, effectiveness, and probability-based values

Table 2 Baseline PSG characteristics by OSA status

Table 3 Demographic and objective characteristics by OSA status

Figure 1 The distribution of PLMI values (x-axis) is given as a cumulative fraction (y-axis) for OSA patients who underwent two PSG nights.

Notes: The vertical gray line indicates PLMI =15 for reference.
Abbreviations: PLMI, periodic limb movement index; OSA, obstructive sleep apnea; PSG, polysomnography; h, hour.
Figure 1 The distribution of PLMI values (x-axis) is given as a cumulative fraction (y-axis) for OSA patients who underwent two PSG nights.

Table 4 Odds ratios values for significant predictors of PLMI ≥15

Figure 2 Structure of the decision model.

Notes: The schematic shows four strategies to approach occult elevation of PLMI. The first branch strategy is to conduct in-laboratory PSG for everyone (PSG all). Within that approach, those with elevated PLMI will have the chance to receive treatment. The next strategy utilizes a prescreen of a clinical tool, based on clinical history, to risk stratify those who should undergo PSG. Again, those testing positive on PSG will have the chance to receive treatment. The third strategy is to perform home testing using a portable device, and those testing positive in that setting can proceed to treatment (no PSG in this arm). Finally, we included a reference strategy with no testing or treatment for anyone (do-nothing arm). Within each of these arms, a Markov node is placed to allow recurrent annual risk of cardiovascular or cerebrovascular event as well as a low probability of death, to occur. For convenience, these states are shown only for one branch (dotted lines).
Abbreviations: PLMI, periodic limb movement index; PSG, polysomnography; PLMS, periodic limb movements of sleep; CT, clinical tool; HD, home device; M, markov node.
Figure 2 Structure of the decision model.

Figure 3 Sensitivity analysis.

Notes: We performed sensitivity analysis on home device accuracy (sensitivity and specificity, on the x and y axes of each plot), across two costs of home device testing per person ($0 and $100) and a range of prevalence of elevated PLMI (rows). Willingness to pay was $50,000 in all cases. Two strategies were never favored in this modeling: clinical tool and PSG for everyone. The red and blue regions indicate parameter space when the preferred choice is the home device arm versus the do-nothing arm, respectively.
Abbreviations: PLMI, periodic limb movement index; PSG, polysomnography; cHD, cost for home device; Sens, sensitivity; Spec, specificity; Prev, prevalence.
Figure 3 Sensitivity analysis.

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