Allocating provider resources to diagnose and treat restless legs syndrome: a cost-utility analysis

Restless legs syndrome (RLS) is a neurological disorder that is frequently misdiagnosed, resulting in delays in proper treatment. The objective of this study was to analyze the cost-utility of training primary care providers (PCP) in early and accurate diagnosis of RLS.

We used a Markov model to compare two strategies: one where PCPs received training to diagnose RLS (informed care) and one where PCPs did not receive training (standard care). This analysis was conducted from the US societal and health sector perspectives over one-year, five-year, and lifetime (50-year) horizons. Costs were adjusted to 2016 USD, utilities measured as quality-adjusted life-years (QALYs), and both measures were discounted annually at 3%. Cost, utilities, and probabilities for the model were obtained through a comprehensive review of literature. An incremental cost-effectiveness ratio (ICER) was calculated to interpret our findings at a willingness-to-pay threshold of $100,000/QALY. Univariate and multivariate analyses were conducted to test model uncertainty, in addition to calculating the expected value of perfect information.

Providing training to PCPs to correctly diagnose RLS was cost-effective since it cost $2021 more and gained 0.44 QALYs per patient over the course of a lifetime, resulting in an ICER of $4593/QALY. The model was sensitive to the utility for treated and untreated RLS. The probabilistic sensitivity analysis revealed that at $100,000/QALY, informed care had a 65.5% probability of being cost-effective.

A program to train PCPs to better diagnose RLS appears to be a cost-effective strategy for improving outcomes for RLS patients.