Cost-effectiveness of Home Respiratory Polygraphy

Overview

Primary objectives: The efficacy of the therapeutic decision taken by respiratory polygraphy (RP) against polysomnography (PSG) using the Epworth scale; Secondary Objective: 1. the cost-effectiveness of diagnosis and therapeutic decision valued using the Epworth Scale and EuroQol 5D. 2. effectiveness of the therapeutic decision by means of: 1) quality of life tests, 2) adherence and compliance to treatment, 3) blood pressure MAP, 4) biochemistry determinations. Design: prospective, randomized, controlled, open, parallel of non-inferiority. 440 patients will be randomized to diagnose and follow treatment based on the RP or the PSG. The follow-up will last 6 months with 4 assessments. Statistical analysis: We will compare the change in the Epworth scale between both arms of treatment through analysis of covariance. The premise of non-inferiority is -2 at the lower limit of 95% IC. Secondary variables will be analyzed using differences in independent means (or non-parametric equivalent) or Chi2 for dichotomous variables. Cost-effectiveness: costs generated by one and another method will be evaluated against the effectiveness of the primary variable using Bayesian techniques

Design: Prospective, randomized, controlled, open, parallel noninferiority. 440 patients will be randomized to diagnose and follow treatment based on the PR or PSG generating four groups: two treated and two untreated with CPAP. The latter did not undergo self-certification home. The monitoring will be 6 months and 4 assessments. Statistical analysis: compare the change of the scale Epworth before and after the intervention between both treatment arms using analysis of covariance. The premise is non-inferiority of -1.6 at the lower limit of 95%. Secondary variables were analyzed by independent means differences (or nonparametric equivalent) or Chi2 for dichotomous variables. Cost-effectiveness: the costs generated by the two methods will be measured against the effectiveness of the primary endpoint using Bayesian techniques