Statistical Process Control Improves The Feasibility Of Remote Physiological Monitoring In Patients With Chronic Obstructive Pulmonary Disease.

Abstract

BackgroundExacerbations of chronic obstructive pulmonary disease (COPD) occur with increasing frequency as the disease progresses and account for poor health status, worse prognosis, and higher healthcare expenditure.MethodsWe developed a networked system for remote physiological monitoring (RPM) at home and optimized it with statistical process control (SPC) with the goal of earlier detection of COPD exacerbations. We enrolled 17 patients with moderate to severe COPD with a mean (SD) age of 71.1 (7.2) years. We obtained daily symptom scores, treatment adherence and activity levels using a programmable device, and measured daily slow and forced spirometry (FEV1, FVC, PEF), inspiratory capacity (IC) and oxygenation (SpO2). To identify exacerbations, we developed rolling prediction intervals for FVC, FEV1, IC and SpO2 using SPC.ResultsThe time taken to perform daily monitoring was reduced from 12.7 (5.4) minutes to 6.5 (2.6) minutes through software refinements during the study. Adherence to forced and slow spirometry was 62.6% and 62.4%, respectively. The within-subject coefficients of variation for FEV1, PEF and IC were 12.2%, 16.2%, and 13.1%, respectively. Event rates per patient-year for exacerbations were: self-reported 0.42, 2/3 Anthonisen Criteria (AC) 0.42, modified AC 2.23, systemic corticosteroid use 0.56, and antibiotic use 0.56.ConclusionWe successfully implemented a networked system for RPM of symptoms, treatment adherence, and physiology at home in patients with COPD. We demonstrated that SPC improves the feasibility of RPM in COPD patients which may increase the likelihood of detecting COPD exacerbations.