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

Abstract

Christopher B Cooper,1 Worawan Sirichana,1,2 Eric V Neufeld,1 Michael Taylor,3 Xiaoyan Wang,4 Brett A Dolezal1 1Exercise Physiology Research Laboratory, Departments of Medicine and Physiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA; 2Division of Pulmonary and Critical Care Medicine, Department of Medicine, Chulalongkorn University, Bangkok, Thailand; 3eResearch Technology Inc., Philadelphia, PA, USA; 4Division of General Internal Medicine and Health Services Research, David Geffen School of Medicine, University of California, Los Angeles, CA, USACorrespondence: Christopher B CooperDavid Geffen School of Medicine, University of California, 10833 Le Conte Avenue, 37-131 CHS Building, Los Angeles, CA 90095-1690, USATel [phone]Fax [phone]Email [email protected]: Exacerbations 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.Methods: We 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.Results: The 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.Conclusion: We 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.Keywords: chronic obstructive pulmonary disease exacerbations, early detection, treatment adherence, home monitoring

Keywords

• chronic obstructive pulmonary disease exacerbations
• early detection
• treatment adherence
• home monitoring