Detecting COPD exacerbations early using daily telemonitoring of symptoms and k-means clustering: a pilot study

• Published in: Medical & biological engineering & computing Vol. 53; no. 5; pp. 441 - 451
• Main Authors: Sanchez-Morillo, Daniel, Fernandez-Granero, Miguel Angel, Jimenez, Antonio Leon
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2015; Springer Nature B.V
• Subjects: Aged; Aged, 80 and over; Algorithms; Antibiotics; Biomedical and Life Sciences; Biomedical engineering; Biomedical Engineering and Bioengineering; Biomedicine; Chronic obstructive pulmonary disease; Cluster Analysis; Clustering; Compliance; Computer Applications; Cost control; Cost engineering; Disease; Economics; Electronics; Experiments; Female; Hospitals; Human Physiology; Humans; Imaging; Male; Medical; Medical diagnosis; Middle Aged; Mortality; Original Article; Patients; Pattern recognition; Physiology; Pilot Projects; Pilots; Pulmonary Disease, Chronic Obstructive - diagnosis; Pulmonary Disease, Chronic Obstructive - physiopathology; Pulmonology; Quality of life; Questionnaires; Radiology; Signal Processing, Computer-Assisted; Studies; Surveys; Telemedicine; Telemedicine - methods; Telemetry - methods; Usability; Spain; Exacerbation; Questionnaire; Telehealth; Symptoms; Early detection; COPD
• ISSN: 0140-0118; 1741-0444; 1741-0444
• DOI: 10.1007/s11517-015-1252-4

COPD places an enormous burden on the healthcare systems and causes diminished health-related quality of life. The highest proportion of human and economic cost is associated with admissions for acute exacerbation of respiratory symptoms (AECOPD). Since prompt detection and treatment of exacerbations may improve outcomes, early detection of AECOPD is a critical issue. This pilot study was aimed to determine whether a mobile health system could enable early detection of AECOPD on a day-to-day basis. A novel electronic questionnaire for the early detection of COPD exacerbations was evaluated during a 6-months field trial in a group of 16 patients. Pattern recognition techniques were applied. A k-means clustering algorithm was trained and validated, and its accuracy in detecting AECOPD was assessed. Sensitivity and specificity were 74.6 and 89.7 %, respectively, and area under the receiver operating characteristic curve was 0.84. 31 out of 33 AECOPD were early identified with an average of 4.5 ± 2.1 days prior to the onset of the exacerbation that was considered the day of medical attendance. Based on the findings of this preliminary pilot study, the proposed electronic questionnaire and the applied methodology could help to early detect COPD exacerbations on a day-to-day basis and therefore could provide support to patients and physicians.