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

Med Biol Eng Comput. 2015 May;53(5):441-51. doi: 10.1007/s11517-015-1252-4. Epub 2015 Mar 1.

Abstract

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Aged
  • Aged, 80 and over
  • Algorithms
  • Cluster Analysis
  • Female
  • Humans
  • Male
  • Middle Aged
  • Pilot Projects
  • Pulmonary Disease, Chronic Obstructive / diagnosis*
  • Pulmonary Disease, Chronic Obstructive / physiopathology
  • Signal Processing, Computer-Assisted
  • Telemedicine / methods*
  • Telemetry / methods*