Predicting the risk of exacerbation in patients with chronic obstructive pulmonary disease using home telehealth measurement data

Artif Intell Med. 2015 Jan;63(1):51-9. doi: 10.1016/j.artmed.2014.12.003. Epub 2014 Dec 18.


Background: The use of telehealth technologies to remotely monitor patients suffering chronic diseases may enable preemptive treatment of worsening health conditions before a significant deterioration in the subject's health status occurs, requiring hospital admission.

Objective: The objective of this study was to develop and validate a classification algorithm for the early identification of patients, with a background of chronic obstructive pulmonary disease (COPD), who appear to be at high risk of an imminent exacerbation event. The algorithm attempts to predict the patient's condition one day in advance, based on a comparison of their current physiological measurements against the distribution of their measurements over the previous month.

Method: The proposed algorithm, which uses a classification and regression tree (CART), has been validated using telehealth measurement data recorded from patients with moderate/severe COPD living at home. The data were collected from February 2007 to January 2008, using a telehealth home monitoring unit.

Results: The CART algorithm can classify home telehealth measurement data into either a 'low risk' or 'high risk' category with 71.8% accuracy, 80.4% specificity and 61.1% sensitivity. The algorithm was able to detect a 'high risk' condition one day prior to patients actually being observed as having a worsening in their COPD condition, as defined by symptom and medication records.

Conclusion: The CART analyses have shown that features extracted from three types of physiological measurements; forced expiratory volume in 1s (FEV1), arterial oxygen saturation (SPO2) and weight have the most predictive power in stratifying the patients condition. This CART algorithm for early detection could trigger the initiation of timely treatment, thereby potentially reducing exacerbation severity and recovery time and improving the patient's health. This study highlights the potential usefulness of automated analysis of home telehealth data in the early detection of exacerbation events among COPD patients.

Keywords: Chronic obstructive pulmonary disease (COPD); Classification and regression tree; Home telehealth system.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Aged
  • Aged, 80 and over
  • Algorithms*
  • Biomarkers / blood
  • Body Weight
  • Decision Trees
  • Diagnosis, Computer-Assisted / methods*
  • Disease Eradication
  • Early Diagnosis
  • Female
  • Forced Expiratory Volume
  • Humans
  • Lung / physiopathology
  • Male
  • Middle Aged
  • Multivariate Analysis
  • Oxygen / blood
  • Predictive Value of Tests
  • Prognosis
  • Pulmonary Disease, Chronic Obstructive / blood
  • Pulmonary Disease, Chronic Obstructive / complications
  • Pulmonary Disease, Chronic Obstructive / diagnosis*
  • Pulmonary Disease, Chronic Obstructive / physiopathology
  • Remote Consultation
  • Reproducibility of Results
  • Risk Assessment
  • Risk Factors
  • Severity of Illness Index
  • Telemedicine / methods*
  • Time Factors


  • Biomarkers
  • Oxygen