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

Mas S Mohktar; Stephen J Redmond; Nick C Antoniades; Peter D Rochford; Jeffrey J Pretto; Jim Basilakis; Nigel H Lovell; Christine F McDonald

doi:10.1016/j.artmed.2014.12.003

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.

Authors

Mas S Mohktar¹, Stephen J Redmond², Nick C Antoniades³, Peter D Rochford⁴, Jeffrey J Pretto⁵, Jim Basilakis⁶, Nigel H Lovell⁷, Christine F McDonald⁸

Affiliations

¹ Graduate School of Biomedical Engineering, University of New South Wales, Sydney, NSW 2052, Australia; Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia. Electronic address: mas_dayana@um.edu.my.
² Graduate School of Biomedical Engineering, University of New South Wales, Sydney, NSW 2052, Australia. Electronic address: s.redmond@unsw.edu.au.
³ Institute for Breathing and Sleep, Department of Respiratory Medicine, Austin Health, Heidelberg, Victoria 3081, Australia. Electronic address: nickantoniades@gmail.com.
⁴ Institute for Breathing and Sleep, Department of Respiratory Medicine, Austin Health, Heidelberg, Victoria 3081, Australia. Electronic address: peter.rochford@austin.org.au.
⁵ Department of Respiratory Medicine, John Hunter Hospital, Newcastle 2305, Australia. Electronic address: masdayana@gmail.com.
⁶ School of Computing and Mathematics, University of Western Sydney, Sydney, NSW 2751, Australia. Electronic address: j.basilakis@uws.edu.au.
⁷ Graduate School of Biomedical Engineering, University of New South Wales, Sydney, NSW 2052, Australia. Electronic address: n.lovell@unsw.edu.au.
⁸ Institute for Breathing and Sleep, Department of Respiratory Medicine, Austin Health, Heidelberg, Victoria 3081, Australia. Electronic address: christine.mcdonald@austin.org.au.

PMID: 25704112
DOI: 10.1016/j.artmed.2014.12.003

Abstract

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

Substances

Biomarkers
Oxygen