title: Predicting the risk of exacerbation in patients with chronic obstructive pulmonary disease using home telehealth measurement data.
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.
  
  
  METHODS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: 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.
authors.forename: Mas S
authors.initials: MS
authors.lastname: Mohktar
authors.affiliation: Graduate School of Biomedical Engineering, University of New South Wales, Sydney, NSW 2052, Australia. Electronic address: s.redmond@unsw.edu.au.
authors.forename: Stephen J
authors.initials: SJ
authors.lastname: Redmond
authors.affiliation: Institute for Breathing and Sleep, Department of Respiratory Medicine, Austin Health, Heidelberg, Victoria 3081, Australia. Electronic address: nickantoniades@gmail.com.
authors.forename: Nick C
authors.initials: NC
authors.lastname: Antoniades
authors.affiliation: Institute for Breathing and Sleep, Department of Respiratory Medicine, Austin Health, Heidelberg, Victoria 3081, Australia. Electronic address: peter.rochford@austin.org.au.
authors.forename: Peter D
authors.initials: PD
authors.lastname: Rochford
authors.affiliation: Department of Respiratory Medicine, John Hunter Hospital, Newcastle 2305, Australia. Electronic address: masdayana@gmail.com.
authors.forename: Jeffrey J
authors.initials: JJ
authors.lastname: Pretto
authors.affiliation: School of Computing and Mathematics, University of Western Sydney, Sydney, NSW 2751, Australia. Electronic address: j.basilakis@uws.edu.au.
authors.forename: Jim
authors.initials: J
authors.lastname: Basilakis
authors.affiliation: Graduate School of Biomedical Engineering, University of New South Wales, Sydney, NSW 2052, Australia. Electronic address: n.lovell@unsw.edu.au.
authors.forename: Nigel H
authors.initials: NH
authors.lastname: Lovell
authors.affiliation: Institute for Breathing and Sleep, Department of Respiratory Medicine, Austin Health, Heidelberg, Victoria 3081, Australia. Electronic address: christine.mcdonald@austin.org.au.
authors.forename: Christine F
authors.initials: CF
authors.lastname: McDonald
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D015415:Biomarkers / Q000097:blood; D001835:Body Weight; D003663:Decision Trees; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D060740:Disease Eradication; D042241:Early Diagnosis; D005260:Female; D005541:Forced Expiratory Volume; D006801:Humans; D008168:Lung / Q000503:physiopathology; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D010100:Oxygen / Q000097:blood; D011237:Predictive Value of Tests; D011379:Prognosis; D029424:Pulmonary Disease, Chronic Obstructive / Q000097:blood / Q000150:complications / Q000175:diagnosis* / Q000503:physiopathology; D019114:Remote Consultation; D015203:Reproducibility of Results; D018570:Risk Assessment; D012307:Risk Factors; D012720:Severity of Illness Index; D017216:Telemedicine / Q000379:methods*; D013997:Time Factors
chemical_list: D015415:Biomarkers; D010100:Oxygen
keywords: Chronic obstructive pulmonary disease (COPD); Classification and regression tree; Home telehealth system