Journal of Telemedicine and Telecare > Volume 15, Number 5 > Pp. 226-231

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RESEARCH Original articles

Predicting need for intervention in individuals with congestive heart failure using a home-based telecare system

Elaine Biddiss ^*   , Simon Brownsell ^*  and Mark S Hawley ^* 

^* School of Health and Related Research, University of Sheffield, Sheffield, UK; Bloorview Research Institute, Bloorview Kids Rehab, Toronto, Canada; Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Canada; Department of Medical Physics and Clinical Engineering, Barnsley Hospital NHS Foundation Trust, Barnsley, UK

Correspondence: Elaine Biddiss, Bloorview Research Institute, 150 Kilgour Road, Toronto, Ontario M4G 1R8, Canada (Fax: +1 416 425 1634; Email: elaine.biddiss{at}utoronto.ca)

We have studied how well the need for a medical intervention can be predicted by a telecare monitoring system. During a study period of about 18 months, 45 elderly individuals with congestive heart failure used a home health monitor to enter daily information pertaining to their symptoms and health status. A total of 8576 alerts were generated by the monitoring system, although in most cases, patient and service provider interaction was not required. When system alerts were considered to be serious, or if symptoms persisted, the patient was contacted. A total of 171 key medical events (6 deaths; 28 hospital admissions; 59 changes in medication; 54 cases of advice given; 24 instances where immediate medical attention was recommended) were recorded in the monitoring logs. A multivariate logistic regression model was developed to predict these medical interventions/events. The model correctly predicted key medical events in 75% of cases with a specificity of 74% and an overall cross-validated accuracy of 74% (95% CI, 68–80%). Key predictors included the number of system alerts, self-rated mobility, self-rated health and self-rated anxiety. This suggests that subjective measures are useful in addition to physiological ones for predicting health status. A multivariate decision support model has potential to supplement practitioners and current telecare systems in identifying heart failure patients in need of medical intervention.

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