Telemedicine systems consist of collection, transmission, and analysis of biometric data essentially based on instrumental measures. Our goal was to evaluate if information collected from patients has an incremental informative value in automatically rating the patient's health status. We present preliminary results of a new telemedicine system (ASCOLTA) obtained by observation of 12 heart failure patients (New York Heart Association Class IIb-III). Instrumental data (electrocardiogram, oxygen saturation level, and respiration rate) were wirelessly collected daily together with clinical data (weight, heart rate, and blood pressure values) and patients' information obtained through a Web-based questionnaire, simulating a virtual medical visit. Health status was independently judged by two blinded cardiologists and by the patient's cardiologist on the basis of 348 daily clinical reports. Random forest classification analysis was applied to 240 complete clinical report variables in order to estimate the judged health status. The use of "patient's information" led to a better predictive ability in comparison with using only physiological parameters assessed by instruments. The complete set of variables (Patient+Instrumental) achieved 84% concordance, compared with 72% for the instrumental-only variables and 69% for the patient-only variables. The receiver operator characteristics curves graphically confirmed the described results. Patients have an active role in home monitoring, and their information appears relevant for a new telemedicine approach integrating subjective and objective vital signs. Combining patient information with instrumental parameters, it is possible to achieve a more correct automatic classification of health status of heart failure patients.
Read full abstract