Telemonitoring System for Early Diagnosis of COPD Exacerbations.

Chronic obstructive pulmonary disease (COPD) is a global health problem throughout the world. It's not only the fourth cause of death with 600 million deaths every year, but it's also the most rapidly increasing pathology in terms of mortality in industrialized countries and in 2020 it will become the second cause of death (WHO data). Moreover it's impact is largely underestimated. Only one patient on four is diagnosed, often with delay, and this obviously reduces therapeutic chance. Consequently, COPD is often identified and cured only in late stages, while it constitutes an important health problem also in younger people (from 45-50 years). Recent studies estimated an average cost of 1.300 euro per patient every year, which can increase to 7.000 euro in more severe stages. A tailored management of COPD patients would obviously allow to reduce costs for hospitalizations and improve quality of life. This management could benefit of the Information and Comunication Technology support, which can offer the possibility of telemonitoring patients without the need of repeated hospital visits and improving the efficacy of healthcare services. Moreover, the high frequency of exacerbations and their often atypical clinical presentation in the aged patient make particularly desirable the availability of a telemonitoring system which could guarantee continuous control and early intervention in case of necessity. The use of telemonitoring systems in COPD patients has already demonstrated a moderate efficacy in reducing hospidalizations and other related outcomes (Pedone C et al. BMC Health Serv Res 2003), but evidence is not homogeneous (Pedone C e Lelli D Pneumonol Alergol Pol 2015). A further improvement of the impact of telemonitoring systems on health status of COPD patients could derive from Decision Support System (DSS), able to predict clinical events (i.e. exacerbations) and to assist healthcare providers in monitoring or predicting events on the basis of variation of clinical parameters, not directly collected with conventional strategies.

Our research group (L.I.A., Università Campus Biomedico di Roma, Asl n. 4 Lanusei) has already developed a predictive algorithm for exacerbations or clinical worrisome events before the onset of symptoms. The project has been financed by Provincia Autonoma di Bolzano (Italy) and presented at the IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI) in Las Vegas on February 24-27th 2016 ["On the Remote Detection of COPD-Related Worrisome Events" - BHI 2016]. The full paper has been published on the IEEE Journal of Biomedical and Health Informatics ["A decision support system for tele-monitoring COPD-related worrisome events"] on March 2017.

Exploiting our results, an innovative medical device for health status monitoring has been developed to detect exacerbations or clinical worrisome events through the consideration of early signs the patient is not still aware of and, accordingly, suggesting him to refer to doctors before the onset of symptoms. The device is made up of a Bluetooth digital pulse oximeter and of an APP. The APP connects with the pulse oximeter that measure heart rate and blood oxygen saturation. The APP, at first, adapts to the physiological characteristics of each patient, thus personalizing its responses, setting its own coefficients for the analysis of the parameters used on the basis of the continuous flow of data received from the pulse oximeter. The initial period of "adaptation" for the basic setting of the machine has been estimated in 5 days, after which the device will continue to model itself on the patient both through self-learning using the input data, and through a possible, but not obligatory, intervention from medical doctors modifying the configuration parameters for a better response from the machine. Our results suggest the algorithm is able to detect potentially critical situations with a sensitivity index of 98% and a specificity of 100%.

3. Study objectives

To test the algorithm on a larger population to verify the performance of the current system. Based on the number of descriptors used by the system, considering a drop-off rate of 15%, error margin of 0.02, p = 0.05, the number of patients enrolled is 120.