Monitoring Chronic Obstructive Pulmonary Disease Patients at Home by a Forced Oscillation Technique Device

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The continuous progresses of medical science and technology and the improvement of life conditions have increased the life expectancy in industrialized countries: the result is the progressive population aging [UN, 2002]. One of the consequences of this trend is the increasing number of people suffe...

The continuous progresses of medical science and technology and the improvement of life conditions have increased the life expectancy in industrialized countries: the result is the progressive population aging [UN, 2002]. One of the consequences of this trend is the increasing number of people suffering from chronic diseases. Among these pathologies chronic obstructive pulmonary disease (COPD) is the more important for number of patients and deaths affecting 80 million people in the world [WHO, 2006]. The time course of the disease is characterized by stable periods broken up by intermittent acute exacerbations of the symptoms, during which a severe inflammatory process occurs, leading to increased airways obstruction. During exacerbations the risk of death is very high [Connors et al, 1996;Soler-Cataluna et al, 2005]. Moreover the frequency and severity of exacerbations correlate with the worsening of the health condition of the patient [Anzueto et al, 2009; Donaldson et al, 2002]. The burden of care of patients with COPD on the national health care systems and society is high. The utilization of health care resources by patients with COPD is primarily due to acute exacerbations requiring care in the emergency rooms and hospital [Dal Negro, 2008] . In the last few years the need to rationalize the health care costs has prompted to the development of new technologies for the home monitoring of these patients mainly aimed at the early detection of the onset of exacerbations. This could help the development of new therapeutic and organizational models aimed at reducing the impact of such events on the quality of life of the patient and on the national health care systems. Published data offer several different follow-up models of home monitoring of COPD. Written and electronic diaries, questionnaires, telephonic assistance by respiratory nurses or specialized clinical personnel and use of web-based call centers have all been suggested for the follow up of patients after discharge from hospitals. Even if positive results are reported in terms of reduction of in hospitalization [Vitacca et al, 2009] many COPD patients tend to underestimate the severity of their condition [Cote et al, 1998] and patient's compliance in recording their symptoms rapidly decreases with time [Cote et al, 1998]. Moreover, other authors address the difficulty to evaluate correctly the impact of such models on patient's quality of life and on the reduction of hospitalization and mortality rate [Ram et al, 2004; Bolton et al, 2010]. An effective home monitoring system for COPD patients should have the following characteristics: easy to be used in unsupervised environment, able to provide sensible and objective information about patient status, low cost. Clinically, diagnosis and monitoring of COPD patients is based on spirometric parameters, such as FEV1 (forced expiratory volume in 1 second), FVC (forced vital capacity) and FEV1/FVC obtained from forced expiratory maneuvers [Gold, 2009]. However, for most patients affected by COPD, it is very difficult to execute spirometry correctly because of their poor ability to perform forced maneuvers. For this reason it is necessary that the test is supervised by a physician [Miller et al, 2005]. Moreover, there is general belief in the medical community that FEV1 is insensitive to changes over short periods of time in patients with COPD so controversy surrounds the reliance of this parameter as an accurate metric for acute respiratory events. These may be some of the reasons why most of the studies where patients have been monitored by home spirometry or peak-flow meters have reported poor results[Brouwer et al, 2010]. A promising technology for the home monitoring of respiratory diseases is represented by the Forced Oscillations Technique (FOT). FOT is based on the analysis of the response of the respiratory system to external pressure stimuli superimposed to the spontaneous breathing of the patient. It does not require the cooperation of the patient and it can provide accurate measurements even without the supervision of specialized personnel. In the last few years new technologies based on FOT and special algorithms were developed by the respiratory research group of the Bioengineering Department of the Politecnico di Milano University, Italy. These methodologies have been already tested proving to be effective for the automatic and quantitative evaluation of several phenomena including: the presence or not of Expiratory Flow Limitation (EFL) breath by breath [Dellaca' et al, 2004]; airways resistance corrected from the artifacts introduced by EFL [Dellaca' et al, 2009]; the degree of airways heterogeneities measured by the analysis of the frequency dependency of the resistance; the response of the respiratory system to the administration of bronchodilators and bronchoconstrictors [Dellaca' et al, 2009]. Recently a spin-off company of respiratory group of the Bioengineering Department of the Politecnico di Milano has made available a special device (RESMONPRO-diary) suitable to collect data at patient's home without medical supervision. Data from a prototype device recorded on asthmatic patients have shown its suitability for remote unsupervised monitoring of chronic conditions. AIM OF THE STUDY The aim of the present study is to evaluate the possibility of monitoring oscillatory parameters of lung mechanics, measured by the Forced Oscillation Technique (FOT), for the early detection of exacerbations in COPD patients. Therefore primary outcome will be the identification of the relationship between changes in patient's symptoms, breathing pattern, lung mechanical impedance indices and occurrence of and exacerbation. A further outcome of the study will be the identification and classification of the occurred exacerbations. STUDY PROTOCOL Pre-study measurements: After the enrollment in the study, every subject will be visited by physician and will undergo a standard pulmonary function test. Moreover the following data will be collected: exacerbation history smoking history disease history presence of co-morbidities Daily home monitoring program: All patients will be monitored at home using a device for oscillometric measurement of lung functionality (RESMON pro). The duration of the measurement will be 2-3 minutes once a day for a period comprised between 6 and 8 months. At the beginning of the measurement the device will ask the patient to fill a simple questionnaire for self-evaluation of their symptoms (dyspnoea, amount and purulence of sputum, wheeze, cough). For the duration of the study, the participant will be asked to wear an Actiwatch Spectrum 24 hours a day. The Actiwatch Spectrum, a small watch-like device, measures activity by recording and analyzing the readings from the on-board accelerometer. Each 1-minute activity epoch is analyzed and the statistical results stored in the on-board memory which can hold up to six-months of data. The Actigraphy data provides an objective record of activity and has been validated to determine normal and abnormal sleep / wake patterns. A nurse will perform a weekly phone interview in order to collect information about presence and timing of the following events: Change in current drug therapy Systemic steroids use/prescription Antibiotic use/prescription Hospitalization due to respiratory causes Emergency Room admission Unscheduled GP call Unscheduled GP visit Specialist visit Worsening in sleep Worsening in dyspnea during daily life activity Fever DATA ANALYSIS The time-series data and their variability will be analyzed using the method of the time irreversibility, sample and multiscale entropy, and Gumbel's extreme values statistic. The predictor of an exacerbation will then be derived by calculating the conditional probability of having a future acute event given the trend or the variability of indices derived from the above methods. Additionally data will be analyzed also using a deterministic approach: linear and non-linear modelling analysis

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