Telemonitoring System for Early Diagnosis of COPD Exacerbations.
Telemonitoring System for Early Diagnosis of COPD Exacerbations.
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 aim of the present study is to test an innovative
 telemonitoring system in patients with COPD.
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.

 To train the algorithm for the recognition of exacerbations, defined as worsening of dyspnoea
 or sputum of entities superior to the normal circadian variability and requiring treatment
 modifications, ascertained through clinical evaluation; To calibrate and validate the
 algorithm for recognition of exacerbation episodes.

 To evaluate the effectiveness of the system in terms of costs and quality of life of the
 patient:

 Efficacy:

 it will be based on the comparison of the frequency of ER and hospital admissions in the
 three years before the intervention and during the intervention period in order to verify the
 expected decline in the need for extraordinary care. To this end, after authorization, the
 File A and File L of the Scheda di Dimissione Ospedaliera (SDO) will be used. In this way,
 all the data concerning ER and hospital admissions, causes and days of hospitalization will
 be retrieved for each patients. A significant reduction in the cumulative frequency of the
 outcome will be considered proof of efficacy. The comparison between the year of intervention
 and the previous year will be supplemented by the analysis of the time series including the
 previous 3 years and the intervention to avoid that an apparently significant difference
 between the year of intervention and the previous year reflects only the evolution of a
 trend. Moreover, since detected parameters can be modified also by incident not only
 respiratory, but also of other origin (e.g. cardiac) pathology, the causes of ER or hospital
 admissions will be detected and catalogued for the purpose of an analytical evaluation of the
 effects of the intervention.

 the analysis of objective outcomes, that are expression of the care needs (efficacy check 1),
 will be complemented by that of the health effects expressed by a baseline and final
 oligodimensional evaluation including a measure of dyspnea (MRC), a physical performance
 index ( 6'WT), a personal autonomy scale (ADL, IADL) and a health status questionnaire
 correlated with respiratory disease (CAT). Since there is no comparison for this outcome, the
 same will be considered achieved in the case of stability of performance compared to
 baseline.

 Cost/effectiveness: assuming a weighted average cost of hospitalization for exacerbated COPD
 and estimating the cost of the proposed intervention (see the attached analytical scheme),
 the intervention will be considered cost / effective if the savings achieved will exceed at
 least 25% the expenditure incurred. This threshold, albeit arbitrary, is convenient and
 reasonable because lower values could be penalized by variously localized defects in the
 procedure for calculating the variables involved.

 Patients with COPD with frequent exacerbations will be enrolled regardless from bronchial
 obstruction degree or GOLD stage. According to the 2018 GOLD guidelines, two or more
 exacerbations every year or at least one leading to hospitalization are considered frequent.
 Exacerbation means any change in respiratory health that requires a change in drug therapy,
 regardless of the need for ER or hospital admission, which conversely characterize the
 proportion of exacerbations constituting the first efficacy outcome. Patients in long-term
 oxygen therapy, with cognitive impairment hampering, in the opinion of the investigator, the
 possibility of adhering to the protocol and with a life expectancy limited to one year, will
 be excluded. No other exclusion criteria will be foreseen, in particular co-morbidity, since
 the study is designed for a real life dimension and the algorithm can be used to convey
 information of interest for worsening of non-respiratory pathology.

 Each patient will be provided with the BPCOmedia Kit for measuring the status of patient's
 health COPD. The kit is composed of a Bluetooth pulse oximeter and an APP for Android system
 downloadable from Google Play and installed on the Android smartphone of the patient from
 version 4.1 on.

 During sperimentation, a dedicated case manager nurse will care, for the professional team
 leader (GP) and for the entire team where the specialist pneumologist is present, an
 impartial and transversal approach for the development of a personalized treatment plan,
 facilitating the coordination and appropriate use of the various services.

 Measurements of oxyhemoglobinic saturation and heart rate will be performed 3 times a day,
 according to a predefined monitoring plan, and whenever the patient experiences symptoms. The
 patient will firstly refer to the case manager nurse or to the GP, if the first is
 unavailable, whenever the algorithm will detect a potentially dangerous situation or when its
 perceived health status will deviate from measurement performed by the telemonitoring system.
 On the other hand, the case manager nurse and the GP will daily monitor the parameters and
 will evaluate if the received values are compatible with an ongoing or imminent deterioration
 of the state of health ("worrisome event"). In this case, the patient will be contacted by
 telephone to investigate the symptoms or the presence of signs of exacerbation (dyspnoea,
 asthenia, increased secretions). On the basis of the obtained data, the worrisome event will
 be reclassified as follows:

 False alarm; Minor event (clinical worsening not requiring therapeutical intervention or
 other interventions) Possible exacerbation. In case of possible exacerbation, the patient
 will be visited by the GP who will establish the actual presence of an exacerbation (with
 consequent modification of therapy, indication of further diagnostic investigations, or
 sending to higher levels of treatment, depending on the indication), or reclassify the event
 as "Minor Event".

 The pneumologist will be always available for the interpretation of situations involving
 specialist knowledge and will be contacted by both the case manager nurse and the GP
 depending on availability. In case of particular situations, for example important
 desaturations, preferential routes will be activated so that the specialist pneumologist can
 intervene promptly.

 In order to obtain a sufficient number of events for the objectives of the project, it is
 planned to enroll at least 120 patients, who will be followed for a period of 12 months.

 The main outcome will be bronchial exacerbations, which are assumed to have a Poisson
 distribution with an average of 2/patient/year. Considering as clinically significant a
 reduction of 0.25 exacerbations/patient/year, using Lehr's equation with Type I and Type II
 error of 5% and 20%, respectively, we obtain an estimate of the sample size of 480 people
 followed for a year; it is considered appropriate to increase to 500 to offset any losses
 during follow-up.

 This sample size is also sufficient for the development of the event prediction algorithm: in
 fact, based on the number of descriptors used by the system and considering a drop-off rate
 of 15%, error margin of 0.02, p = 0.05 , the number of patients required is 120.

 The instrumentation will be delivered within a training session in which the patient will be
 instructed on the functioning of the telemonitoring system and on the behavior to be taken in
 case of changes in health status.

 Appropriate understanding will be verified at the end of the session, and the study staff
 will offer the patient additional sessions even after some time in case of need.

 The patient will also be specified that the investigating system is not substitutive of the
 usual assistance and that in case of any variation of the symptoms it is necessary to follow
 the usual procedures by contacting the primary care physician or the specialist as needed.

 All patients will sign an informed consent. The telemonitoring system used is not intended as
 a substitute for normal assistance and the study procedures do not provide emergency
 interventions, therefore participants will be advised to contact their doctor for the
 evaluation of any health problems and to activate the usual emergency procedures in case of
 critical situations.

 Data analysis Data will be analyzed according to descriptive statistics methods to evaluate
 the performance, calculating accuracy, precision, specificity and sensitivity. Initially, the
 training phase will use data from a reference population. Subsequently, using the leave-one
 person out, the system training will be evaluated again using the data acquired during the
 present experimentation, then proceeding to the estimate of the performances.

 5. Telemonitoring system

 Basic characteristics are summarized below:

 The system consisting of the app and of a pulse oximeter and is certified as a medical device
 (next June 2017); The patient receives a kit consisting of a pulse oximeter and the Android
 app that can be downloaded from Google Play and can be installed on your Android smartphone
 from version 4.1 and up; The patient uses the app to perform measurements at home, without
 the need for external support; The app works in three modes: configuration, training /
 calibration, monitoring; During configuration, the user creates an account associated with
 the purchased pulse oximeter and the app, in order to reduce the abuse of the device by
 people who have not purchased it; During the training phase, the app acquires the necessary
 information to calibrate the algorithm and to make it able to identify the occurrence of one
 of the aforementioned dangerous events; During the monitoring phase the patient uses the app
 to monitor the onset of one of the aforementioned dangerous events; The user, using the
 credentials provided during the configuration, can access a web portal called the patient
 portal where he can check the history of the performed measurements; The patient portal
 allows the end user to view their data in a graphical and intuitive form and to share
 measurements with their doctor; Doctors access a portal dedicated to them and called the
 medical portal; In the medical portal, the doctor can consult the measurements performed by
 his patients provided that the patient has enabled the doctor to consult his data;
 Possibility for the doctor to obtain reports of the measures performed by the patients; The
 app and both the patient portal and the medical portal support the main European languages
 starting from Italian and English.

 Installation and configuration phase The user downloads the Android app from the Google Play
 store. At the time of installation, the app guides the user to register a new account, if not
 already in possession of one, and to register the pulse oximeter purchased, by entering the
 alphanumeric commercial code available inside the package.

 A single account can be linked to the commercial code.

 During the account creation and association with the pulse oximetry wizard, the app requires
 the minimum information necessary for registration and user profiling:

 Name and surname Email address Telephone number Username and password In order to proceed
 with registration, the patient must view the privacy statement and consent to the processing
 of personal health data as required by current legislation.

 At the end of the installation / configuration phase the app starts in training / calibration
 mode.

 Training and calibration phase For the correct functioning, the app needs an initial training
 phase of the predefined duration of N = 5 days.

 The training phase is based on a monitoring plan consisting of three measurements / day
 divided by predefined time period: morning, afternoon, evening. A measurement is required for
 each time slot.

 The monitoring plan includes a series of reminders that, through the push notification
 associated with a sound signal, remind the patient of the measurements to be performed. The
 monitoring plan can be consulted in read only on the app in a special section.

 At each measurement indicated by M the app acquires the value of the percentage of hemoglobin
 saturation SpO2, the value of the HR heart rate measured by the pulse oximeter and the date
 and time when the measurement takes place. The input data are formally indicated with the
 triad (SPO2, HR, t), where the value t indicates both the date and the time of measurement M
 and therefore serves to classify the band of measurement: morning, afternoon, evening.

 At the end of the 5 days, ie of the training phase, using the measurements and a dataset of
 patient profiles pre-stored in the APP, it is automatically passed during the monitoring
 phase.

 The APP displays in a special band always clearly visible the phase in which it finds:
 training or monitoring.

 During the training phase, the app stores the data of the individual measurements and the
 result of the calibration on the backend at the end of the training phase. The app also works
 in an independent way from the backend so the data are synchronized in the background in a
 transparent way to the user even in the absence of connectivity.

 Monitoring phase Once the training and calibration phase is complete, the app goes into the
 monitoring phase in which the patient performs the measures to keep his health checked and
 monitored.

 The information to be memorized in this phase are:

 HR heart rate; SpO2 hemoglobin saturation; Date and Time of detection According to the
 monitoring plan, the app invites the user to perform the measurement by wearing and turning
 on the pulse oximeter. Upon detection, the app alerts the user by inviting him to remove the
 pulse oximeter. The single measurement, completely transparent to the user, is implemented as
 a series of measures more appropriately filtered (indicatively the average of the
 measurements performed in 5 - 10 seconds).

 At each measurement, the app, received the parameters from the pulse oximeter, queries the
 algorithm by sending SpO2, heart rate, date and time and displays the result of the
 processing by the algorithm. This result is presented to the patient through easily
 comprehensible text and with appropriate icons that provide a clear and immediate
 understanding of the patient's state of health.

 The app is able to perform the measurement even in the absence of connectivity, so all
 configuration parameters (see monitoring plan) are stored locally to the app and from time to
 time synchronized with what is present remotely to the backend.

 After the measurement, the parameters of the measurement itself and the result of the
 processing are stored locally and, if possible, on the cloud. In case it is not possible to
 immediately store them on the cloud due to lack of connection, the alignment of the data will
 happen in a transparent way to the patient.

 The app also keeps a measurement history locally so as to allow the patient to consult the
 measurements directly from the app. These measures are presented in tabular and graphic mode.

 During the monitoring phase the patient must have the possibility to contact the relative
 doctor in case of a worsening of the perceived conditions and if he finds a disagreement
 between his / her perceived condition and the result of the app. It is therefore possible
 directly from the app to start a new training / training phase to recalibrate the algorithm.

 Settings The app provides a special section dedicated to personal settings. In this section
 the patient can consult his personal data, the monitoring plan and perform a reset to restart
 the training and calibration phase.

 Algorithm library The algorithm is encapsulated in a library developed by the Biomedical
 Campus of the University of Rome and owned by the L.I.A of Giuseppe Capasso.

 Patients' interface

 Using the credentials chosen during the initial configuration phase, the patient can access a
 web portal dedicated to patients. After access to this portal the patient has the possibility
 of:

 Manage your personal data and change the password for accessing the portal and the app.

 Consult the standard monitoring plan available as read-only. Consult the list of measurements
 made and display them in graphical mode, with the possibility to filter and view them for
 time periods.

 Set up your own doctor with whom you can then share your monitoring data. The patient invites
 the doctor to access his data in a manner similar to that used by the most common social
 networks to share information.

 Doctors' interface This portal allows treating physicians to access their patients' data via
 the web. By following the appropriate link 'Register as a doctor on the login page of the
 medical portal, the doctor can register at the portal. The activation must take place after
 adequate verification by the administrators, who may request further documentation to
 ascertain the identity of the doctor requesting registration.

 Once registered, the doctor can use his credentials to access the portal where he can manage
 his personal data entered during registration and access the measurements of patients who
 have shared access. The doctor can in turn search for patients and send a request for access
 to data. The patient can then accept the request or not.

 The doctor can not make any changes to the patient's data, he has them read-only. There are
 known fields where you can enter other information deriving from a medical history or a
 visit.

 Doctors' interface - specific features In order to support the achievement of the objectives
 of the study / experimentation, a series of functionalities are foreseen in order to collect
 a set of "certain" data, that is validated by the doctor near the detected measure so that
 the same doctor has the possibility to confront the patient and confirm or redirect the
 result generated by the algorithm. From a technical point of view it is about collecting the
 data sent by the app in the backend but allows the doctor to confirm the data or reclassify
 the result that, according to his opinion, should have been generated. Therefore, as already
 foreseen, the doctor will access the back end with the possibility to select the patient in
 question, to examine the measures interested in the judgment confirming the outcome or
 modifying it according to a series of labels that will be indicated by the scientific
 committee.

 The information can then be extracted ad hoc and provided for analysis at the Biomedical
 Campus.

 Working scheme The following diagram represents the logical architecture of the whole system.
 The diagram also shows which parts reside in the smartphone (Smarpthone area) and which are
 instead ancillary components that reside in the web (Web area). It is important to keep in
 mind that the medical device consists of the app and the algorithm included in the area
 marked as Smartphone. The users of the system are the patient and the doctor. The patient
 uses the smartphone for monitoring and consultation, while the web part is used for simple
 consultation both by the patient and by the doctor in the appropriate web portals.

 Principal components are described below.

 App This represents the app installed in the patient's smartphone. The app receives
 measurements from the pulse oximetry during the monitoring phase and interacts with the
 predictive algorithm to determine the patient's health status. The data useful for processing
 health status and for consultation are then saved in a local database so that you can work
 independently from the web-side connectivity. The app interacts with the backend for the sole
 purpose of saving measurements and health data remotely.

 Predictive algorithm Although it is physically integrated into the app, the algorithm is
 locally a block in itself, given its criticality. The app takes care of managing the time
 slots of the measures based on the monitoring plan and to call accordingly the predictive
 algorithm passing the information collected by the pulse oximeter and other information
 processed from the historical data stored in the local database. Starting from this
 information, the algorithm determines the patient's health status.

 Local database The local database is used by the app to maintain locally the necessary
 measures in the first instance to the algorithm to perform the processing and to determine
 the health status of the patient, and secondly to allow the patient to consult the previous
 measures directly on the smartphone . For the correct processing the algorithm requires the
 measurements of the last 90 days. These are the measures stored in the local database.

 Pulse oximeter. It is the medical device used by the patient to perform the measurement of
 SpO2 and heart rate. Interacts via bluetooth with the app to share the measurement performed.

 Backend

 This is the remote hardware / software component on which the measurements made by the
 various patients and the patient profiles are saved. Here the data are stored in a remote
 database and are always available to be downloaded on smartphones in case of need, to be
 consulted by patients and doctors through the appropriate portals, or to be used by
 researchers of the Rome Biomedical Campus to refine and further develop the algorithm. The
 following section dedicated to the detailed architecture will also describe the various
 protocols used for the interaction with the other components of the system.

 Remote Database Remote database on which patient measurements and patient and medical profile
 data are saved.

 Web interface These are the components of the system that allow patients and physicians to
 consult the data stored in the backend.

 Structure The following diagram represents the physical architecture of the whole system and
 how the components of the whole system interact / communicate with each other.
Inclusion Criteria:

 -  Patients with COPD with two or more exacerbations every year or at least one leading
 to hospitalization.

 Exclusion Criteria:

 -  long-term oxygen therapy

 -  cognitive impairment

 -  a life expectancy limited to one year