Clinical Trial Details
— Status: Recruiting
Administrative data
| NCT number |
NCT03739957 |
| Other study ID # |
10/2018/CE |
| Secondary ID |
|
| Status |
Recruiting |
| Phase |
N/A
|
| First received |
|
| Last updated |
|
| Start date |
January 1, 2019 |
| Est. completion date |
January 1, 2022 |
Study information
| Verified date |
February 2020 |
| Source |
Laboratori di Informatica Applicata |
| Contact |
Antonio De Vincentis, MD |
| Phone |
003906225411 |
| Email |
a.devincentis[@]unicampus.it |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Interventional
|
Clinical Trial Summary
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.
Description:
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.
