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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_4566_Библиотеки_им_академика_М_И_Перельмана
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New digital diagnostic tools
https://t.me/medicina_free
for respiratory sleep
disorders
Renaud Tamisier, Maelle Guellerin and Jean-Louis Pépin
Covering the diagnosis of OSA in the general population remains an immense task
due to its high prevalence worldwide, the heterogeneity of clinical presentation and
the need to prioritise subpopulations of patients with chronic diseases. On one hand,
we must examine symptomatic patients in the general population; those with a
moderate to high positive predictive value based on clinical symptoms (sleepiness,
nocturia, snoring, tiredness or choking during sleep) and measurements (BMI,
neck circumference, Mallampati class or high BP) (figure 1). Therefore, because of
the prevalence of sleepiness (∼10%), overweight or obesity (60%) and the nearly
30% of the population who snore, sleep physicians see a huge number of patients
who are referred for testing. On the other hand, the prevalence of OSA reaches
>50% in specific subpopulations already diagnosed with a chronic disease such as
chronic HF, resistant hypertension, stroke or diabetes (figure 2). However, most of
these patients with chronic disease do not present the usual OSA symptoms. The
pauci-symptomatic presentation of the latter group indicates testing all patients in
the group despite a low pre-test clinical diagnostic positive predictive value. Moreover,
in addition to representing a large population to be tested, these patients require
specific management. The timing of the diagnosis can be as determinant as the
status of the chronic disease, which may interfere with assessment of the severity of
OSA. Moreover, these asymptomatic patients might not consider OSA diagnosis as a
priority and might not agree to undergo PSG. Finally, even though prevalence is high,
numerous individuals will undergo PSG to confirm that they do not have OSA. These
latter examinations should be avoided.
Key points
• Diagnosis of OSA is an immense and dicult task that PSG alone
cannot fulfil.
• New devices are continually emerging and oer new digital solutions that
need to be positioned in the decision tree of OSA diagnosis.
• Long-term evaluations must be undertaken to ensure that these new
diagnostic tools are not only reliable for OSA diagnosis, but also equivalent
to PSG in terms of monitoring symptom normalisation and treatment
adherence.
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New digital diagnostic tools
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Specific testing in
symptomatic patients
?
OSA
?
?
?
?
?
Figure 1. The problem of screening all symptomatic patients in a large population. A huge
number of symptomatic patients are referred to sleep physicians for testing.
?
Non-OSA
Faced with these realities, although considered as the gold standard, but mostly only
available in specialised sleep clinics, performing a PSG for all these patients appears
to be unrealistic. Indeed, PSG fulfils almost all requirements when investigating
OSA: sleep macrostructure (REM or non-REM), sleep instability, body position and
respiratory events during sleep. It allows the specialist to determine the precise
polysomnographic phenotype of OSA. However, sleep centres cannot respond to such
high demand for testing, and thus diagnosis, and consequently, treatment will be
delayed for those who particularly need them. Moreover, the relative fragility of sleep
health structures that was revealed during the coronavirus disease 2019 (COVID-19)
crisis further increases the need to move to a dierent mode of diagnosis.
What alternatives to PSG could be proposed? A simple diagnostic test would be
welcome for the two populations previously mentioned, i.e. those in the general
population with symptomatic OSA and asymptomatic individuals in specific chronic
disease groups (figures 1 and 2). How would a simple diagnostic test dier from PSG?
PSG involves multiple channel acquisitions, is complicated to handle and analyse
and requires extensive training to run smoothly with expertise. In contrast, a simple
diagnostic test should be easy to implement and have reliable automatic analysis with
the results directly available to the user, and require only simple training. A similar
transition from PSG to a ‘simple diagnostic test’ should be feasible, as was the
transition in diagnosis of COVID-19 from requiring an analytical laboratory PCR test to
the combination of a simple self-test and a confirmatory PCR test. Possible candidates
for this transition (although not exhaustive) are discussed later in this chapter.
How could a simple test be used? It has already been demonstrated that in patients
with a high clinical positive predictive value, a simple polygraph recording is reliable
enough for diagnosis and provides sucient confidence to implement treatment. The
only discrepancies that remain compared to PSG are when sleep is interrupted or is
insucient during the recording.
In a population of patients with chronic diseases and with high OSA prevalence,
comorbidities and their treatment may impair sleep, and thus measuring sleep
quantity and quality is highly recommended.
355ERS Handbook: Respiratory Sleep Medicine
New digital diagnostic tools
https://t.me/medicina_free
Screening testing in
high OSA prevalent
population
Severe OSA
Mild-to-moderate
OSA
Non-OSA
Figure 2. The problem of carrying out diagnostic tests in pauci-symptomatic patients in a
targeted disease subpopulation (stroke, diabetes, depression, etc.). Without clinical exams or
questionnaires, the only way to dierentiate patients is to perform the test.
Respiratory polygraphy (PG) is an easier, reliable alternative for diagnosis when handled
by well-trained professionals. However, an incompressible period of time is required
for settling the patient and scoring. The installation of the polygraph and positioning
of all the sensors is oen delegated to caregivers who are insuciently trained, or
even to the patients themselves, which reduces the quality of the test. Performing
scoring and diagnosis using records with poor-quality signals is problematic.
There is a need for new solutions that can be implemented in a large population, are
easy to use and do not impose an extra burden on sleep healthcare sta, and that will
be accepted by patients. In addition, the proposed solutions should be recognised
by the research community and by scientific learned bodies, and covered by health
insurance providers.
Over the past decade, technological developments have led to several possible solutions
using miniaturised sensors directly linked to a digital server that transfers data to be
processed by artificial intelligence algorithms. An online application provides the result
almost immediately to both sleep physicians and the patients themselves. Some of
these new solutions are described in chapter 16.1 of this Handbook (refer to the table
and figure therein for examples). Here we focus on solutions that have obtained or are
close to obtaining marketing authorisation in Europe. Although not exhaustive, the
two examples are intended to give the reader some idea of the objectives and results
these solutions must achieve in terms of population targeted, diagnosis performances
compared to PSG, methodology and regulations (data collection and data processing
regulations and medical device approval).
The detection and analysis of mandibular movements enables us to monitor
ventilation, respiratory eort and arousals from sleep, and obtain an estimation of
sleep stages. The ability to assess these with a single sensor simplifies the diagnosis
of OSA. This system employs a sensor positioned on the skin of the chin using
adhesive tape. The system is easily used by the patient, who can follow a step-by-step
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New digital diagnostic tools
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procedure on a smartphone app. There are several publications reporting the ability
of this system to accurately score respiratory events (compared to PSG), to monitor
respiratory eort (against oesophageal pressure) and detect sleep fragmentation
(against EEG monitoring). Moreover, there is a better agreement in respiratory event
scoring between the mandibular movement detection algorithm and conventional
international reference centre data than between two dierent international reference
centres. This device is currently being evaluated as a solution compared to PSG;
the primary outcome is diagnostic delay, and secondary outcomes are therapeutic
acceptance and adherence to treatment (CPAP, mandibular advancement devices).
Another approach is to use sensors that are able to record tracheal sounds. One such
system uses a sensor that is positioned on the skin of the neck using adhesive tape, over
the trachea. Again, the system is simple to use for the patient, who can follow step-bystep instructions on a smartphone app. Although this system is reported as being able to
diagnose OSA with a specificity of 96.8% and sensitivity of 92.7%, there is no function
included in the current device to monitor sleep stages and/or sleep fragmentation. One
aspect shared by these two sensors is the ability to be used to collect data over several
nights, increasing both the sensitivity and specificity of the diagnosis.
There are other strategies that use more sophisticated complex devices. One example is
a system using a multicaptor design (peripheral arterial tonometry with chest and wrist
sensors) that is able to measure the pulse wave arterial signal, heart rate, oximetry, chest
and peripheral movements, and body position. All these channels enable this system to
detect OSA with good confidence compared to PSG. Due to the simplicity of the set-up
and its automatic algorithm, the variability between scorers is decreased. Again, this
type of device allows measurements to be repeated over several nights.
Even though there are currently several promising diagnostic solutions commercially
available or in development, all need to be approved as medical devices. Sensors,
algorithms, devices, soware and phone applications need to fulfil medical device
regulations to obtain this approval. Clinicians should not use these solutions for
diagnosis in a clinical decision-making setting before this approval has been obtained.
Faced with the huge demand for and complexity of performing OSA diagnosis using
PSG or respiratory PG, sleep physicians and healthcare providers must carefully
consider investing in the new technologies that are reaching the field of sleep
medicine. However, while these new digital tools promise simplicity, objectivity and
rapidity in the diagnosis of OSA, all need to be validated through processes that still
need to be delineated. One aspect that will clearly be a huge improvement is the
possibility of repeating data collection over several nights.
Further reading
• Bailly S, et al. (2016). Obstructive sleep apnea: a cluster analysis at time of diagnosis. PLoS
One; 11: e0157318.
• Benjafield AV, et al. (2019). Estimation of the global prevalence and burden of obstructive
sleep apnoea: a literature-based analysis. Lancet Respir Med; 7: 687–698.
• Chai-Coetzer CL, et al. (2011). A simplified model of screening questionnaire and home
monitoring for obstructive sleep apnoea in primary care. Thorax; 66: 213–219.
• Kapur VK, et al. (2017). Clinical practice guideline for diagnostic testing for adult obstructive
sleep apnea: an American Academy of Sleep Medicine clinical practice guideline. J Clin Sleep
Med; 13: 479–504.
357ERS Handbook: Respiratory Sleep Medicine
New digital diagnostic tools
https://t.me/medicina_free
• Kelly JL, et al. (2022). Diagnosis of sleep apnoea using a mandibular monitor and machine
learning analysis: one-night agreement compared to in-home polysomnography. Front
Neurosci; 16: 726880.
• Pepin J-L, et al. (2022). Mandibular movements are a reliable noninvasive alternative to
esophageal pressure for measuring respiratory eort in patients with sleep apnea syndrome.
Nat Sci Sleep; 14: 635–644.
• Punjabi NM, et al. (2020). Variability and misclassification of sleep apnea severity based on
multi-night testing. Chest; 158: 365–373.
• Randerath W, et al. (2017). Definition, discrimination, diagnosis and treatment of central
breathing disturbances during sleep. Eur Respir J; 49: 1600959.
• Yalamanchali S, et al. (2013). Diagnosis of obstructive sleep apnea by peripheral arterial
tonometry: meta-analysis. JAMA Otolaryngol Head Neck Surg; 139: 1343–1350.
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Digital health innovations
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for optimisation and
follow-up of therapy
Renaud Tamisier, Sébastien Baillieul and Jean-Louis Pépin
A complete resumption of all respiratory events is associated with successful treatment
of OSA. This normalisation of breathing is a first step before improving symptoms
related to OSA. With the aim of providing the highest response to therapeutics, optimal
treatment should be attempted; for CPAP treatment, this means adequate pressure
settings, a mask that is perfectly adapted to the patient’s face and skin, and support
from caregivers. With great therapeutics comes great compliance to CPAP. Therefore,
monitoring compliance is a goal for healthcare providers, insurance companies
and sleep physicians. Indeed, the more the patient adheres to CPAP treatment and
increases the duration of use, the more symptoms will improve. However, compliance
to CPAP is not only related to OSA severity, or treatment comfort; it is also related to
sociological and cultural factors, life circumstances and comorbidities that require
dedicated support.
Background: improvement of treatment quality
Several targets should be focused upon to improve treatment acceptance and quality.
Among these, harms of OSA, tricks and lessons from peers and helpers, and sideeects of therapeutics (CPAP, mandibular advancement devices) should be considered.
Digital health innovations may be used for the optimisation and follow-up of therapy,
with which patients may improve their knowledge. Digital health tools may also be
the medium to provide continuous support, promoting lifestyle changes and specific
medical interventions. Here, we focus on three specific aspects: continuous support,
patient engagement and specific medical interventions.
Key points
• Phone apps have demonstrated their usefulness in supporting patients during
the first months of treatment with CPAP.
• Access to CPAP telemonitoring is a powerful tool providing sleep caregivers
with precise feedback on how treatment is conducted and whether the
patient faces diculties.
• Remote consultations are now of sucient quality to be considered as an
alternative to face-to-face clinical appointments; however, added value and
cost-eciency still need to be demonstrated.
359ERS Handbook: Respiratory Sleep Medicine
Digital health innovations for optimisation and follow-up of therapy
https://t.me/medicina_free
Continuous support during treatment trajectories
Digital health tools can provide positive feedback from well-conducted treatment,
record its eectiveness in reducing apnoeas, mutual support (peers, groups) and
reactiveness to technical issues, side-eects or diculties related to concomitant
diseases.
Engaging patients in a better lifestyle
It is well known that the more patients engage with a positive lifestyle, the more
compliant to therapeutics they become. Therefore, giving feedback about patients’
needs will initiate positive engagement. Among them, lifestyle support to increase
physical activity and improve nutrition and sleep or monitoring of physiological
parameters such as BP, heart rate, weight and blood glucose may also be associated
with an improvement in awareness of lifestyle, promoting healthy behaviours.
Specific medical interventions
Digital support may also target specific medical needs, for example cognitive
behavioural therapy for patients having comorbid OSA and insomnia (COMISA),
motivational enhancement therapy, diet programmes or exercise rehabilitation. These
e-interventions remain under medical supervision, but digital media may be sucient
for some patients who do not need direct guidance from healthcare givers and coaches.
Digital health media for treatment support
Electronic support may be able to sustain medical interventions over time. From
the first studies using phone call support to newer smartphone app support, several
studies have demonstrated that providing continuous support helps patients to
continue CPAP and improves adherence. However, despite evidence that a 3-month
telemonitoring/coaching programme increases CPAP use in the short-term, once the
programme stops, it does not lead to sustained long-term improvements. In contrast,
it is likely that longer (12-month) programmes may lead to sustained improvements,
although these still diminish once coaching ceases.
Telemonitoring from CPAP machines
Telemonitoring from CPAP machines enables all parameters recorded and analysed
by these devices to be monitored at any moment (figure 1). Telemonitoring can follow
medical events that destabilise residual (r)AHI under CPAP. This allows patients,
healthcare providers and sleep physicians to evaluate both the quality and quantity
of CPAP therapy. If the possibilities are countless, there is a paucity of data that have
evaluated the impact of this continuous flow of information objectively. Indeed,
few randomised trials have investigated the impact of multimodal telemonitoring.
‘Multimodal’ brings together telemonitoring from CPAP (compliance, residual events
and leaks), dedicated positive messages to patients to support treatment, lifestyle
counselling and physiological measurement (BP control, nocturnal oximetry and
physical activity). Multimodal telemonitoring in two groups of patients with low and
high cardiovascular risk brought dierent outcomes: while there was little evidence
of dierence in the patients with low cardiovascular risk, in the high cardiovascular
risk group, a larger decrease in BP was present in the monitored patients. One aspect
noticed in both trials was that setting multimodal telemonitoring generates many
alerts that imply an action from healthcare providers or physicians. Therefore, there is
a need to set algorithms able to extract meaningful alerts from monitoring to which
either technical or medical teams would react and set appropriate interventions.
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Digital health innovations for optimisation and follow-up of therapy
2019
2019
2019
2019
2020
2020
2020
2020
2020
2020
2020
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B
Leaks
A
C
–1
Leaks
20
15
–1
10
Events·h
5
0
20
–1
15
10
Events·h
5
0
7 Sep
16 Aug
2018
AHI
A
10 Sep
10 Oct
4 Nov
2018
2018
AB
19 Nov
12 Oct
24 Dec
2018
29 Nov
2018
28 Jan
4 Mar
24 Dec
2018
AHI
8 Apr
18 Jan
2019
B
C
12 Feb
9 Mar
3 Apr
2019
2019
Compliance h
C
13 May
17 Jun
Compliance
Leaks
A
22 Jul
2019
Compliance
Leaks
26 Aug
28 Apr
2019
–1
Leaks L·min
Figure 1. Telemonitoring can be used to follow a medical event that destabilises rAHI in
a well-treated, compliant patient using CPAP. High doses of steroids were used against
thrombocythaemia in January 2019 and January 2020. Condition A is when the subject was
not taking steroids; condition B is when the patient was taking steroids; condition C shows
decreasing doses of steroids.
Leaks L·min
A French working group proposed a decision tree for telemonitoring for healthcare
providers; herein we propose a complement to this decision tree, more adapted to
digital application, to optimise follow-up (figure 2). This decision tree is based on
a 7-day moving window measuring compliance, residual (r)AHI and leaks. If CPAP
use is >4 h, no leaks are detected (threshold may dier between CPAP brands) and
rAHI is <10 events·h−1, CPAP treatment could be considered as having stabilised the
condition. rAHI <10 events·h−1 may be reduced to <5 events·h−1.
Remote consultation
Remote consultation should be part of the toolset of caregivers and sleep physicians
to improve the treatment of SDB. However, there are some essentials relating to the
digital nature of the remote consultation. Access to a high-speed internet connection
is mandatory to be able to conduct a consultation with good technical settings.
Physicians should be able to share their screen, showing the patient medical charts and
telemonitoring files. All these features enable good communication between patients
and physicians, and should be as close as possible to a face-to-face meeting. Soware
should enable patients’ and physicians’ faces to be seen, allowing facial expressions
to be noted and fluidity of sound for quick question-and-answer communication.
361ERS Handbook: Respiratory Sleep Medicine
Digital health innovations for optimisation and follow-up of therapy
https://t.me/medicina_free
CPAP telemonitoring based on a 7-day moving
window on CPAP use, rAHI and leaks
• Checking for leaks or high rAHI
• Screening for insomnia
No
• Assessing patient’s motivation
• Checking for other causes of inobservance
• Continuous leaks: checking circuit and
mask fitting
No
• Discontinuous leaks: checking for
malposition of mask or mouth opening
• Checking device signals directly if available
• Obstructive events: checking for side-eects
of oronasal mask, insucient pressure or
No
comfort settings
• Central events: decreasing pressure on
medical guidance
or a clinical appointment is mandatory
If the issue is unresolved, physician contact
Leaks below manufacturer’s
Yes
recommendation
or patient’s sensitivity
rAHI <10 events·h
CPAP use
>4 h per day
Yes
Yes
–1
Figure 2. Proposal of a decision tree for the remote monitoring of patients treated with CPAP.
Availability of CPAP telemonitoring is mandatory to conduct remote consultations,
and in our opinion an electronic questionnaire completed in advance by patients
should be a backbone of the consultation. Finally, although the added value should be
evaluated, we believe that auto-measurement of arterial BP, sleep diaries and physical
activity measured using accelerometers, if possible, would also increase the value and
eciency of remote consultations.
Conclusion
Digital health innovations in sleep medicine may become pre-eminent over the next
decade. These encompass phone apps that could increase the patient’s knowledge
about SDB and how treatments work. Additionally, these apps provide continuous
support that have demonstrated benefit in terms of increasing adherence to
treatment, although this does not persist once the app use is stopped. The progressive
generalisation of CPAP telemonitoring is helping sleep physicians, caregivers and
healthcare providers to monitor quality of treatment. However, these are a source
of uncontrolled alerts that need to be managed using artificial intelligence assisted
decision (see chapter 16.4 of this Handbook). Finally, among these technological
innovations is remote consultation, which requires validation to demonstrate added
value and eectiveness.
Further reading
• Crosby ES, et al. (2022). Motivational interviewing eects on positive airway pressure therapy
(PAP) adherence: a systematic review and meta-analysis of randomized controlled trials. Behav
Sleep Med; in press [https://doi.org/10.1080/15402002.2022.2108033].
• Gasa M, et al. (2013). Residual sleepiness in sleep apnea patients treated by continuous
positive airway pressure. J Sleep Res; 22: 389–397.
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Digital health innovations for optimisation and follow-up of therapy
https://t.me/medicina_free
• Hwang D, et al. (2018). Eect of telemedicine education and telemonitoring on continuous
positive airway pressure adherence. The Tele-OSA Randomized Trial. Am J Respir Crit Care Med;
197: 117–126.
• Patel SR, et al. (2022). Impact of an extended telemonitoring and coaching program on
continuous positive airway pressure adherence. Ann Am Thorac Soc; 19: 2070–2076.
• Pépin JL, et al. (2019). Multimodal remote monitoring of high cardiovascular risk patients with
OSA initiating CPAP: a randomized trial. Chest; 155: 730–739.
• Prigent A, et al. (2020). Télésuivi des patients traités par pression positive continue pour un
syndrome d’apnées/hypopnées obstructives du sommeil: proposition d’un arbre décisionnel.
[Telemonitoring in continuous positive airway pressure-treated patients with obstructive sleep
apnoea syndrome: an algorithm proposal]. Rev Mal Respir; 37: 550–560.
• Sparrow D, et al. (2010). A telemedicine intervention to improve adherence to continuous
positive airway pressure: a randomised controlled trial. Thorax; 65: 1061–1066.
• Tamisier R, et al. (2020). Impact of a multimodal telemonitoring intervention on CPAP
adherence in symptomatic OSA and low cardiovascular risk: a randomized controlled trial.
Chest; 158: 2136–2145.
• Weaver TE, et al. (2007). Relationship between hours of CPAP use and achieving normal levels
of sleepiness and daily functioning. Sleep; 30: 711–719.
363ERS Handbook: Respiratory Sleep Medicine
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