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Big data and artificial
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intelligence: opportunities
and challenges
Renaud Tamisier, Sébastien Bailly and Jean-Louis Pépin
The volume of data created, captured, exchanged and consumed has grown in an
exponential manner, from 2 ZB in 2010 to 64.2 ZB in 2020. Health-related data have
demonstrated the same development, pushing health organisations, researchers,
care sta and health industries to redesign data management and organisation. In
respiratory sleep disorders, the flow of data was initially made up of CPAP machine
recordings and health insurance electronic claims. More recently, these have been
joined by medical record databases, research databases and physiological medical
recordings, such as PSG raw data, BP, physical activity or glucose monitors and
connected devices. A growing interest emerges in the ‘internet of things’, initially
designed to be used in the field of wellbeing. The volume of data demonstrate an
interest in the analysis of signals and factors that impact sleep health.
These data present several challenges for researchers and health stakeholders. Each
source of data has its own structure; therefore, pooling several sources requires that
scientists create structured data ready for statistical analysis (figure 1). Among the
tools available, application programming interfaces (APIs) embedded into a system
allow the continuous harvesting of data from dierent systems or databases. Data
management is the practice of collecting, keeping and using data securely, eciently
and cost-eectively. This means that data use must be within the bounds of policy
and regulation. Researchers, industries and health stakeholders will be able to
perform statistical analysis, but will also be able to run dierent artificial intelligence
(AI) algorithms, such as machine learning, deep learning, neural networks, computer
vision or natural language processing, to support their respective activities.
Key points
• Electronic data holds an important position in monitoring health and
wellbeing, capturing lifestyle factors, risk factors, clinical presentation,
therapeutic management and outcomes.
• Analysis of big data provides research opportunities to improve our knowledge
about clinical presentation, therapeutic management and predicting
outcomes.
• AI applications are not yet routinely used in clinical settings or therapeutic
devices; however, owing to major progress made in the past decade, several
solutions are arising.
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and research assistants
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Claims
Research database data lake
Medical records
Electronic raw data
• Diagnostics (PSG)
• Internet of things
Claims chaining
API
Multimodal data
entry solution
Structuration
API
Deidentification
Aggregated electronic
case report form
• Compliant to regulatory policies
• FDA21 CFR PART 11 compliant
• Independent data management
Questionnaires
completed by patients
Therapeutics data
telemonitoring
Completed by physicians
Figure 1. Setting proposal, covering from unstructured data to implemented structured data
from multicommunicant databases that respect GDPR. An API is a soware intermediary that
allows two applications to talk to each other.
Initially, analysis was performed for human decision-makers or researchers. However,
AI technologies can take the next step and make some decisions or propose a
recommended action.
SDB data, from CPAP machines, health electronic claims or research databases, have
gone the same way. Initially, large datasets issued for CPAP device telemonitoring were
able to demonstrate the evolution of AHI calculated from CPAP devices or duration of
use. As an example, these data can demonstrate short-term compliance to CPAP therapy
in adults and children or in developing countries. Analysis of AHI from machine patterns
is insightful, showing dierent patterns of CPAP-emergent CSA. Interestingly, other data
illustrate how CSA is normalised using ASV, with an increase in compliance.
To target long-term adherence behaviour in a previous study, we used a French health
electronic claims database, which showed that aer 3 years, 52% of the patients
remained on CPAP. More importantly, these datasets allowed the performance of new
analysis on mortality and morbidity relating to treated or untreated sleep apnoea. Using
the same database, we illustrated how treatment with CPAP was protective against
cardiovascular mortality and morbidity, with risk reduction measured by hazard ratio (HR)
0.61 (95% CI 0.57–0.65; p<0.01, log-rank test). This protective eect for major adverse
cardiovascular events was confirmed and even clarified to CPAP compliance (hours per
night), with HR 0.87 (95% CI 0.73–1.04) for 4–6 h·night−1, HR 0.75 (95% CI 0.62–0.92)
for 6–7 h·night−1 and HR 0.78 (95% CI 0.65–0.93) for >7 h·night−1 (p=0.0130).
From CPAP telemonitoring residual AHI data, we identified several clusters of
trajectories and demonstrated that these may predict important outcomes, from
treatment adherence to CPAP failure (high AHI or leaks). This was performed using a
hidden Markov model segmentation technique.
Another example is the problem area around CPAP masks, and particularly mask
change. Because better adherence and reduced termination is associated with
a regular supply of masks, healthcare providers and physicians may be inclined to
365ERS Handbook: Respiratory Sleep Medicine
Big data and artificial intelligence
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change masks to obtain the best comfort, and therefore optimal treatment, for
patients. However, inadequate face masks may be associated with partial failure.
Therefore, monitoring residual AHI during these actions may identify failure aer
mask change, using a Bayesian structural time series applied to telemonitoring data.
Big data and AI in sleep respiratory disorders are likely to expand over the coming
decade. Indeed, there are unfulfilled needs both in data analysis and new digital
tools using AI. There is a particular interest in developing AI tools that will assist with
healthcare management for patients with SDB, improving adherence and treatment
quality. This needs to be developed in accordance with the general data protection
regulation (GDPR) of data privacy in the EU and many external countries.
Further reading
• Bailly S, et al. (2016). Obstructive sleep apnea: a cluster analysis at time of diagnosis. PLoS
One; 11: e0157318.
• Bailly S, et al. (2020). Partial failure of CPAP treatment for sleep apnoea: analysis of the French
national sleep database. Respirology; 25: 104–111.
• Benjafield AV, et al. (2021). Positive airway pressure therapy adherence with mask resupply:
a propensity-matched analysis. J Clin Med; 10: 720.
• Bhattacharjee R, et al. (2020). Adherence in children using positive airway pressure therapy:
a big-data analysis. Lancet Digit Health; 2: e94–e101.
• Cistulli PA, et al. (2019). Short-term CPAP adherence in obstructive sleep apnea: a big data
analysis using real world data. Sleep Med; 59: 114–116.
• Drager LF, et al. (2021). Adherence with positive airway pressure therapy for obstructive sleep
apnea in developing vs. developed countries: a big data study. J Clin Sleep Med; 17: 703–709.
• Gerves-Pinquie C, et al. (2022). Positive airway pressure adherence, mortality and cardiovascular
events in patients with sleep apnea. Am J Respir Crit Care Med; 206: 1393–1404.
• Liu D, et al. (2017). Trajectories of emergent central sleep apnea during CPAP therapy. Chest;
152: 751–760.
• Midelet A, et al. (2021). Hidden Markov model segmentation to demarcate trajectories
of residual apnoea-hypopnoea index in CPAP-treated sleep apnoea patients to personalize
follow-up and prevent treatment failure. EPMA J; 12: 535–544.
• Midelet A, et al. (2022). Bayesian structural time series with synthetic controls for evaluating
the impact of mask changes in residual apnea-hypopnea index telemonitoring data. IEEE J
Biomed Health Inform; 26: 5213–5222.
• Pépin J-L, et al. (2018). Adherence to positive airway therapy aer switching from CPAP to ASV:
a big data analysis. J Clin Sleep Med; 14: 57–63.
• Pépin J-L, et al. (2020). Big data in sleep apnoea: opportunities and challenges. Respirology;
25: 486–494.
• Pépin J-L, et al. (2021). CPAP therapy termination rates by OSA phenotype: a French nationwide
database analysis. J Clin Med; 10: 936.
• Pepin J-L, et al. (2022). Relationship between CPAP termination and all-cause mortality:
a French nationwide database analysis. Chest; 161: 1657–1665.
• Verbraecken J (2021). Telemedicine in sleep-disordered breathing: expanding the horizons.
Sleep Med Clin; 16: 417–445.
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Patient empowerment/
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participation in care of
respiratory sleep disorders
Piet-Heijn van Mechelen
Diagnosis and treatment of OSA present a number of challenging problems:
• Underdiagnosis: if OSA is not diagnosed and treated in time, it can gradually lead
to physical damage that is irreparable.
• Longer waiting times for the patient to receive a diagnosis (3–6 months) due to the
exploding number of patients that attend for diagnosis at sleep clinics.
• A steep rising workload of diagnosis and treatment for medical professionals.
• The impact of lifelong treatment for apnoea is dicult for patients, which makes
information and guidance in the trial period of utmost importance.
• Lifelong treatment requires periodic control. This is dicult to achieve with the
rising number of patients being treated.
Digital health has enabled the development of new practices, as described throughout
this Handbook. In this chapter we look at the extent to which they succeed in
empowering patients. We have used detailed information about the daily practice of
diagnosis and treatment in the Netherlands; we understand from patient organisations
and professionals in Europe that similar problems exist in other countries.
Underdiagnosis and its eects
Although OSA is not fatal, it has a great impact and is very disabling. In our numerous
meetings with OSA patients, we hear personal stories that have a lot in common:
increased daytime sleepiness, decreased energy, procrastination, decreased cognitive
functioning and mood swings, leading to tension at home and conflicts at work.
A person like that becomes an increasingly unpleasant partner, father, mother,
colleague, manager or employee.
Key points
• Digital information and online questionnaires can create awareness and
give an indication of the likelihood of having OSA, lightening the burden of
untreated OSA.
• Sleep studies at home are preferred by most patients and help optimise the
diagnostic process, reducing patient waiting time.
• Telemonitoring reduces visits to the hospital and enables focused support and
guidance for patients who need it most.
• Platforms for patients are important for self-management.
367ERS Handbook: Respiratory Sleep Medicine
Patient empowerement/participation in their care
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Most of it is confirmed in our surveys. 70% of patients report that they must have
developed OSA >4 years prior to the diagnosis; 39% think it is even ⩾8 years. These
numbers have not changed over the past 15 years. This leads to our conclusion that
there is still significant underdiagnosis.
At the time of their OSA diagnosis, patients reported an average of 3.5 comorbidities.
Although they were treated by a medical specialist (e.g. cardiologist, internist,
ophthalmologist, psychiatrist), the majority of patients were not asked about sleeping
problems by these specialists.
Our findings are supported by a study of 80 000 patients: OSA patients can be detected
8 years prior to the OSA diagnosis by very high medical consumption. The direct and
indirect costs of an untreated OSA patient amounted on average to EUR 3860 annually
(2011 prices), while treatment with CPAP would cost EUR 300–350 annually.
Underdiagnosis and digital health
Information can help to create awareness. A good indication comes from the
website of the Dutch Apnea Association (www.apneuvereniging.nl). The section
‘Do I have apnoea?’ is most viewed. Here visitors can find information about
symptoms, comorbidities and tests such as the ESS. The same picture emerges from
the Association’s Facebook groups, where the questions most asked by potential
patients are what are the symptoms of apnoea; can it be treated; and which tests and
treatments exist?
The Philips Sleep Apnea Quiz (www.apneutesten.nl/vragenlijst) is oen used. It gives
respondents an indication of the likelihood of having OSA. This questionnaire has
been completed >15 000 times in 2 years.
Rising numbers at sleep clinics and digital health
In the Netherlands, the number of patients who consult sleep clinics is increasing
steeply. In 2012, the clinics conducted 60 000 sleep studies. In 2018, this had risen
to 114 000. Several digital approaches are used to manage this growing number of
patients, to optimise processes and bring more comfort to the patient, while making
treatment faster and cheaper.
• Improving pre-test probability of OSA brings fewer patients to clinic, or keeps them
out of a costly diagnosis process. Pilot projects include oxygen desaturation index
measurement in primary care, and questionnaires.
• The prevalence of AHI above the diagnostic threshold is tremendous in the general
population. Overdiagnosis is a risk. To avoid costly and time-consuming sleep
studies, a simple method is to schedule anamnesis with a somnologist in the sleep
clinic before the sleep study.
• Wearables are promising, if rightly embedded to prevent the opposite problem:
a larger number of false-positive ‘patients’.
• In the Netherlands we have seen a steady shi from clinical PSG to ambulatory
polygraphy (PG). In 2011, 67% of sleep studies were clinical, whereas in 2019,
83% were ambulatory. In 2018, according to patients, 44% were PSG and 50%
PG. The majority of sleep clinics (59%) report that PG is the most common sleep
study. A number of clinics use arterial pressure measurement with automatic
remote readout (e.g. WatchPAT) as an equivalent alternative to PSG.
• Digital patient records are increasingly important, because of the frequency of
comorbidity. Patients do not need to repeat the same story too oen, and do not
want to do so.
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• E-consultations proved their value during the coronavirus disease 2019 pandemic.
They will be part of the future diagnostic process. Although we have not yet seen
studies evaluating their eects, the impression is that aer the initial awkwardness
of online consultations instead of seeing a doctor in person, patients begin to see
the advantages, e.g. not having to travel to a hospital.
• Big data can be used to define phenotypes, enabling customisation of treatment.
This is especially relevant in the Netherlands where seven treatment opportunities
for OSA are covered by basic health insurance.
CPAP trial period and digital health
Remote monitoring of CPAP makes it possible to detect problems in the trial period
at an early stage and to direct attention, guidance and information to patients who
really need it. It has helped to foster adherence considerably. Platforms for selfmonitoring empower patients and raise therapy adherence even further. Patientreported outcome measures are being developed. They enable the simultaneous:
creation of an inventory of symptoms and complaints; customisation of treatment;
and assessment of whether the treatment is eective.
Lifelong treatment
There has been a sharp rise in people treated for OSA. In 2004, 9000 people in the
Netherlands used CPAP therapy. In 2020, the total number of people treated for OSA
was >300 000.
Lifelong treatment supposes ‘lifelong’ periodic control. This is dicult for the sleep
clinics on top of the growing number of new patients. At the same time, periodic control
for well-regulated patients is not always needed. People who use CPAP for >5 years are
capable of self-management and can signal possible problems when they arise.
Digital health and lifelong treatment
Telemonitoring has proven its value. In the Netherlands, homecare providers play
an important role in monitoring the patient, thus diminishing the workload at sleep
clinics. Homecare providers signal problems and refer to the sleep clinic only when
necessary. The platforms for self-monitoring empower patients and make selfmanagement possible.
Further reading
• Bailly S, et al. (2021). Clusters of sleep apnoea phenotypes: a large pan-European study from
the European Sleep Apnoea Database (ESADA). Respirology; 26: 378–387.
• Jennum P, et al. (2011). Health, social and economical consequences of sleep disordered
breathing: a controlled national study. Thorax; 66: 560–566.
• Pépin J-L, et al. (2022). Relationship between CPAP termination and all-cause mortality:
a French nationwide database analysis. Chest; 161: 1657–1665.
• Van Mechelen PH, et al. (2021). Monograph About Care Pathway for Sleep Apnea.
ApneuVereniging (Dutch Apnoea Association). https://apneuvereniging.nl/monographabout-dutch-care-pathway-osa/
• Verbraecken J (2021). Telemedicine in sleep-disordered breathing: expanding the horizons.
Sleep Med Clin; 16: 417–445.
369ERS Handbook: Respiratory Sleep Medicine
Development of breathing
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and sleep, and pathophysiology
of apnoea in the first years of life
Refika Ersu and Ha Trang
The scope of this chapter is to describe the dramatic changes that occur in the first
months and years of life regarding sleep organisation and breathing control, and use
this as a background to explain the pathophysiology of apnoea and brief resolved
unexplained events (BRUEs) in infants. Circadian sleep/wake rhythms are already
present in utero, and are observed during the last trimester of gestation. These fetal
rhythms are lost at birth, but they reappear during the first weeks and months of life.
The organisation of sleep/wake rhythms is driven by both an endogenous biological
clock and numerous external elements, such as day/night variations and parental/
social factors. These may at least in part underlie the large variability observed in sleep
development and characteristics among individuals.
Development of sleep in the first years of life
Sleep and wakefulness states can be identified in utero from the beginning of the third
trimester of gestation. In human newborns, sleep is divided into two defined states:
active sleep (AS) and quiet sleep (QS). AS is characterised by low voltage and rapid EEG,
inhibited EMG and bursts of saccadic and REMs, associated with rapid and irregular
respiratory and heart rates. In contrast, QS is characterised by high voltage and slow
EEG, inhibited EMG and bursts of saccadic and REMs, associated with slow and regular
respiratory and heart rates. Thus, AS shares some features with future REM sleep and
QS shares some features with future NREM sleep, states which are observed in older
children and adults.
During the first month of life, sleep duration is almost 16–17 h per day, structured on
an ultradian rhythm with alternate diurnal and nocturnal sleep. Newborns begin in
AS, which occupies a high percentage of sleep (50% of total sleep time). Sleep cycles
with alternate AS and QS last ∼50 min. No early and late-night dierences in AS/QS
distributions are observed.
Key points
• Breathing irregularities are common in infancy but most resolve
spontaneously by age 3–6 months.
• Breathing and cardiovascular control are inextricably linked and should be
evaluated together.
• Higher-risk BRUEs may not be benign and may impact long-term outcome.
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Significant changes in sleep characteristics and organisation are observed in the first
6 months of life, with development of the main features of adult sleep. Beyond the
first months of age, AS/REM sleep decreases significantly, whereas QS/NREM sleep
increases and dierentiates into stages 1, 2 and 3 sleep with specific EEG waveforms.
Sleep onset no longer occurs in REM sleep aer 6 months of age. Organisation of
sleep following a circadian rhythm is established by 1 year of age.
With increasing age, the mean duration of total sleep time per day decreases
progressively (an average of 14–15 h at 6 months of age, 13–14 h at 1 year of age,
12–13 h at 3 years of age, and 11 h at 6 years of age). The disappearance of diurnal
naps at 2–6 years of age is associated with substantial reorganisation of nocturnal
sleep. Sleep cycles which include REM and NREM periods last longer (55 min at
3 months of age, rising to 75 min at 2 years) of age. The percentage of REM sleep
decreases rapidly aer 9 months of age. REM sleep latency increases progressively
(15 min at 3 months of age, 70 min at 2 years of age, and 143 min at 6–7 years of
age). The duration and percentage of REM sleep increase progressively overnight, and
are longer in the second half of the night compared to the first.
Finally, parental/social factors and day/night variations also play a key role in the
development of the circadian rhythm of sleep, which could influence the occurrence
of sleep disorders.
Development of the control of breathing
The purpose of breathing is to match oxygen (O 2) supply and demand and eliminate
carbon dioxide (CO 2) – a precarious task in infancy when the metabolic rate is high,
body O 2 stores are low and breathing is irregular. Specific chemoreceptors facilitate
this task by constantly monitoring O 2 and CO 2 levels in the blood and tissues.
Functional chemoreceptors are not required to initiate or maintain breathing at birth
(neurogenic/brain activity is sucient to achieve this), but over subsequent days/
weeks their input (neural ‘drive’) becomes progressively more important for sustaining
breathing rhythm, especially during sleep. Chemoreceptor dysfunction consequently
underpins and exacerbates sleep-related breathing disorders.
CO
2
CO 2 stimulates the peripheral (carotid body) and central (brainstem) chemoreceptors
at all ages. The ventilatory response (VR) of the term-born infant and adult are moreor-less equivalent, so CO2 sensitivity is normally well-developed if not fully mature
at birth. The infant curve is, however, displaced to the le (it shis rightwards with
age), which means V’E in infancy is greater at any P
(weight-adjusted) metabolic rate/CO2 production. In infants who are born preterm, the
VR does appear to increase aer birth, although whether this is due to an increase in
CO2 chemosensitivity or improved lung compliance and rib cage stability is uncertain.
Hypoxia
Hypoxia mainly stimulates the peripheral chemoreceptors. Newborns are known to
be less sensitive and responsive to hypoxia. Two events transform this situation: an
increase in carotid body chemosensitivity, and the waning of hypoxia’s depressant
actions on the brain. The first process is rapid and is normally well advanced by the
second postnatal day; it augments mainly the amplitude of the VR. The second process
occurs slowly over weeks to months and increases the duration of the response, i.e.
the increase in V’E/hyperpnoea lasts longer; this largely explains why the response
becomes less ‘biphasic’ and more ‘monophasic’ with age.
; this reflects a two-fold greater
aCO
2
371ERS Handbook: Respiratory Sleep Medicine
Development of breathing and sleep
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Asphyxia
Asphyxia refers to a low O2–high CO2 state. It can develop for a variety of reasons, e.g.
central apnoea (no breathing eort) or airway obstruction at the level of the mouth
and nose, pharynx or larynx. Tolerance to asphyxia is particularly low. It can quickly
lead to severe organ damage and death and poses a particular danger in infancy
because it develops rapidly (for the reasons given above), and because sensitivity to
hypoxia (although not to CO2) is low. Normally (in adults) when both stimuli occur
together, cardiorespiratory activation and arousal is dramatically heightened. This
‘multiplicative’ interaction (which occurs within the carotid body and brainstem) is
weak at birth but gradually develops as the carotid chemoreceptors reset, although
the time course is uncertain.
The upper airway
The upper airway muscles are the accessory muscles of breathing. They are normally
activated in parallel with the diaphragm and intercostals to regulate airway calibre
and resistance. During inspiration, the pharyngeal and laryngeal abductors tense and
dilate the airway to lower resistance and facilitate lung inflation. During expiration,
the adductors constrict the larynx (in particular) to slow expiration and maintain lung
volume above passive relaxation volume. The tone and phasic activity of these muscles
is influenced by drive from the chemoreceptors and airway, and laryngeal sensors
that monitor pressure, flow and pH. Upper and lower airway muscle coordination
improves with age; if poorly coordinated, it may cause obstruction or significantly
reduce airflow, especially when chemoreceptor drive is strong.
Breathing and sleep
During sleep, behavioural drives are lost or suppressed and tonic drive from
the chemoreceptors helps sustain a normal breathing rhythm. Consequently,
chemoreceptor dysfunction is oen unmasked and breathing irregularities are
accentuated, particularly during QS.
The rate and depth of breathing fall at sleep onset; both become more irregular and
variable during REM sleep and reach a nadir during NREM sleep (QS). Responsiveness
to CO2 and hypoxia is also attenuated during QS and falls even further during REM
sleep, accentuated by chest wall instability due to muscle atonia. Short respiratory
pauses are more frequent, particularly during REM sleep. Premature and some
full-term infants exhibit bouts of periodic breathing (cycles of regular breathing
eorts separated by at least three central pauses lasting >3 s). Cyclical hypoand hyperventilation may cause significant hypo/hypercapnia and intermittent
desaturation. Periodic breathing is common early on but decreases dramatically
in frequency and duration with age as the chemoreceptors and central rhythm
generators mature. Persisting immaturity in either or both may accentuate periodic
breathing and apnoea.
Plasticity
Development in general does not follow a fixed trajectory but is shaped by experience.
Long-lasting changes in chemoreception can be triggered by unusual perinatal
circumstances, such as pre-term birth, fetal exposure to drugs (nicotine, for example),
repetitive apnoea, asthma and lung disease. These changes can manifest as structural
or functional ‘reprogramming’ of the chemoreceptors themselves, of processing
centres within the brainstem, or both. Exposure to chronic or repetitive intermittent
hypoxia and/or hypercapnia, for example, can reduce or increase the VR, depending
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on the developmental stage at exposure. Circumstances that alter, delay or otherwise
reprogramme chemoreflex development may exacerbate cardiorespiratory failure and
vulnerability to asphyxia.
Pathophysiology, diagnosis and assessment of apnoea and BRUEs
Developmental aspects of respiratory rhythm in health
The breathing pattern of the newborn infant is oen characterised by apnoeas of
varying duration and frequency, and by periodic breathing. Breathing irregularities
are evident in almost all infants born extremely preterm (<28 weeks) and ∼30% of
those born at full term. The propensity for apnoea and periodic breathing in infancy
has been ascribed to a weak response to hypoxia and hypercapnia, and/or a low CO2
apnoeic threshold. Most breathing irregularities resolve spontaneously by 3–6 months
of age due to maturation of central rhythm generators and chemoreceptors, the two
systems essential for maintaining a normal breathing rhythm during sleep.
Aetiology of breathing irregularities
Apnoea is a symptom rather than a disease. Breathing irregularities occur in diseases
of the central nervous system, lungs, muscles, metabolism, upper airways, etc.
Apnoea may be aggravated by many factors: immature or abnormal chemoreception,
lung disease, upper airway dysfunction, systemic infection, intracranial haemorrhage,
hypo- or hyperthermia, glucose/electrolyte imbalance, anaemia, gastro-oesophageal
reflux (GOR), and patent ductus arteriosus.
Clinical diagnosis is complicated by the intricate interaction between central and
peripheral control mechanisms. Central mechanisms control respiratory rhythm, as
well as the diaphragm and accessory muscles of breathing. Weak or unstable central
drive can therefore result in central, obstructive or mixed apnoea, as well as irregular
breathing. Similarly, upper airway, lung and respiratory muscle disease may further
modify central drive via altered sensory feedback, and exacerbate central irregularities.
Clinical aspects of apnoea
Control of breathing is inextricably linked with cardiovascular control, particularly
during sleep. Dysfunction in one aects the other and vice versa, and can rapidly lead to
a dangerous or fatal downward spiral of events (see below). In clinics, the two systems
should be carefully evaluated in tandem. Thus, duration and frequency of central,
obstructive and mixed apnoea must be linked with concomitant changes in blood
gases as well as in heart rate and BP. Brief apnoea is oen regarded as physiological,
but if frequent and associated with significant blood gas and circulatory changes (e.g.
bradycardia) it may compromise health (figure 1).
BRUEs
BRUEs are episodes of short duration (typically <1 min), characterised by a change
in breathing, consciousness, muscle tone (hyper- or hypotonia) and/or skin colour
(cyanosis or pallor). The mixture of breathing, circulatory and neuromuscular
symptoms makes separating coexisting and causative factors dicult. BRUEs can be
divided into higher- and lower-risk events. If any of the following criteria are present,
it is considered higher risk: <60 days of age, <32 weeks and corrected gestation
<45 weeks, cardiopulmonary resuscitation (CPR) provided by a trained provider, event
lasted >1 min, more than one event or high-risk concerns from history or physical
examination. Clinical management should focus on excluding serious underlying
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