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Identification of high-risk patients
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Sleepiness
EDS is the most common daytime symptom associated with OSA but not all patients
report EDS, especially those with a mild disorder. This aspect is especially important
in identifying patients with a clinical OSAS, and EDS is increasingly recognised as an
important independent variable in predicting comorbidity risk. The association of EDS
with OSA is especially important when interpreting recent RCT reports, such as from
the SAVE (Sleep Apnea cardioVascular Endpoints) trial that failed to demonstrate a
benefit from CPAP therapy in the secondary prevention of cardiovascular comorbidity,
as these trials were largely restricted to non-sleepy subjects, who may be less likely to
have an increased independent risk of cardiovascular comorbidity.
The evaluation of sleepiness in patients with OSA is typically performed using
subjective measures, most commonly using the ESS score. However, this measure
is open to subjective bias, although bed partner input may improve accuracy where
relevant. In case of doubt, objective evaluation of sleepiness by measures such as the
MSLT and/or maintenance of wakefulness test (MWT) may provide a more accurate
measure of sleepiness, but this is limited in clinical practice by the labour-intensive
nature of these tests.
Elevated BP
Systemic hypertension is the most prevalent comorbidity associated with OSA, being
present in up to 50% of patients with OSA, and loss of nocturnal dipping of BP is
very common. Recent reports indicate that non-dipping nocturnal BP is a strong
independent predictor of OSA and may represent a potentially useful biomarker for
the disorder. Furthermore, non-dipping nocturnal BP is associated with an increased
risk of cardiovascular comorbidity and is thus a relevant variable that may help identify
high-risk patients.
Recent reports that CPAP therapy benefits to BP levels are largely confined to
hypertensive patients with a non-dipping nocturnal BP profile support the likelihood
that loss of nocturnal dipping is a central feature of the BP response to OSA.
Additional potentially relevant factors in identifying patients at high risk
for comorbidity
Biomarkers
Much research is ongoing regarding the identification of biomarkers that may help
predict susceptibility of patients with OSA to additional comorbidity. To date, no
biomarker has been identified that provides a clinically reliable indicator of comorbidity
risk. Markers of inflammation, such as high-sensitivity C-reactive protein, have been
proposed as a relevant biomarker but the reliability of inflammatory biomarkers in
this context remains unproven. Markers of autonomic function may also help predict
comorbidity risk, although they remain an open area of investigation. Markers of
metabolic dysfunction such as leptin, adipocytokines, dyslipidaemia and impaired
glucose tolerance are also relevant in this context. Overall, it appears likely that
individual or (more likely) a combination of biomarkers will help identify patients at
increased risk of comorbidity, but this topic remains an open area of investigation.
Genetic factors
There is growing interest in genetic factors as potential biomarkers of comorbidity
risk. While there is wide acceptance that genetic factors play an important role in
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determining a susceptibility to developing OSA, genetic factors that may predispose to
comorbidity in patients with OSA remain unproven, although they represent an area
of active investigation.
Driving risk
OSA is widely recognised as being associated with an increased risk for motor vehicle
accidents (MVA). Many studies have quantified this increased risk, which is reported
to be in the region of two to three times that of the general population. Since eective
treatment with CPAP has been reported to normalise the risk of MVA, the identification
of patients at high accident risk is important for both the aected patient and the
population at large. Many jurisdictions have introduced regulations regarding the
ability of OSA patients to continue driving, which typically specify a threshold of
OSA severity and a requirement that patients with OSA above that threshold should
discontinue driving until eectively treated. The most widely applicable regulation was
introduced by the European Union in 2015 and is mandatory throughout the member
states. This regulation specifies that drivers with OSA who have an AHI >15 events·h−1
and report significant sleepiness should not drive until eective treatment of the
disorder has been demonstrated.
The features of OSA that influence accident risk remain unclear, especially the relative
importance of AHI and EDS. Overall, EDS appears to be the most important variable in
this context, but the objective evaluation of sleepiness is dicult in large populations.
Furthermore, the identification of subjects at risk for OSA and/or accidents remains
elusive. There is a role for screening questionnaires, driving simulators and other
techniques to evaluate sleepiness and/or impaired vigilance. Studies of the impact of
treatment on MVA risk in aected drivers highlight the evidence gaps regarding the
identification of OSA patients at risk for MVA.
These diculties complicate the ability of clinicians to determine the fitness of
OSA patients to drive and prompted the establishment of a European Respiratory
Society task force on sleep apnoea, sleepiness and driving risk, which recently
reported an ocial statement on the topic. The statement included the following
recommendations for clinicians advising on fitness to drive in OSA patients:
• OSA severity assessed by AHI alone does not predict fitness to drive in OSA patients.
• Excessive sleepiness is a major factor in determining accident risk in OSA but does
not relate to AHI and could be influenced by other non-OSA factors.
• Where doubt exists regarding the validity of self-reported sleepiness, further
investigation such as the MWT is warranted, especially in professional drivers.
• Eective and compliant treatment with CPAP largely reverses the increased
accident risk and driving can resume once demonstrated.
Conclusion
The identification of high-risk patients with OSA requires the integration of several key
facets of the disorder. Future research is likely to identify additional parameters and
the overall direction of OSA research is to consider the disorder and its consequences
beyond the traditional severity measure of AHI.
Further reading
• Bonsignore MR, et al. (2021). European Respiratory Society statement on sleep apnoea,
sleepiness and driving risk. Eur Respir J; 57: 2001272.
115ERS Handbook: Respiratory Sleep Medicine
Identification of high-risk patients
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• Crinion SJ, et al. (2019). Nondipping nocturnal blood pressure predicts sleep apnea in patients
with hypertension. J Clin Sleep Med; 15: 957–963.
• Drager LF, et al. (2013). Obstructive sleep apnea: a cardiometabolic risk in obesity and the
metabolic syndrome. J Am Coll Cardiol; 62: 569–576.
• Drager LF, et al. (2017). Sleep apnea and cardiovascular disease: lessons from recent trials and
need for team science. Circulation; 136: 1840–1850.
• Gleeson M, et al. (2022). Bidirectional relationships of comorbidity with obstructive sleep
apnoea. Eur Respir Rev; 31: 210256.
• Gottlieb DJ, et al. (2020). Diagnosis and management of obstructive sleep apnea: a review.
JAMA; 323: 1389–1400.
• McNicholas WT, et al. (2016). Mild obstructive sleep apnoea: clinical relevance and approaches
to management. Lancet Respir Med; 4: 826–834.
• McNicholas WT, et al. (2022). Obstructive sleep apnea: transition from pathophysiology to an
integrative disease model. J Sleep Res; 31: e13616.
• Pevernagie DA, et al. (2020). On the rise and fall of the apnea–hypopnea index: a historical
review and critical appraisal. J Sleep Res; 29: e13066.
• Randerath W, et al. (2018). Challenges and perspectives in obstructive sleep apnoea. Eur Respir J;
52: 1702616.
• Yeghiazarians Y, et al. (2021). Obstructive sleep apnea and cardiovascular disease: a scientific
statement from the American Heart Association. Circulation; 144: e56–e67.
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Methods of dierent
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sleep tests
Renata L. Riha
Methods developed to acquire signals from the human body during sleep have
been in use for over 100 years. Initial studies focused on refining sleep EEG with
recognition of dierent sleep stages being made possible with the addition of EMG and
EOG in the 1950s, followed by the addition of sensors for monitoring and recording
electrophysiological signals around respiration, oxygen saturation, carbon dioxide (CO2)
monitoring, body position and limb movements beginning in the 1970s. In 1994, the
American Academy of Sleep Medicine (AASM), American College of Chest Physicians
and the American Thoracic Society divided sleep monitoring into four major types:
• Type I: full attended PSG (≥7 channels) in a laboratory setting.
• Type II: full unattended PSG (≥7 channels).
• Type III: limited channel devices (4–7 channels).
• Type IV: one or two channels, usually using oximetry as one of the parameters.
The four types of sleep and respiratory monitoring are discussed in this chapter.
PSG (type I and type II monitoring)
The most widely applied method for diagnosing sleep-related respiratory disorders
is overnight PSG, either attended (type I) or unattended (type II). PSG is considered
the ‘gold standard’ for measuring sleep, respiration and movement during sleep. PSG
simultaneously and continuously monitors the following physiological signals: nasal
and/or oral airflow, thoracoabdominal movement related to respiration, snoring,
EEG, EOG, EMG of the chin muscles and limbs, body position, oxygen saturation
and occasionally, transcutaneous CO2. Most modern systems incorporate video
recording, which may assist in the identification of abnormal movements during
sleep (e.g. parasomnias, seizure activity, dissociative behaviour) and reasons for
change in airflow and desaturations.
Key points
• Attended video PSG is still considered the ‘gold standard’ for assessing sleep
and associated breathing, movement and other parameters.
• Limited studies, without EEG, are considered acceptable tools for screening
and diagnosing SDB, particularly in an attended setting.
• Applied in isolation, oximetry alone is not recommended as a diagnostic test
for SDB.
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Signal identification and collection are computerised, allowing for automatic,
semi-automatic or manual scoring of the trace (or a combination of any of the
above). Advances in the deployment of artificial intelligence have allowed for the
generation of automated scoring algorithms, but they are still imperfect. Knowledge
of and ability to score all the traces manually/visually is recommended, and these
complex studies should always be inspected by appropriately qualified healthcare
professionals with the appropriate expertise. The scoring of the sleep EEG and the
respiratory and movement signals has been the subject of numerous changes in
international guidelines in an eort to standardise the outcomes. With respect to
the EEG, the 1968 Rechtschaen and Kales (R&K) rules remained unchallenged
until 2007 when the AASM revised the criteria and in some respects simplified EEG
montage and scoring. Respiratory and movement scoring were also revised. The
2007 guidelines were then subjected to annual changes in the ‘rules’, with the most
recent incarnation being AASM version 3 (Troester et al., 2023). The evidence base
for the visual scoring ‘rules’ is slim, and new additions and changes will continue to
be made as evidence for the best way to classify a breathing or movement disorder
emerges over time. An additional consideration with PSG is the artificial nature of the
surroundings in which sleep is undertaken (type I studies), with results not always
accounting for the ‘first night eect’, and the variability of up to 20% in SDB on a
night-to-night basis. However, the benefits include incorporating EEG (type I and II
studies) to obtain a sleep denominator, a controlled environment where electrode
attachments can be monitored and home distractions are eliminated (e.g. a sick
cat that needs to be put outside at 2 am every morning), video recording as well as
personal observation of any movements, breathing disorders, etc., bedroom safety
and determination of sleepiness; those who are excessively sleepy will fall asleep in
most environments irrespective.
Nevertheless, in the context of SDB, a negative study should be viewed with scepticism
if the clinical suspicion of OSAS remains high and can always be repeated. Split-night
studies, in which the first half of the study night is used for diagnosis and the second
for monitoring treatment response using CPAP, are considered accurate and costeective when the pre-test probability of OSA presence is high, the study is technically
satisfactory, the monitoring unequivocally demonstrates obstructive events, and the
patient is willing to commence a trial of CPAP half-way through the sleep period.
Sleep EEG
The primary purpose of the EEG is to record electrical potentials generated by the
cortex and deep brain structures, in particular the thalamus. The basis of the trace is
the relative recorded potential between two recording electrodes, with one electrode
being negative in comparison to the other. It is important to remember that negative
charges deflect upwards on the trace. The system used for recording the EEG is based
on an abbreviated version of the ‘10–20’ EEG montage used in neurological diagnosis
(in particular, epilepsy) and the trace is recorded with a 30-s time base. The lehand sided channels are odd-numbered, with the general rule being to read from the
le-hand channels; with the right-hand channels acting as a back-up. The original
Rechtschaen and Kales montage used the following electrode placements (figure 1):
• C4–A1 (A being the reference electrode)
• C3–A2
• EOG on the le eye referenced to A1
• EOG on the right eye referenced to A1
• EMG mentalis
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A2
A2
FrontVertex
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E2
E1
A1
EMG
Methods of dierent sleep tests
Le eye–A1
Right eye–A1
C4 C3
Figure 1. Placement of electrodes for sleep staging according to Rechtschaen et al. (1968).
Reproduced and modified from Riha (2012) with permission.
C4-A1
A1
In 2007, the AASM introduced an improved placement, which is in use today:
• F4–M1 (M being the reference electrode placed on the mastoid process); best for
picking up delta waves.
• C4–M1; best for capturing spindles generated in the thalamic area.
• O2–M1; best for capturing alpha rhythm.
Figure 2 shows the AASM montage in use since 2007 with the placements for le and
right electrodes.
The EOG
The EOG is not a recording of the eye muscle movements, but rather captures the
corneo–retinal potential dierence as the eyes move.
Le
side
Back
Figure 2. AASM (2007) electrode placement. Reproduced and modified from Riha (2012) with
permission.
Right
side
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The placement of these electrodes according to the AASM is at the outer canthus of
the right eye (ROC) oset by 1 cm above the horizontal plane and the LOC is oset by
1 cm below the horizontal plane at the outer canthus of the le eye.
Referencing of the ROC is to M1 and the LOC is to M2. Additional electrodes can be
placed for multiple sleep latency testing when capturing and recording REM sleep is
essential.
The EMG
In the AASM guidelines, up to three EMG electrodes can be placed on the face to
allow for alternative signal derivation should one of the electrodes malfunction.
The mentalis and submentalis EMG are essential to the scoring of REM-sleep and
associated parasomnias. Bruxism can be recorded using EMG placement over the
masseter muscles.
EMG is also essential to the recording of leg movements and arm movements
during sleep; electrodes are placed on the flexor hallucis longus and flexor digitorum
longus, respectively. The latter is not incorporated consistently in every montage
but is especially important in the context of suspected parasomnias and abnormal
movements during sleep, such as restless legs syndrome.
Sensors and associated monitoring
Many dierent types of electrodes are available for obtaining signals using EEG, EOG
and EMG. For the EEG, gold-cup, silver-cup and disposable electrodes can be used. A
variety of pastes and glues are available and specification for their use is dependent
on the type of monitoring equipment deployed. Electrocardiogram (ECG) is recorded
using the standard configuration. Body position is also monitored, as is snoring using
a microphone attached to the throat. When applying sensors, the aim is to keep the
impedance to <5 kΩ.
The measurement of rib cage and abdominal movement should now be largely
undertaken using piezoelectric transducers according to the AASM guidelines.
Inductive plethysmography, strain gauges and pneumatic bands have been used
extensively in the past, but have numerous drawbacks and are not the recommended
standard. Oesophageal monitoring is very precise, but largely poorly tolerated. It
should not be used routinely in the measurement of SDB at a clinical level.
Flow is assessed using several dierent techniques. Current recommendations are for
the use of a thermistor to score obstructive apnoeas and nasal prongs for the scoring
of hypopnoeas.
Thermistors are small sensors whose electrical characteristics, comprising voltage
and resistance, depend on temperature. The device is placed under the nose and
flow is derived by measuring the variation in the electrical properties of the sensor
when exposed to temperature changes in flow (room air temperature on inspiration
and ∼37°C at expiration). The signal is semiquantitative. Nasal prongs comprise
conventional oxygen prongs connected to a pressure transducer. Airflow turbulence
at the nostrils induces pressure directly related to the magnitude of flow with a
dynamic response. Since the relationship between pressure and flow is nonlinear, an
overestimation of flow magnitude may occur. However, they allow for more accurate
detection of hypopnoeas. Both systems are subject to problems related to mouth
breathing, blocked nasal passages, increased resistance in terms of size (too small in
the case of nasal prongs) and movement during the sleep period.
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Oxygen saturation is detected using pulse oximetry, which is generally derived from
a finger or ear probe. However, since there is still no standardisation of technical
equipment used to acquire PSG signals, only broad specifications are in place.
According to the AASM, a sampling rate of 25 Hz with a signal averaging time of ≤3 s
at a heart rate of 80 beats per min is desirable.
Monitoring CO
The most common method of measuring CO2 levels during sleep is via transcutaneous
monitoring. Although not always exact when compared with arterial blood gases,
it can nevertheless reveal a trend in the direction in which the CO2 level is moving
during the night, especially in relation to hypoventilation. Please see the most recent
AASM manual (Troester et al., 2023) for further information.
Signal acquisition and measurement techniques
Prior to recording the PSG, important checks need to be undertaken on the sensitivity,
filter settings and amplification requirements. Aer the electrodes and monitors are in
place, physiological calibration is conducted to assess proper functioning. Numerous
instruction series are in existence and should be performed with the patient to ensure
that electrode placements are appropriate, baseline data are collected, and comparison
is then possible with events scored when the PSG is visually and manually analysed.
The recording speed of the EEG is generally 10 mm per second.
Scoring the EEG
The PSG is generally scored in sequence using well-defined visual scoring rules (in general
practice, internationally, this means the AASM scoring rules). The EEG is scored first in
30-s epochs and each epoch assigned a state or sleep stage: awake, movement, artefact,
or sleep (N1, N2, N3 or REM sleep). Events can be scored within or across epochs.
Sleep staging of the EEG is generally followed by the scoring of any respiratory events,
followed by the scoring of any limb movements, assessment of the ECG, and finally, by
the scoring of arousals on the EEG to ascertain whether they are occurring in response
to breathing irregularities or movement. Any abnormal movements during sleep or
parasomnias are also assessed on inspection of the video. Descriptions of the latter or
any other events during the night (e.g. nocturia, eating and drinking, reading) should
also be described and recorded in the final report and contextualised. Detailed and
exhaustive guides to scoring sleep stages and associated events during sleep are to
be found in the most recent AASM manual (which has been updated frequently since
2007) and the reader is referred there. The R&K scoring guidelines are no longer
in widespread use, but the reader should have a general awareness of them when
consulting pre-2007 publications or sleep studies.
Summaries and examples of sleep stage characteristics are presented in tables 1–6
and figures 3–6.
2
Scoring EEG arousals
These are defined as a brief interruption of sleep continuity, >3 s in duration but
shorter than 15 s in duration (half an epoch). The AASM defines an arousal as an
abrupt shi in EEG frequency from the underlying rhythm, e.g. from theta to alpha. In
NREM sleep, arousals do not require an accompanying rise in EMG tone as they do in
REM sleep. Generally, arousals are clearest in the occipital and central EEG and can be
scored in any stage of sleep. They cannot be scored in wakefulness.
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Table 1. Stage W (Wakefulness)
Recording Characteristics
EOG Voluntary control, REMs or none, blinks, slow rolling eye movements
when drowsy
EEG Eyes open: low voltage, mixed frequency; eyes closed: posterior
dominant rhythm#, prominent in occipital, attenuates with attention
Posterior
dominant
#
rhythm
8–13 Hz, seen mostly in occipital channels, mainly associated with
wakefulness, also associated with microarousals, 10–20% of patients
will not have a detectable posterior dominant rhythm# on EEG
EMG High; tonic activity; voluntary movement
#
: also known as alpha-rhythm. Reproduced and modified from Riha (2012) with permission.
Table 2. Stage N1 (NREM 1)
Recording Characteristics
EOG Slow rolling eye movements
EEG Low voltage, mixed frequency; may be θ rhythm 2–7 Hz, up to 50–75 µV
range; vertex sharp waves (200 µV)
θ waves 4–7 Hz; low voltage, mixed frequency backgrounds; oen appear as
sharp vertex waves; mostly associated with stage 1
EMG Tonic activity, slight decrease compared with waking
Reproduced and modified from Riha (2012) with permission.
Table 3. Stage N2 (NREM 2)
Recording Characteristics
EOG SEMs occasionally near sleep onset
EEG Low voltage, mixed frequency
Sleep spindles Bursts of 12–14 Hz activity, ≥0.5 s long, no amplitude
K complexes Sharp negative wave followed by positive component, ≥0.5 s
long, no amplitude requirement, mostly associated with stage 2,
EMG Tonic activity, low level
SEM: slow rolling eye movement. Reproduced and modified from Riha (2012) with permission.
Table 4. Stage N3 (NREM 3)
Recording Characteristics
EOG None, reflects EEG
EEG δ rhythm >20% of epoch, high amplitude waves (>75 µV), low frequency
(≤2 Hz), prominent in frontal regions, sleep spindles may persist but not
δ waves <4 Hz, >75 Hz in amplitude, must be <2 Hz to qualify for N3, mostly
EMG Tonic activity, low level
Reproduced and modified from Riha (2012) with permission.
122
requirement, mostly associated with stage 2
maximal over frontal derivations
necessary for scoring
associated with N3
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Table 5. Stage R (REM)
Recording Characteristics
EOG Phasic REMs
EEG Low voltage, mixed frequency, sawtooth waves, θ activity,
posterior dominant rhythm
#
Sawtooth waves 2–6 Hz serrated bursts of activity, maximal centrally and
precede REMs
EMG Tonic suppression, phasic twitches
Phasic muscle activity Bursts of EMG activity lasting <0.25 ms, detected on chin
EMG, anterior tibialis, EOG–EEG leads
#
: also known as alpha-rhythm. Reproduced and modified from Riha (2012) with permission.
Table 6. Normative data for sleep stages across the lifespan
Age (years)
20–29 30–39 40–49 50–59 >60
TST, min 374.9 375.8 370.2 366.6 348.8
Sleep eciency, % 94.4 94.4 90.2 90.4 85.8
Sleep latency, min 6.3 10.0 8.4 6.1 8.2
Awakenings, n 6.3 4.7 8.4 9.7 12.3
Stage R, % TIB 22.2 23.1 20.4 20.9 16.4
Stage N1, % TIB 3.0 2.5 4.3 4.7 4.0
Stage N2, % TIB 50.5 52.8 54.6 56.7 57.6
Stage N3, % TIB 18.8 16.1 10.9 8.1 7.7
Sleep eciency is defined as (TST×100)/TIB. Sleep latency is defined as the time to initial sleep
onset from TIB. TST: total sleep time; TIB: time in bed. Reproduced and modified from Hirshkowitz
(2004), with permission.
EOG
EOG
EEG waves
EEG
EMG
Figure 3. Stage W (Wakefulness). Note α-waves on EEG. Reproduced and modified from
Riha (2012) with permission.
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