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Table 1. Alternative parameters to the AHI that include information on event duration
Parameter Description
ApDur, s Single apnoea event duration
HypDur, s Single hypopnoea event duration
AHDI, % The percentage of sleep time the patient has
been in apnoea, hypopnoea and desaturation
TAD, % The total duration of apnoeas as a percentage
of total sleep time
Total apnoea time, s The total duration of apnoeas
THD, % The total duration of hypopnoeas as a
percentage of total sleep time
Total hypopnoea time, s The total duration of hypopnoeas
TAHD or AHT or ObsDur, % Alternative names for the total duration of
apnoeas and hypopnoeas as a percentage of
total sleep time
Total event time, s The total duration of apnoeas and hypopnoeas
AHDI: apnoea–hypopnoea desaturation index; TAD: total apnoea duration; THD: total hypopnoea
duration; AHT: apnoea-hypopnoea time; ObsDur: obstruction duration.
subjects with a high loop gain (with unstable ventilatory control) may also terminate
respiratory events more quickly than subjects with a low loop gain. However, respiratory
event duration has its limitations, since it does not take into account the desaturation
depth and degree of hypoxaemia. An overview of the alternative parameters to the
AHI that include information on event duration is shown in table 1.
Measurement of dierent oxygen parameters
Night-time hypoxaemia
Night-time hypoxaemia is a very appealing parameter to explore, as intermittent
hypoxia is the hallmark of OSA. Episodes of short intermittent high-frequency
hypoxaemia are pivotal in the presence of OSA, while prolonged low-frequency
hypoxaemia is seen in chronic pulmonary diseases. The traditional oxygen
desaturation index (ODI) represents the average number of desaturation events (also
referred to as dips) that drop at least 3% (ODI3) or at least 4% (ODI4), per hour of
sleep (or recording time), regardless of their duration and morphology. This does not
necessarily mean that oxygen saturation falls below 90%: drops from 95% to 91%
are oen observed. In the European Sleep Apnoea Database (ESADA), it has been
found that the ODI is a better predictor of systemic hypertension in OSA than the AHI.
The discrepancy between the AHI and the ODI arises because a hypopnoea can be
scored in association with an arousal (without desaturation). Also, small changes in
ventilation due to partial airway obstruction may never be observed as changes in the
desaturation profile, as a consequence of the non-linear haemoglobin desaturation
curve. Moreover, the ODI does not assess the length of oxygen desaturation, nor its
depth. Other hypoxaemia metrics are the time spent with peripheral oxygen saturation
(S
) below 90% (T90 or T90%), the mean oxygen saturation and the nadir of oxygen
pO
2
saturation (S
a threshold of 88% (T88) has been considered as well. T90 has been shown to be
minimum) (table 2). The 90% threshold is somewhat arbitrary, and
pO
2
predictive of important outcomes, like platelet aggregation as well as overall mortality.
In contrast, in the SAVE (Sleep Apnea cardioVascular Endpoints) trial, the desaturation
indices did not show predictive value related to MACE (major adverse cardiac events), a
composite end-point frequently used in cardiovascular research. T90 was significantly
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Table 2. Parameters integrating desaturation
Parameter Description
ODI, events·h
−1
Oxygen desaturation index (the number of
oxygen desaturations per hour of sleep)
Mean S
Minimum S
T90 or T90% or time <90%, min Time spent with S
T88 or time <88%, min Time spent with S
DesDur, s Single desaturation event duration
, % Mean oxygen saturation throughout the night
pO
2
, % Nadir of the oxygen saturation
pO
2
below 90%, in minutes
pO
2
below 88%, in minutes
pO
2
DesDur, % Total desaturation duration normalised with total
sleep time
DesSev, % Total desaturation area normalised with total
sleep time
associated with cardiovascular mortality in the SHHS, but not in the MrOS Sleep
Study (Osteoporotic Fractures in Men Study) cohort, while the reverse was true for
the ODI. This points at limitations for oxygen desaturation metrics. For example, a
patient with an ODI of 20 events per h of sleep with long oxygen desaturation events
can have a two-fold higher T90 and a lower minimal saturation than a patient with
an ODI of 40 events·h−1 but shorter events, associated with mild levels of oxygen
desaturation. The mean S
OSA. Additionally, the nadir or minimum S
must be taken to ensure this value represents a true minimum. Hence, there is no
value is expected to decrease with increasing severity of
pO
2
value can be prone to artefact and care
pO
2
individual variable which clearly and consistently outperforms the ODI. Furthermore,
the chosen frequency definition (ODI3 or ODI4) has an impact on the strength of
relationships with CVD. Altogether, while such simplified indices are convenient, they
are likely to fail to capture important pathophysiological characteristics.
Hypoxic burden
The desaturation area is a manifestation of both desaturation depth and duration,
and quantifies the area between the baseline and the S
Algorithms currently allow capture of the area under the oxyhaemoglobin saturation
curve as a metric of the overall hypoxic burden. The hypoxic burden can be obtained
by adding individual desaturation areas and dividing the total area by the sleep
duration, with the unit of hypoxic burden being %·min·h−1. For example, a hypoxic
burden of 40%·min·h−1 is equivalent to 40 min of 1% desaturation per h or 10 min
of 4% desaturation per h. Studies have demonstrated that cardiovascular mortality
increases progressively with increasing hypoxic burden. In the SHHS, patients in the
upper quintile of hypoxic burden had an adjusted hazard ratio of 1.96 for cardiovascular
mortality. These findings indicate that not only the frequency but also the depth and
duration of sleep-related respiratory events are relevant disease-modifying features.
A number of alternative parameters that include information on oxygen desaturation
are shown in table 2. Codes for hypoxic burden calculations are available online
(https://github.com/pdechazal/Hypoxic-Burden: for processing, Matlab is required;
https://zenodo.org/record/6198838: only the EDF file of oximetry is required). One
major limitation is that observed S
time, sampling rate and amplitude resolution settings of the pulse oximeter used.
Parameters integrating event duration and hypoxic load
Dierent parameters have been proposed that integrate event duration and degree and
severity of desaturation, such as total apnoea–hypopnoea duration as a percentage of
trace during desaturation.
pO
2
values may vary depending on the averaging
pO
2
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Table 3. Parameters integrating event duration and hypoxic load
Parameter Description
TAHD% × average desaturation
depth, %
ObsSev, s·%; or SBII, %·min2·h
Adjusted AHI A composite index integrating event duration,
ObsSev: obstruction severity; SBII: sleep breathing impairment index.
2
The product of the TAHD as a percentage of total
−1
sleep time, and the average desaturation depth
Alternative names for the sum of products of the
duration of each apnoea and hypopnoea event
and the associated desaturation event area
normalised by total sleep time
desaturation areas and the total analysed time
total sleep time (TAHD%) multiplied by average desaturation depth, and obstruction
severity (table 3). The adjusted AHI is a composite index integrating event duration,
desaturation areas and the total analysed time and has predictive power regarding the
incidence of cardiovascular events and all-cause mortality.
Measurement of arousals
Arousal intensity
Arousal intensity has been described as a potentially important physiological variable,
with some arousals being rather subtle (and not detected by traditional EEG) and
others being more powerful (leading to full awakening from sleep). Arousal intensity
can be measured and scaled between 0 and 9, and arousals can be categorised into
low (scale <5) and high (>5) intensities. It has been shown that the average arousal
intensity is independent of the preceding respiratory stimulus, is positively correlated
with arousal duration, time to arousal and rate of change in epiglottic pressure,
and is negatively correlated with BMI. Consequently, since respiratory responses
increase with arousal intensity, patients with higher arousal intensity may be prone
to respiratory control instability. Arousal intensity has also been related to changes
in heart rate (the sleep apnoea-specific pulse rate response), supporting the concept
that arousal intensity is a marker of autonomic activation. Recently, a more in-depth
analysis of an arousal was proposed, namely the area under the curve (AUC) of a
respiratory arousal EEG recording, or pooled arousal signal, which could be considered
as an additional quantitative surrogate marker of OSA. The arousal-AUC metric has
been shown to closely correlate with hypoxic burden, AHI and arousal index.
Arousal morphology
An additional method to study arousal microstructure is by means of the arousal
morphology. The cyclic alternating pattern (CAP) consists of transient arousals
(phase A) that periodically interrupt the tonic theta/delta activities of NREM sleep.
Three dierent subtypes of A phases can be identified that correspond to dierent
levels of arousal power. It has been shown that the CAP can be used to assess and
distinguish the severity of OSA and its symptoms.
Timing of arousal
Negative intrathoracic pressure appears to be a key trigger for respiratory-induced
arousals; hence, they should occur during inspiration. However, over one-third of
respiratory-induced arousals occurs during expiration, related to neuromuscular
and respiratory load compensation mechanisms, such as increases in peak tensor
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palatini EMG, increased P
arousals may be a marker of enhanced neuromuscular compensation. This could
and chemical drive. It was concluded that expiratory
aCO
2
have important implications for potential treatment targets. Identification of timing
of arousals in relation to respiratory phase may be helpful in advancing respiratory
phenotyping eorts in OSA.
Sleep depth
The odds ratio product (ORP) is a new metric that quantifies sleep depth. It can be
derived from analyses of the EEG, using various power spectral measures. ORP values
can vary significantly, with a range of 0–2.5 (ORP <1.0 predicts sleep and ORP >2.0
predicts wakefulness in >95% of 30-s epochs, while ORP in the 1.0–2.0 range can
simply be considered as unstable sleep). ORP may be a measure of susceptibility
to adverse neurocognitive outcome in OSA, while its relationship to cardiovascular
outcomes is untested. ORP measured during wakefulness may also provide valuable
objective mechanistic insight into physiological hyperarousal: high ORP while awake
predicted poor sleep quality in two large independent cohorts. ORP could be considered
as an added dimension to the evaluation of a patient’s sleep quality, and may help
explain symptoms that might not be explained by the conventional sleep report.
Arousal threshold
The arousal threshold is one of the endotypic traits that can be calculated from a
baseline clinical PSG and can be described as the respiratory eort associated with
waking. A low respiratory arousal threshold is a common endotype in patients with
OSA who are not obese. A low arousal threshold is one of the pathophysiological
traits associated with a favourable response to sedative drugs, and a poor response to
hypoglossal nerve stimulation therapy. Determination of the arousal threshold asks
for sophisticated and time-consuming calculations and is currently only available in
a research setting.
Corticoperipheral neuromuscular disconnection
A recent development is the quantitative assessment of corticomuscular (CM)
coupling or coherence between cortical sensorimotor areas and lower facial motor
units by analysing EEG (C3 and C4 leads) and chin EMG from PSG recordings. It was
shown that this compensatory CM coupling weakens progressively with increasing
OSA severity. This new CM coupling metric could be used in the development of new
therapeutic targets (e.g. pharmacological neuromodulation) in patients with OSA.
Measurement of autonomic parameters
Pulse transit time and pulse arrival time
The pulse transit time (PTT) is the time delay of pulse propagation between two
points in the arterial tree, and requires two pulse detection sites, which is technically
challenging during sleep. For practical reasons, the pulse arrival time (PAT) is
widely used as a surrogate of the PTT, using ECG and photoplethysmography (PPG)
waveforms from peripheral pulse oximetry. The PAT can be defined as the time
taken for the pulse wave to travel from the aortic valve to the periphery, and can be
extracted from the time delay between the ECG R-wave peak of the QRS complex
and the onset (foot) of the PPG waveform. Prior studies have shown the association
of OSA with overall increased baseline arterial stiness resulting from increased BP,
as measured by ‘static’ single PTT measurements. Recently, a more dynamic ‘PAT
change’ approach was introduced: for each significant PAT decline, the PAT response
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was defined as the area under the PAT decline waveform following respiratory events.
The OSA-specific PAT response is defined as the average of all individual areas, and
has been independently associated with markers of subclinical CVD and incident
cardiovascular events.
Peripheral arterial tonometry
Peripheral arterial tonometry (also abbreviated as PAT) is a novel measurement
of endothelial function, and has been proven to be valuable in cardiovascular risk
stratification in several populations. It determines the peripheral arterial vascular
tone by tracking pulsatile volume changes in peripheral arterial beds using a
plethysmographic method on the finger. Sympathetic activation events cause
vasoconstriction of the digital artery, which constitutes a change in peripheral arterial
tone. This will be reflected by a decrease in the amplitude of the PPG signal, as a
decreased vascular calibre causes a decreased perfusion of the peripheral tissues.
Peripheral arterial tonometry technology allows continuous monitoring of the pulse
amplitude, reflecting blood flow and enabling profiling of endothelial function.
Pulse wave analysis
Pulse wave analysis has been proposed as a useful and additive tool for improved
classification of cardiovascular risk in OSA patients. Pulse wave parameters assessed
during daytime rest, like pulse wave velocity or vascular stiness determined by
peripheral arterial tonometry, have been extensively studied to assess cardiovascular
risk. Additionally, pulse wave analysis can be performed during sleep. Pulse wave
parameters can be computed, such as the pulse wave attenuation index, the pulse
propagation time, pulse rate variability, and pulse rate acceleration index. These
parameters have been associated with functional vascular properties. Aer additional
processing, a cardiac risk index can be generated that allows categorisation into high/
low added cardiovascular risk. Pulse wave attenuation frequency, duration and slope
were independent predictors of prevalent hypertension in the HypnoLaus study.
Pulse wave amplitude drops resulting from autonomic vasoconstriction represent
another sensitive marker of autonomic activations. The analysis allows extraction of
various parameters, including relative timing, amplitude, duration, descending slope,
ascending slope and AUC. A lower amplitude indicates an increase in sympathetic
activity. The highest cardiovascular risk has been found in subjects with a low pulse
wave amplitude drop index, indicating a blunted autonomic activity and endothelial
dysfunction. These pulse wave amplitude drops can easily be detected by finger PPG,
and automatic methods have been developed for this purpose. Therefore, it can be used
for the diagnostic but also prognostic evaluation of patients with OSA. The algorithm is
freely available for download in the Open Science Framework (OSF) repository (https://
osf.io/c2eup/?view_only1/4a2890a0f06704cf1a281eee5727d8790).
Heart rate variability
The estimation of heart rate variability oers prognostic information beyond that
provided by the evaluation of traditional CVD risk factors. Heart rate variability
outcomes include integrated indices (parasympathetic function and total variability),
time domain indices (e.g. the standard deviation of NN intervals and the root mean
square of the successive dierences between normal heartbeats) and frequency
domain indices (e.g. high-frequency, low-frequency and very-low-frequency power,
and the ratio of low-frequency to high-frequency power). In a landmark study, all heart
rate variability measures except the ratio of low-frequency to high-frequency power
were significantly associated with risk for a cardiac event. During sleep, low-frequency
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power can reflect the impairment of autonomic function in OSA, and the ratio of lowfrequency to high-frequency power may play an important role in OSA diagnosis. The
short-term heart rate variability response diers based on the desaturation severity
and the respiratory event rate in patients with suspected OSA.
Pulse rate response
Apnoea-specific pulse rate response or OSA-related heart rate response (ΔHR) is
defined as the dierence between the maximum pulse rate aer airway opening
and an event-related minimum pulse rate (during respiratory events). High ΔHR
(>10.1 beats·min−1) is associated with cardiovascular events and all-cause mortality
in longitudinal follow-up of the SHHS cohort. The risk associated with a high ΔHR is
particularly high in those with a substantial hypoxic burden (≥62%·min·h−1). ΔHR may
reflect both the parasympathetic and sympathetic responses to an event. A higher
ΔHR may reflect a more pronounced vagally induced bradycardia during an event
(larger decrease in heart rate during an event) and a more pronounced sympathetic
response to hypoxaemia and hypercapnia (larger increase in heart rate). Such ΔHR
relies only on measurement of respiration and pulse oximetry, so is easy to collect
with home polygraphy (PG).
Cardiopulmonary coupling
Cardiopulmonary coupling (CPC) is a technique that generates a sleep spectrogram
by calculating the cross-spectral power and coherence of heart rate variability
and respiratory tidal volume fluctuations, allowing the dynamic tracking of
cardiopulmonary interactions. It can be easily assessed from a continuous,
single-lead ECG. CPC has shown that NREM sleep in adults is associated with
spontaneous abrupt transitions between high- and low-frequency CPC regimes,
with characteristic features in health and disease. Such an approach could be used
to gain complementary information to assess sleep stability when scoring NREM
sleep stages, while correlations of CPC-derived measures with conventional metrics
(such as AHI) have also been reported.
Respiratory eort burden: total time of sleep spent with respiratory eort
Increased respiratory eort is one of the main features of OSA and is associated
with sympathetic overactivity. Available evidence suggests that respiratory eort
contributes to increases in nocturnal BP. Recently, a new metric was introduced that
automatically incorporates increased respiratory eort derived from measurement of
mandibular jaw movements, expressed as a proportion of total sleep time (REMOV
%TST). This parameter was identified as a potential new reliable metric to predict
prevalent hypertension in patients with OSA.
Genetics
It has been suggested that genetic factors are likely to play an important role in the
individual dierences in OSA (minimally symptomatic, those with disrupted sleep and
those with EDS). It is only recently that the genetic causes of OSA and its component
endotypes are being explored. For example, recent analyses have shown higher
heritability indices with respiratory event duration and a multicomponent score
comprising six traits (AHI, event duration, two ODIs, snoring and sleepiness). The
significant heritability for event duration is of particular interest, given its correlation
with an individual’s ventilatory control sensitivity. Overall, this approach is likely to help
explain individual dierences and susceptibility to the clinical consequences of OSA.
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Biomarkers
Panels of biomarkers have the added value of identifying causal pathways aected
by OSA, such as systemic inflammation, autonomic dysfunction and oxidative stress.
Interleukin (IL)-6 and IL-10 plasma levels have the potential to be good biomarkers in
identifying or excluding the presence of OSA in adults, while kallikrein-1, uromodulin,
urocortin-3 and orosomucoid-1, when combined, have enough accuracy to be an
OSA diagnostic test in children. Along the same lines, high-sensitivity C-reactive
protein, glycated haemoglobin (HbA1c), erythropoietin, uric acid, tumour necrosis
factor-α, homocysteine and cysteine have been proposed, but none are sensitive
and specific enough to predict individual susceptibility to cardiovascular morbidity.
Exhaled volatile organic compounds may have value as biomarkers, but again the lack
of specificity is a major challenge. Using combinatorial approaches and cut-o values
for overnight changes of these biomarkers enables a more reliable prediction of OSA.
Such approaches not only yield diagnostic and monitoring opportunities but also could
encompass prognostic and predictive information. New developments in this area are
the assessment of specific microRNAs related to hypoxia, exosomes, and metabolic,
lipidomic, proteomic and gene expression profiles. Also, metabolomics (the study
of complete sets of metabolites on cells, tissues and organisms) and proteonomics
(the set of technologies applied to explore and evaluate hundreds or thousands of
proteins to discover biomarkers) have been explored in OSA and are promising. There
are obvious limitations (costs, reproducibility and significant delays for results) in the
clinical applicability of these biomarkers for supporting OSA diagnosis.
Conclusion
Novel parameters, also including genetics and biomarkers, indicate significant
dierences in severity of OSA in patients having similar AHIs, and could bring
new valuable information to support the diagnostics of the severity of OSA. These
parameters will also give direction to the concept of dierent disease subtypes with
dierent endotypes and phenotypes with dierent predominant pathophysiological
features and therapeutic targets.
Further reading
• Amatoury J, et al. (2018). New insights into the timing and potential mechanisms of
respiratory-induced cortical arousals in obstructive sleep apnea. Sleep; 41: zsy160.
• Azarbarzin A, et al. (2019). The hypoxic burden of sleep apnoea predicts cardiovascular
disease-related mortality: the Osteoporotic Fractures in Men Study and the Sleep Heart Health
Study. Eur Heart J; 40: 1149–1157.
• Azarbarzin A, et al. (2021). The sleep apnea-specific pulse-rate response predicts cardiovascular
morbidity and mortality. Am J Respir Crit Care Med; 203: 1546–1555.
• Bahr K, et al. (2021). Intensity of respiratory cortical arousals is a distinct pathophysiologic
feature and is associated with disease severity in obstructive sleep apnea patients. Brain Sci;
11: 282.
• Betta M, et al. (2020). Quantifying peripheral sympathetic activations during sleep by means
of an automatic method for pulse wave amplitude drop detection. Sleep Med; 69: 220–232.
• Blekic N, et al. (2022). Impact of desaturation patterns versus apnea–hypopnea index in the
development of cardiovascular comorbidities in obstructive sleep apnea patients. Nat Sci
Sleep; 14: 1457–1468.
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• Butler MP, et al. (2019). Apnea–hypopnea event duration predicts mortality in men and
women in the Sleep Heart Health Study. Am J Respir Crit Care Med; 199: 903–912.
• Gouveris H, et al. (2020). Corticoperipheral neuromuscular disconnection in obstructive sleep
apnoea. Brain Commun; 2: fcaa056.
• Kulkas A, et al. (2013). Novel parameters indicate significant dierences in severity of
obstructive sleep apnea with patients having similar apnoea–hypopnoea index. Med Biol Eng
Comput; 51: 697–708.
• Kwon Y, et al. (2021). Pulse arrival time, a novel sleep cardiovascular marker: the multi-ethnic
study of atherosclerosis. Thorax; 76: 1124–1130.
• Lebkuchen A, et al. (2021). Advances and challenges in pursuing biomarkers for obstructive
sleep apnea: implications for the cardiovascular risk. Trends Cardiovasc Med; 31: 242–249.
• Malantis-Ewert S, et al. (2022). A novel quantitative arousal-associated EEG-metric to predict
severity of respiratory distress in obstructive sleep apnea patients. Front Physiol; 13: 885270.
• Malhotra A, et al. (2021). Metrics of sleep apnea severity: beyond the apnea–hypopnea index.
Sleep; 44: zsab030.
• Martinot J-B, et al. (2022). Respiratory eort during sleep and prevalent hypertension in
obstructive sleep apnoea. Eur Respir J; in press [https://doi.org/10.1183/13993003.014862022].
• Muraja-Murro A, et al. (2012). Total duration of apnea and hypopnea events and average
desaturation show significant variation in patients with a similar apnea–hypopnea index.
J Med Eng Technol; 36: 393–398.
• Strassberger C, et al. (2021). Beyond the AHI – pulse wave analysis during sleep for recognition
of cardiovascular risk in sleep apnea patients. J Sleep Res; 30: e13364.
• Terrill PI (2020). A review of approaches for analysing obstructive sleep apnoea-related
patterns in pulse oximetry data. Respirology; 25: 475–485.
• Thomas RJ, et al. (2009). Prevalent hypertension and stroke in the Sleep Heart Health Study:
association with an ECG-derived spectrographic marker of cardiopulmonary coupling. Sleep;
32: 897–904.
• Tsuji H, et al. (1996). Impact of reduced heart rate variability on risk for cardiac events. The
Framingham Heart Study. Circulation; 94: 2850–2855.
• Vizzardi E, et al. (2014). Noninvasive assessment of endothelial function: the classic methods
and the new peripheral arterial tonometry. J Investig Med; 62: 856–864.
• Wang Z, et al. (2023). Heart rate variability changes in patients with obstructive sleep apnea:
a systematic review and meta-analysis. J Sleep Res; 32: e13708.
• Younes M, et al. (2015). Odds ratio product of sleep EEG as a continuous measure of sleep
state. Sleep; 38: 641–654.
31ERS Handbook: Respiratory Sleep Medicine
Evaluation of obstructive
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sleep apnoea severity
Dirk Pevernagie, Sophia E. Schiza and Winfried Randerath
OSA is a medical construct rooted in respiratory pathophysiology. The presence
of recurrent obstructive respiratory disturbances that are generally associated
with arousal from sleep is the basic finding on sleep recordings. SDB events are
called “apnoeas” when a complete collapse of the upper airway occurs, whereas
“hypopnoeas” denote only partial obstruction. The number of all these respiratory
events divided by the total sleep time is the AHI.
OSA is a sleep disorder causing symptoms of disrupted nocturnal sleep and impaired
functioning during the daytime. As a consequence of several systemic eects, including
intermittent hypoxia, subsequent sympathetic activation and sleep fragmentation,
OSA is also implicated in metabolic and cardiovascular pathophysiology. OSA may
induce organic damage in the long term and may decrease life expectancy in severely
aected patients. When OSA is associated with symptoms, signs or comorbidities, the
term OSAS is used.
Features of OSA suitable for severity grading
The AHI is currently used as an identifier for establishing a diagnosis of OSA and as a
quantifier for assessing the severity of this disorder. However, the AHI has never been
properly validated and is actually a deficient metric. Biomarkers that are better suited
for diagnostic and severity grading purposes are needed. Recently, indices of systemic
eects have been introduced as new “candidate” markers.
Besides markers derived from PSG or body fluids, disease manifestations of OSA are
also suitable for severity assessment. Suggestive symptoms of OSA are: complaints
of sleep disruption (not being able to initiate or maintain sleep); nonrestorative sleep;
snoring; fatigue; excessive sleepiness during the daytime; and cognitive impairment.
Key points
• The concept of OSA is based on respiratory pathophysiology.
• The AHI is a pathophysiological marker used for identification and
quantification of this disorder.
• The validity of the AHI has been questioned due to poor correlation with
clinical outcomes.
• New biomarkers for better prediction of OSA severity are being developed.
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OSA may also aect mood, safety and social functioning. Furthermore, OSA may
compromise metabolic and cardiovascular health. These characteristics may qualify
as relevant variables suitable for severity grading of OSA. However, many of these
clinical features lack specificity because they could also be caused by other conditions.
Uncertainty about causal relationships between pathophysiological variables and
disease manifestations is a limitation of the current OSA disease model.
Subjective manifestations of OSA are patient-reported outcomes, which are assessed
using questionnaires. When employed properly, patient-reported outcome measures
(PROMs) may reliably represent disease severity and can be used to assess therapeutic
outcome. Generic sleep questionnaires can be applied to gauge severity of OSArelated symptoms. The Pittsburgh Sleep Quality Index (PSQI) and the ESS are the
best known and most commonly used assessment tools. Furthermore, specific OSA
questionnaires have been made available to rate disease severity and to evaluate
improvement under treatment. The Patient-Reported Apnea Questionnaire (PRAQ) is
a recently published, well-validated instrument.
Meanwhile, models for integrating pathophysiological markers and disease
manifestations have been proposed. These models may contribute to a more holistic,
integrative approach to OSA. Again, resolving the specificity issues is mandatory to
assure the validity of these new constructs.
Assessment of OSA severity derived from sleep study parameters
The AHI
Based on the observation that the number of apnoeas and hypopnoeas per hour of
sleep is elevated in patients with symptomatic OSA, the AHI has been used as the
primary predictor of the disorder. By expert consensus, the following parameters were
proposed for severity determination (American Academy of Sleep Medicine, 1999):
• AHI <5 events·h−1: no OSA
• AHI ≥5 events·h−1: mild OSA
• AHI ≥15 events·h−1: moderate OSA
• AHI ≥30 events·h−1: severe OSA
The application of these severity grades has become ubiquitous in research, clinical
practice and administrative regulations. Currently, many ocial bodies use the AHI to
judge medical fitness and the patient’s ability to drive, as an indication for treatment
and to determine the reimbursement of healthcare costs. Oen, a distinction is
made between mild OSA (AHI ≥5 events·h−1) and moderate-to-severe OSA (AHI
≥15 events·h−1). In many countries, categorisations as the latter is mandatory for
reimbursement of treatment with PAP devices or mandibular advancement devices.
Both categories also appear in the ICSD, published by the American Academy of
Sleep Medicine (AASM). According to this classification, a diagnosis of OSA can
be established in the mild category when concurrent signs and symptoms are
present, and also in the moderate-to-severe category, even in the absence of clinical
manifestations.
However, the AHI fails as a metric and as a measure. Depending on the criteria and
methods used, computation of the AHI can yield dierences of up to two to three
factors. Its numerical value cannot be easily reproduced when a given quantity of
respiratory disturbances is measured using dierent methods of assessment, all of
which have their own grading system. There is also a poor correlation between AHI and
the relevant markers of disease, including subjective sleepiness, objective sleepiness,
33ERS Handbook: Respiratory Sleep Medicine
Соседние файлы в папке Библиотека им академика М.И. Перельмана