Добавил:
Sekretar
kiopkiopkiop18@yandex.ru
t.me/Prokururor I Вовсе не секретарь, но почту проверяю
Опубликованный материал нарушает ваши авторские права? Сообщите нам.
Вуз:
Предмет:
Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_4566_Библиотеки_им_академика_М_И_Перельмана
.pdf
Assessment of EDS
https://t.me/medicina_free
Table 3. Medication and other substances that may interfere with sleep architecture
Acetylcholine modulators
Adenosine modulators
Alpha-2-delta ligands
Antidepressants
Antihistamines, sedating
Antipsychotic agents
Antihypertensives
Steroids
Wake-promoting agents
Benzodiazepines/NBRAs
Dopamine agonists
Lithium
Melatonin agonists
Opioid agonists
Orexyn/hyocretin antagonists
Sodium oxybate
Stimulants
Marijuana
NBRAs: non-benzodiazepine receptor agonists. Reproduced and modified from Krahn et al. (2021)
with permission.
The MWT is used to evaluate the subject’s response to treatment of or interventions
for conditions (not necessarily diseases) that are associated with excessive sleepiness.
It is also used to assess the level of alertness in individuals who must remain awake
for safety reasons.
As with the MSLT, rigorous protocols should be applied before and during the execution
of the MWT (table 2). Adequate sleep should be documented using a sleep log and/or
actigraphy for 2 weeks before testing. The test should be performed when the subject
is clinically stable and when planned treatments or interventions are well-established
and eective. In patients with SDB, the ecacy of and adherence to PAP therapy should
be assessed before testing; where this is suboptimal, treatment should be adjusted and
the MWT should be rescheduled. Considerations regarding comorbid sleep disorders as
well as concomitant medication are similar to that previously described for MSLT.
The need to perform PSG on the night before the MWT is controversial and is usually
le to the discretion of the clinician. However, there are a number of circumstances in
which performing PSG before the MWT has been suggested to be useful, as reported
in table 4.
The definition of normal values in the MWT is even more controversial than in the
MSLT. The literature suggests that the mean± sleep latency for normal controls is
Table 4. Circumstances in which performing PSG before the MWT is useful
Misperception of daytime sleepiness
High motivation to stay awake during the day
High motivation to demonstrate daytime sleepiness (i.e. prescription of stimulating
drugs or refusal to perform specific work activities)
Suspicion of sleep deprivation
Contemporary assessment of the ecacy of therapy and daytime sleepiness
Correlation of sleep eciency and sleep latency at MWT
Circadian evaluation of sleep latency at each MWT
144
ERS Handbook: Respiratory Sleep Medicine
Assessment of EDS
https://t.me/medicina_free
30.4±11.2 min. As each trial is stopped at 40 min there is an obvious ceiling eect.
According to the AASM, a MSL of <8 min is the cut-o for pathological sleepiness,
compared with ≥30 min for normal alertness. A MSL of 8–30 min is therefore
considered a ‘grey zone’. Dogramji et al. (1997) proposed alternative normative values
for the definition of sleep latency:
• When sleep latency is calculated as the first period of any sleep stage: short
latency, MSL<12.8 min; borderline latency, MSL 12.8–32.6 min; normal latency,
MSL 32.6–40 min.
• When sleep latency is calculated as at least three consecutive periods of sleep
stage 1 or a single period of another sleep stage: short latency, MSL <19.4 min;
normal latency, MSL >35.2 min.
In addition to sleep latency, which is the main result of the MWT, very short episodes of
sleep (microsleeps) may occur during the test, which could be more sensitive markers of
sleepiness as they reflect drowsiness, i.e. the intermediate state between wakefulness
and established sleep. Drowsiness preceding sleep is associated with a lack of control
and maybe a crucial determinant of car accidents. The latency of the first microsleep
during the MWT is shorter than sleep latency, and in some patients, there is a large
dierence between the latencies. It is worth noting that many subjects experience a
number of microsleep episodes before the onset of sleep, particularly those with shorter
sleep latencies. In contrast, isolated episodes of microsleep may occur in subjects who
do not reach sleep during the trial. It has also been shown that normal subjects may
display episodes of microsleep during extended monotonous tasks or aer partial sleep
deprivation, with the prevalence of microsleep estimated to be 53–80%.
The OSLER
The OSLER has been proposed as an alternative to the MWT in order to overcome
some of the diculties encountered during the latter’s use: the need for EEG
monitoring, the necessary attendance of a technician during the test, the length of
time needed for analysis, the requirement of a standard sleep room, etc. The OSLER
makes it possible to use non-assisted portable techniques to measure the subject’s
ability to maintain wakefulness.
As reported in table 2, the test consists of four trials, each lasting 40 min, with a similar
procedure to that of the MWT. The occurrence of sleep is assessed behaviourally rather
than via EEG monitoring. The standard way to analyse the OSLER is to determine the
sleep latency, calculated as the time from the start of the test and the appearance of
seven consecutive flashes without response. Research demonstrated that OSLER is
a sensitive test for identifying sleepiness in OSA patients and fluctuation in vigilance
throughout the day, but normative data are not available.
Further reading
• Bennett LS, et al. (1997). A behavioural test to assess daytime sleepiness in obstructive sleep
apnoea. J Sleep Res; 6: 142–145.
• Doghramji K, et al. (1997). A normative study of the maintenance of wakefulness test (MWT).
Electroencephal Clin Neurophysiol; 103: 554–562.
• Garbarino S, et al., eds (2014). Sleepiness and Human Impact Assessment. Milan, Springer.
• Harrison Y, et al. (1996). Occurrence of ‘microsleeps’ during daytime sleep onset in normal
subjects. Electroencephalogr Clin Neurophysiol; 98: 411–416.
145ERS Handbook: Respiratory Sleep Medicine
Assessment of EDS
https://t.me/medicina_free
• Hertig-Godeschalk A, et al. (2020). Microsleep episodes in the borderland between wakefulness
and sleep. Sleep; 43: zsz163.
• Krahn LE, et al. (2021). Recommended protocols for the multiple sleep latency test and
maintenance of wakefulness test in adults: guidance from the American Academy of Sleep
Medicine. J Clin Sleep Med; 17: 2489–2498.
• Littner MR, et al. (2005). Practice parameters for clinical use of the multiple sleep latency test
and the maintenance of wakefulness test. Sleep; 28: 113–121.
• Mazza S, et al. (2002). Analysis of error profiles occurring during the OSLER test. Am J Respir
Crit Care Med; 166: 474–478.
• Morrone E, et al. (2020). Microsleep as a marker of sleepiness in obstructive sleep apnoea
patients. J Sleep Res; 29: e12882.
• Peiris MTR, et al. (2006). Frequent lapses of responsiveness during an extended visuomotor
tracking task in non-sleep deprived subjects. J Sleep Res; 15: 291–300.
• Philip P, et al. (2008). Maintenance of wakefulness test, obstructive sleep apnea syndrome and
driving risk. Ann Neurol; 64: 410–416.
• Pizza F, et al. (2009). Daytime sleepiness and driving performance in patients with obstructive
sleep apnea: comparison on the MSLT, the MWT, and a simulated driving task. Sleep; 32: 382–391.
• Priest B, et al. (2001). Microsleep during a simplified maintenance of wakefulness test.
A validation study of the OSLER test. Am J Respir Crit Care Med; 163: 1619–1625.
• Sagaspe P, et al. (2007). Maintenance of wakefulness test as a predictor of driving performance
in patients with untreated obstructive sleep apnea. Sleep; 30: 327–330.
• Sullivan SS, et al. (2008). Multiple sleep latency test and maintenance of wakefulness test.
Chest; 134: 854–861.
146
ERS Handbook: Respiratory Sleep Medicine
Diagnostic algorithms
https://t.me/medicina_free
based on an individualised
patient approach
Sophia E. Schiza, Winfried Randerath and Özen K. Basoglu
OSA is a highly prevalent disorder that aects millions of adults worldwide. Despite its
high prevalence, many cases remain undiagnosed and untreated, impairing patient
quality of life and increasing the risk of adverse events, morbidity and mortality. This
risk can be reduced with appropriate management of the disorder. Early diagnosis and
eective treatment of OSA is therefore an urgent health priority.
The gold standard diagnostic test for OSA relies on in-lab all-night PSG, which is
expensive, time-consuming and labour-intensive. As most sleep laboratories face
long waiting lists, further delays in diagnosing OSA increase disease burden. Given
these shortcomings, it would be useful to develop diagnostic algorithms based on
an individualised patient approach for patients with suspected sleep apnoea, which
could reliably determine a priori probability before PSG is considered, and could
prioritise patients who required PSG in accordance with the probability of a positive
result. The American Academy of Sleep Medicine (AASM) supported this idea in its
latest guidelines. The guidelines recommend that clinical tools, questionnaires and
prediction algorithms can be more helpful in identifying patients with an increased
OSA risk, rather than using them to diagnose OSA in the absence of PSG.
Dierent approaches have been used in order to predict whether an individual
has OSA. As several clinical risk factors are associated with OSA, such as male sex,
Key points
• OSA diagnosis is based on comprehensive sleep evaluation using screening
questionnaires or clinical prediction models and appropriate diagnostic
testing.
• Diagnostic algorithms that are based on an individualised patient approach
may establish a priori probability before considering PSG, and prioritise
patients in need of PSG.
• Portable monitoring devices exhibit good diagnostic performance in adult
patients with a high pre-test probability of moderate-to-severe OSA.
• PSG is recommended for diagnosis of OSA in adult patients suspected of
having comorbid sleep disorders or sleep disorders other than OSA.
• PSG is recommended in patients with significant medical conditions.
147ERS Handbook: Respiratory Sleep Medicine
Diagnostic algorithms
https://t.me/medicina_free
older age, obesity and neck circumference, it is possible to predict the condition by
taking only these simple and readily available predictors into consideration. High
levels of sensitivity or specificity can also be achieved, depending on whether a ruleout or rule-in approach is chosen.
Additional clinical parameters (such as laboratory blood tests, physical measurements,
history of comorbidities, self-reported symptoms and other routinely available medical
information) may also be useful when prioritising patient referral in further sleep
studies. Several recent studies reported that OSA can occur in association with a variety
of comorbidities, some of which represent risk factors for OSA, such as CHF, arterial
hypertension, atrial fibrillation (AF), stroke, diabetes, pulmonary hypertension (PH) and
chronic lung diseases, including COPD, asthma and idiopathic pulmonary fibrosis (IPF).
OSA should be also suspected in patients who experience EDS, snoring, witnessed
apnoeas, waking with choking and nonrestorative sleep, particularly in the presence
of risk factors such as obesity, male sex and advanced age. However, patients with a
high clinical suspicion of OSA and significant comorbidities may be asymptomatic or
have sleep disorders other than OSA, such as comorbid insomnia.
Taking into consideration all of the above in today’s rapidly digitising world, machine
learning could be used for extracting available patient data from electronic health
records or real-time physiological data from connected wearable devices, in order to
develop powerful diagnostic frameworks.
In order to develop a more personalised approach to patient evaluation and to identify
the pre-test probability of OSA, cluster analysis (a statistical method for investigating
the relationship between groups of patients or variables) could also be used to
determine whether there are dierent subgroups of patients with distinctive clinical
presentations, or phenotypes. Dierent clusters created from predictive variables
express dierent disease risks, consequently allowing the definition of phenotypes
with a high risk for OSA.
Clinical phenotypes of OSA have been defined based on the clinical characteristics
of the disease, by combining anthropometrics, sleep data and comorbidities, such
as patients with EDS and/or insomnia, minimally symptomatic patients, those
with few or no comorbidities, elderly patients, female patients, supine- and/or
REM-predominant patients, etc. However, these clinical phenotypes may present
with atypical clinical features, leading to misdiagnosis of another sleep disorder.
Furthermore, although OSA phenotypes may be considered as a reflection of the
disease’s early manifestations and natural history, OSA is not stable and evolves over
time, necessitating additional research into evaluating phenotyping fluctuations and
determining their long-term diagnostic implications. Therefore, a comprehensive
sleep evaluation along with dierent screening questionnaires should be always
be performed for the identification of patients with a likelihood of SDB. Several
questionnaires are used to assess sleepiness (e.g. the ESS), sleep quality (e.g. the
Pittsburgh Sleep Quality Index) and the pre-test probability of OSA (e.g. the STOP,
STOP-Bang and Berlin questionnaires). The STOP-Bang Questionnaire has the highest
sensitivity for detecting OSA but appears to lack specificity.
Aer screening with appropriate questionnaires or clinical prediction models for the
probability of OSA, diagnostic sleep testing is imperative in adult patients suspected
to having SDB.
It is worth noting that the optimal diagnostic approach in patients with suspected CSA
is less well-established, as the current methods of identifying sleep apnoea based on
148
ERS Handbook: Respiratory Sleep Medicine
Diagnostic algorithms
https://t.me/medicina_free
patient history and clinical risk factors fail in these patients. Although male sex, age,
HF and chronic opioid use are established risk factors for CSA, there are no clinical
symptoms, physical examination findings or serological abnormalities that are specific
to CSA. In addition, screening questionnaires for OSA have not been validated in CSA
patients. The heterogeneity of CSA mandates an individualised diagnostic approach,
and patients with atypical symptoms along with risk factors for CSA should undergo
an attended in-laboratory PSG.
According to the AASM guidelines, PSG is the gold standard diagnostic test for
the diagnosis of OSA in adult patients with suspicion of sleep apnoea based on a
comprehensive sleep evaluation. PSG should also be performed for the diagnosis of
OSA in patients with negative or technically inadequate portable monitoring (PM)
results. The AASM guidelines further assert PSG to be used for the diagnosis of OSA
in patients with significant cardiorespiratory disease (e.g. CHF and COPD), potential
respiratory muscle weakness due to neuromuscular conditions, awake hypoventilation
or suspicion of sleep-related hypoventilation, chronic opioid medication use, history
of stroke or a suspected sleep disorder other than OSA (e.g. CSA, severe insomnia,
parasomnia, narcolepsy). However, this process requires an overnight stay, a sleep
technician and manual scoring, and elderly patients may encounter accessibility
diculties. Thus, less expensive, portable diagnostic tools may be considered. In
selected adult patients with suspicion of OSA, portable devices (also referred to as
home sleep apnoea testing (HSAT), out-of-centre sleep testing (OCST) or PM) can be
used as an alternative to in-laboratory PSG for the diagnosis of sleep apnoea.
In uncomplicated patients who present with signs and symptoms that indicate an
increased risk of moderate-to-severe OSA, PSG or polygraphy (PG) can be used for
OSA diagnosis. Increased risk is defined as the presence of EDS and at least two of
the following clinical features of OSA: habitual loud snoring; witnessed apnoeas,
gasping or choking; and diagnosed arterial hypertension. Patients who work in a
mission-critical job in which falling asleep would have a major negative impact on
the patient and others (e.g. healthcare workers, pilots, bus/taxi/truck/train drivers,
soldiers, police ocers) should be tested using attended in-laboratory PSG so that
OSA diagnosis is not missed.
Selection of the appropriate diagnostic test (PSG or PG) in patients with mild OSA is a
controversial issue. Mild OSA is highly prevalent in the general population, and most
patients are asymptomatic or minimally symptomatic; however, its clinical significance
remains uncertain. For most patients with suspected mild OSA, in-laboratory PSG
is the diagnostic test of choice, as portable diagnostic techniques measure total
recording time rather than sleep time and do not reliably identify arousals, thereby
potentially underestimating AHI.
In order to improve sleep apnoea diagnosis and treatment, patients should initially
be screened using comprehensive sleep history-taking, sleep questionnaires and
ambulatory monitoring devices with oximetry and/or airflow (type IV devices).
The goal here is to develop clinical prediction models. Patients with a high pretest probability of OSA can then be identified and referred to a sleep centre for PG.
Patients with low pre-test probability of OSA along with minimal or no symptoms
and a high risk of CVD could also be referred for PG. In this two-step approach,
if the suspicion of OSA remains and PM is negative or technically inadequate, inlaboratory PSG can be performed (figure 1). PG should not be used when sleep
disorders other than OSA, such as CSA or severe insomnia, are suspected; PG use is
also inappropriate for screening patients with significant comorbidities or missioncritical workers.
149ERS Handbook: Respiratory Sleep Medicine
Diagnostic algorithms
https://t.me/medicina_free
Negative PSG
PG
High pre-test probability of OSA
(no suspicion of hypoventilation)
Negative PSG
PG
plus
High risk of CVD comorbidities
Low pre-test probability of OSA
PSG
#
other sleep disorders
Mission-critical worker
Significant comorbidities
Suspicion of comorbidities or
: significant comorbidities are defined as significant cardiorespiratory disease, potential
#
150
of OSA
Clinical suspicion
Figure 1. Diagnostic algorithm for use in patients with suspected OSA.
respiratory muscle weakness due to a neuromuscular condition, hypoventilation when awake or suspicion of sleep-related hypoventilation, chronic opioid medication
use, history of stroke and severe insomnia.
ERS Handbook: Respiratory Sleep Medicine
Diagnostic algorithms
https://t.me/medicina_free
To develop a full picture of portable assessment, further studies are needed to
clarify its use in patients with a low pre-test probability of OSA, as well as in specific
populations, such as dierent ethnic groups, children, elderly patients and female
patients.
Further reading
• Abrahamyan L, et al. (2018). Diagnostic accuracy of level IV portable sleep monitors versus
polysomnography for obstructive sleep apnea: a systematic review and meta-analysis. Sleep
Breath; 22: 593–611.
• Bernhardt L, et al. (2022). Diagnostic accuracy of screening questionnaires for obstructive
sleep apnoea in adults in dierent clinical cohorts: a systematic review and meta-analysis.
Sleep Breath; 26: 1053–1078.
• Collop NA, et al. (2007). Clinical guidelines for the use of unattended portable monitors in the
diagnosis of obstructive sleep apnea in adult patients. Portable Monitoring Task Force of the
American Academy of Sleep Medicine. J Clin Sleep Med; 3: 737–747.
• Cooksey JA, et al. (2016). Portable monitoring for the diagnosis of OSA. Chest; 149: 1074–1081.
• Corral-Peñafiel J, et al. (2013). Ambulatory monitoring in the diagnosis and management of
obstructive sleep apnoea syndrome. Eur Respir Rev; 22: 312–324.
• El Shayeb M, et al. (2014). Diagnostic accuracy of level 3 portable sleep tests versus level 1
polysomnography for sleep-disordered breathing: a systematic review and meta-analysis.
CMAJ; 186: E25–E51.
• Epstein LJ, et al. (2009). Clinical guideline for the evaluation, management and long-term care
of obstructive sleep apnea in adults. J Clin Sleep Med; 5: 263–276.
• 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.
• Kushida CA, et al. (2005). Practice parameters for the indications for polysomnography and
related procedures: an update for 2005. Sleep; 28: 499–521.
• Malhotra A, et al. (2018). Research priorities in pathophysiology for sleep-disordered breathing
in patients with chronic obstructive pulmonary disease. An ocial American Thoracic Society
Research Statement. Am J Respir Crit Care Med; 197: 289–299.
• McNicholas WT, et al. (2016). Mild obstructive sleep apnoea: clinical relevance and approaches
to management. Lancet Respir Med; 4: 826–834.
• Rosen IM, et al. (2018). Clinical use of a home sleep apnea test: an updated American Academy
of Sleep Medicine Position Statement. J Clin Sleep Med; 14: 2075–2077.
151ERS Handbook: Respiratory Sleep Medicine
Screening with limited
https://t.me/medicina_free
sleep tests to increase
pre-test probability
Sophia E. Schiza, Winfried Randerath and Marta Drummond
The vast heterogeneity in the clinical presentation, predisposing factors,
pathophysiological mechanisms and consequences of sleep apnoea (OSA and CSA)
requires a diagnostic approach that is personalised to each disease and takes into
account the pre-test probability of each disease. A two-step process in which patients
are first screened with a comprehensive sleep history and sleep questionnaires, and
are then assessed with ambulatory monitoring devices, could increase the pre-test
probability of OSA and facilitate the referral of high-risk patients to a sleep centre for
diagnostic study (figure 1).
However, pre-test probability itself is challenging to determine. For that reason, several
scientific societies have accepted the complementary role that simplified sleep study
methods play in improving the estimation of pre-test probability. Devices have been
developed that can be used as a screening tool for OSA or as an aid for OSA diagnosis,
increasing the pre-test probability of moderate-to-severe OSA. These tools include
type III and type IV devices. Type III devices record at least four cardiorespiratory
signals (which include airflow, chest movement, pulse oximetry, and heart rate or
electrocardiogram), without using electrophysiological signals to measure sleep. Type
IV devices use only one or two recording channels, one of which is generally oximetry.
The use of type III devices for OSA screening has increased greatly due to their lower
cost, lower technical complexity and greater convenience when compared with PSG.
When combined with appropriate clinical pre-screening tools (e.g. the STOP-Bang
Questionnaire) they can be used as a screening or ‘disease-finding’ tool for OSA
diagnosis in both uncomplicated patients and in patients with CHF, hypertension and
Key points
• Limited sleep tests have been developed that can be used both as a screening
tool and as a diagnostic aid for OSA, increasing the pre-test probability of
moderate-to-severe OSA.
• Their application as a screening tool should also be considered in populations
where OSA is highly underdiagnosed and in patients with significant
comorbidities.
• Patients and recordings must be reviewed in an accredited sleep centre under
the supervision of a board-certified sleep medicine physician.
152
ERS Handbook: Respiratory Sleep Medicine
Clinical suspicion
https://t.me/medicina_free
of OSA
Screening with limited sleep tests
High pre-test
probability of OSA
Refer for diagnosis
Low pre-test probability of OSA
High clinical suspicion of OSA
plus
Two-stage screening
Type III/IV study
High risk of OSA
Low pre-test probability of OSA
plus
CVD comorbidities
(e.g. CAD, AF, stroke)
Low risk of OSA
Figure 1. Two-step approach for patients with suspected OSA. CAD: coronary artery disease;
AF: atrial fibrillation.
stroke. Concerns have been raised about the limitations of these devices: technical
failures have been encountered (dislodged sensors, poor-quality signals due to a
lack of real-time signal visualisation); they do not measure sleep variables; and they
are unable to detect hypopneas associated with arousals. Moreover, there are no
universally defined technical standards in place for these devices. Nevertheless, they
are convenient and accessible when used as a screening tool. It is important to note
that patients should be reviewed by medical practitioners who are certified in sleep
disorders, so that the complexities of the clinical assessment of OSA can be fully
explored and comorbid conditions can be identified.
Type IV monitoring devices can also be used for screening purposes, allowing a more
simplified home testing than PSG, at a lower cost. Type IV devices typically record one
to two variables and provide limited information. The most commonly measured
physiological variables are pulse oximetry and airflow. Consequently, extracted
information typically includes: frequency of apnoeas; frequency of hypopnoeas; baseline
oxygen saturation (S
duration of oxyhaemoglobin desaturation; degree of oxyhaemoglobin desaturation;
and nadirs of S
of both PSG and type III study, can be a useful adjunct to patient history and physical
pO
2
); mean S
pO
2
; frequency of oxyhaemoglobin desaturation;
pO
2
. In selected cases, pulse oximetry, which is an important component
examination. Furthermore, novel technologies, such as mandibular movement
monitoring with machine learning analysis, show promise as valid surrogates for
sleep staging and respiratory pattern evaluation, maximising the screening ability of
type IV devices. In settings and populations where demand is high and the capacity
of PSGs is limited, type IV devices can prioritise patients and increase early OSA
identification and prompt management. It is also worth noting that type IV devices
could be also used in populations where OSA is highly underdiagnosed, including
patients with significant comorbidities, ensuring that patients and recordings are
reviewed in an accredited sleep centre under the supervision of a board-certified
sleep medicine physician.
153ERS Handbook: Respiratory Sleep Medicine
Соседние файлы в папке Библиотека им академика М.И. Перельмана