Keywords oximetry - actigraphy - sleep apnea - obstructive - Treacher Collins syndrome - Pierre
Robin syndrome
Introduction
Obstructive sleep apnea (OSA) is characterized by recurrent events of partial or complete
upper airway obstruction, typically accompanied by oxyhemoglobin desaturation and
arousal, which can significantly impact child development.[1 ]
[2 ] In the general pediatric population, the prevalence of OSA ranges from 2 to 5%,
and it is more common in specific clinical conditions, such as craniofacial anomalies.[2 ]
In this context, the presence of cleft palate (CP) in isolation already determines
a three-fold higher risk of OSA compared to the general pediatric population. This
risk is further elevated in the presence of syndromes and anomalies associated with
CP, including Treacher Collins Syndrome (TCS) and Robin sequence (RS).[3 ]
[4 ] A rare congenital condition, TCS presents an approximate prevalence of 1 in every
50 thousand live births.[5 ] Its characteristics include mandibular hypoplasia and/or retrognathia, orbital dimorphism,
zygomatic hypoplasia (with or without CP), auricular and pharyngeal hypoplasia, among
other alterations.[5 ] Conversely, RS is a heterogeneous congenital craniofacial anomaly characterized
by micro/retrognathia, glossoptosis, and, in most cases, a U-shaped CP.[6 ]
[7 ]
[8 ]
[9 ] It has a multifactorial etiology, with prevalence ranging from 1.2 to 40.4 per 100
thousand live births, leading to severe feeding difficulties and respiratory complications,
which are associated in up to 60% of the cases with syndromic conditions and/or other
congenital anomalies, which, in turn, increases the complexity of the clinical management[6 ]
[8 ]
[9 ]
[10 ]
The association between craniofacial anomalies and elevated OSA risk underscores the
need for an accurate diagnosis. While polysomnography (PSG) combined with clinical
evaluation remains the gold standard to assess OSA in the pediatric population,[1 ]
[11 ]
[12 ] its high cost and logistical challenges often limit its implementation. Therefore,
portable monitoring (PM) has emerged as a promising objective OSA screening instrument,
especially because it is low-cost, easy to use, it enables greater mobility, eliminating
the bias related to maintaining a forced dorsal position, and it facilitates the performance
of serial examinations.[13 ]
[14 ]
[15 ]
High-resolution oximeter plus actigraphy combined with a cloud-based algorithm (Biologix
Sleep Test, Biologix Sistemas S.A., São Paulo, SP, Brazil) is a new PM device that
has been validated for OSA diagnosis in adults when compared to PSG and traditional
PM used at home.[14 ]
[16 ] However, there is lack of evidence regarding its applicability and feasibility in
the pediatric population.[13 ] Therefore, the objective of the present study was to verify the feasibility of performing
the Biologix Sleep Test in children with craniofacial anomalies and to identify the
frequency of OSA in the study sample.
Materials and methods
Study Design
The present is a prospective, cross-sectional study that recruited school-aged children
of both sexes from the Outpatient Clinic of (Hospital for Rehabilitation of Craniofacial
Anomalies, Universidade de São Paulo). The study included children previously submitted
to primary surgical palate repair with a genetically confirmed diagnosis of TCS, non-syndromic
RS (NSRS), or non-syndromic CP (NSCP). Patients with other syndromes and associated
anomalies/malformations, those previously submitted to orthognathic maxillary advancement
surgery, subjects presenting tracheostomy at the time of evaluation, history of mandibular
distraction osteogenesis and neuromuscular disorders, difficulty understanding the
research instruments, and those in chronic use of medications, including respiratory
system depressors, and/or in use of antibiotic therapy for upper-airway infection
in the previous 3 months were excluded.
The present study was approved by the institutional Ethics Committee (report 5.880.145,
CAAE:51879521.3.0000.5441 and report 5.144.944, CAAE; 52373721.0.0000.5441). All procedures
were conducted in full compliance with the Declaration of Helsinki and its subsequent
amendments or comparable ethical standards. The legal guardians and the participants
signed an informed consent form and an assent form authorizing the collection of clinical
data, images of examinations, and reports used for scientific purposes.
Clinical Assessment
Sociodemographic data (sex and age) and surgical history were assessed through the
application of a structured questionnaire. Anthropometric data were verified, and
the body mass index (BMI) was calculated and corrected for age and sex using the World
Health Organization's WHO AnthroPlus software (free) as reference, scoring the participants
according to their nutritional status using the Z-score.[17 ]
Sleep Study
The patients underwent the Biologix Sleep Test, which consists of a high-resolution
oximeter (Oxistar, Biologix Sistemas S.A.) with a built-in accelerometer, connected
via Bluetooth to a smartphone application (app) that records snoring. The Oxistar
firmware acquires 100 samples per second, generating beat-to-beat raw data of oxygen
saturation (SpO2 ) with a resolution of 0.1%. A moving average of four cardiac beats is applied. All
collected data are transferred via the smartphone app to the cloud and automatically
analyzed by a proprietary algorithm.[14 ]
[16 ]
Following the process, the oxygen desaturation index (ODI) is calculated with the
number of dessaturations (defined as a reduction > 3% in SpO2 ) per hour of valid recording time. The ODI was used for the OSA diagnosis, and values
from 1 to 5 were indicative of mild OSA, from 5 up to 10, of moderate OSA, and above
10, severe OSA.[1 ]
[18 ] Other variables provided by the Biologix Sleep Test, including sleep ODI, hypoxic
burden, estimated sleep efficiency, SpO2 < 90% and snoring time (%), were also evaluated as secondary outcomes. A minimum of
6 hours of recording was considered valid for analysis.
Furthermore, we analyzed the Oxistar signal quality in measuring SpO2 in children. This involved removing signal segments considered invalid due to movement
artifacts, poor sensor positioning, or very low perfusion index. After the cleaning
of the signal, only valid segments were retained to calculate the ODI and other variables.
Considering that the present study is focused on a pediatric population, the processed
data was compared to the adult population from the Biologix database.
The objective of this comparison was to verify if the proportion of valid recording
time was equivalent between adults and children. This comparison enabled the assessment
of the accuracy and reliability of SpO2 measurements in a population for which the Biologix Sleep Test has not yet been validated.
Statistical Analysis
The sample size calculation was performed considering the prevalence of 26.5% of sleep-disordered
breathing (SDB) symptoms assessed by a study[19 ] that applied the Brazilian version of the Sleep Disturbance Scale for Children (SDSC);
and the prevalence of OSA (22%) was assessed in another study[20 ] by PSG in children and adolescents with RS aged 1 to 18 years. Since the total population
of children and adolescents with RS in the study was of 250 individuals under active
treatment during the study period, the sample size calculation resulted in 52 participants
with NSRS and 52 children with NSCP, considering the expected prevalence of 22% of
OSA, adopting an error margin of 10% and a test power of 80%. Regarding the subgroup
with TCS, the formal sample calculation was performed considering an alpha error of
5%, a beta error of 20%, a minimum difference to be detected in SpO2 levels of 2%, and a standard deviation (SD) of ± 2.527, obtaining a minimum of 14
individuals to compose the sample.
Data were analyzed by descriptive analysis and expressed as absolute frequencies (n)
and mean and SD, median, minimum and maximum values, and quartiles (25% and 75%).
The variables studied in the three groups (NSCP, NSRS, and TCS) were compared using
the Kruskal-Wallis's test, which was also used to analyze the degrees of ODI, the
hypoxic burden, the snoring time, ODI sleep, estimated sleep efficiency, and time
of SpO2 < 90%, considering all study participants for the variables of interest. Multiple
linear regression analysis was applied to the same variables of the patients in the
three groups. Statistical analyses were performed on the Jamovi software (free and
open source), version 2.2.
Results
A total of 176 children were approached according to the primary diagnosis of TCS,
NSRS and NSCP, and the final sample consisted of 64 children ([Fig. 1 ]). The main cause of exclusion was lack of return after the initial approach, followed
by refusal to undergo the sleep study. There were 16 children with TCS (25%), 29 children
with NSRS (45%), and 19 children with NSCP (30%). In general, a lower mean age was
observed among children with NSRS (8.72 ± 2.12 years), with significant differences
in the sample regarding mean age and BMI Z-score in relation to the TCS group. Regarding
the BMI Z-score, a prevalence of eutrophic and thin profile was observed, ruling out
obesity and overweight bias in the population as influencing the changes in ODI present
in the study. The anthropometric characteristics of the population studied are presented
in [Table 1 ].
Fig. 1 Flowchart of patient recruitment and inclusion. Abbreviations: TCS, Treacher Collins syndrome; NSCP, non-syndromic cleft palate; NSRS, non-syndromic
Robin sequence.
Table 1
Baseline characteristics of the study sample.
Variables
All patients
(N = 64)
NSCP group
(N = 19)
NSRS group
(N = 29)
TCS group
(N = 16)
p -value
Age (years)
0.005*
Mean ± SD
9 ± 2.74
9 ± 2.22
8 ± 2.12
12 ± 3.14
Min–Max
6–12
6–12
6–12
7–12
Sex
0,735χ2
Female: n (%)
37 (57.81)
11 (57.89)
18 (62.07)
8 (50.00)
BMI Z-score
0.006*
Mean ± SD
-0.24 ± 1.18
0.11 ± 0.61
0.07 ± 0.73
-1.23 ± 1.75
Min–Max
-3.2-2.8
-0.7-1.8
-0.7-2.5
-3.2-2.8
ODI (events/hour)
0.408
Mean ± SD
4.39 ± 3.12
4.14 ± 3.90
4.22 ± 2.59
4.98 ± 3.10
Min–Max
0.4–17.4
0.6–17.4
0.4–10.4
0.8–12.4
Sleep ODI (events/hour)
0.492
Mean ± SD
3.01 ± 2.56
2.86 ± 3.06
2.92 ± 2.48
3.36 ± 2.18
Min–Max
0–13.6
0–13.6
0.1–10.6
0.8–7.6
Hypoxic burden (%, minutes/hour)
0.106
Mean ± SD
21.28 ± 14.40
18.92 ± 16.82
19.44 ± 12.41
27.44 ± 13.79
Min–Max
0.8–74.6
3.3–74.6
0.8–56.6
9.6–52.3
SpO2 : minimum (%)
0.723
Mean ± SD
89.09 ± 3.20
88.79 ± 3.69
89.45 ± 2.90
88.81 ± 3.23
Min–Max
79–95
79–95
82–93
81–93
SpO2 : mean (%)
0.028*
Mean ± SD
96.88 ± 1.28
97.21 ± 1.23
97.14 ± 0.99
96.00 ± 1.46
Min–Max
93–99
95–99
95–98
93–98
SpO2 : maximum (%)
0.879
Mean ± SD
99.64 ± 0.84
99.74 ± 0.65
99.69 ± 0.71
99.44 ± 1.21
Min–Max
96–100
98–100
97–100
96–100
HR: minimum (bpm)
0.426
Mean ± SD
54.95 ± 11.75
52.68 ± 7.02
55.31 ± 6.66
57.00 ± 20.65
Min–Max
39–131
44–74
44–77
39–131
HR: mean (bpm)
0.515
Mean ± SD
77.05 ± 13.35
75.00 ± 9.23
77.72 ± 9.64
78.25 ± 21.56
Min–Max
61–151
64–100
62–99
61–151
HR: maximum (bpm)
0.630
Mean ± SD
124.44 ± 12.45
123.53 ± 7.40
126.34 ± 14.09
122.06 ± 14.16
Min–Max
103–164
110–136
107–163
103–164
Total sleep time (minutes)
0.155
Mean ± SD
377.91 ± 107.79
401.20 ± 95.93
382.16 ± 113.31
342.56 ± 112.10
Min–Max
39–526
175–526
53–526
39–526
Time awake after sleep (minutes)
0.482
Mean ± SD
64.97 ± 40.97
66.53 ± 38.52
68.22 ± 44.82
57.22 ± 35.97
Min–Max
1.5–211
3.5–142
1.5–211
2–132
Sleep efficiency (%)
0.287
Mean ± SD
78.94 ± 9.13
80.79 ± 9.90
79.10 ± 7.74
76.44 ± 10.45
Min–Max
55–93
55–89
62–89
59–93
Snoring time (%)
0.330
Mean ± SD
14.09 ± 20.76
11.74 ± 17.31
16.59 ± 24.80
12.38 ± 16.75
Min–Max
0–115
0–58
0–115
0–62
Snoring time (minutes)
0.334
Mean ± SD
59.22 ± 81.24
61.84 ± 92.14
68.34 ± 89.22
39.56 ± 45.77
Min–Max
0–387
0–327
0–387
0–142
Abbreviations: BMI, body mass index; bpm, beats per minute; HR, heart rate; Max, maximum; Min, minimum;
NSCP, non-syndromic cleft palate; NSRS, non-syndromic Robin sequence; ODI, oxyhemoglobin
desaturation index; SD, standard deviation; SpO2 , partial oxyhemoglobin saturation; TCS, Treacher Collins syndrome.
Notes: BMI Z-score: BMI corrected for age and sex, Z-score. All variables were evaluated
with Kruskal-Wallis's test. Except for the gender variable where the chi square test
was used. All records of the Nocturnal Hypoxia Index were equal to zero. χ2 Chi-squared test. *Statistically significant difference: p < 0,05.
In the current study, we identified the presence of excellent signal quality, with
a means of 95%, and reduced error events, with percentages below 5%. The detailed
signal analysis is presented in [Table 2 ]. A total of 58 patients successfully underwent the exam on the 1st night, and 6 patients required an additional night to record more than 6 hours. Overall,
a successful rate of examinations of 90% was observed in the first night.
Table 2
Evaluation of high-resolution oximeter plus actigraphy combined signal quality in
children with craniofacial anomalies.
Signal quality across patients (%)
Variable
Mean
± SD
Mean signal quality (total)
94.53
± 5.29
Mean signal quality (Biologix databank)
97.24
± 5.08
Duration of errors across patients (%)
Variables (errors)
Mean
Status variationa
3.93
Poor signal qualityb
1.45
Off fingerc
0.12
Abbreviation: SD, standard deviation.
Notes:
a “Status variation” indicates significant fluctuation between good and poor signal
quality, often due to patient movement or sensor instability. b “Poor signal quality” indicates insufficient signal quality due to external interference
such as body movements or inadequate sensor positioning. c “Off fingers indicate absence of finger signal, typically caused by sensor disconnection
or turning off by the user, leading to absence of physiological data.
Out of 64 patients evaluated, 59 patients (92%) presented OSA: 36 (56%) mild cases,
19 (30%) moderate cases, and 4 (6%) severe cases. Descriptive data regarding the sleep
study results are shown in [Table 2 ]. The frequency of OSA was similar among patients with NSCP, NSRS and TCS. The mean
SpO2 was significantly lower in the TCS group (p = 0.028)
In the study sample, a positive relationship was observed involving changes in ODI
indicative of OSA (above 1) and greater hypoxic burden, presence of more episodes
of SpO2 < 90%, and lower sleep efficiency, as shown in [Table 3 ]. When performing the multiple regression analysis, regarding the anthropometric
variables, a significant association was observed between older age and longer snoring
time (in minutes) in the STC group (p = 0.049), as shown in [Table 4 ]. No significance was observed in the other relationships evaluated (p > 0.05). If we consider the total group of children, in the multiple regression analysis,
a significant association was observed between changes in total ODI with lower estimated
sleep efficiency and sleep ODI, as shown in [Table 5 ].
Table 3
Correlation of ODI with the sleep variables.
ODI
Normal
Mild
Moderate
Severe
p -value
N = 5
N = 36
N = 19
N = 4
Sleep ODI
Normal
5
6
−
−
< 0.001*
Mild
−
30
15
−
Moderate
−
−
4
3
Severe
−
−
−
1
Hypoxic burden
(%, minute,/hour)
Mean
6.7
15.23
32.7
40
< 0.001*
Minimum SpO2 (%)
< 80
−
−
1
−
< 90
−
17
11
2
0.004*
≥ 90
5
19
7
2
Mean SpO2 (%)
< 80
−
−
−
−
0.512
< 90
−
−
−
−
≥ 90
5
36
19
4
Minimum HR (bpm)
< 50
−
8
5
2
0.746
50 to 100
5
27
14
2
> 100
−
1
−
−
Mean HR (bpm)
< 50
−
−
−
−
0.356
50 to 100
5
35
19
4
> 100
−
1
−
−
Maximum HR (bpm)
< 50
−
−
−
−
0.161
50 to 100
−
−
−
−
> 100
5
36
19
4
Time awake after sleep (minutes)
Mean
46.5
59.5
80.8
62.4
0.291
Sleep efficiency (%)
< 85
1
22
16
3
0.001*
≥ 85
4
14
3
1
Snoring time (%)
Mean
21.6
12.6
12.6
25.8
0.364
Snoring time (minutes)
Mean
46.6
49.1
64.5
140.8
0.367
Abbreviations: bpm, beats per minute; HR, heart rate; ODI, oxyhemoglobin desaturation index; SpO2 , partial oxyhemoglobin saturation.
Notes: All variables were avalied with Kruskal-Wallis's test. *Statistically significant
difference: p < 0.05.
Table 4
Multiple regression analysis considering the anthropometric variables of the study
sample.
Variable
NSCP group
NSRS group
TCS group
Age
r2
= 0.304
r2
= 0.226
r2
= 0.536
ODI
p = 0.315
p = 0.528
p = 0.814
ODI during sleep
p = 0.332
p = 0.245
p = 0.832
Hypoxic burden
p = 0.720
p = 0.576
p = 0.499
Snoring time (%)
p = 0.828
p = 0.239
p = 0.140
Snoring time (minutes)
p = 0.705
p = 0.771
p = 0.049*
Sleep efficiency
p = 0.549
p = 0.614
p = 0.383
Snoring intensity
p = 0.695
p = 0.805
p = 0.324
BMI
r2
= 0.292
r2
= 0.171
r2
= 0.504
ODI
p = 0.474
p = 0.304
p = 0.504
ODI during sleep
p = 0.488
p = 0.140
p = 0.171
Hypoxic burden
p = 0.846
p = 0.238
p = 0.282
Snoring time (%)
p = 0.609
p = 0.760
p = 0.193
Snoring time (minutes)
p = 0.756
p = 0.996
p = 0.400
Sleep efficiency
p = 0.556
p = 0.173
p = 0.202
Snoring intensity
p = 0.308
p = 0.556
p = 0.591
BMI Z-score
r2
= 0.542
r2
= 0.247
r2
= 0.137
ODI
p = 0.206
p = 0.243
p = 0.894
ODI during sleep
p = 0.129
p = 0.086
p = 0.884
Hypoxic burden
p = 0.968
p = 0.167
p = 0.846
Snoring time (%)
p = 0.203
p = 0.840
p = 0.541
Snoring time (minutes)
p = 0.270
p = 0.519
p = 0.793
Sleep efficiency
p = 0.063
p = 0.053
p = 0.481
Snoring intensity
p = 0.124
p = 0.508
p = 0.845
Abbreviations: BMI, body mass index; ODI, oxyhemoglobin desaturation index; NSCP, non-syndromic
cleft palate; NSRS, non-syndromic Robin sequence; TCS, Treacher Collins syndrome.
Notes: BMI Z-score: BMI corrected for age and sex, Z-score. *Statistically significant difference:
p < 0.05
Table 5
Multiple linear regression analysis of the ODI scores considering the variables of
the sleep examination in the study sample.
ODI
r2
= 0.773
p -value
Sleep ODI
< .001*
Hypoxic burden
0.982
Minimum SpO2
0.473
Mean SpO2
0.195
Maximum SpO2
0.087
Minimum HR
0.218
Mean HR
0.283
Maximum HR
0.464
Time awake after sleep
0.925
Sleep efficiency
0.004*
Snoring time in percentages
0.663
Snoring time in minutes
0.290
Abbreviations: HR, heart rate; ODI, oxyhemoglobin desaturation index; SpO2 , partial oxyhemoglobin saturation.
Note: *Statistically significant difference: p < 0.05
Discussion
In the present study, we could observe good applicability in the use of a high-resolution
oximeter plus actigraphy in home sleep assessments in children with craniofacial anomalies
based on the high success rate of the exam on the first night and the signal quality
demonstrated. The high occurrence of altered ODI (92%) was also evident in the group
evaluated with a predominance of ODI indicative of mild and moderate OSA (86%). These
results point to the importance of evaluating OSA in school-aged children with craniofacial
anomalies.
Excellent signal quality and a low percentage of signal errors that could compromise
the quality of the physiological data obtained for analysis were observed. These data
demonstrate that the use of PM without supervision by a technician is feasible from
an operational standpoint, with a small need for repeat examinations (10%), considering
the 6-hour recording time as adequate. Examinations with inadequate signal were not
observed. Although the viability of its use in adult populations as an alternative
method to level-I PSG (PSG I) is well defined,[14 ]
[16 ] its viability in children must be better elucidated.[13 ] It is also worth noting that the adoption of measures such as prior guidance for
primary caregivers, provision of written guidance and information, as well as adequate
remote support for children's caregivers contributed to the successful completion
of exams.
In the context of the use of PM for the diagnosis of OSA in children with craniofacial
anomalies, there are many reports of use in children of different age groups, especially
when PSG is not available,[21 ]
[22 ]
[23 ] with the benefit of speed and relative safety in the diagnosis of suspected moderate
and severe SDB, especially if performing PSG would result in delayed diagnosis due
to inaccessibility, high cost or complex logistics, while the use of type-IV polygraph
is associated with early decision-making and no delay in establishing the appropriate
treatment.[21 ]
[24 ]
In the present study, we observed a high frequency of ODI alteration, compatible with
SDB, of 92% of the total sample, with 56% classified as mild, 30%, as moderate, and
6%, as severe. Similar data with higher prevalence of mild and moderate cases were
also observed by authors who evaluated children with craniofacial anomalies.[20 ]
[25 ]
[26 ] There was a significant difference among the groups only regarding the mean SpO2 , with lower mean saturation observed in the TCS group (p = 0.028). The frequencies observed corroborate the literature reports[2 ]
[20 ]
[25 ]
[26 ] of high prevalence of SDB in children with craniofacial anomalies, which is significantly
higher than the frequency observed in the general pediatric population, including
the presence of snoring above the estimated rate in the general pediatric population
(between 3% and 15%), with frequencies ranging from 11 to 17%.[1 ]
[27 ] These data are consistent with those of the literature, which demonstrates higher
prevalence and severity associated with the presence of craniofacial anomalies when
compared to the general pediatric population.[3 ]
[28 ]
Regarding the anthropometric variables, the TCS group presented lower Z-scores and
higher mean age, with a statistically significant difference (p < 0.05). A correlation was also observed between higher mean age in the TCS group
and longer snoring time in minutes, and these findings may be related to greater impairment
of the upper airway in this population and complications that would decrease adequate
weight gain.[5 ]
[25 ]
[26 ] Moreover, there is reduction in the reporting of symptoms by the population with
NSRS due to the “catch-up” of mandibular growth between 6 and 8 years of age.[29 ]
[30 ]
In the present study, a significant relationship was found regarding ODI (above 1)
and changes in ODI during sleep, more episodes of SpO2 < 90%, higher percentages of hypoxic burden, and lower estimated sleep efficiency
regardless of the group evaluated, thus demonstrating consistency in the data, indicating
a lower probability of false negative results. It is important to note that data regarding
the different desaturation levels and times, as well as calculations of hypoxic burden,
are calculated by the algorithm with data obtained from oximetry, not specifically
linked to the calculation of ODI, demonstrating the importance of using algorithms
to improve the accuracy of the method.[31 ]
[32 ] Additionally, hypoxic burden is associated with changes in the apnea-hypopnea index
(AHI), SpO2 nadir, and sleep time with SpO2 > 90%; it is also associated with a higher risk of cardiovascular disease.[33 ]
Studies[31 ] have shown a high correlation between the AHI derived from PSG and the ODI, both
when analyzed as an independent channel of PSG and by high-resolution oximeters, with
sensitivity variations from 32 to 98.5% and specificity between 47.7% and 98%. The
sleep ODI data is a refinement through the algorithm that identifies the desaturation
occurring only in the period of effective sleep, with better refinement for use in
the clinical practice.[16 ]
[31 ]
[34 ]
[35 ]
[36 ] There is evidence that the cumulative time spent with SpO2 < 90% and the measurement of the variability of oxyhemoglobin saturation are important
data to be compared with the AHI to improve the diagnostic accuracy of OSA.[31 ]
[37 ]
The analysis of estimated sleep efficiency is like PSG, although the PM relies on
SpO2 , heart rate (HR), accelerometer and snoring signals to estimate the total sleep time,
since it does not have electroencephalogram data. It is important to note that there
was consistency between changes in ODI and lower sleep efficiency, demonstrating a
correlation between these two aspects, which is clinically explained, since OSA negatively
impacts sleep quality in general.[38 ] Recent studies[38 ] have observed an association regarding shorter sleep time, lower estimated sleep
efficiency, severe cases of OSA, the male sex, and advanced age, without significant
variation between data observed in PSG and in home sleep tests. In the present study,
a relationship was observed between altered ODI and worse outcomes in terms of estimated
sleep efficiency.
The strengths of the present study are a significant sample of children with craniofacial
anomalies and proof of the applicability of PM, which consists of a high-resolution
oximeter with a built-in actigraphy, with the clear advantage of reducing the first
night effect and costs associated with sleep examination using a PSG.[13 ]
[15 ] Additionally, it makes logistics simpler and facilitates the screening of children
with craniofacial anomalies for OSA, optimizing diagnosis and favoring the performance
of serial exams that are well accepted by children.[39 ]
[40 ]
[41 ] Among the limitations we can list the lack of comparison of data with results from
the PSG defined as the gold standard to evaluate OSA. Therefore, new studies are needed
on the sensitivity and specificity of PM, in comparison with PSG, in school-aged children
with craniofacial anomalies.
Conclusion
The present study demonstrates that PM in children is technically feasible and presents
good applicability in children with craniofacial anomalies, with high frequency of
ODI compatible with OSA. These data indicate the need to establish a routine to evaluate
children with craniofacial anomalies for OSA. Further studies to validate PM in relation
to the gold standard of PSG in children are needed to better elucidate this question.
Bibliographical Record
