1. Introduction
Obstructive sleep apnea (OSA) is characterized by intermittent episodes of upper airway collapse during sleep. It is a chronic and severe breathing-related disorder, which affects approximately 2 to 3% of school-aged children [
1,
2]. A substantial volume of evidence has linked pediatric OSA and cardiovascular/metabolic dysfunctions [
1,
2]. Some research has also suggested a connection between pediatric OSA and neurocognitive/behavioral impairment, but the results are still inconsistent and inconclusive [
3]. Adenotonsillectomy remains the preferred treatment for pediatric OSA [
4,
5]; however, older children have been reported to have less satisfactory surgical outcomes [
6]. Therefore, delay in the diagnosis of severe pediatric OSA has been one of the major attributable causes for compromised treatment outcomes for pediatric sleep-disordered breathing.
Snoring is a hallmark of pediatric OSA, and around 20% of children who are frequent and loud snorers have OSA [
7]. However, there are only a few studies using objective evaluations of snoring sound for the diagnosis of pediatric OSA [
8,
9]. Standard in-laboratory nocturnal polysomnography is the best method to diagnose OSA; however, it is time-consuming and labor-intensive, and hence the waiting list is usually very long [
10]. Furthermore, the high cost and poor tolerability of the polysomnography apparatus can also delay a prompt diagnosis and treatment in children with OSA [
11,
12]. An increasing number of rapid screening tools have been developed to better stratify children with snoring for their needs of polysomnography and treatment.
For example, the OSA-18 questionnaire has been used to screen for pediatric OSA [
13]. Additionally, adenoid and tonsillar hypertrophy, traditional risk factors for pediatric OSA, have been used to predict disease severity [
14]. Nocturnal pulse oximetry, a typical home sleep apnea test, has shown excellent ability to identify pediatric OSA [
15]. Recently, snoring sound analysis, an objective evaluation of snoring sounds has been demonstrated to have potential in screening for OSA [
9,
16]. Even though the pathophysiology behind these rapid screening tests is distinctly different, they can all be used to predict the severity of OSA. The utility and limitations of anatomy-based classification systems and snoring sound analysis have been elucidated in some previous research. Nevertheless, to the best of our knowledge, no previous study has compared the diagnostic ability of these screening tools simultaneously in the same pediatric cohort [
9,
17,
18]. It is also a novel approach to develop various combinations of the aforementioned tests and report their performance in identifying severe OSA.
We hypothesized that clinical variables, home snoring sound analysis, and home sleep pulse oximetry had different performances in screening severe OSA among children with habitual snoring. The first aim of the study was to evaluate the prediction performance for severe pediatric OSA of the aforementioned various measures, including home snoring sound analysis and home sleep pulse oximetry. The second aim of the study was to develop a combined predictive model for severe pediatric OSA with measurements from these existing screening tools to facilitate timely diagnosis and early intervention.
4. Discussion
Home sleep apnea tests with technically adequate devices have been used to diagnose moderate to severe OSA in uncomplicated adult patients [
40] and school-aged children [
41,
42] with sleep-disturbed breathing; however, these devices are not recommended for the diagnosis of pediatric OSA [
43]. Home sleep apnea tests have fewer first-night effects compared to overnight in-lab polysomnography [
44]. A familiar environment and simple devices may minimize the interference on one’s usual sleep and also improve children’s compliance [
45]. A combination of simplified cardiorespiratory montage and video recording have been proven to accurately evaluate OSA in pediatric patients with adenotonsillar hypertrophy [
46]. In this study, the ODI3 might provide greater accuracy than the SSE of 801–1000 Hz did.
Home sleep pulse oximetry is a promising tool to screen for OSA in symptomatic children, primarily due to the significant difference in ODI3 between children with and without OSA [
47,
48,
49,
50]. Brouillette reported that children identified by pulse oximetry testing were likely to have clinically significant disease severity [
51]. Furthermore, using home sleep apnea tests to estimate the severity of OSA could assist clinicians with timely referrals in areas with limited access to polysomnography and reduce the workload of sleep technicians. However, easy dislodgement of the finger sensor is a major disadvantage of unattended pulse oximetry. Approximately 10% to 20% of hospital and home sleep pulse oximetry failed to provide technically acceptable data due to poor contact between the sensor and the child’s finger [
33,
42,
52]. Therefore, Bhattacharjee commented that the failure rate of home pulse oximetry and cardiorespiratory measures was relatively high for studies that were attended by night technologists and for older children [
53].
In this study, we provided a feasible protocol to collect valid SpO
2 signals to assess disease severity. The majority of children (139/149 (93%)) accomplished the first 90-min recordings of SpO
2 at home. Notably, the first sleep cycle is usually a slow-wave sleep with the smallest number of apneas [
54]. However, those with significant oxygen desaturations during this sleep stage could be assumed to be at risk of severe OSA. Additionally, parents are more likely to keep vigilance during this early night sleeping period, assist the measurement, and re-connect the pulse oximetry device when dislodged.
Previous studies have adopted different cut-off values of ODI3 for screening pediatric OSA [
55,
56,
57,
58]. The diversity could be caused by varied sample size and screening of the different OSA severity in each study. An ODI3 cut-off value of 5 events/h was reported to identify children with moderate abnormalities [
56], while the cut-off value of 25 events/h was defined to diagnose severe pediatric OSA [
58]. The optimal ODI3 corresponding to the polysomnography-defined severe OSA was established as ≥6.0 events/h in our discovery study and demonstrated a sensitivity of 90% and a specificity of 83%. This discrepancy might be due to the difference in ODI3 between the early night and the full night. However, technically unacceptable data of the full night sleep limit the accuracy of SpO
2 analysis, and artificial intelligence may help to precisely remove motion artifacts, poor-quality signals, and awakenings [
59].
Furthermore, Hornero reported that home sleep pulse oximetry with an automated neural network algorithm accurately detected severe OSA with an AUC of 0.91 [
15]. Therefore, single-channel nocturnal pulse oximetry may be an alternative to respiratory polygraphy and polysomnography in an unattended setting to screen for OSA among children [
15,
50]. In this study, we used a commercially available pulse oximetry system to assess SpO
2, improved the validity and efficacy of this test, and found that ODI3 during a 90 min period after the first snoring signal could predict severe OSA with an AUC of 0.87. Additionally, ODI3 performed better than the other independent predictors in the detection of severe OSA. For example, ODI3 detected severe OSA with greater sensitivity (90% and 86%, respectively) and specificity (83% and 67%, respectively) than ANR. A recent study also reported that pulse oximetry provided satisfactory diagnostic performances in detecting moderate and severe OSA [
60]. Our findings further support the use of home sleep pulse oximetry to screen for severe OSA among children with snoring.
In another home sleep apnea test, we used a non-contact technology to detect snoring sounds to avoid the problem of sensor dislodgement and therefore classified the severity of OSA more efficiently. An elevated SSE of 850–2000 Hz has been reported to be a warning sign of severe OSA in adults [
37]. Furthermore, an increased SSE of 801–1000 Hz has been shown to predict more severe OSA (AHI > 15 events/h) and to indicate multiple-level obstructions due to a low surgical success rate in children with OSA [
9]. In this study, the accuracy of SSE of 801–1000 Hz ≥ 22.0 dB was poorer than that of ODI3 ≥ 6.0 events/h for predicting severe OSA (67% versus 87%).
Clinicians usually evaluate the risk of severe OSA for children with clinical history, physical examination, and questionnaire-based tools. However, these approaches are with limited sensitivity and specificity [
61]. In the present study, differences in age, sex, BMI z-score, and OSA-18 between subgroups in the discovery cohort could not differentiate severe OSA from non-severe OSA at the first evaluation. Both the ANR and tonsil size could provide a more reliable prediction of severe pediatric OSA than the OSA-18 instrument. The sensitivity of OSA-18 to predict severe OSA was 57% in our study, which was compatible with the pooled sensitivity of a recent meta-analysis [
62]. Our result further supports that the predictive value of the OSA-18 for severe OSA is weak [
63].
The optimal cut-off value of the ANR was 0.78 for predicting severe OSA with the largest AUC comparing with other single predictors. The ANR is significantly associated with age [
64] and AHI [
19]. Clinically, an ANR ≥ 0.73 may be considered indicative of pathological enlargement of the adenoids [
65]. Accordingly, our results strengthened the utility of the ANR to screen severe OSA in children with snoring. Furthermore, the anatomical model (either ANR ≥ 0.78 or tonsil size = 4) predicted severe OSA more precisely than the single anatomic predictors.
This study provides a practical perspective for clinicians to predict severe pediatric OSA more efficiently. In children with habitual snoring, we suggest using the anatomical model or the combined model to screen for severe OSA since they both have acceptable accuracy (82% and 92%, respectively). However, the predictive performances of both the anatomical and combined modules obviously reduced in the validation cohort. These results might be because of the younger age of the validation cohort, which reduced the predictive performance of the ANR and tonsil size (
Table 6). Nevertheless, our simple predictive models may assist clinicians to better stratify at-risk children for severe OSA and facilitate a timely diagnosis and early intervention. This proactive management could reduce OSA-related health costs in the future.
Our study had some limitations. First, the study was retrospective cross-sectional and had a small sample size. Second, despite the fact that variables obtained from nocturnal pulse oximetry were distinguishable from those from polysomnography in terms of testing environments and inclusion of respiratory events, ODI3 might be partially autocorrelated with the AHI since oxygen desaturation was part of the definition of hypopneas. Third, an adaption night standard polysomnography was not included in our protocol. Although an adaption night is considered necessary only for nocturnal respiratory pattern investigations, the first-night effect may still influence the sleep architectures in subjects [
66]. Fourth, selection bias that might have existed concerning the single ethnicity, a predominance of boys, a subgroup of children with snoring, a confined age spectrum of 5–12 years, and a lack of a normal control group. Nevertheless, the combined model reached a considerable accuracy in the subsequent Study 2 cohort. Further prospective investigation with a sample larger in size and more heterogenous in demographics (e.g., different ethnic groups) is warranted to verify the findings of this research.