DISCUSSION
Our goal was to identify the components of the SRBD survey useful for
OSA screening in children with asthma, a patient population with
heightened susceptibility to OSA. Our cohort of consecutively recruited
children with asthma demonstrated a high prevalence of OSA, confirming
results from previous studies. We report four novel findings. First, of
the 22 SRBD survey questions, only loud snoring, morning dry mouth and
being overweight were associated with OSA. Second, a combined model of
reported loud snoring, morning dry mouth and being overweight generated
a probability index (MOP) with greater OSA screening accuracy compared
to the composite SRBD score. Third, the BMI z-score also had greater OSA
screening accuracy compared to the composite SRBD score, but had similar
OSA screening accuracy compared with the combined model of reported loud
snoring, morning dry mouth and being overweight. Fourth, children with
moderate to severe persistent asthma had twofold higher odds of having
OSA compared to those with mild persistent and intermittent asthma.
These findings confirm the high burden of OSA amongst children with
asthma, particularly those with concomitant obesity, and suggest that a
simple yet objective anthropometric measure such as the BMI z-score can
be used to effectively screen for OSA in children with asthma, thereby
identifying those at risk for adverse health consequences.
The objective of initial OSA screening is to identify the persons with a
high likelihood of having OSA in a subsequent sleep study. Another
rationale is to recognize persons who might have OSA in the context of
their underlying disease. For children with asthma, undetected OSA
presents an extra‐ordinary high risk for morbidity. Since in-laboratory
polysomnography, the only objective and clinically valid method of OSA
assessment in children is expensive and often difficult to obtain, a
simple yet accurate low-cost approach is critical to identify children
with asthma who need sleep studies in a timely manner.
Several studies investigating sleep disordered breathing in children
have reported a higher prevalence of OSA in children with asthma
compared to the general population1,19–21. Ramagopal
et al reported an OSA prevalence of 81% in children with asthma
referred for adenotonsillectomy21. Similarly,
Kheirandish-Gozal et al studied children with poorly controlled asthma
and showed an OSA prevalence of 63%1. Furthermore, a
recent study by Ehsan et al reported an OSA prevalence of 47% in a
retrospective patient chart review of children with asthma referred for
sleep studies2. As seen in our study, the prevalence
of OSA was 40%, confirming the relatively higher OSA prevalence in
children with asthma. Our result along with those of previous reports
suggest that asthma contributes to OSA pathophysiology, which might
explain the increased prevalence. Moreover, in our cohort, children with
moderate to severe persistent asthma had twofold higher odds of having
OSA compared to those with mild persistent and intermittent asthma
(Table 4 ). Interestingly, we also found that boys had
three-fold higher odds of OSA compared to girls (Table 4 )
possibly due to inclusion of children in the post-pubertal age
group22,23 in whom differences in anatomy have begun
to emerge resulting in different susceptibilities for OSA. It is likely,
however, that the gender difference in predilection for asthma24,25 contributed to the higher odds for OSA in boys
compared to girls, but such an inference will need to be examined and
verified in studies that have controls without asthma.
Currently, OSA screening in children is often done with the SRBD
questionnaire, a 22-item survey derived from the Pediatric Sleep
Questionnaire14, for which a score greater than 0.33
indicates a high OSA risk. In the general population, the SRBD
questionnaire has a sensitivity of 78% and a specificity of 72% as
reported by Chervin et al8. In that study, OSA was
defined as an AHI ≥ 1 events/hr. and was enriched with children
scheduled to have adenotonsillectomy. Ehsan et al conducted a validation
study of the SRBD in children with asthma, which revealed a screening
sensitivity of 81.6% and specificity of 14.4% at an OSA definition of
AHI≥2 events/hr2. In our study, we found a screening
sensitivity of 47.3% and specificity of 68.8% at the same OSA and SRBD
thresholds. The differences in our results may be because their study
population comprised patients specifically referred for clinical sleep
studies unlike our study that investigated consecutive research
participants, thus lowering but not eliminating the potential bias
related to sleep study referrals. Interestingly, we found that our MOP
model that comprised loud snoring, morning dry mouth and BMI z-score was
comparatively better than the SRBD. In fact, the BMI z-score alone had
an NPV and PPV of 72.2% and 76.3% compared to 65.5% and 51.0% for
the SRBD (Figure 3 ). From a clinical utility and practical
standpoint, the BMI z-score may provide the best combination of OSA
screening accuracy and ease of application. At the optimal cut-off of
2.07, the predictive values suggest that 76% of patients above this
threshold will have an AHI≥ 2 events/hr, hence a 24% false positive
rate. Conversely, 72% of the patients with BMI z-scores below 2.07 will
have an AHI< 2 events/hr, leaving a 28% chance for false
negatives. Based on this paradigm, therefore, children with asthma that
have BMI z-scores < 2.07, should still be further evaluated
for indicators or risk factors for upper airway obstruction including
enlarged tonsils, rhinitis, and asthma severity, particularly if they
also report loud snoring or have dry mouth in the morning. Bearing in
mind that in our cohort, the odds of having OSA was higher in boys
compared to girls (Table 4 ), future work will need to explore
whether the OSA screening accuracy of the BMI z-score, MOP model and
SRBD survey, differs between boys and girls.
Previous studies have shown that children with asthma who have OSA had
higher BMI z-scores compared to those without
OSA26,27. Our results confirm these reports, as we
showed that children with asthma with BMI z-scores ≥ 2.07 had 8-fold
higher odds of having OSA compared to those with BMI z-scores
< 2.07. Obesity may also contribute to asthma
morbidity28–30, although the causal mechanisms remain
unclear. Since OSA has been associated with asthma morbidity, obesity
related increase in OSA risk may be one of the links between obesity and
worse asthma control. On the other hand, increased upper airway
inflammation in asthma may lead to higher susceptibility for OSA thus
setting up a vicious cycle of cumulative
morbidity5,31. While the potential bi-directional
OSA-asthma relationship is complex, the shared risk factor in obesity
presents a target for mitigating both the OSA and asthma
burden32, which as suggested by our study, could be
achieved by monitoring BMI z-scores and identifying those at risk for
OSA early.