Background
Obstructive sleep apnea (OSA) is one of the most common types of sleep-disordered breathing. One notable characteristic of OSA is recurrent episodes of partial/complete pharyngeal collapse and subsequently reduced oronasal airflow, or even cessation of breathing, during sleep [
1]. OSA is closely associated with impairments in daily activities and social functioning due to daytime sleepiness and fatigue, in turn caused by sleep fragmentation and clinical sequelae such as hypertension, diabetes, dyslipidemia, cognitive dysfunction, cardiovascular events and even all-cause mortality [
2‐
7]. The prevalence of OSA is high and has increased with the obesity epidemic according to data gathered since the 1990s [
8‐
10]; however, most cases remain undiagnosed and thus the disease is undertreated, resulting in a high social and economic burden.
Although awareness of the prevalence of OSA and its clinical sequelae have increased recently based on data from western countries, the wait time for polysomnography (PSG) ranges from 2 to 60 months [
11]; thus, limited access to PSG, which also involved significant expense, remains a major issue. Chinese patients with OSA also need time to reach the aforementioned treatment plan due to a lack of readily available PSG labs. A delayed diagnosis in turn results in delayed OSA treatment and, consequently, more comorbidities. Thus, a simple-to-use and reliable method to identify and triage patients at high risk for OSA is urgently needed.
Well-designed questionnaires [i.e., the STOP-Bang questionnaire (SBQ), STOP questionnaire (STOP), Epworth Sleepiness Scale (ESS) and the Berlin questionnaire (BQ)] have been designed as substitute methods for diagnosing OSA in the absence of standard PSG. However, the results have been unsatisfactory. In a recent meta-analysis, pooled specificity was low, ranging from 42 to 65% [
12]. Furthermore, questionnaires can be susceptible to bias and snoring is not amenable to self-report, as OSA symptoms occur during sleep. Uncertainty exists about the accuracy and clinical utility of the above-mentioned screening tools [
13]. An optimized diagnostic tool that combines multiple, objective clinical biomarkers, to avoid bias, has yet to be developed.
No study has determined whether using a combination of objective, clinical biomarkers confers a good diagnostic ability for OSA. Therefore, the aim of our study was to develop and validate a simple-to-use nomogram that incorporated objective demographic, biochemical, and anthropometric parameters to determine whether this nomogram minimized the number of missed OSA diagnoses.
Discussion
In this study, we developed and validated an easy-to-use nomogram as a new approach to diagnose OSA in a large clinical sample. The nomogram incorporates eight items: age, sex, glucose, ApoB, insulin, BMI, NC, and WC. To our knowledge, this is the first study to establish an objective model, including common demographic, anthropometric, and biochemical variables, to distinguish OSA from non-OSA. The nomogram showed good accuracy and discrimination.
In total, 18 candidate variables were used for construction of the nomogram, which was reduced to eight potential predictors using the LASSO regression method. LASSO is suitable for analyzing large sets of clinical factors and avoids overfitting [
20]. Our nomogram suggested that obesity and presence of a glucose metabolic disorder may be good predictors of OSA. Furthermore, the nomogram may serve as a useful tool for optimal identification of patients at high risk for OSA. Thus, therapeutic decisions will be better informed and the likelihood of early intervention for high risk patients would be increased, particularly in clinics lacking standard PSG equipment.
Luo M, et al. also established nomogram encompasses lots of subjective variables through an ordinal logistic regression procedure [
21]. They found that the discrimination accuracies of this nomogram for non-OSA, moderate-severe OSA, and severe OSA were 83.8, 79.9, and 80.5%, respectively. Our study has a bit lower sensitivities, but with similar AUC (nearly 0.8). The biggest difference from the above-mentioned research is our study used objective parameters which could be avoiding bias caused by questionnaire recalled by patients. Besides, our study also had some advantages such as using LASSO regression for analyzing clinical factors, 10-fold subjects than previous study and had validation group. Other studies also used clinical nomograms to calculate AHI and even estimate median survival time/event-free survival [
7,
22]. We will also extend the usage of nomograms in OSA in further prospective studies.
OSA remains underdiagnosed and undertreated in clinical settings, given the substantial burden that it confers. Thus, several questionnaire-based prediction tools for OSA have been developed and validated. For example, in a recent meta-analysis, the sensitivity (specificity) of the BQ, SBQ, STOP, and ESS for detecting mild OSA was 76% (59%), 88% (42%), 87% (42%), and 54% (65%), respectively; for moderate OSA, the corresponding values were 77% (44%), 90% (36%), 89% (32%), and 47% (62%); and for severe OSA they were 84% (38%), 93% (35%), 90% (28%), and 58% (60%) [
12]. Although the SBQ seems to be more accurate than the other three questionnaires for screening OSA, its relatively low specificity limits its clinical utility [
12]. One study assessed the ability of a simple, two-part questionnaire to predict OSA in a clinical setting; it showed high sensitivity (96.6%) but relatively low specificity (40.4%) [
23]. However, the sample size (128 patients) in that study was relatively small. Shah et al. established a predictive model for sleep apnea that included age, BMI, snoring, and sex, and had a sensitivity of 0.77 and specificity of 0.75 [
24]. However, a portable PSG rather than standard PSG was used in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) study, which may therefore have underestimated the severity of sleep apnea and also did not assess sleep stage, duration, or fragmentation (as noted by the authors [
24]. The NoSAS score, a new screening tool that encompasses NC, BMI, snoring, age, and sex, was developed and validated in the HypnoLaus and EPISONO sleep cohort studies [
25]. The NoSAS score seems marginally superior versus the BQ and ESS; however, its sensitivity and specificity are also low. Our objective nomogram seemed to perform better than traditional subjective questionnaires, however, this conclusion needs to be treated with caution because of had no validation in other ethnic groups.
Our study was performed according to previously described screening principles (WHO, 1968). We employed a rigorous methodology and an appropriate cross-sectional study design. Although our study had strengths, such as a large sample size, and all samples were evaluated by standard PSG, certain limitations should also be addressed. First, we only used demographic, anthropometric, and biochemical data to establish the nomogram; however, OSA is also affected by genetic factors, as evidenced by genome-wide association studies, and we did not consider genomic characteristics. Second, although our nomogram was developed in the context of a large sample size, and internal validation and external validation were performed, it has not been validated in different ethnic groups and populations. Third, the validation group was derived from the same institution as the training group, which makes it difficult to generalize the results to other populations. Therefore, we plan to externally validate our predictive model at other institutions. Fourth, the prevalence of OSA in our sleep center is higher than that in the general population; this high prevalence of OSA may have affected our evaluation of the predictive parameters by inflating their positive predictive value. Fifth, the assessment of biochemical variables raises concerns in term of money, time, and effort when compared with simple screening questionnaires. Lastly, the use of AASM 2007 scoring rules was not justified and could also be one of the potential limitations of our study.