OSAS is one of the most significant health problems in middle-aged people and leads to daytime sleepiness and cognitive deficiencies as well as many systemic diseases. It is a highly prevalent disorder, and at least 4% of middle-aged males and 2% of middle-aged females are estimated to be affected [
In this observational study, we retrospectively evaluated 390 consecutive, unselected subjects who were referred to the sleep laboratory of a university hospital to evaluate presumed sleep-disordered breathing and who had undergone PSG. Each individual included in this study was referred to sleep laboratory by otolaryngology department after detailed ear, nose, and throat examination which was performed by the same specialist. Demographic data (age, gender, smoking history, and alcohol and psychotropic drug use), anthropometric measurements (height, weight, body mass index, and circumferences of neck, waist, and hip), and medical history were evaluated.
BMI, the most commonly used method to measure obesity, was calculated by dividing weight in kilograms by the square of height in meters (kg/m2). Neck circumference was measured in centimeters at the level of the cricothyroid membrane. Waist circumference was measured in centimeters at the level above the iliac crest. Hip circumference was measured in centimeters while patients were standing still and their feet were fairly close and at the level of the point where the maximum circumference over the buttocks was measured.
After asking patients about their complaints, the following parameters emerged: snoring, witnessed apnea, the existence of nasal obstruction, smoking, and alcohol use. Subjective daytime sleepiness was assessed by using the Turkish version of the Epworth Sleepiness Scale (ESS). Scores higher than 10 were considered to be sleepiness. Pulmonary function tests (including spirometry and flow volume curves), chest X-rays, pulse oximeters in polyclinics to evaluate peripheral oxygen saturation (plus MED-pulse oximeter, plus 50-DL, Contec Medical Systems), arterial blood gas analysis, and a full-night in-laboratory PSG were performed on all subjects.
Detailed otolaryngologic physical examinations were performed by the same specialist. Parameters obtained in these examinations were included in statistical evaluations and included the following: protrusion and retrusion of the mandibles, tonsil sizes, the relationship between the soft palate and the neutral position of the tongue, the tongue-base size, nasal septum deviation, the inferior turbinate size, the endoscopic nasal cavity, and nasopharynx examinations using a 0° rigid endoscope, endoscopic larynx, and hypopharynx examinations using a 70° rigid endoscope (4 mm, 18 cm, Korl Storz Hopkins, Tuttlingen, Germany).
Mandibular retrognathism was evaluated in accordance with the position of the pogonion when the virtual imaginary line between the vermilion line and the chin on a patient sitting in a Frankfort horizontal position is taken into consideration. The examinations of the oral cavity started with an inspection of the relative position of the hard and of the soft palate to the tongue inside the mouth, without protrusion using the modified Mallampati index (MMI), ranging from Class I to Class IV, with Class I representing the highest visibility (the tonsils, the pillars, and the soft palate being visible) and Class IV representing the lowest level of visibility (only the hard palate being visible) of the posterior oropharynx [
All subjects underwent a full overnight in-laboratory diagnostic PSG (Compumedics E Series, Australia or Alice 5 Diagnostic Sleep System, Philips, Respironics, USA). Electroencephalography electrodes were positioned according to the international 10–20 system. PSG consisted of monitoring of sleep by electroencephalography, electrooculography, electromyography, airflow, and respiratory muscle effort and included measures of electrocardiographic rhythm and blood oxygen saturation. Thoracoabdominal plethysmograph, oronasal temperature thermistor, and nasal-cannula pressure transducer system were used to identify apneas and hypopneas. Transcutaneous finger pulse oximeter was used to measure oxygen saturation. Sleep was recorded and scored according to the standard method [
SPSS version 17.0 was used for statistical analysis. First, the correlation between the AHI and all of the variables included in the study was analyzed in order to identify variables to be used to predict the AHI. Mann-Whitney
The demographic data (age, gender, smoking history, and alcohol and psychotropic drug use), anthropometric measurements (height, weight, body mass index, and circumferences of the neck, waist, and hips), and medical history variables are shown in Table
Characteristics of the study population.
Variables | Study population ( |
---|---|
Age (year)* | 50.1 ± 11.1 |
Sex ( |
Male: 289 (73.9%) |
Female: 101 (26.1%) | |
Current Smoker ( |
116 (29.7) |
Alcohol Consumption ( |
67 (17.2%) |
Comorbidities ( |
|
Hypertension | 156 (40 %) |
Diabetes mellitus | 76 (19.5 %) |
Coronary artery disease | 31 (7.9 %) |
BMI (kg/m2)* | 30.8 ± 5.5 |
Neck Circumference (cm)* | 40.9 ± 4.2 |
Waist Circumference (cm)* | 106.1 ± 14.6 |
Hip Circumference (cm)* | 110.1 ± 11.2 |
Epworth Sleepiness Score* | 9.8 ± 6.0 |
Apnea-hypopnea index (/hour)* | 33.2 ± 30.3 |
BMI: Body mass index, NC: Neck circumference, WC: Waist circumference, HC: Hip circumference, SpO2: Oxygen saturation, FVC: Forced vital capacity, FEV1%: Forced expiratory volume ratio in one second, ESS: Epworth Sleepiness Scale’s score, TS: Tonsil size. *Values are expressed as mean (SD).
Variables that were integrated into the regression analysis after we found that they were correlated with the AHI in Spearman’s rho test were the BMI, NC, WC, hip circumference (HC), smoking rate in packages/year, force vital capacity (FVC), forced expiratory volume ratio in one second (FEV1%), FEV1/FVC ratio, Pao2, PaCO2, ESS score, oxygen saturation level via a pulse oximeter, the Epworth Scale score, and tonsil size. Correlation coefficients of these variables in nonparametric Spearman’s rank order correlation analysis are shown in Table
Variables identified to be related to AHI in Spearman’s rho test and their correlation coefficients.
Variable | Correlation coefficient | Variable | Correlation coefficient |
---|---|---|---|
BMI | 0.491 | FVC | −0.149 |
NC | 0.358 | FEV1% | −0.101 |
WC | 0.371 | FEV1/FVC | −0.085 |
HC | 0.112 | PaO2 | −0.203 |
Smoking | 0.190 | PaCO2 | 0.161 |
ESS | 0.103 | SpO2 | −0.242 |
TS | 0.431 |
Thereafter, each variable was integrated into the regression analysis in order to identify how these variables as a whole affected the AHI via a step-by-step multiple linear regression analysis. In the seventh phase, the explanatory power of the model reached its peak:
Parameters used to identify independent predictors of apnea-hypopnea index.
Parameter | Beta |
|
|
---|---|---|---|
Body mass index | 0.797 | 2.132 | 0.034 |
Neck circumference | 2.286 | 6.696 | <0.0001 |
Oxygen saturation | −1.272 | −9.094 | <0.0001 |
Waist circumference | 0.314 | 2.274 | 0.24 |
Tonsil size | 5.114 | 2.261 | 0.024 |
We found this model to be statistically significant (
Distribution of predicted AHI values regarding real AHI values.
Some studies have shown that data based on medical history and the findings of the physical examination may be useful to detect OSAS in patients. Hoffstein and Szalai obtained a sensitivity of 60% and a specificity of 63% in the detection of OSAS in patients in a study that included 594 patients and aimed to analyze the statistical relevance of data related to medical history and physical examinations [
Sleep apnea, despite being the most common reason for EDS, was not considered to be a useful clinical feature for detecting OSAS in patients because 30 to 50% of the society claim moderate to sleepiness [
Obesity is an important risk factor for OSAS, and 70% of OSAS patients suffer from it. The most commonly used measurements in the diagnosis of obesity are BMI, WC, and HC. Bouloukaki et al. stated that BMI was a better indicator than other obesity indicators, such as waist-to-hip ratio, in OSAS predictability [
It is a challenging issue for otolaryngologists to identify and to improve the location of obstruction level(s) in the upper airway (UAW) in patients suspected of OSAS. The UAW is assumed to be narrower and/or more prone to collapse multilevelly in OSAS patients. The relationship between anatomic abnormalities and the severity of OSAS has not been well established in previous studies because UAW abnormalities are not the only causative factors [
There is no consensus regarding the effect of nasal airway blockage on OSAS pathogenesis and the severity of the disease. While some authors argued that the nasal passage affects OSAS development and is related to AHI, other authors claimed the converse [
The Mallampati index, which was defined by anesthesiologists in 1985 to identify difficult intubation, was later suggested as an MMI to be used in predicting OSAS [
In many studies, pulmonary function tests were not included in the data to detect OSAS predictors. Herer et al. evaluated 102 obese patients by clinical features, pulmonary function tests, arterial blood gas tensions, and oximetry for prediction of OSAS prior to PSG and stated that none of these data is sufficient to prove the existence of OSAS [
However, such formulas include different variables within different populations, whose differences may be a significant limitation of this study. Therefore, this method may not be sufficient since a screening technique and its validity need to be further tested in other series and would need to include more patients. It is our next aim to design a prospective study to evaluate the predictability of the formula obtained from this study.
As a result, a mathematical model created via data obtained through simple office procedures to predict OSAS may reduce redundant PSG and result in more rapid diagnoses and treatment processes. The potential advantage of this approach is its ease of use, its cost effectiveness, its applicable nature in office environments, and its contribution to more rapid decisions. Validity of this prediction formula needs to be further tested in other prospective researches. It is not possible to make a clear clinical statement about this formula yet. Validated prediction models, which will be created via collective studies by more sleep centers and with more groups of patients, are needed because no single, sufficient model has yet been described.
The authors declare that there is no conflict of interests regarding the publication of this paper.