Abstract
Study Objectives:
To evaluate predictions of moderate to severe obstructive sleep apnea (MS-OSA) by the neck circumference/height ratio (NHR) and waist circumference/height ratio (WHR) and compare to the derived STOP-Bang Questionnaire (dSBQ) prediction.
Methods:
Included were 6,167 participants from the Sleep Heart Health Study baseline evaluation who completed polysomnograms and had anthropometric measurements and data to compute proxy dSBQ item answers. The sample was divided randomly into derivation (n = 2,035) and validation (n = 4,132) subsets. The derivation sample was used to estimate the NHR and WHR cut points to detect MS-OSA; the validation sample was used to evaluate sensitivity and specificity.
Results:
Mean age was 63.1 years, and 47.2% were men for the overall sample. In the derivation sample, a cut point ≥ 0.21 for NHR yielded a sensitivity of 92.0% and a specificity of 25.0%; a cut point ≥ 0.52 for WHR yielded a sensitivity of 91.2% and a specificity of 25.0% for MS-OSA. Using the validation sample, the NHR, WHR, and dSBQ areas under the receiver operator curves were 69.8%, 65.2%, and 70.5%, respectively, for MS-OSA. There was no statistical difference with listwise comparison of the NHR and dSBQ areas under the receiver operator curves (P = .997); however, there was a significant difference between the WHR and dSBQ areas under the receiver operator curves (P = .015) for MS-OSA.
Conclusions:
The NHR is a viable obstructive sleep apnea screening tool comparable to the dSBQ, independent of witnessed apneas and body mass index, that can be used for different body types.
Citation:
Vana KD, Silva GE, Carreon JD, Quan SF. Using anthropometric measures to screen for obstructive sleep apnea in the Sleep Heart Health Study cohort. J Clin Sleep Med. 2021;17(8):1635–1643.
Keywords: anthropometric measurements, neck circumference, waist circumference, height, stature, STOP-Bang Questionnaire, obstructive sleep apnea
BRIEF SUMMARY
Current Knowledge/Study Rationale: Excessive weight may be distributed differently in persons with obstructive sleep apnea (OSA), who may not have a sleep witness. An easy-to-administer OSA screening tool that accounts for different body frames, independent of witnessed apneas or body mass index (kg/m2), is essential for quick identification of persons with OSA.
Study Impact: The neck circumference/height ratio is a viable OSA screening tool, comparable to the derived STOP-Bang Questionnaire, that can be used for different body types. The neck circumference/height ratio is incorporated easily into electronic health records for quick identification of persons at risk for OSA and for subsequent referral to a sleep provider.
INTRODUCTION
Approximately 13% of men and 6% of women aged 30–70 years are estimated to have moderate to severe obstructive sleep apnea (MS-OSA; apnea-hypopnea index [AHI] ≥ 15 events/h) in the United States. 1 The seriousness of the related comorbidities warrants the need for screening tools that can identify asymptomatic individuals reliably. 2 Individuals with a high risk of obstructive sleep apnea (OSA) may not have access to sleep clinics due to immobility, transportation difficulties, or living in rural areas. Excessive weight is an important characteristic of persons with OSA and is used to identify persons at risk. An easy-to-administer OSA screening tool that accounts for different body frames, independent of witnessed apneas or body mass index (BMI; kg/m2), is essential for quick identification of these individuals. Specifically, a free, easy-to-administer OSA screening tool that is compatible with sleep and primary care electronic health records and that may be completed reliably by clinicians is ideal for the quick identification of individuals at high risk for OSA for subsequent referral to sleep providers. 3 This study investigates the predictive abilities of anthropometric ratios for OSA.
BMI
Anthropometric ratios, such as BMI, have been used in OSA screening tools, such as the STOP-Bang Questionnaire (SBQ) developed by Chung and respective colleagues, 4–6 to account for differences in body habitus. Although the BMI uses height in its ratio, is determined easily, and is socially acceptable, 7 the World Health Organization 8 BMI cutoffs for identifying overweight (BMI ≥ 25 and < 30 kg/m2) and obese (BMI ≥ 30 kg/m2) adults may not be applicable to all populations. The same BMI may occur in individuals who vary widely in their amount of abdominal fat. 9,10 BMI does not account for varied fat distribution: general, abdominal, and cervical. 7,9,11–14 BMI cutoffs may not be appropriate for different races and ethnicities. 7,9,15 For example, Ko et al 16 measured 5,153 Hong Kong Chinese adults, aged 18‒89.5 years, and proposed that BMI cutoffs for overweight and obesity should be 23 kg/m2 and 26 kg/m2, respectively. Similarly, Kim and colleagues 14 noted that the AHI in 125 Korean men correlated best with a BMI ≥ 25 kg/m2. We also noted in a previous study of 47 sleep clinic patients who completed polysomnograms (66% female and 34% Hispanic) that replacing the BMI item in the SBQ with a BMI ≥ 30 kg/m2 correctly identified more individuals with OSA than a BMI ≥ 35 kg/m2, 17 which was similar to Ong et al’s 18 findings for Singapore sleep clinic patients screened for OSA with the SBQ. Furthermore, the World Health Organization BMI cutoffs for overweight and obesity do not account for differences in age, sex, or body composition. 9,19–21
Other anthropometric ratios
Because of these BMI limitations, additional anthropometric ratios have been proposed to account for body habitus differences. Current literature indicates that the neck circumference/height ratio (NHR) and the waist circumference/height ratio (WHR) may better represent obesity patterns that place individuals at risk for OSA than the BMI.
NHR
The NHR has been proposed to measure neck adiposity, a risk factor for upper airway collapse. 22–24 Ho et al 22 used the NHR to normalize the neck circumference for American participants, aged 5‒18 years; she and her colleagues determined that a ratio > 0.25 was associated with an odds ratio of 3.47 for children with AHI > 2 events/h and an odds ratio of 18 for adults with AHI > 5 events/h. Neck circumference was measured just superior to the cricoid cartilage to the nearest 0.5 cm. 22 In Canadian children (N = 53, 55% male), aged 8‒18 years, Narang et al 24 noted that a ratio ≥ 0.25 was associated significantly with the presence of OSA (P = .01). Mazzuca et al 23 noted that the NHR and hip circumference explained 33.9% of the AHI variability when they entered a stepwise multiple regression for 105 Italian women, but the NHR was not a significant variable for 423 Italian men. In men, the BMI, waist circumference, hip circumference, and NHR explained 30.2% of the AHI variability in the stepwise multiple regression, with the first 2 variables being most significant. 23 Narang et al 24 and Mazzuca et al 23 measured the neck circumference at the cricothyroid level while the participant stood with head erect.
WHR
Waist measurements are accepted as indirect measures of central obesity. 9 Although many waist measurement protocols have been proposed, Ostchega et al 25 noted that the measurements varied by less than 1.5 cm. Measurements may vary for many reasons: recent food and water intake, respiratory movements, and participants sucking in their abdomens when measured. 10,26 Additionally, measurements may be taken halfway or at the smallest measurement between the last rib and superior iliac crest, at the umbilicus, or at the superior iliac crest. 10,27
Flegal et al 12 assessed 12,901 American adults from the National Health and Nutrition Examination Surveys, 1999‒2014, and noted that the WHR slightly better correlated with the percentage of body fat than the waist circumference, measured just superior to the iliac crest; the differences were statistically significant only at ages 20‒39 years for men (P < .0001) and women (P < .0001). Kelishadi et al 13 measured the waist midway between the last rib and superior iliac crest to the nearest 0.1 cm after a normal expiration and used a WHR ratio ≥ 0.5 to denote abdominal obesity in 4,200 Iranian children and adolescents aged 7‒18 years.
Purpose
Approximately 60% of MS-OSA in adults can be linked to excessive weight. Given the evidence that the NHR and WHR can serve as metrics for the severity of obesity, the purpose of this study was to determine whether the NHR and/or the WHR can be used in lieu of the SBQ, a commonly used screening instrument for OSA. 28
METHODS
The Sleep Heart Health Study (SHHS) is a prospective multicenter cohort study that investigated the relationship of sleep-disordered breathing and cardiovascular diseases in the United States; details of the study design have been published elsewhere. 29 The study’s 1995 initial baseline recruitment enrolled 6,441 participants over age 40 years from current, geographically distinct, respiratory and cardiac general population cohorts, initiated between 1976 and 1995. 30 These cohorts included the Offspring Cohort and the Omni Cohort of the Framingham Heart Study in Massachusetts; the Hagerstown, MD, and Minneapolis, MN, sites of the Atherosclerosis Risk in Communities Study; the Hagerstown (MD), Pittsburgh (PA), and Sacramento (CA) sites of the Cardiovascular Health Study; 3 hypertension cohorts (Clinic, Worksite, and Menopause) in New York City; the Tucson Epidemiologic Study of Airways Obstructive Diseases and the Health and Environment Study; and the Strong Heart Study of American Indians in Oklahoma, Arizona, North Dakota, and South Dakota. 29 Participants were recruited to undergo an overnight home polysomnogram and limited physical examination and to complete sleep and health questionnaires. 30 The SHHS was approved by institutional review boards at all sites, and all participants signed consent forms. 29
The SHHS baseline data were used in this cross-sectional cohort study and included participants who completed polysomnograms, had neck and waist circumferences and height measurements, and had the SHHS variables used to construct proxy answers to each STOP item of the derived STOP-Bang Questionnaire (dSBQ). 31 The SHHS polysomnogram montage included the following: electroencephalogram, bilateral electrooculogram, thoracic and abdominal excursions measured by inductive plethysmography, airflow and oximetry, electrocardiogram, body position, and ambient light. The sensors were placed and calibrated by a certified sleep technician during an evening home visit. 29 The polysomnographic data were scored according to the guidelines developed by Rechtschaffen and Kales. 32 Apneas were defined as a complete or almost complete cessation of airflow lasting at least 10 seconds. Hypopneas were identified if the reduction in flow or volume was at least 25% lower than the baseline breathing for at least 10 seconds and did not meet the criteria for apneas. For this study, the AHI derived from apneas and hypopneas that resulted in a 4% or greater oxyhemoglobin desaturation was used. AHI was categorized into no OSA (AHI < 5 events/h), mild OSA (AHI ≥ 5 events/h to < 15 events/h), moderate OSA (AHI ≥ 15 events/h to < 30 events/h), and severe OSA (AHI ≥ 30 events/h). These severity categories were used to compare demographic characteristics. The categories of MS-OSA (AHI ≥ 15 events/h) and severe OSA (AHI ≥ 30 events/h) were employed to evaluate NHR, WHR, and dSBQ predictive abilities.
dSBQ
The SBQ developed by Chung, Yang, and colleagues 5 has been validated as a screening tool for OSA in diverse target populations. 3 The SBQ uses the BMI cutoff > 35 kg/m2, as 1 of 8 items: “Snoring,” “Tired,” “Observed” apnea, “Blood Pressure,” “BMI,” “Age over 50 years old,” “Neck circumference” (≥ 41 cm for women or ≥ 43 cm for men), and “Gender” male. 5 Because the SBQ was developed after the SHHS baseline evaluation, SHHS participant data were used to create proxy answers to 8 dSBQ items and to score the dSBQ with the alternative scoring method proposed by Chung and colleagues 5 for the SBQ. Specifically, the SHHS sleep habits questionnaire items were used to construct proxy answers to the STOP items on the dSBQ. 31
The variable, snore, was coded as present if the participants agreed to snoring loudly (louder than talking or loud enough to be heard through closed doors). 31 Tiredness or sleepiness during the day was noted as present if the participants reported feeling unrested during the day, no matter how many hours that they slept (“often” and “almost always” = yes; “never,” “rarely,” and “sometimes” = no) or reported feeling tired (“all of the time,” “most of the time,” and “a good bit of the time” = yes; “some of the time,” “a little bit of the time,” and “none of the time” = no). The variable, observed stop breathing, was present if the participant answered affirmatively to the question, “Based on what you have noticed or household members have told you, are there times when you stop breathing during your sleep?” Having high blood pressure was defined as positive if the participant answered “yes” to being treated with medication for high blood pressure. 31 High risk of sleep-disordered breathing was defined as answering affirmative to ≥ 2 questions. Low risk of sleep-disordered breathing was defined as answering affirmative to < 2 questions on the STOP. 5
The Bang items of the dSBQ were evaluated as present for BMI > 35 kg/m2, age > 50 years, neck circumference ≥ 43 cm for men and ≥ 41 cm for women, and male gender. 5 One point was assigned for each affirmative answer; 0 for no answers. Scores for the dSBQ were defined as low risk (0‒2 points), intermediate risk (3‒4 points), and high risk (5‒8 points) or if affirmative responses to 2 of the STOP items and 1 affirmative answer to BMI, neck circumference, or male gender. 5
Anthropometric measurements
The SHHS weight, height, and neck and waist measurements were recorded in centimeters. The participants’ NHR was calculated to the nearest tenth by dividing the participants’ neck circumference by the height. Likewise, the participants’ WHR was calculated to the nearest tenth by dividing the participants’ waist circumference by the height.
Statistical analyses
There were 6,305 SHHS participants with evaluable baseline data. Of these participants, 134 participants withdrew their permission to remain in the SHHS study and were excluded from analyses. Additionally, extreme or outlier values were observed for neck or waist measurements for some participants. Participants with neck and waist circumference measurements greater than ± 3 standard deviations from the mean (n = 138) were excluded before finalizing the sample. Thus, a total of 6,167 participants were included in this study. The participants then were divided randomly into one-third for derivation (n = 2,035) and two-thirds for validation (n = 4,132) analyses, similar to Nahapetian et al’s 33 split of SHHS data. The derivation sample was used to define the anthropometric ratio cut points to identify at least 90% of participants who had MS-OSA with a specificity (SP) of 25.0%. These ratios then were used to identify participants at risk for MS-OSA in the validation sample.
Continuous demographic data were evaluated with t tests, whereas dichotomous demographical data were evaluated with chi-square tests. The sensitivities (SNs), specificities (SPs), positive predictive values (PPVs), negative predictive values (NPVs), areas under receiver operator curves (AUCs), odds ratios, and the percentage correctly classified by OSA severity were evaluated for the NHR and WHR in predicting MS-OSA and compared to the same values of the dSBQ in the derivation and validation samples. The AUCs of the NHR and WHR were compared to the dSBQ AUC by DeLong’s t tests for 2 receiver operator curves in the derivation and validation samples. The statistical significance was set at a P value of ≤ .05. The analyses were completed with R (R Foundation for Statistical Computing, Vienna, Austria).
RESULTS
For all participants, the mean age was 63.1 years (standard deviation = 11), 47% were male, and the mean AHI was 8.52 events/h (standard deviation = 11.93). There were statistically significant differences between AHI categories and all variables of interest ( Table 1 and Table 2 ). Participants with severe OSA had significantly larger necks and waist circumferences, were taller and heavier, and had a higher BMI than participants without severe OSA. There were no significant differences for the variables of interest when comparing the derivation data with the validation data ( Table 3 ).
Table 1.
Descriptive categorical variables by AHI category (N = 6,167).
| Total | No OSA* (n = 3,362; 54.5%) | Mild OSA (n = 1,749; 28.4%) | Moderate OSA (n = 703; 11.4%) | Severe OSA (n = 353; 5.7%) | P† | |
|---|---|---|---|---|---|---|
| Snoring | ||||||
| No | 3,147 (87.2) | 1,669 (92.2) | 930 (86.4) | 372 (80.3) | 176 (67.7) | |
| Yes | 464 (12.8) | 142 (7.8) | 147 (13.6) | 91 (19.7) | 84 (32.3) | < .001 |
| Tired | ||||||
| No | 516 (8.4) | 258 (7.7) | 156 (8.9) | 62 (8.8) | 40 (11.4) | |
| Yes | 5,643 (91.6) | 3,099 (92.3) | 1,592 (91.1) | 640 (91.2) | 312 (88.6) | .04 |
| Breathing‡ | ||||||
| No | 2,380 (76.3) | 1,407 (83.8) | 646 (74.6) | 246 (65.3) | 81 (41.3) | |
| Yes | 739 (23.7) | 273 (16.2) | 220 (25.4) | 131 (34.7) | 115 (58.7) | < .001 |
| BP medication | ||||||
| No | 3,717 (60.5) | 2,203 (65.6) | 988 (56.6) | 372 (53.4) | 154 (44.1) | |
| Yes | 2,431 (39.5) | 1,154 (34.4) | 758 (43.4) | 324 (46.6) | 195 (55.9) | < .001 |
| BMI > 35 kg/m2 | ||||||
| No | 5,503 (90.8) | 3,144 (95.2) | 1,516 (88.2) | 575 (83.2) | 268 (77) | |
| Yes | 556 (9.2) | 158 (4.8) | 202 (11.8) | 116 (16.8) | 80 (23) | < .001 |
| Age > 50 y | ||||||
| No | 832 (13.5) | 586 (17.4) | 158 (9) | 58 (8.3) | 30 (8.5) | |
| Yes | 5,334 (86.5) | 2,776 (82.6) | 1,590 (91) | 645 (91.7) | 323 (91.5) | < .001 |
| Large neck§ | ||||||
| No | 5,264 (85.8) | 3,117 (93.1) | 1,422 (81.6) | 509 (73.2) | 216 (61.7) | |
| Yes | 872 (14.2) | 231 (6.9) | 321 (18.4) | 186 (26.8) | 134 (38.3) | < .001 |
| Sex | ||||||
| Male | 2,911 (47.2) | 1,238 (36.8) | 960 (54.9) | 458 (65.1) | 255 (72.2) | |
| Female | 3,256 (52.8) | 2,124 (63.2) | 789 (45.1) | 245 (34.9) | 98 (27.8) | < .001 |
| dSBQ > 3 | ||||||
| No | 2,081 (33.7) | 1,519 (45.2) | 422 (24.1) | 103 (14.7) | 37 (10.5) | |
| Yes | 4,086 (66.3) | 1,843 (54.8) | 1,327 (75.9) | 600 (85.3) | 316 (89.5) | < .001 |
| NHR | ||||||
| < mean | 3,125 (51.8) | 2,174 (66.1) | 675 (39.4) | 207 (30.3) | 69 (20) | |
| ≥ mean | 2,905 (48.2) | 1,115 (33.9) | 1,037 (60.6) | 477 (69.7) | 276 (80) | < .001 |
| WHR | ||||||
| < mean | 3,077 (53.3) | 2,007 (64.5) | 731 (44.1) | 244 (36.6) | 95 (28.2) | |
| ≥ mean | 2,697 (46.7) | 1,106 (35.5) | 926 (55.9) | 423 (63.4) | 242 (71.8) | < .001 |
Values are presented as n (%). *No OSA = AHI < 5 events/h; mild OSA = AHI ≥ 5 events/h to < 15 events/h; moderate OSA = AHI ≥ 15 events/h to < 30 events/h; severe OSA = AHI ≥ 30 events/h. †P value for chi-square tests. ‡Observed stopped breathing. §Neck circumference ≥ 43 cm for men and ≥ 41 cm for women. AHI = apnea-hypopnea index, BMI = body mass index, BP = blood pressure, dSBQ = derived STOP-Bang Questionnaire, NHR = neck circumference/height ratio, OSA = obstructive sleep apnea, WHR = waist circumference/height ratio.
Table 2.
Descriptive continuous variables by AHI category (N = 6,167).
| Total (N = 6,167) | No OSA* (n = 3,362) | Mild OSA (n = 1,749) | Moderate OSA (n = 703) | Severe OSA (n = 353) | P† | |
|---|---|---|---|---|---|---|
| Age (y) | 63.12 (11.00) | 61.40 (11.12) | 65.13 (10.52) | 65.44 (10.35) | 64.97 (10.66) | < .001 |
| AHI (events/h) | 8.52 (11.93) | 1.79 (1.43) | 8.93 (2.80) | 20.66 (4.16) | 46.43 (15.17) | < .001 |
| Neck circumference (cm) | 37.83 (4.09) | 36.51 (3.79) | 38.87 (3.77) | 39.93 (3.90) | 41.00 (3.81) | < .001 |
| Waist circumference (cm) | 97.24 (13.44) | 93.24 (12.81) | 100.57 (12.53) | 103.49 (12.59) | 105.53 (12.08) | < .001 |
| Height (cm) | 167.33 (9.58) | 166.30 (9.41) | 168.01 (9.70) | 169.13 (9.66) | 170.09 (9.15) | < .001 |
| Weight (kg) | 79.22 (16.26) | 74.65 (14.47) | 82.59 (15.97) | 86.81 (16.90) | 90.91 (17.01) | < .001 |
| BMI (kg/m2) | 28.19 (4.86) | 26.91 (4.35) | 29.19 (4.84) | 30.25 (5.21) | 31.25 (5.02) | < .001 |
Values are presented as mean (standard deviation). *No OSA = AHI < 5 events/h; mild OSA = AHI ≥ 5 events/h to < 15 events/h; moderate OSA = AHI ≥ 15 events/h to < 30 events/h; severe OSA = AHI ≥ 30 events/h. †P value for t tests. AHI = apnea-hypopnea index, BMI = body mass index, OSA = obstructive sleep apnea.
Table 3.
Descriptive characteristics for derivation and validation data.
| Total (N = 6,167) | Derivation (n = 2,035) | Validation (n = 4,132) | P* | |
|---|---|---|---|---|
| Age (y) | 63.12 (11.00) | 62.95 (11.05) | 63.21 (10.98) | .38 |
| Race/ethnicity, n (%) | .43 | |||
| White | 4,852 (78.7) | 1,599 (78.6) | 3,253 (78.7) | |
| Black | 486 (7.9) | 146 (7.2) | 340 (8.2) | |
| Asian/Pacific Islander | 88 (1.4) | 26 (1.3) | 62 (1.5) | |
| Native American | 464 (7.5) | 163 (8.0) | 310 (7.3) | |
| Hispanic/Mexican | 274 (4.4) | 100 (4.9) | 174 (4.2) | |
| Other | 3 (0.1) | 1 (0.0) | 2 (0.1) | |
| AHI (events/h) | 8.52 (11.93) | 8.46 (11.76) | 8.55 (12.01) | .76 |
| Neck circumference (cm) | 37.83 (4.09) | 37.73 (4.08) | 37.88 (4.10) | .18 |
| Waist circumference (cm) | 97.24 (13.44) | 97.65 (13.57) | 97.04 (13.37) | .11 |
| Height (cm) | 167.33 (9.58) | 167.36 (9.55) | 167.31 (9.60) | .87 |
| Weight (kg) | 79.22 (16.26) | 79.23 (15.90) | 79.21 (16.44) | .96 |
| BMI (kg/m2) | 28.19 (4.86) | 28.22 (4.89) | 28.17 (4.85) | .71 |
| Snoring, n (%) | .68 | |||
| No | 3,147 (87.2) | 1,041 (86.8) | 2,106 (87.3) | |
| Yes | 464 (12.8) | 158 (13.2) | 306 (12.7) | |
| Tired, n (%) | .53 | |||
| No | 516 (8.4) | 164 (8.1) | 352 (8.5) | |
| Yes | 5,643 (91.6) | 1,871 (91.9) | 3,772 (91.5) | |
| Breathing†, n (%) | .36 | |||
| No | 2,380 (76.3) | 781 (75.3) | 1,599 (76.8) | |
| Yes | 739 (23.7) | 256 (24.7) | 483 (23.2) | |
| BP medication, n (%) | .45 | |||
| No | 3,717 (60.5) | 1,239 (61.1) | 2,478 (60.1) | |
| Yes | 2,431 (39.5) | 788 (38.9) | 1,643 (39.9) | |
| BMI > 35 kg/m2, n (%) | .48 | |||
| No | 5,503 (90.8) | 1,809 (90.5) | 3,694 (91.0) | |
| Yes | 556 (9.2) | 191 (9.6) | 365 (9.0) | |
| Age > 50 y, n (%) | .67 | |||
| No | 832 (13.5) | 280 (13.8) | 552 (13.4) | |
| Yes | 5,334 (86.5) | 1,755 (86.2) | 3,579 (86.6) | |
| Large neck‡, n (%) | .55 | |||
| No | 5,264 (85.8) | 1,744 (86.2) | 3,520 (85.6) | |
| Yes | 872 (14.2) | 280 (13.8) | 592 (14.4) | |
| Sex, n (%) | .64 | |||
| Male | 2,911 (47.2) | 952 (46.8) | 1,959 (47.4) | |
| Female | 3,256 (52.8) | 1,083 (53.2) | 2,173 (52.6) | |
| dSBQ > 3, n (%) | .68 | |||
| No | 2,081 (33.7) | 694 (34.1) | 1,387 (33.6) | |
| Yes | 4,086 (66.3) | 1,341 (65.9) | 2,745 (66.4) | |
| NHR, n (%) | .12 | |||
| < mean | 3,125 (51.8) | 1,060 (53.3) | 2,065 (51.1) | |
| ≥ mean | 2,905 (48.2) | 930 (46.7) | 1,975 (48.9) | |
| WHR, n (%) | .54 | |||
| < mean | 3,077 (53.3) | 1,018 (53.9) | 2,059 (53.0) | |
| ≥ mean | 2,697 (46.7) | 871 (46.1) | 1,826 (47.0) |
Data are presented as mean (standard deviation) for continuous measures and n (%) for categorical measures. *P value for chi-square tests for categorical data and t tests for continuous data. †Observed stopped breathing. ‡Neck circumference ≥ 43 cm for men and ≥ 41 cm for women. AHI = apnea-hypopnea index, BMI = body mass index, BP = blood pressure, dSBQ = derived STOP-Bang Questionnaire, NHR = neck circumference/height ratio, WHR = waist circumference/height ratio.
In the derivation sample (n = 2,035), the NHR cut point of ≥ 0.21 resulted in an SN of 91.9% and an SP of 26.1% for AHI ≥ 15 events/h ( Table 4 ). The derivative WHR cut point of ≥ 0.52 resulted in an SN of 91.0% and an SP of 23.5% for AHI ≥ 15 events/h. The NHR and WHR cut points for women and men differed by only 0.01‒0.02. The dSBQ had an SN of 86.3% and a higher SP of 38.4% for AHI ≥ 15 events/h. For MS-OSA, the PPVs were very similar: 20.6%, 20.4%, and 22.6% for NHR, WHR, and dSBQ, respectively. Likewise, the NPVs were similar: 93.9%, 92.4%, and 93.1% for NHR, WHR, and dSBQ, respectively. For AHI ≥ 5 events/h, the PPVs were higher and the NPVs were lower for NHR, WHR, and dSBQ, with nearly identical AUCs to those found for MS-OSA.
Table 4.
Derivation sample: predictive parameters of the NHR, WHR, and dSBQ for OSA by AHI category (n = 2,035).
| AHI ≥ 5 events/h (n = 919) | AHI ≥ 15 events/h (n = 351) | AHI ≥ 30 events/h (n = 117) | |||||||
|---|---|---|---|---|---|---|---|---|---|
| NHR | WHR | dSBQ | NHR | WHR | dSBQ | NHR | WHR | dSBQ | |
| SN, % (95% CI) | 88.8 (86.8–88.8) | 88.5 (86.2–88.5) | 79.4 (76.8–82.0) | 91.9 (88.9–91.9) | 91.0 (88.1–91.0) | 86.3 (82.7–89.9) | 94.0 (88.8–94.0) | 92.9 (87.5–92.9) | 89.7 (84.2–95.2) |
| SP, % (95% CI) | 32.8 (29.9–32.8) | 28.7 (25.9–28.7) | 45.3 (42.3–48.2) | 26.1 (23.9–26.1) | 23.5 (21.4–23.5) | 38.4 (36.0–40.7) | 24.0 (22.1–24.0) | 21.7 (19.8–21.7) | 35.6 (33.4–37.7) |
| PPV, % (95% CI) | 52.2 (51.0–52.2) | 51.4 (50.2–51.4) | 54.4 (51.8–57.1) | 20.6 (19.9–20.6) | 20.4 (19.7–20.4) | 22.6 (20.4–24.8) | 7.1 (6.7–7.1) | 7.0 (6.6–7.0) | 7.8 (6.4–9.3) |
| NPV, % (95% CI) | 77.9 (74.5–77.9) | 74.4 (70.5–74.4) | 72.8 (69.5–76.1) | 93.9 (91.7–93.9) | 92.4 (89.9–92.4) | 93.1 (91.2–95.0) | 98.5 (97.2–98.5) | 98.0 (96.5–98.0) | 98.3 (97.3–99.2) |
| AUC, % (95% CI) | 70.0 (67.7–72.3) | 67.3 (64.8–69.7) | 62.3 (59.6–65.1) | 70.5 (67.5–73.5) | 65.2 (62.1–68.3) | 62.3 (59.4–65.3) | 74.5 (69.8–79.1) | 64.2 (59.4–68.9) | 62.7 (58.8–66.5) |
| OR (95% CI) | 3.85 (3.03–4.92) | 3.06 (2.40–3.94) | 3.19 (2.62–3.90) | 3.98 (2.71–6.07) | 3.11 (2.13–4.69) | 3.93 (2.88–5.47) | 4.92 (2.45–11.73) | 3.61 (1.86–8.11) | 4.83 (2.75–9.31) |
| Correctly classified (95% CI) | 58.1 (56.3–58.1) | 56.2 (54.3–56.2) | 60.7 (60.7–60.7) | 37.5 (35.6–37.5) | 35.4 (33.7–35.4) | 46.6 (46.6–46.7) | 28.1 (26.3–28.1) | 25.9 (24.1–25.9) | 38.7 (38.7–38.7) |
AHI = apnea-hypopnea index, AUC = area under receiver operator curve, CI = confidence index, dSBQ = derived STOP-Bang Questionnaire, NHR = neck circumference/height ratio, NPV = negative predictive value, OR = odds ratio, OSA = obstructive sleep apnea, PPV = positive predictive value, SN = sensitivity, SP = specificity, WHR = waist circumference/height ratio.
Listwise comparisons were used to determine statistical significance between pairs of AUCs for AHI ≥ 15 events/h in the derivation sample. The AUCs for the NHR, WHR, and dSBQ were 70.5%, 65.2%, and 62.3%, respectively. The listwise comparison of the NHR AUC (70.5%) with the dSBQ AUC (70.5%) was statistically insignificant (P = .997). Comparison of the WHR AUC (65.2%) with the dSBQ AUC (69.8%) showed statistical significance (P = .015). The odds ratios were 3.98 (95% confidence interval, CI, 2.7–6.07; NHR), 3.11 (95% CI, 2.13–4.69; WHR), and 3.93 (95% CI, 2.88–5.47; dSBQ) for MS-OSA. Although the NHR had a higher SN than the dSBQ (91.1% and 86.3%, respectively), the dSBQ was better for correctly classifying participants (46.6% vs 37.5%) due to the higher SP. The dSBQ (46.6%) also was better than the WHR (35.4%) for correctly classifying participants.
The validation data were very similar to the derivation data for AHI ≥ 15 events/h. In the validation sample (n = 4,132), the NHR cut point of ≥ 0.21 resulted in an SN of 92.3% and an SP of 24.0% for AHI ≥ 15 events/h ( Table 5 ). The WHR cut point of ≥ 0.52 resulted in an SN of 91.2% and an SP of 25.1% for AHI ≥ 15 events/h. The dSBQ had an SN of 87% and a higher SP of 37.8%. The NHR again had a higher SN than the dSBQ (92.3% and 87.0%, respectively). The NHR, WHR, and dSBQ PPVs were similar: 19.9%, 20.2%, and 22.3%, respectively. The NPVs also were similar: 93.8% (NHR), 93.2% (WHR), and 93.4% (dSBQ). For AHI ≥ 5 events/h, the PPVs were higher and the NPVs were lower for NHR, WHR, and dSBQ, with nearly identical AUCs to those found for MS-OSA.
Table 5.
Validation sample: predictive parameters of the NHR, WHR, and dSBQ for OSA by AHI category (n = 4,132).
| AHI ≥ 5 events/h (n = 1,886) | AHI ≥ 15 events/h (n = 705) | AHI ≥ 30 events/h (n = 236) | |||||||
|---|---|---|---|---|---|---|---|---|---|
| NHR | WHR | dSBQ | NHR | WHR | dSBQ | NHR | WHR | dSBQ | |
| SN, % (95% CI) | 89.6 (88.3–89.6) | 87.4 (85.9–87.4) | 80.2 (78.4–82.0) | 92.3 (90.2–92.3) | 91.2 (88.9–91.2) | 87.0 (84.5–89.4) | 93.9 (90.8–93.9) | 93.3 (89.8–93.3) | 89.4 (85.5–93.3) |
| SP, % (95% CI) | 30.3 (28.3–30.3) | 30.6 (28.7–30.6) | 45.1 (43.1–47.2) | 24.0 (22.6–24.0) | 25.1 (23.7–25.1) | 37.8 (36.2–39.4) | 22.1 (20.8–22.1) | 23.3 (21.9–23.3) | 35.0 (33.5–36.5) |
| PPV, % (95% CI) | 51.8 (51.0–51.8) | 51.9 (51.1–51.9) | 55.1 (53.3–57.0) | 19.9 (19.4–19.9) | 20.2 (19.7–20.2) | 22.3 (20.8–23.9) | 6.8 (6.5–6.8) | 7.0 (6.7–7.0) | 7.7 (6.7–8.7) |
| NPV, % (95% CI) | 77.7 (75.2–77.7) | 74.0 (71.4–74.0) | 73.1 (70.8–75.4) | 93.8 (92.2–93.8) | 93.2 (91.6–93.2) | 93.4 (92.1–94.7) | 98.4 (97.5–98.4) | 98.3 (97.4–98.3) | 98.2 (97.5–98.9) |
| AUC, % (95% CI) | 71.1 (69.5–72.7) | 65.8 (64.1–67.5) | 62.7 (60.8–64.6) | 69.8 (67.6–71.9) | 65.1 (62.9–67.3) | 62.4 (60.3–64.4) | 71.3 (68.0–74.6) | 67.2 (63.9–70.4) | 62.2 (59.5–64.9) |
| OR (95% CI) | 3.75 (3.15–4.48) | 3.06 (2.59–3.62) | 3.34 (2.90–3.85) | 3.76 (2.84–5.08) | 3.47 (2.65–4.63) | 4.05 (3.23–5.12) | 4.36 (2.62–7.89) | 4.25 (2.59–7.53) | 4.54 (3.04–7.07) |
| Correctly classified (95% CI) | 57.3 (56.1–57.3) | 56.8 (55.6–56.8) | 61.2 (61.1–61.2) | 35.5 (34.3–35.5) | 36.5 (35.2–36.5) | 46.2 (46.2–46.2) | 26.2 (24.9–26.2) | 27.4 (26.0–27.4) | 38.1 (38.1–38.1) |
AHI = apnea-hypopnea index, AUC = area under receiver operator curve, CI = confidence index, dSBQ = derived STOP-Bang Questionnaire, NHR = neck circumference/height ratio, NPV = negative predictive value, OR = odds ratio, OSA = obstructive sleep apnea, PPV = positive predictive value, SN = sensitivity, SP = specificity, WHR = waist circumference/height ratio.
Listwise comparisons were completed on the validation data for AHI ≥ 15 events/h. The AUCs for the NHR, WHR, and dSBQ were 69.8%, 65.1%, and 62.4%, respectively. Listwise comparison of the NHR AUC (69.8%) and the dSBQ AUC (71.1%) showed no statistical significance (P = .20). Listwise comparisons of the WHR and dSBQ AUCs did show statistical significance (P < .001). The odds ratios were 3.76 (95% CI, 2.84–5.08; NHR), 3.47 (95% CI, 2.65–4.63; WHR), and 4.05 (95% CI, 3.23–5.12; dSBQ). In the validation sample, again the dSBQ correctly classified more participants than the NHR (46.2% vs 35.5%) due to a higher SP. The dSBQ (46.2%) also was better than the WHR (36.5%) for correctly classifying participants.
The derivation and validation PPVs for participants with an AHI ≥ 30 events/h were much lower than those for AHI ≥ 15 events/h, as expected, due to the cut-off values for the NHR and WHR being determined for MS-OSA. Interestingly, more participants with an AHI ≥ 5 events/h were classified correctly.
DISCUSSION
Participants with severe OSA were more likely to have higher BMIs and anthropometric measurements: neck circumference, waist circumference, height, and weight, which underscores the role of obesity and stature in the degree of OSA. BMI cutoffs have been shown not to accurately reflect different types of obesity in different ethnic and racial groups or by sex. Additionally, accurate weights may be difficult to obtain from individuals who do not have a scale or refuse to have their weight taken due to society shaming. Some individuals are not comfortable in determining or disclosing their BMI, if known. Furthermore, many individuals live alone and do not have a sleep witness for snoring and apneas, which are required items of the SBQ.
The NHR is a statistically sound alternative method for screening individuals for MS-OSA. In both the derivation and validation samples, the SNs were higher for the NHR and WHR than for the dSBQ for MS-OSA; however, the dSBQ correctly classified more participants than did the NHR and WHR, due to a higher SP than that of the NHR or WHR. However, when using listwise comparisons for the AUCs, the NHR and dSBQ were not statistically different for MS-OSA prediction.
There was no statistically significant difference between the AUCs for the dSBQ and the NHR for MS-OSA, which suggests that their abilities to predict the presence of MS-OSA are similar, suggesting construct validity. Furthermore, the NHR does not require a sleep witness and may be calculated easily by nonsleep professionals, which is important in remote areas that do not have sleep clinics. The NHR does not rely on BMI, which may not predict obesity type accurately by sex, race, and ethnicity.
Screening tools with high SPs may be useful in ruling out MS-OSA false positives in low-risk community populations. These high-SP tools are ideal when screening funds are limited. The dSBQ demonstrated moderate SPs of 38.4% and 37.8% for the derivation and validation samples, respectively, for MS-OSA. However, the NHR does not increase screening costs, because the neck circumference and height measurements are collected traditionally at clinic visits. Sleep clinicians may prefer to use tools with higher SNs to avoid missing cases of MS-OSA that may lead to subsequent adverse health problems and higher health care costs; the NHR has a higher SN and minimally impacts costs while personalizing health care. Its SN and AUC are comparable to those of the dSBQ for MS-OSA, it is included easily within clinics’ electronic health records, and it is simple to understand. Patients with at-risk NHRs (≥ 0.21) may be identified and referred to sleep clinics promptly.
Strengths
The research sample has many strengths. The NHR, WHR, and dSBQ predicted MS-OSA for a large multicenter, community sample of individuals who were not preselected to attend sleep clinics. This sample also was diverse in race and ethnicity and explored persons with respiratory and cardiovascular diseases related to MS-OSA. Additionally, the NHR demonstrated construct validity with the dSBQ for MS-OSA screening.
Limitations
One of the limitations of this study is that the baseline data were collected before the SBQ was created. 5 Therefore, the researchers constructed the dSBQ variables from the SHHS data. Additionally, global studies are needed to test NHR prediction in a multitude of races/ethnicities. The SHHS participants do not represent all races and ethnicities. Neck, waist, and height measurements may vary between providers’ and patients’ measurements and may not be completed routinely at all visits.
CONCLUSIONS
The NHR is a viable OSA screening tool, independent of witnessed snoring, apneas, and BMI, that is statistically comparable to the dSBQ for MS-OSA. Individuals at risk for MS-OSA may have different body types, no bed partners, and limited access to sleep clinics. The NHR is a free, easy-to-administer MS-OSA screening tool that is compatible with electronic health records in primary care and sleep clinics and is person-centered, cost-effective, valid, and efficient. Large, prospective, multicenter studies are needed to investigate the validity and reliability of the NHR in predicting MS-OSA in different settings. The NSR should be tested globally with different racial and ethnic groups, and its reliability should be tested by lay providers or family members. Screening results may be used to determine who should be encouraged to access sleep clinics.
DISCLOSURE STATEMENT
All authors have seen and approved this manuscript. This work was funded by U01HL53938 and U01HL53938-07S (University of Arizona). The authors report no conflicts of interest.
ABBREVIATIONS
- AHI
apnea-hypopnea index
- AUC
area under the receiver operator curve
- BMI
body mass index
- CI
confidence interval
- dSBQ
derived STOP-Bang Questionnaire
- MS-OSA
moderate-to-severe obstructive sleep apnea
- NHR
neck circumference/height ratio
- NPV
negative predictive value
- OSA
obstructive sleep apnea
- PPV
positive predictive value
- SBQ
STOP-Bang Questionnaire
- SHHS
Sleep Heart Health Study
- SN
sensitivity
- SP
specificity
- WHR
waist circumference/height ratio
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