Sleep and Breathing

, Volume 15, Issue 4, pp 819–826 | Cite as

The NAMES assessment: a novel combined-modality screening tool for obstructive sleep apnea

  • Shyam Subramanian
  • Sean E. Hesselbacher
  • Raymond Aguilar
  • Salim R. Surani
Original Article

Abstract

Purpose

Obstructive sleep apnea (OSA) remains underdiagnosed, despite our understanding of its impact on general health. Current screening methods utilize either symptoms or physical exam findings suggestive of OSA, but not both. The purpose of this study was to develop a novel screening tool for the detection of OSA, the NAMES assessment (neck circumference, airway classification, comorbidities, Epworth scale, and snoring), combining self-reported historical factors with physical exam findings.

Methods

Subjects were adults without previously diagnosed OSA, referred to a community sleep center for suspicion of OSA. General health, Epworth Sleepiness Scale (ESS), and Berlin questionnaires were completed, and a physical exam focusing on modified Friedman (MF) grade, body mass index (BMI), and neck circumference (NC) was performed prior to polysomnography. OSA was defined by a respiratory disturbance index ≥15. Each variable was dichotomized, and cutoff values were determined for the NAMES tool in a pilot group of 150 subjects. The NAMES score was calculated from NC, MF, comorbidities, ESS, and loud snoring values. The performances of the NAMES, Berlin questionnaire, and ESS screening tests in predicting OSA were then compared in a validation group of 509 subjects.

Results

In the pilot population, the cutoff value for the composite NAMES tool was calculated at ≥3 points. In the validation group, NAMES demonstrated similar test characteristics to the Berlin questionnaire, and sensitivity was better than that seen with the Epworth scale. The addition of BMI and gender to the tool improved screening characteristics.

Conclusions

The NAMES assessment is an effective, inexpensive screening strategy for moderate to severe OSA.

Keywords

Obstructive sleep apnea Screening Sensitivity and specificity 

Introduction

Background

Obstructive sleep apnea (OSA) is a common medical disorder with general health and quality-of-life implications [1]. Associations with important medical conditions, including diabetes mellitus (DM), coronary arterial disease (CAD), hypertension (HTN), and cerebrovascular accident (CVA) are well-documented [1, 2, 3, 4, 5, 6]. Untreated OSA may result in excessive daytime sleepiness, impaired decision making, and automobile accidents [7, 8, 9]. Despite the importance of recognizing and treating OSA, it remains underdiagnosed [10, 11]. While continuous overnight polysomnography (PSG) performed in a sleep laboratory remains the current gold standard for diagnosis, its widespread use is limited by issues related to access as well as high cost. With the recognized association between OSA and comorbid conditions, patients with likely milder OSA are being referred to sleep laboratories, further increasing the strain on existing resources. Screening strategies to better detect sleep apnea in the community and, more importantly, guide triage decisions for referrals to overnight PSG thus become very important. Current screening strategies, such as the Epworth Sleepiness Scale (ESS) and the Berlin questionnaire, have relied primarily on self-reported patient symptoms and have provided mixed results [12, 13, 14]. Other investigators have focused on the utility of morphologic features, including the upper airway in predicting the presence of OSA [15]. The neck circumference (NC) is another anthropometric measure that has been shown to correlate significantly with the severity of OSA [16]. There is no screening strategy that makes systematic use of these anatomic criteria. Given the heterogeneity of presenting complaints and the need for a more broad-based screening strategy for OSA, formulation of more comprehensive recognition profiles may be the need of the hour.

Rationale and objective

A novel screening tool, the NAMES assessment (neck circumference, airway classification, comorbidities, Epworth scale, and snoring), was designed to incorporate past medical history, current symptoms, and physical exam into a single assessment for OSA. The method was developed and verified in a high-risk population for OSA, patients referred for PSG because of suspected OSA. Additionally, cardiovascular and metabolic morbidity is clearly defined for those with moderate to severe OSA (RDI ≥15), though it remains unknown if the same remains true for those with mild OSA [1, 3, 5]. Thus, OSA was prospectively defined for this high-prevalence population as a respiratory disturbance index (RDI) of ≥15 per hour by PSG.

Methods

Study participants

Data were collected from 1,418 consecutive eligible patients referred to the Torr Sleep Center (Corpus Christi, TX) for PSG evaluation of suspected OSA between February 5, 2007 and April 21, 2008. All subjects were ≥18 years of age without a prior diagnosis of OSA. Subjects were excluded from the final analysis if any of the NAMES components were incomplete or if they failed to undergo PSG and recording of the RDI. The study was exempted from IRB approval at participating institutions, since all data used in this study were routinely collected on each patient referred for sleep studies prior to the conception of this study.

Baseline evaluation

Prior to undergoing PSG, the subjects completed questionnaires. A general health questionnaire obtained information about demographics, comorbidities, specifically DM, HTN, CAD, and CVA and general sleep health, including snoring, witnessed apneas, and subjective daytime sleepiness. Questions directed at comorbidities requested that the subject answer yes/no to the following questions: “Do you snore?,” “Do you have diabetes?,” “Do you have hypertension?,” “Have you had a stroke?,” “Do you have coronary artery disease?,” and “Are you sleepy during the day?” Secondly, the subjects completed Epworth and Berlin questionnaires, which have been described elsewhere [12, 14]. Physical examination was performed by trained technologists. The general examination included height and weight measurement. Measurements were taken of each subject’s neck, hip, and waist circumference. Neck circumference was measured in the transverse plane at the level of the cricoid cartilage. A detailed oral airway evaluation was performed, and each subject was classified based on the modified Freidman (MF) staging system, equivalent to the modified Mallampati grade [15]. Figure 1 depicts the data sheet completed by a technologist for each subject, with the historical data based on the questionnaires and the exam data based on their physical exam findings.
Fig. 1

Data collection sheet used in the development and validation of the NAMES screening test. Neck circumf, neck circumference, in centimeters; M. Friedman, modified Friedman grade, as illustrated below; Co-morbid, comorbidities; Heart, coronary artery disease

Polysomnographic evaluation

Overnight comprehensive PSG was performed in the sleep laboratory, with multichannel recordings monitoring electroencephalogram, electrocardiogram, electrooculograms, submentalis electromyogram, airflow, respiratory effort, oxygen saturation, and anterior tibialis electromyogram. The data were scored manually according to the American Academy of Sleep Medicine Scoring Guidelines by a blinded technologist [17]. The RDI was calculated by summing the number of obstructive apneas, hypopneas, and respiratory effort-related arousals per hour. Airflow was measured using a nasal thermistor, and hypopneas required a 4% decrease in oxygen saturation in addition to the defined reduction in airflow. Technologists were chosen with minimum experience of scoring 500 PSG, and intra- and interscorer variability were standardized by means of a point system in place at the sleep center [18].

Statistics

In the pilot group of 150 subjects, each continuous variable (NC, MF, comorbidities, and ESS) was dichotomized. The cutoff values for ESS and NC were based on established literature. The cutoff values for MF and comorbidities were chosen so as to maximize sensitivity. Each continuous variable and one dichotomous variable, snoring, was assigned one point for a positive result and summed, giving a total score out of 5 possible points. The NAMES, Berlin, and Epworth tools were then applied to a distinct validation group of 509 subjects. Correlation analysis was performed by calculating the Spearman’s rank correlation coefficient. A p value of <0.05 was considered statistically significant.

Results

Clinical characteristics and demographics

Of 1,418 eligible subjects, 659 subjects had complete data collected and were included in the final analysis. The vast majority of subjects excluded from analysis (94%) were missing historical data (ESS, snoring, or comorbidities). The baseline clinical characteristics and demographics of the pilot and verification groups are shown in Table 1. The groups were similar in terms of demographics and basic clinical characteristics. The subjects predominantly identified themselves as Hispanic or Caucasian in both groups, and the mean body mass index (BMI) fell within the range of obesity; subject characteristics were otherwise unremarkable. The baseline characteristics of subjects that did not provide complete data were not different than the subjects included in the final analysis, except they were slightly younger (53 ± 14 years vs. 55 ± 14 years).
Table 1

Clinical and demographic information of the study population

Group

Pilot

Verification

Number

150

509

Age, mean (±s.d.)

56 ± 13

55 ± 14

Height, cm (± s.d.)

170 ± 11

171 ± 11

Weight, kg (± s.d.)

67 ± 14

69 ± 18

Body mass index, mean (±s.d.)

35 ± 7

35 ± 9

Male, no. (%)

86 (57%)

299 (59%)

Ethnicity, no. (%)

  

Hispanic

75 (50%)

219 (43%)

Caucasian

75 (50%)

274 (54%)

Black

0 (0%)

15 (3%)

Asian

0 (0%)

1 (0%)

Determination of cutoff values for the screening test

The receiver–operator curves (ROC) of each continuous variable component were plotted (Fig. 1a). The test with the greatest area under the receiver–operator curve (AUROC) was NC, at 0.66. Based on established literature, the cutoff values for NC were ≥16 cm in females and ≥17 cm in males, and ESS ≥11 points. The cutoff value chosen for the MF grade was ≥2 and for comorbidities was ≥1. For snoring, “yes” was a positive value. The optimal cutoff value for the composite NAMES test was ≥3 points, which yielded excellent sensitivity (91%) while maintaining some degree of specificity (23%). A cutoff of ≥2 points gave a sensitivity of 100%, but a very low specificity of 3%. The components of the NAMES tool are summarized in Table 2.
Table 2

Components of the NAMES and NAMES2 screening tools for obstructive sleep apnea

 

NAMES

NAMES2

Component

Points assigned

Historical

Comorbidities (≥1)

1

1

Epworth Sleepiness Scale (≥11)

1

1

Loud snoring (yes)

1

1

Gender (male)

0

1

Physical examination

Neck circumference (males ≥17; females ≥16)

1

1

Modified Friedman grade (≥2)

1

1

Body mass index (≥30)

0

1

Positive score/total possible

3/5

4/7

Alternative combinations

Alternative combinations were retrospectively created using all of the NAMES components, along with BMI and gender. In the pilot group, the cutoff for BMI that maximized sensitivity while maintaining specificity was ≥28, though that was rounded up to 30, in keeping with cutoff points established by the World Health Organization [19]. The AUROC for BMI (Fig. 1a) was similar to that of NC, 0.66. Male gender was assigned a positive value. Each positive result was assigned one point and summed to give a total for each combination; in some combinations, characteristics that demonstrated the best discriminative ability (BMI, NC, MF) were weighted double. The characteristics of the alternative combinations are shown in Table 3. The combination with the highest AUROC was chosen as the best alternative tool (NAMES2), and a screening cutoff was chosen that produced a sensitivity ≥90%. NAMES2 consisted of the NAMES combination plus a point each for BMI and male gender, showing a sensitivity and specificity of 91% and 27% at a cutoff of ≥4 points. The ROCs for NAMES and NAMES2 are shown in Fig. 1b.
Table 3

Performance of combinations for the obstructive sleep apnea screening tool

Combination

AUROC

Screening cutoff (points)

Sensitivity (%)

Specificity (%)

NC + MF + CM + ESS + S + BMI

0.6577

3

98

13

NC + MF + CM + ESS + S + M

0.6572

3

96

16

NC + MF + CM + ESS + S + BMI + M (NAMES2)

0.6690

4

91

27

NC + MF + M + ESS + S

0.6583

2

99

9

BMI + MF + CM + ESS + S + M

0.6436

3

98

9

(NC + MF) × 2 + CM + ESS + S

0.6661

4

90

26

(NC + BMI) × 2 + M + ESS + S

0.6433

3

94

26

(NC + MF) × 2 + M + ESS + S

0.6484

3

91

13

(NC + BMI) × 2 + CM + ESS + S

0.6426

3

91

21

(NC + MF + BMI) × 2 + CM + ESS + S + M

0.6478

5

93

23

AUROC area under the receiver–operator curve; Screening cutoff cutoff for abnormal result giving ≥90% sensitivity, CM comorbidities; S snoring; M male gender

Validation of the NAMES screening tool

In the validation group, OSA was detected by PSG in 58.5% of the subjects. The mean RDI was 38 ± 8 (range, 0–155) and the mean SaO2 nadir 78 ± 11% (range, 21–98%). The sensitivity and specificity of NAMES for the detection of OSA were similar to that seen with the pilot group, 91% and 23%, respectively. The positive (PPV) and negative (NPV) predictive values were 62% and 63% while the positive (LR+) and negative (LR−) likelihood ratios were 1.17 and 0.41, respectively. The Berlin questionnaire demonstrated similar sensitivity (93%) and lower specificity (14%) to the NAMES tool. The Epworth scale revealed modest performance in both sensitivity (57%) and specificity (53%). The NAMES test performed similarly to the Berlin questionnaire and superiorly to the ESS in the characteristics used to stratify screening tests, LR− (0.41 vs. 0.49 and 0.82) and NPV (63% vs. 59% and 46%), as shown in Fig. 2. None of the three tests performed well in specificity, PPV or LR+. The NAMES2 assessment showed similar sensitivity (92%) and superior LR− (0.25) and NPV (0.74) to the other screening tests, though specificity was again low at 34% (Fig. 3).
Fig. 2

Receiver–operator curves for the individual components and composite NAMES screening test. a Receiver–operator curves of the individual components of the NAMES screening test. The cutoff chosen for comorbidities (blue) was 1 with AUROC = 0.5679; for modified Friedman grade (green), 2/4 with AUROC = 0.6019; for neck circumference (red), 16 cm for females and 17 cm for males with overall AUROC = 0.6564; for Epworth scale (orange), 11/24 points with AUROC = 0.5908, and for body mass index (purple), 30 kg/m2 with AUROC = 0.6633. b Receiver–operator curves of NAMES and NAMES2 assessments. The cutoff score chosen (arrow) for NAMES (orange) was 3/5 points with AUROC = 0.6425 and for NAMES2 (green), 4/7 points with AUROC = 0.6690

Fig. 3

The utility of the NAMES screening tool in comparison with existing tools. a The sensitivity and specificity of NAMES and NAMES2 are plotted as percentages (%), alongside those of the Berlin questionnaire and the Epworth scale. b The positive (PPV) and negative (NPV) predictive values of each screening tool are plotted as percentage. c The positive (LR+) and negative (LR−) likelihood ratios of each screening tool are plotted as a change from the baseline of 1.0

Including those subjects with mild OSA (RDI, 5–15), the prevalence of OSA was 84.9%. In this group, test characteristics for the detection of all OSA were similar to those seen when using the stricter criteria (RDI ≥15). The sensitivity and specificity of NAMES were 88% and 29%, respectively and for NAMES2 were 85% and 42%. These compare with values of 92% and 18% for the Berlin questionnaire, and 54% and 56% for the ESS.

Five individual components correlated significantly with RDI: NC (r = 0.44, p < 0.0001); ESS (r = 0.14, p = 0.002); BMI (r = 0.36, p < 0.0001); MF grade (r = 0.14, p = 0.0019); and male gender (r = 0.18, p < 0.0001). Of all the individual components evaluated, BMI and NC had the greatest areas under the receiver–operator curves when predicting OSA in the pilot group, and correlated the strongest with RDI and lowest SaO2 in the validation group. Additionally, the total NAMES (r = 0.32, p < 0.0001) and NAMES2 (r = 0.41, p < 0.0001) scores correlated significantly with RDI; the correlations were greater than those yielded by the Berlin questionnaire score (r = 0.27) or ESS alone (Table 4). The NAMES (r = −0.31, p < 0.0001) and NAMES2 (r = −0.37, p < 0.0001) scores also correlated the best with lowest SaO2 when compared with the Berlin score (r = −0.20) and ESS (r = −0.18).
Table 4

Correlation of screening test variables with features of obstructive sleep apnea

Test/variable

Correlation with RDI

Correlation with lowest SaO2

NAMES

0.32*

−0.31*

 Neck circumference

0.44*

−0.40*

 Modified Friedman

0.14*

−0.14*

 Comorbidities

0.08

−0.07

 Snoring

0.08

−0.05

NAMES2

0.41*

−0.37*

 Body mass index

0.36*

−0.39*

 Male gender

0.18*

−0.10*

Epworth scale

0.14*

−0.18*

Berlin questionnaire

0.27*

−0.20*

*p < 0.05

Discussion

The NAMES assessment tool demonstrated a high sensitivity for the detection of moderate to severe OSA. Its utility as a screening tool is confirmed by strong sensitivity, NPV, and LR− when validated in a high-prevalence population. The test characteristics improved with the inclusion of BMI and male gender. The strength of the NAMES tool lies in the combination of reported symptoms and physical exam findings to assess for OSA. The most important factors in this population appear to be NC and BMI, which are not routinely measured in many common screening tests.

Previous studies have looked at using screening tests to effectively identify patients with OSA. The ESS was specifically designed as “a simple, self-administered questionnaire which is shown to provide a measurement of the subject’s general level of daytime sleepiness.” In the initial analysis [12], the ESS successfully distinguished normal subjects from subjects with sleep disorders. Though not designed to distinguish OSA patients specifically, the ESS score correlated significantly with RDI and lowest SaO2. Further analysis [13] showed that the ESS could discern between subjects that snore and subjects with OSA. The data from this follow-up study showed sensitivity and specificity for detection of OSA (defined as RDI ≥5) was 62% and 74%, respectively, similar to the performance of ESS in our validation group.

The Berlin questionnaire was developed as a means to identify patients with OSA among the primary care population, using questions focused on risk factors for OSA. In a validation study [14], a positive test had a sensitivity of 86%, specificity of 77%, and PPV of 89% for OSA, as defined by an RDI >5. Our validation group demonstrated similar sensitivity but, much worse, specificity. Surveys such as the Berlin questionnaire are subject to recall bias, which may manifest when a patient is referred for a sleep study. Additionally, patients referred to a sleep center have already been screened in the primary care center, often with questions derived from the Berlin questionnaire. These factors substantially decrease the specificity and highlight the need for a combined-modality screening tool such as NAMES.

The NAMES screening tool differs from these other tests in a number of ways. First, it is specifically designed to detect OSA, evaluating for features known to occur in OSA. The ESS was designed to detect excessive daytime sleepiness which occurs in OSA, but also any of a number of sleep disorders such as narcolepsy, restless legs syndrome, and insomnia of any cause. The Berlin questionnaire focuses on OSA, but was simplified in order to make it accessible to primary care providers. Secondly, NAMES incorporates physical exam features in addition to self-reported symptoms adding to the sensitivity, especially in cases of atypical presentations of OSA.

A recent study [20] also employed a screening test with a combination of symptoms and objective findings for the diagnosis of OSA. The final formulae were devised using the Berlin risk classification, ESS, OSAHS score (Mallampati grade, tonsil size, and BMI), and a snoring score. Two formulae were created for this screening and used to assign a positive or negative score to each subject. This method resulted in sensitivity and specificity for detection of OSA (AHI, ≥5) of 82% and 84%, respectively. It was suggested that this tool be used in circumstances when PSG is not feasible or when the cost of false positive diagnosis is unacceptably high. In contrast, the NAMES tool utilizes a single formula to screen for OSA, which can be both an advantage and a drawback. By using one scale with one cutoff value, test characteristics demonstrate an inverse relationship: in order to achieve maximum sensitivity, specificity is sacrificed; however, a score such as NAMES is simple to calculate and useful in quickly ruling out disease. NAMES has been shown here to effectively screen for OSA in a high-risk population and is simple enough to use in the ambulatory setting.

Some limitations to the present study may hinder the generalization of NAMES to a broader population. First, the tool was developed and validated in a single center. This has the advantage of requiring only a few well-trained staff, but does not show how the test would apply to centers and staff with variable resources and training. Second, this population has a high prevalence of OSA and skewed proportions of ethnicities, due to the location of the sleep center. In order to counteract any bias introduced by the high prevalence of OSA in the population, OSA was defined more restrictively by an RDI ≥15; however, the results were similar when we analyzed our data using an RDI ≥5 as the cutoff for OSA though screening for such mild disease may require lower cutoffs for a positive result. Barriers to routine use of the NAMES tool include its low specificity and dependence on an evaluator trained to accurately assess the MF score and NC. It should also be noted that a large proportion (54%) of the subjects screened for inclusion into the study did not have complete data, raising the possibility that the tool is too complicated to apply to a large population or that the study population was subject to bias. The majority of missing data was due to noncompliance by the subjects in completing the questionnaires. While questionnaires, including the ESS and Berlin questionnaires, are routinely used in clinical practice, they serve to augment, not supplant, clinician communication with patients, and therefore should not limit the implementation of a screening test into the appropriate population. The subjects that did not have complete data collected were similar in their clinical and demographic characteristics, including sleepiness scores and RDI, to those that did have complete data. Thus, there is not likely to be a systematic bias due to this incomplete data collection.

Overall, the NAMES performed well in the detection of OSA in a high-prevalence population and may be a step toward optimizing utilization of sleep center resources. We propose this screening tool as a simple means to triage high-risk patients in the community that should be referred for a sleep study. Alternatively, because portable cardiopulmonary sleep studies are indicated in the diagnosis of moderate and severe OSA [21], this tool may also be used by referral sleep centers to triage those patients that would benefit from a portable study, thereby conserving the laboratory PSG for those with milder disease. Future studies may focus on validating the NAMES protocol in a broader, more diverse population in multiple centers, including the primary care setting.

Notes

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Shyam Subramanian
    • 1
  • Sean E. Hesselbacher
    • 1
  • Raymond Aguilar
    • 2
  • Salim R. Surani
    • 1
    • 2
    • 3
  1. 1.Department of Medicine, Section of Pulmonary, Critical Care, and Sleep MedicineBaylor College of MedicineHoustonUSA
  2. 2.Torr Sleep CenterCorpus ChristiUSA
  3. 3.Department of MedicineTexas A&M UniversityCorpus ChristiUSA

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