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1097
Journal of Clinical Sleep Medicine, Vol. 14, No. 7 July 15, 2018
Study Objectives: To develop and validate a practical model for obstructive sleep apnea (OSA) screening in adults based on objectively assessed criteria,
and then compare it with two widely used tools, namely STOP-BANG and NoSAS.
Methods: This is a retrospective study of an existing database of consecutive outpatients who were referred for polysomnography for suspected sleep-
disordered breathing by their primary care physicians. Area under the curve (AUC) and 2 × 2 contingency tables were employed to obtain the performance of
the new instrument.
Results: A total of 4,072 subjects were randomly allocated into two independent cohorts: one for derivation (n = 2,037) and one for validation (n = 2,035).
A mnemonic model, named No-Apnea, with two variables (neck circumference and age) was developed (total score: 0 –9 points). We used the cutoff ≥ 3 to
classify patients at high risk of having OSA. OSA severity was categorized by apnea-hypopnea index (AHI): any OSA (AHI 5 ≥ events/h; OSA-5), moderate/
severe OSA (AHI 15 ≥ events/h; OSA-15); and severe OSA (AHI 30 ≥ events/h; OSA-30). In the derivation cohort, the AUCs for screening of OSA-5, OSA-15,
and OSA-30 were: 0.784, 0.758, and 0.754; respectively. The rate of subjects correctly screened was 78.1%, 68.8%, and 54.4%, respectively for OSA-5,
OSA-15, and OSA-30. Subsequently, the model was validated conrming its reproducibility. In both cohor ts, No-Apnea discrimination was similar to STOP-
BANG or NoSAS.
Conclusions: The No-Apnea, a 2-item model, appears to be a useful and practical tool for OSA screening, mainly when limited resources constrain
referral evaluation. Despite its simplicity when compared to previously validated tools (STOP-BANG and NoSAS), the instrument exhibits similar
performance characteristics.
Keywords: clinical assessment, obstructive sleep apnea, polysomnography, scoring
Citation: Duarte RL, Rabahi MF, Magalhães-da-Silveira FJ, de Oliveira-e-Sá TS, Mello FC, Gozal D. Simplifying the screening of obstructive sleep apnea
with a 2-item model, No-Apnea: a cross-sectional study. J Clin Sleep Med. 2018;14(7):10 97–1107.
INTRODUCTION
Obstructive sleep apnea (OSA) is characterized by frequent
partial or complete collapse of the upper airway during sleep,
resulting in periodic hypoxemia, increased respiratory eort,
and arousals.1 There is growing evidence of OSA playing a role
in the pathogenesis of cardiovascular and metabolic diseases1
and also a strong association with increased mortality rates.2
Recent studies3–5 indicate a clear increase in the prevalence of
OSA: a Brazilian study showed that OSA was diagnosed in
32.8% of the participants3; two other studies have also shown
SCIENTIFIC INVESTIGATIONS
Simplifying the Screening of Obstructive Sleep Apnea With a 2-Item Model,
No-Apnea: A Cross-Sectional Study
Ricardo L.M. Duarte, MD, MSc1,2; Marcelo F. Rabahi, MD, PhD3; Flavio J. Magalhães-da-Silveira, MD1; Tiago S. de Oliveira-e-Sá, MD4,5;
Fernanda C.Q. Mello, MD, PhD2; David Gozal, MD, MBA6
1Sleep - Laboratório de Estudo dos Distúrbios do Sono, Centro Médico BarraShopping, Rio de Janeiro, Brazil; 2Instituto de Doenças do Tórax - Universidade Federal do Rio de
Janeiro, Rio de Janeiro, Brazil; 3Faculdade de Medicina, Universidade Federal de Goiás, Goiás, Brazil; 4Hospital de Santa Marta - Centro Hospitalar Lisboa Central, Portugal;
5NOVA Medical School - Faculdade de Ciências Médicas, Universidade Nova de Lisboa, Por tugal; 6Department of Pediatrics, Pritzker School of Medicine, Biological Sciences
Division, The University of Chicago, Chicago, Illinois
pii : j c-17-0055 6 http://dx.doi.org /10.5664/jcsm.7202
similar ndings, with a prevalence of moderate to severe OSA
of 13% being recorded among men and of 6% among women4
and of 49.7% in men and 23.4% in women.5 Aging populations6
and the strong association between body mass index (BMI)
and OSA7 may account for such increases in OSA prevalence.
Indeed, obesity has been linked with increases in neck circum-
ference (NC), which can reduce the upper airway diameter and
alter the mechan ical properties of the upper airway altogether.8,9
The gold standard for OSA diagnosis consists of full poly-
somnography (PSG); however, it is not readily available for
the large number of patients with suspected OSA, such that
BRIEF SUMMARY
Current Knowledge/Study Rationale: There are several clinical questionnaires for screening of obstructive sleep apnea (OSA), aiming to nd the
patients at high risk for this disease, which is often undiagnosed. These questionnaires often use subjective data related to sleep, which often requires
bed partner information, reducing their practical applicability.
Study Impact: The No-Apnea, a practical model with only two variables (neck circumference and age) may be useful to identify patients at high risk
for OSA, indicating home sleep studies and reducing long waiting lines found in several sleep laboratories, especially in primary care settings and
when limited resources constrain referral evaluation. When compared to STOP-BANG (an 8-item model) and NoSAS (a 5-item model), the No-Apnea
(a 2-item model) had similar performance characteristics.
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Journal of Clinical Sleep Medicine, Vol. 14, No. 7 July 15, 2018
RL Duarte, MF Rabahi, FJ Magalhães-da-Silveira, et al. A 2-Item Model for the Screening of OSA
a screening tool could be useful to stratify patients at risk
for OSA, and enable improved access to PSG testing and di-
agnosis.10 Several OSA screening questionnaires have been
described,11–16 among which the Berlin and STOP-BANG
instruments are most frequently cited. The Berlin question-
naire12 includes items that access the presence and frequency
of snoring, sleepiness or fatigue, and history of obesity or
hypertension. The STOP questionnaire (snoring, tiredness,
observed apnea, and hypertension), and the STOP-BANG
questionnaire (STOP plus BMI, age, NC, and gender) were
rst developed and validated in surgical patients.13 Both ques-
tionnaires are self-administered consisting of 4 or 8 yes-or-
no questions, respectively. For OSA diagnosis in a sample of
surgical patients, the STOP-BANG questionnaire showed the
following parameters: sensitivity 83.6%, specicity 56.4%,
positive predictive value (PPV) 81.0%, and negative predic-
tive value (NPV) 60.8%.13
Despite the extensive eorts to develop highly performing
questionnaires, objectively based scoring tools for screening
of OSA have rarely been evaluated. Indeed, the existing OSA
screening models rely on subjective information, such as snor-
ing, observed apnea, and choking/gasping, usually provided by
the bed par tner, and therefore not always available. To overcome
such constraints in the screening of OSA, our study comprised
two parts: (1) development and validation of a very practical
screening model and (2) comparison with STOP-BANG ques-
tionnaire13 and the recently reported NoSAS score.16
METHODS
Study Design and Patient Selection
This is a cross-sectional retrospective study of an existing da-
tabase from January 2015 to December 2016 of consecutive
outpatients who were referred for PSG evaluation for sus-
pected sleep-disordered breathing (SDB) by their primary care
physicians. If the same patient underwent more than one test,
only the test that had the highest total sleep time was retained.
All subjects were grouped into two dierent and independent
samples, using a randomization process as prescribed by the
SPSS statistical package (version 17.0; SPSS; Chicago, Illi-
nois, United States), in which all patients enrolled in the study
were divided equally into two cohorts (50% of the patients
were allocated to the derivation cohort and the remaining 50%
were allocated to the validation cohort). Our study protocol
(#666.608) was approved by the Institutional Review Board of
Federal University of Rio de Janeiro and waived the patient
consent requirement.
For both groups, inclusion criteria consisted of age 18 years
or older and suspected OSA, whereas they were excluded for
any of the following reasons: previously diagnosed OSA, use
of portable or split-night studies, incomplete clinical data,
and technically inadequate PSG. On the evening of the PSG,
clinical data were collected in all patients: sex, age, BMI, NC,
self-reported comorbidities (hypertension, diabetes mellitus,
and smoking), and Epworth Sleepiness Scale (ESS),17 which
is a well-validated 8-item questionnaire that measures subjec-
tive sleepiness, with a score of 10 points or higher considered
indicative of excessive daytime sleepiness. Patients were mea-
sured for weight and height, and the BMI was calculated by
dividing the weight in kilograms by the square of the height in
meters (kg/m2). The NC was systematically measured, using
a tape measure, in centimeters, as follows: all subjects were
asked to stand erect with their head positioned in the Frankfort
horizontal plane. The superior border of a tape measure was
placed just below the laryngeal prominence and applied per-
pendicular to the long axis of the neck.18
Aiming to compare with other validated models, data from
the STOP-BANG questionnaire13 and NoSAS score16 were also
collected by trained sleep technicians. The STOP-BANG13
consists of 8 yes-or-no questions: loud snoring, tiredness, ob-
served apnea, hypertension, BMI > 35 kg/m2, age older than
50 years, NC > 40 cm, and male sex. A score of 3 or higher
is considered as high risk for presence of OSA. The NoSAS
score16 allocates 4 points for having a NC > 40 cm, 3 points for
having a BMI of 25 kg/m2 to less than 30 kg/m2 or 5 points for
having a BMI ≥ 30 kg/m 2, 2 points for snoring, 4 points for age
older than 55 years, and 2 points for being male. Using NoSAS,
a cuto of ≥ 8 points identies subjects at risk of clinically
signicant SDB.16
Sleep Studies
All Brazilian studies were conducted in a single sleep center
with two dierent units: one in Niteroi City and one in Rio
de Janeiro City. All patients underwent an attended, in-labora-
tory PSG (EMBLA S7000, Embla Systems, Inc., Broomeld,
Colorado, United States), consisting of continuous monitoring
of electroencephalography, electrooculography, electromy-
ography (chin and legs), electrocardiography, airow (nasal
pressure), thoracic and abdominal impedance belts, oxygen
saturation (SpO2), microphone for snoring, and sensors for
body position. PSG records were scored manually and were
interpreted in a blinded way by two board-certied sleep phy-
sicians in accordance with existing guidelines.19 Apneas were
classied with a drop ≥ 90% of baseline in airow lasting at
least 10 seconds, whereas hypopneas were classied as fol-
lows: a drop ≥ 30% of preevent during ≥ 10 seconds associ-
ated with ≥ 3% oxygen desaturation or an arousal.19 Diagnosis
of OSA was based on an apnea-hypopnea index (AHI) ≥ 5
events/h and its severity was classied as follows: ≥ 5 events/h
as any OSA (OSA-5), ≥ 15 events/h as moderate/severe OSA
(OSA-15), and ≥ 30 events/h as severe OSA (OSA-30).
Modeling
We aimed to elaborate a very practical model that contains
only continuous and numerical variables, and as such three
parameters, previously recognized as predictors of OSA, were
chosen for analysis: NC, age, and BMI. First, these variables
were evaluated through linear regression in order to verify
the occurrence or not of multicollinearity. Multicollinearity
is a linear association between two or more explanatory vari-
ables being detected by tolerance and variance ination factor
(VIF).20 The VIF is simply the reciprocal of the tolerance level
(1/tolerance), being that the VIFs measure the ination in the
variances of the parameter estimates due to collinearities that
exist among the predictors.20
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Journal of Clinical Sleep Medicine, Vol. 14, No. 7 July 15, 2018
RL Duarte, MF Rabahi, FJ Magalhães-da-Silveira, et al. A 2-Item Model for the Screening of OSA
After excluding multicollinearity, these three parameters
were evaluated in their continuous form, through binary lo-
gistic regression at three dierent AHI thresholds (5, 15, and
30 events/h), with comparisons being conducted through the
regression coecient (β). To further simplify the model, the
two main parameters were retained. For the elaboration of the
scoring system, these two parameters were grouped based on
three cuto points obtained by median (50% percentile) and
interquartile range (IQR: 25% and 75% percentiles) and then
evaluated with binary logistic regression. Assignment of points
to parameters was calculated as follows: the β coecient of
each variable was divided by the lowest β-coecient value and
rounded to the nearest whole integer. Subsequently, all points
accrued were summed to create the scoring index.
Statistical Analysis
Data analysis was conducted using SPSS for Windows (ver-
sion 17.0; SPSS; Chicago, Illinois, United States). Results are
shown as median and IQR for continuous variables and as fre-
quency with percentage for categorical variables. Groups were
compared using the chi-square test for categorical variables,
whereas all numeric variables were evaluated with the non-
parametric Mann-Whitney U test. Cor relation was evaluated
by Spearman correlation coecient (rs). Through linear regres-
sion, absence of multicollinearity was assumed if VIF < 5.0.
Parameters predictive of OSA (age, NC, and BMI) were en-
tered into a logistic regression analysis in parallel, with the
Wald test being used to explore if explanatory variables in a
model were signicant. A scoring system was derived using
weightings from β-regression coecients.
To examine the apparent performance (internal validity) of
our developed model, discrimination, calibration, and over-
all performance were calculated in both cohorts evaluated.
Discrimination, the ability of a scoring system to distinguish
between patients with and without dierent outcomes, was
estimated from the area under the curve (AUC).21 The AUC
may theoretically range from 0.5 (discrimination equivalent
to that of chance) to 1.0 (perfect discrimination).21 Calibra-
tion refers to the agreement between observed outcomes and
predictions; being that it was assessed by Hosmer-Lemeshow
chi-square test (P < .05 indicates poor calibration).21 Over-
all performance (how well the model predicts the likelihood
of an outcome in an individual patient) was assessed us-
ing the Nagelkerke R2, which ranges from 0 to 1.21 As the
Hosmer-Lemeshow chi-square test is sensitive to sample
size, we chose smaller subsets of randomly selected patients
(n = 1,000) to evaluate the model calibration in both cohorts
evaluated. After completion of the model development, the
cuto point determined to identify patients at risk for SDB
was chosen to achieve a high sensitivity, while preserving a
moderate specicity. Using the 2 × 2 contingency tables, the
following parameters were calculated: sensitivity, specicity,
PPV, NPV, accuracy, likelihood ratios, and odds ratio (OR)
with their respective 95% condence interval (CI). The re-
ceiver operating characteristic (ROC) curves and AUC were
assessed at three AHI thresholds (5, 15, and 30 events/h).
The AUCs obtained by all screening models were compared
using prior algorithm.22 Posttest probability of each score
was calculated by logistic regression. A two-tailed value of
P < .05 was considered statistically signicant.
RE S ULTS
Of a total of 4,476 subjects referred for diagnostic PSG, 404
patients (9.0%) were subsequently excluded based on exclusion
criteria. The exclusions consisted of 260 with incomplete clini-
cal data, 76 tested with portable or split-night studies, 54 with
technically inadequate PSG, and 14 with a previous diagnosis
of OSA. Thus, 4,072 subjects were randomly allocated into two
independent cohorts: one for derivation (n = 2,037) and one for
validation (n = 2,035). Based on Tabl e 1, no clinical or PSG
parameter was statistically dierent. Within the derivation co-
hort, median age was 45.0 years (IQR: 35.0–55.0) and 55.9%
were male, whereas in the derivation cohort, median age was
44.0 years (IQR: 34.0–55.0) and 53.2% were male. Similarly,
the prevalence of OSA-5, OSA-15, and OSA-30 was not statis-
tically dierent between the derivation cohort and validation
cohort (77.9% versus 76.4%, 55.2% versus 54.7%, and 34.5%
and 35.8%; respectively). In the derivation cohort, prevalence of
OSA-5, OSA-15, and OSA-30 was higher in men than women:
86.9% versus 66.5%, 67.6% versus 39.5%, and 46.7% versus
19.0%; respectively; all with P < .001. Similarly, in the validation
cohort, prevalence of OSA-5, OSA-15, and OSA-30 were also
higher in men than women: 87.2% versus 64.1%, 69.3% versus
38.1%, and 48.9% versus 20.8%; respectively; all with P < .001,
suggesting sex inuences leading to higher prevalence of severe
forms of OSA. Men had a higher median NC than women in
the derivation cohort (42.0 cm [IQR: 40.0–46.0] versus 38.0 cm
[IQR: 35.0–40.0]), and in the validation cohort (43.0 cm [IQR:
40.0–46.0] versus 38.0 cm [IQR: 35.0–40.0]), both with P < .001.
Men had a lower median age than women in the derivation co-
hort (42.0 years [IQR: 33.0–53.0] versus 47.0 years [IQR: 35.0–
56.0]) and in the validation cohort (41.0 years [IQR: 33.0–53.0]
versus 47.0 years [IQR: 36.0–58.0]); both with P < .001.
The No-Apnea Development
After excluding multicollinearity among the three parameters
of interest (NC with VIF = 1.484, age with VIF = 1.016, and
BMI with VIF = 1.503), these variables were evaluated through
binary logistic regression (Tab le 2). As can be seen in Tab l e 2,
for all AHI thresholds (5, 15 or 30 events/h), when comparing
the three clinical parameters in their continuous form, we found
that the magnitude of the regression coecient was always
higher for NC, followed by age and BMI. Moreover, BMI did
not emerge as an independent variable for screening of OSA-5
with OR: 1.014 (95% CI: 0.996–1.033; P = .119). Therefore, BMI
was then excluded from the model for three reasons: (1) in the
binary logistic regression, it was the third-ranked parameter for
all AHI thresholds; (2) it did not emerge as an independent pa-
rameter for screening of OSA-5; and (3) reducing the model to
two variables obviously further simplied the tool.
Tab le 3 shows the binary logistic regression with the two
parameters chosen (NC and age) categorized from the 25%,
50% and 75% percentiles: (1) for NC: 37 cm, 40 cm, and 43 cm;
and (2) for age: 35 years, 45 years, and 55 years; respectively.
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RL Duarte, MF Rabahi, FJ Magalhães-da-Silveira, et al. A 2-Item Model for the Screening of OSA
Tab l e 1—General and sleep characteristics for both cohorts.
Parameter Derivation Cohort (n = 2,037) Validation Cohort (n = 2,035) P
Clinical data
Male, n (%) 1,138 (55.9) 1,083 (53.2) .095
Caucasian, n (%) 1,636 (80.3) 1,645 (80.8) .864
Age, years 45.0 (35.0–55.0) 44.0 (34.0–55.0) .975
BMI, kg/m232.2 (26.7–38.8) 32.5 (26.7–38.9) .552
NC, cm 40.0 (37.0–43.0) 40.0 (37.0–44.0) .660
ESS score ≥ 10, n (%) 1,017 (50.0) 1,063 (52.3) .167
Current smokers, n (%) 202 (9.9) 202 (9.9) > .999
Hypertension, n (%) 819 (40.2) 790 (38.8) .370
Diabetes mellitus, n (%) 224 (11.0) 250 (12.3) .204
PSG data
Total sleep time, min 352.1 (310.4–393.0) 358.5 (307.8–395.5) .064
Sleep efciency, % 82.2 (71.7–90.5) 83.0 (71.4–90.6) .198
Sleep latency, min 22.4 (9.0–48.3) 22.4 (9.8–47.0) .950
REM latency, min 126.0 (86.5–188.2) 125.5 (85.2–187.0) .847
Stage N1, % 3.0 (1.0–7.0) 3.0 (1.0–7.0) .863
Stage N2, % 65.5 (58.0–74.0) 65.0 (57.1–74.1) .354
Stage N3, % 12.2 (4.2–18.8) 12.0 (4.7–18.5) .730
Stage R, % 16.5 (11.2–21.5) 16.8 (11.2–22.0) .315
Arousal index, events/h 21.5 (11.4–42.4) 20.8 (10.6–42.3) .442
AHI, events/h 16.9 (6.2–41.3) 17.3 (5.5–40.8) .531
Awake SpO2, % 95.8 (94.5–96.9) 95.8 (94.5–97.0) .488
Mean SpO2, % 94.2 (92.0–95.9) 94.3 (92.0–95.9) .864
Nadir SpO2, % 84.0 (77.0–89.0) 84.0 (77.0–89.0) .222
AHI, events/h
≥ 5, n (%) 1,587 (77.9) 1,554 (76.4) .247
≥ 15, n (%) 1,124 (55.2) 1,114 (54.7) .801
≥ 30, n (%) 702 (34.5) 728 (35.8) .393
Data are presented as median (interquartile range) or n (%). AHI = apnea-hypopnea index, BMI = body-mass index, ESS = Epworth sleepiness scale,
NC = neck circumference, PSG = polysomnography, REM = rapid eye movement, SpO2 = oxygen saturation.
Tab l e 2 —Binary logistic regression of the continuous predictors evaluated according to AHI thresholds (derivation cohort:
n = 2,037).
βSE Wald df P OR (95% CI)
AHI ≥ 5 events/h
NC, cm 0.235 0.018 174.078 1 < .001 1.265 (1.222–1.311)
Age, years 0.047 0.005 100.267 1 < .001 1.048 (1.039–1.058)
BMI, kg/m20.014 0.009 2.436 1 .119 1.014 (0.996–1.033)
AHI ≥ 15 events/h
NC, cm 0.204 0.015 196.975 1 < .001 1.226 (1.191–1.261)
Age, years 0.033 0.004 74.138 1 < .001 1.033 (1.026–1.041)
BMI, kg/m20.024 0.008 10.227 1 .001 1.025 (1.010–1.040)
AHI ≥ 30 events/h
NC, cm 0.215 0.015 207.453 1 < .001 1.240 (1.204–1.277)
Age, years 0.025 0.004 39.967 1 < .001 1.025 (1.017–1.033)
BMI, kg/m20.021 0.008 6.746 1 .009 1.021 (1.005–1.037)
The P value was obtained from the Wald test. All parameters were entered into logistic regression in parallel. AHI = apnea-hypopnea index, β = regression
coefcient, BMI = body mass index, CI = condence interval, df = degrees of freedom for the Wald test, NC = neck circumference, OR = odds ratio,
SE = standard error.
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Then, each variable was grouped and scored, according to
β-coecient, as follows: the NC (in cm) was scored in three
dierent values: 1 (37.0–39.9), 3 (40.0–42.9), and 6 (≥ 43.0),
whereas the age (in years) was scored in three dierent val-
ues: 1 (35–44), 2 (45–54), and 3 (≥ 55). The points for each
variable were added, totaling a nal score of 0–9 points and
this mnemonic tool was termed “No-Apnea” (Ta ble 4 ). The
most frequent No-Apnea score was 3 points (n = 352), followed
by 6 points (n = 314) and 4 points (n = 263). Corresponding
to the increase in No-Apnea scores (from 0 to 9 points), there
was a linear increase in the prevalence of OSA-5 (from 33.0%
to 95.6%), OSA-15 (from 13.6% to 85.3%), and OSA-30 (from
3.9% to 68.4%); all with P < .0 01.
The No-Apnea Performance
In the derivation cohort, for screening of OSA-5 (the AHI
threshold chosen to score the No-Apnea parameters), the devel-
oped model showed the following characteristics: (1) discrim-
inatory power based on ROC curves with AUC: 0.784 (95%
CI: 0.761–0.808); (2) calibration with Hosmer-Lemeshow chi-
square test: 10.270 (P = .247); and (3) overall performance with
Nagelkerke R2: 0.265. Moreover, for screening of OSA-15 and
OSA-30, the No-Apnea showed the follow ing characteristics: (1)
discriminator y power with AUCs: 0.758 (95% CI: 0.737–0.779)
and 0.754 (95% CI: 0.733–0.776), respectively; (2) calibration
with Hosmer-Lemeshow chi-square test: 11.591 (P = .170) and
10.046 (P = .262), respectively; and (3) overall performance
with Nagelkerke R2: 0.259 and 0.248, respectively.
In the validation cohort, for screening of OSA-5, OSA-15,
and OSA-30, the No-Apnea showed the following character-
istics: (1) discriminatory power with AUCs: 0.781 (95% CI:
0.757–0.805), 0.752 (95% CI: 0.731–0.773), and 0.752 (95% CI:
0.730–0.773), respectively; (2) calibration with Hosmer-Leme-
show chi-square test: 10.976 (P = .203), 13.647 (P = .091), and
5.498 (P = .703), respectively; and (3) overall performance with
Nagelkerke R2: 0.259, 0.243, and 0.234, respectively.
The No-Apnea Predictive Parameters (Derivation Cohort)
Predictive performance of the No-Apnea is shown in Tabl e 5
(deriva t ion coh o r t ; n = 2, 037 ). We used a cut o of ≥ 3 to cl a s sify
patients at high risk (75.9%) versus at low risk (24.1%) of hav-
ing OSA-5, OSA-15, and OSA-30. The accuracies obtained
were of 78.1%, 68.8%, and 54.4%, respectively for OSA-5,
OSA-15, and OSA-30. Using a cuto of ≥ 3, the posttest prob-
abilities for OSA-5, OSA-15, and OSA-30 were 86.8%, 65.8%,
and 42.6%, respectively. In addition, the posttest probabilities
for OSA-5, OSA-15, and OSA-30 increased proportionally with
the increase in the No-Apnea scores (from 0 to 9 points; data
not shown).
Tab le 6 was created aiming to compare our model with two
previously reported screening tools: STOP-BANG and NoSAS.
For screening of OSA-5, OSA-15, and OSA-30, No-Apnea model
showed the following parameters: sensitivity ranged from 84.7%
to 94.0%, specicity ranged from 54.9% to 33.6%, whereas the
accuracy ranged from 78.1% to 54.4%, respectively. For OSA-5
diagnosis, STOP-BANG showed the higher sensitivity (88.8%),
whereas the higher specicity was obtained with NoSAS
(68.7%). For OSA-15 diagnosis, NoSAS showed the higher spec-
icity (57.6%), whereas the higher sensitivity was obtained with
STOP-BANG (92.5%). For OSA-30 diagnosis, NoSAS showed
the higher specicity (49.2%), whereas the higher sensitivity
was obtained with STOP-BANG (95.7%). Based on AUCs, No-
Apnea discrimination did not show statistically signicant dif-
ferences compared to the STOP-BANG for screening of OSA-5
Tab l e 3 —Binary logistic regression of the categorized predictors according to AHI ≥ 5 events/h (derivation cohort: n = 2,037).
βPoints* SE Wald df P OR (95% CI)
Neck circumference, cm
< 37.0 – 0 – 221.440 3 < .001 –
37.0–39.9 0.732 + 1 0.153 22.941 1 < .001 2.080 (1.541–2.807)
40.0–42.9 1.422 + 3 0.157 82.538 1 < .001 4.145 (3.050–5.633)
≥ 43.0 3.028 + 6 0.215 198.101 1 < .001 20.660 (13.552–31.497)
Age, years
< 35 – 0 – 100.516 3 < .001 –
35–44 0.492 + 1 0.154 10.209 1 .001 1.636 (1.210–2.213)
45–54 0.981 + 2 0.163 36.120 1 < .001 2.668 (1.937–3.675)
≥ 55 1.681 + 3 0.175 91.951 1 < .001 5.369 (3.808–7.569)
The P value was obtained fro m the Wald test. * = poi nt s as si gned to No- Apnea fro m the regression co ef cie nt . AHI = apnea-hypop ne a in dex, β = regress io n
coefcient, CI = condence interval, df = degrees of freedom for the Wald test, OR = odds ratio, SE = standard error.
Tab l e 4 —No-Apnea scoring system.
Parameter Points
Neck circumference, cm
< 37.0 0
37.0–39.9 + 1
40.0–42.9 + 3
≥ 43.0 + 6
Age, years
< 35 0
35–44 + 1
45–54 + 2
≥ 55 + 3
The points for each variable are added, totaling a nal score of 0–9
points.
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(P = .645), OSA-15 (P = .946), and OSA-30 (P = .589). Similarly,
No-Apnea discrimination did not signicantly dier from that
of NoSAS for screening of OSA-5 (P = .555), OSA-15 (P = .946),
an d OSA-30 (P = .858). Furthermore, STOP-BANG discrimina-
tion was similar to NoSAS for diagnosis of OSA-5, OSA-15, and
OSA-30: P = .896, P = .892, and P = .473; respectively. All mod-
els tested (No-Apnea, STOP-BANG, and NoSAS) were corre-
lated with AHI (rs = 0.530, rs = 0.545, and rs = 0.529; respectively;
all with P < .001). In addition, ESS has not proved useful as
a screening tool for OSA-5, OSA-15, and OSA-30: AUC: 0.573
(95% CI: 0.543–0.603), AUC: 0.559 (95% CI: 0.534–0.584), and
AUC: 0.591 (95% CI: 0.565–0.617); respectively.
The No-Apnea Predictive Parameters (Validation Cohort)
Based on AUCs summarized in the Tab le 7 (validation cohort;
n = 2,035), No-Apnea discrimination did not show statistically
signicant dierences compared to the STOP-BANG for
screening of OSA-5 (P = .232), OSA-15 (P = .087), and OSA-
30 (P = .074). Similarly, No-Apnea discrimination did not sig-
nicantly dier from that of NoSAS for screening of OSA-5
(P = .957), OSA-15 (P = .788), and OSA-30 (P > .999). In ad-
dition, STOP-BANG discrimination was similar to NoSAS for
diagnosis of OSA-5, OSA-15, and OSA-30: P = .212, P = .085,
and P = .074; respectively. For screening of OSA-5, OSA-15,
and of OSA-30, No-Apnea model showed the following pa-
rameters: sensitivity ranged from 83.1% to 91.5%, specicity
ranged from 58.2% to 36.8%, whereas the accuracy ranged
from 77.2% to 56.4%, respectively. All models tested (No-
Apnea, STOP-BANG, and NoSAS) were correlated with AHI
(rs = 0.528, rs = 0.584, and rs = 0.534; respectively; all with
P < .001). All AUCs obtained by the three models are shown
in Figure 1.
Tab l e 5 —Predictive parameters of No-Apnea (derivation cohort: n = 2,037).
No-Apnea Scores
≥ 2 versus < 2 ≥ 3 versus < 3 ≥ 4 versus < 4
AHI ≥ 5 events/h
Sensitivity 92.1 (91.2–93.1) 84.7 (83.6–85.8) 68.1 (67.0–69.1)
Specicity 38.2 (34.8–41.5) 54.9 (51.0–58.6) 74.7 (70.8–78.2)
PPV 84.0 (83.1–84.9) 86.9 (85.8–88.0) 90.5 (89.0–91.8)
NPV 57.9 (52.8–62.9) 50.4 (46.9–53.9) 39.9 (37.8–41.8)
Accuracy 80.2 (78.7–81.7) 78.1 (76.4–79.8) 69.6 (67.9–71.1)
LR + 1.49 (1.39–1.59) 1.87 (1.70–2.07) 2.68 (2.29–3.17)
LR − 0.20 (0.16–0.25) 0.27 (0.24–0.32) 0.42 (0.39–0.46)
Odds ratio 7.23 (5.51–9.50) 6.73 (5.30–8.53) 6.29 (4.93–8.04)
Posttest probability (%) 84.0 86.8 90.4
AHI ≥ 15 events/h
Sensitivity 95.1 (93.9–96.2) 90.6 (89.0–92.0) 76.2 (74.4–78.0)
Specicity 26.5 (25.0–27.8) 42.1 (40.2–43.8) 63.0 (60.7–65.2)
PPV 61.4 (60.6–62.1) 65.8 (64.7–66.8) 71.7 (70.0–73.4)
NPV 81.5 (76.8–85.5) 78.4 (74.8–81.6) 68.3 (65.8–70.7)
Accuracy 64.4 (63.0–65.5) 68.8 (67.1–70.4) 70.3 (68.3–72.3)
LR + 1.29 (1.25–1.33) 1.56 (1.48–1.63) 2.06 (1.89–2.24)
LR − 0.18 (0.13–0.24) 0.22 (0.18–0.27) 0.37 (0.33–0.42)
Odds ratio 7.01 (5.09–9.66) 6.97 (5.45–8.92) 5.46 (4.48–6.64)
Posttest probability (%) 61.4 65.8 71.7
AHI ≥ 30 events/h
Sensitivity 97.3 (95.8–98.3) 94.0 (92.1–95.5) 81.9 (79.3–84.4)
Specicity 20.8 (20.1–21.4) 33.6 (32.6–34.4) 53.6 (52.2–54.8)
PPV 39.3 (38.7–39.7) 42.7 (41.8–43.4) 48.1 (46.6–49.6)
NPV 93.6 (90.1–96.0) 91.4 (88.7–93.6) 84.9 (82.7–87.0)
Accuracy 47.2 (46.2–47.9) 54.4 (53.1–55.4) 63.3 (61.5–65.0)
LR + 1.22 (1.19–1.25) 1.41 (1.36–1.45) 1.76 (1.65–1.86)
LR − 0.13 (0.08–0.20) 0.17 (0.13–0.24) 0.33 (0.28–0.39)
Odds ratio 9.45 (5.76–15.67) 7.93 (5.63–11.22) 5.22 (4.16–6.54)
Posttest probability (%) 39.1 42.6 48.1
No-Apnea scoring system is a 2-item model: neck circumference is scored as follows: 37.0–39.9 cm (1 point), 40.0–42.9 cm (3 points), and ≥ 43.0 cm (6
points), whereas age is scored as follows: 35 –44 years (1 point), 45–54 years (2 points), ≥ 55 years (3 points); totaling a score of 0–9 points. Data are
presented as estimates (95% condence intervals) unless otherwise stated. AHI = apnea-hypopnea index, LR = likelihood ratio, NPV = negative predictive
value, PPV = positive predictive value.
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DISCUSSION
Our ndings show that, in a sleep-laboratory setting containing
a sample of subjects referred for evaluation of suspected OSA,
an extremely simple tool, No-Apnea, designed with only two
numeric and objectively acquired variables (NC and age) ex-
hibits excellent and reproducible performance when screening
for OSA at any level of severity. In both derivation and valida-
tion cohorts, the No-Apnea discrimination (a 2-item tool) did
not signicantly dier from that of STOP-BANG (an 8-item
tool) and NoSAS (a 5-item tool). As expected, both cohorts
presented a high prevalence of OSA, an anticipated nding
Tab l e 6 —Comparing predictive performances of the No-Apnea, STOP-BANG, and NoSAS (derivation cohort: n = 2,037).
Screening Tools
No-Apnea STOP-BANG NoSAS
AHI ≥ 5 events/h (prevalence: 77.9%)
Sensitivity 84.7 (83.6–85.8) 88.8 (87.7–89.8) 71.6 (70.5–72.6)
Specicity 54.9 (51.0–58.6) 47.1 (43.4–50.7) 68.7 (64.7–72.4)
PPV 86.9 (85.8–88.0) 85.5 (84.5–86.5) 89.0 (87.6–90.3)
NPV 50.4 (46.9–53.9) 54.4 (50.1–58.5) 40.7 (38.3–42.9)
Accuracy 78.1 (76.4–79.8) 79.6 (78.0–81.2) 70.9 (69.2–72.6)
LR + 1.87 (1.70–2.07) 1.67 (1.55–1.82) 2.28 (1.99–2.63)
LR − 0.27 (0.24–0.32) 0.23 (0.20–0.28) 0.41 (0.37–0.45)
Odds ratio 6.73 (5.30–8.53) 7.05 (5.49–9.04) 5.52 (4.37–6.97)
AUC 0.784 (0.761–0.808) 0.777 (0.752–0.801) 0.775 (0.752–0.799)
AHI ≥ 15 events/h (prevalence: 55.2%)
Sensitivity 90.6 (89.0–92.0) 92.5 (91.1–93.8) 79.2 (77.3–80.9)
Specicity 42.1 (40.2–43.8) 33.5 (31.8–35.1) 57.6 (55.4–59.8)
PPV 65.8 (64.7–66.8) 63.1 (62.2–64.0) 69.7 (68.1–71.2)
NPV 78.4 (74.8–81.6) 78.5 (74.4–82.1) 69.2 (66.5–71.8)
Accuracy 68.8 (67.1–70.4) 66.1 (64.5–67.5) 69.5 (67.5–71.5)
LR + 1.56 (1.48–1.63) 1.39 (1.33–1.44) 1.86 (1.73–2.01)
LR − 0.22 (0.18–0.27) 0.22 (0.17–0.28) 0.36 (0.31–0.40)
Odds ratio 6.97 (5.45–8.92) 6.24 (4.76–8.17) 5.17 (4.23–6.31)
AUC 0.758 (0.737–0.779) 0.759 (0.738–0.779) 0.757 (0.736–0.778)
AHI ≥ 30 events/h (prevalence: 34.5%)
Sensitivity 94.0 (92.1–95.5) 95.7 (94.0–97.0) 85.3 (82.8–87.6)
Specicity 33.6 (32.6–34.4) 27.0 (26.1–27.6) 49.2 (47.9–50.4)
PPV 42.7 (41.8–43.4) 40.8 (40.1–41.3) 46.9 (45.5–48.2)
NPV 91.4 (88.7–93.6) 92.3 (89.2–94.6) 86.4 (84.1–88.5)
Accuracy 54.4 (53.1–55.4) 50.7 (49.5–51.5) 61.7 (59.9–63.2)
LR + 1.41 (1.36–1.45) 1.31 (1.27–1.34) 1.68 (1.58–1.76)
LR − 0.17 (0.13–0.24) 0.15 (0.10–0.22) 0.29 (0.24–0.35)
Odds ratio 7.93 (5.63–11.22) 8.27 (5.54–12.40) 5.63 (4.42–7.18)
AUC 0.754 (0.733–0.776) 0.763 (0.742–0.784) 0.751 (0.730–0.773)
Data are presented as estimates (95% condence intervals) unless otherwise stated. No-Apnea scoring system is a 2-item model: NC is scored as follows:
37.0–39.9 cm (1 point), 40.0–42.9 cm (3 points), and ≥ 43.0 cm (6 points), whereas age is scored as follows: 35–44 years (1 point), 45–54 years (2
points), ≥ 55 years (3 points); totaling a score of 0 –9 points (score ≥ 3 was considered as high-risk for presence of any OSA, moderate/severe OSA, and
severe OSA). STOP-BANG questionnaire is an 8 -item model (1 point for each positive answer): loud snoring, tiredness, observed apnea, hypertension,
BMI > 35 kg/m2, age > 50 years, NC > 40 cm, and male sex; totaling a score of 0–8 points (score ≥ 3 was considered as high-risk for presence of any OSA,
moderate/severe OSA, and severe OSA). NoSAS score is a 5-item model: NC > 40 cm (4 points), BMI 25.0–29.9 kg/m2 (3 points), BMI ≥ 30.0 kg/m2 (5
points), snoring (2 points), age > 55 years (4 points), male sex (2 points); totaling a score of 0–17 points (score ≥ 8 was considered as high risk for presence
of any OSA , moderate/severe OSA, and severe OSA). AHI = apnea-hypopnea index, AUC = area under the curve, BMI = body mass index, LR = likelihood
ratio, NC = neck circumference, NPV = negative predictive value, OSA = obstructive sleep apnea, PPV = positive predictive value.
because these were clinically referred sleep-laboratory pa-
tients, a population known to have a high prevalence of OSA.
According to previous studies,8,10,23–25 our ndings showed that
males had higher NC than females, whereas females were older
when compared with males. Similarly, we also observed that
men had a higher rate of OSA compared to women.3–5,8,10,23–25
The cuto used to classify patients with high pretest prob-
ability of OSA was 3 points for OSA-5, OSA-15, and OSA-30.
The inclusion of this single cuto was chosen to obtain a high
sensitivity with consequent moderate specicity. Sensitivity
and specicity of a screening model are usually inversely re-
lated, and the high sensitivity often comes at the expense of
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Tab l e 7—Comparing predictive performances of the No-Apnea, STOP-BANG, and NoSAS (validation cohort: n = 2,035).
Screening Tools
No-Apnea STOP-BANG NoSAS
AHI ≥ 5 events/h (prevalence: 76.4%)
Sensitivity 83.1 (81.9–84.2) 88.9 (87.8–89.9) 71.3 (70.1–72.4)
Specicity 58.2 (54.5–61.8) 51.4 (47.8–54.7) 71.7 (68.0–75.2)
PPV 86.5 (85.3–87.7) 85.5 (84.5–86.5) 89.1 (87.6–90.4)
NPV 51.6 (48.3–54.7) 58.8 (54.8–62.7) 43.6 (41.3–45.7)
Accuracy 77.2 (75.4–78.9) 80.0 (78.3–81.6) 71.4 (69.6–73.1)
LR + 1.98 (1.80–2.20) 1.82 (1.68–1.98) 2.52 (2.19–2.92)
LR − 0.29 (0.25–0.33) 0.21 (0.18–0.25) 0.40 (0.36–0.43)
Odds ratio 6.83 (5.42–8.61) 8.42 (6.59–10.77) 6.30 (4.99–7.96)
AUC 0.781 (0.757–0.805) 0.803 (0.781–0.825) 0.780 (0.757–0.803)
AHI ≥ 15 events/h (prevalence: 54.7%)
Sensitivity 88.7 (87.1–90.2) 93.4 (92.0–94.6) 78.3 (76.4–80.1)
Specicity 45.3 (43.3–47.1) 37.6 (35.9–39.1) 59.6 (57.4–61.8)
PPV 66.2 (65.0–67.3) 64.4 (63.4–65.2) 70.1 (68.4–71.7)
NPV 76.8 (73.5–79.9) 82.4 (78.7–85.6) 69.4 (66.8–71.9)
Accuracy 69.0 (67.3–70.7) 68.1 (66.6–69.5) 69.8 (67.8–71.8)
LR + 1.62 (1.53–1.70) 1.49 (1.43–1.55) 1.93 (1.79–2.09)
LR − 0.25 (0.20–0.29) 0.17 (0.13–0.22) 0.36 (0.32–0.41)
Odds ratio 6.48 (5.14–8.19) 8.45 (6.39–11.19) 5.31 (4.36–6.48)
AUC 0.752 (0.731–0.773) 0.777 (0.760–0.801) 0.756 (0.735–0.777)
AHI ≥ 30 events/h (prevalence: 35.8%)
Sensitivity 91.5 (89.4–93.3) 97.1 (95.6–98.1) 84.5 (82.0–86.7)
Specicity 36.8 (35.6–37.8) 30.5 (29.7–31.1) 51.9 (50.5–53.1)
PPV 44.6 (43.6–45.5) 43.8 (43.1–44.2) 49.4 (48.0–50.8)
NPV 88.6 (85.8–91.0) 95.0 (92.4–96.8) 85.7 (83.4–87.8)
Accuracy 56.4 (54.9–57.6) 54.3 (53.3–55.1) 63.5 (61.8–65.2)
LR + 1.44 (1.38–1.49) 1.39 (1.36–1.42) 1.75 (1.65–1.85)
LR − 0.23 (0.17–0.29) 0.09 (0.06–0.14) 0.29 (0.24–0.35)
Odds ratio 6.25 (4.67–8.39) 14.79 (9.26–23.84) 5.86 (4.64–7.41)
AUC 0.752 (0.730–0.773) 0.778 (0.761–0.803) 0.752 (0.731–0.774)
Data are presented as estimates (95% condence intervals) unless otherwise stated. No-Apnea scoring system is a 2-item model: NC is scored as follows:
37.0–39.9 cm (1 point), 40.0–42.9 cm (3 points), and ≥ 43.0 cm (6 points); whereas age is scored as follows: 35–44 years (1 point), 45–54 years (2
points), ≥ 55 years (3 points); totaling a score of 0 –9 points (score ≥ 3 was considered as high-risk for presence of any OSA, moderate/severe OSA, and
severe OSA). STOP-BANG questionnaire is an 8 -item model (1 point for each positive answer): loud snoring, tiredness, observed apnea, hypertension,
BMI > 35 kg/m2, age > 50 years, NC > 40 cm, and male sex; totaling a score of 0–8 points (score ≥ 3 was considered as high-risk for presence of any OSA,
moderate/severe OSA, and severe OSA). NoSAS score is a 5-item model: NC > 40 cm (4 points), BMI 25.0–29.9 kg/m2 (3 points), BMI ≥ 30.0 kg/m2 (5
points), snoring (2 points), age > 55 years (4 points), male sex (2 points); totaling a score of 0–17 points (score ≥ 8 was considered as high risk for presence
of any OSA , moderate/severe OSA, and severe OSA). AHI = apnea-hypopnea index, AUC = area under the curve, BMI = body mass index, LR = likelihood
ratio, NC = neck circumference, NPV = negative predictive value, OSA = obstructive sleep apnea, PPV = positive predictive value.
specicity. For a disease such as OSA, it is possibly more im-
portant that a screening test has a high sensitivity, and does not
miss patients with OSA, rather than a high specicity, espe-
cially in a population with high pretest probability of disease.10
However, this strategy may not be unanimous. A previous
study26 reports on the strategy of using dierent cut os to rule-
in or rule-out OSA. In a sleep-laboratory setting, where the
main objective is to identify subjects with more severe forms
of OSA and requiring treatment with continuous positive air-
way pressure (CPAP), a higher cuto for a given model may
be preferred, whereas in a primary care setting where the pri-
ority is not to miss any disease, a lower cuto may be more
appropriate.26 Similar to prior studies on STOP-BANG,27–29 as
the No-Apnea score increased, the posttest probability of hav-
ing OSA-5, OSA-15, and OSA-30 also increased.
OSA is a very prevalent and often underdiagnosed dis-
ease.3 0, 31 In addition, symptoms suggestive of OSA, despite
being common, are not consistently investigated during rou-
tine clinical visits.32 Accordingly, screening tools can be used
to identify patients at high risk for SDB, thus prioritizing the
use of portable methods in areas with limited resources. How-
ever, the performance of an OSA questionnaire may have con-
siderable variability according to the patient population and
AHI thresholds employed.10,33 The Berlin questionnaire, for
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example, possibly has a better performance in primary care
settings than sleep laboratory settings.26,34 In a sleep clinic
population, the STOP-BANG showed sensitivity of 90%, 94%,
and 96% to detect OSA-5, OSA-15, and OSA-30, respectively;
however, specicity was relatively low (49%, 34%, and 25%;
respectively).35 Conversely, STOP-BANG alone was insu-
cient to conrm the occurrence of signicant OSA in military
veterans undergoing unattended sleep studies, mainly OSA-15,
in whom a score of 3 showed a high sensitivity (99.1%), but also
a very low specicity (4.9%).36
The STOP-BANG is an instrument with high sensitivity that
increases in parallel with increasing AHI thresholds (from 5 to
30 events/h).13 Conversely, it exhibits moderate to low specic-
ity based on the AHI thresholds used herein, such that decreases
in specicity result in a large number of false-positive results,
thereby reducing accuracy, especially in the more severe forms
of OSA. This issue is critical, particularly in sleep laboratories
targeting the choice of portable diagnostic methods or when
predicting which subjects will be requiring CPAP treatment.
The abilities of the 4-Variable screening tool, STOP, STOP-
BANG, and ESS questionnaires in identifying subjects at risk
for SDB were previously evaluated37: for predicti ng OSA-15, the
STOP-BANG had the highest sensitivity (87.0%) with an AUC
of 0.64, whereas the 4-Variable screening tool had the highest
speci city (93.2%) and accuracy (79.4%). Moreover, predictive
parameters for OSA-30 showed that the STOP-BANG had the
highest sensitivity (70.4%), whereas the 4-Variable screening
tool had the highest specicity (93.2%) and accuracy (86.7%)
with an AUC of 0.67. Similar ndings are shown in a study38
comparing ve dierent questionnaires (STOP, STOP-BANG,
Berlin, ESS, and 4-Variable screening tool): the STOP-BANG
had the highest sensitivity (97.6%) and the largest AUC (0.73),
but the lowest specicity (12.7%) for OSA-15. Conversely, the
4-Variable screening tool had the highest specicity (74.4%)
followed by ESS (67.0%).
According to NoSAS, although this instrument may pos-
sibly present good performances when other AHI thresholds
are explored, it was implemented using an AHI cuto of 20
events/h.16 Conversely, our model was tested at three dier-
ent AHI thresholds that are widely recognized and accepted
when assessing the severity of OSA. The NoSAS score16 has
ve variables (NC, BMI, age, sex, and snoring), whereas our
tool requires only two objective parameters, a feature that we
think can translate into greater practical applicability and ease
of implementation. Moreover, NoSAS approach uses the pres-
ence of snoring as an integral parameter, thus requiring infor-
mation based on a bed partner, thereby potentially resulting in
information bias.
A recent study39 was developed to validate the NoSAS score
in a multiethnic Asian cohort and compare its performance
Figure 1—Receiver operating characteristic curves showing the discrimination of No-Apnea, STOP-BANG, and NoSAS.
Values shown as area under the curve and 95% condence interval. Top panels: derivation cohort (n = 2,037). Bottom panels: validation cohort (n = 2,035).
OSA severity was classied based on AHI as follows: ≥ 5 events/h as any OSA, ≥ 15 events/h as moderate/severe OSA, and ≥ 30 events/h as severe OSA.
AHI = apnea-hypopnea index, OSA = obstructive sleep apnea.
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with STOP-BANG and Berlin questionnaires: both question-
naires performed in a manner similar to that of the NoSAS
score, with AUCs of all of them clustered around 0.682–0.748.
Our study also did not verify performance dierences between
STOP-BANG and NoSAS, therefore dierent from the previ-
ous study of derivation and validation of NoSAS, in which it
performed better than the STOP-BANG (P < .0001) and Ber-
lin questionnaires (P < .0001) in both cohorts (HypnoLaus and
EPISONO).16
Strengths and Limitations
Our study had some limitations: patient selection for genera-
tion of the tool was predicated on sleep laboratory subjects, and
therefore the possibility of selection bias is plausible and its
implication for the general population may be limited. In gen-
eral, patients referred to a sleep laboratory are often suspected
of having OSA and they reect selected patients with a high
pretest probability. Furthermore, possible specic dierences
in the tool proper ties among sexes may require future optimiz-
ing adjustments. A nding that deserves to be emphasized is
that our 2-item model uses a parameter (NC) that can suer
from measurement error, which can cause information bias. In
addition, it can be inuenced by populations with their own
anthropometric characteristics (Asian or African populations),
thus requiring further validation in these settings. Similarly,
measures of regional obesity were not included, and might pro-
vide important additional information to the referral decision-
making algorithm.
Conversely, we should also point out several features of
the current study that should strengthen the ability to imple-
ment the proposed instrument. First, the tool is a mnemonic
and concise model with only two numerical and readily mea-
sured objective variables along with absence of any subjec-
tively reported items, thereby allowing for easier acquisition
and calculation while reducing potential biases. Second, it was
developed and then validated in two large and independent co-
horts, with all individuals enrolled undergoing full PSG and
with the same diagnostic criteria,19 aiming to explore the ro-
bustness of our model. Third, the developed scoring system
presented adequate performance (overall performance, dis-
crimination, and calibration) in the derivation cohort as well as
in a validation cohort.
CONCLUSIONS
In conclusion, our tool shows favorable promise for screen-
ing of OSA at any level of severity, based on commonly and
widely used AHI thresholds. This tool should enable alloca-
tion of patients to dierent severity types and corresponding
priorities, and thus enable improved patient prioritization and
resource allocation. In addition, there was no superiority of
one model over the other, which highlights a very great practi-
cal applicability of the No-Apnea because it contains only two
objective variables easily obtained during the evaluation of a
patient with suspected OSA. Therefore, it can also be used in
individuals who sleep alone, in whom subjective information
about sleep is not necessarily available. As with any population
study, future prospective exploration for other world regions
and dierent clinical settings will be critical for widespread
implementation of such simple tool.
ABBREVIATIONS
AHI, apnea-hypopnea index
AUC, area under the curve
BMI, body mass index
CI, condence interval
CPAP, continuous positive airway pressure
ESS, Epworth Sleepiness Scale
IQR, interquartile range
NC, neck circumference
NPV, negative predictive value
OR, odds ratio
OSA, obstructive sleep apnea
PPV, positive predictive value
PSG, polysomnography
ROC, receiver operating characteristic
SDB, sleep-disordered breathing
SpO2, oxygen saturation
VIF, variance ination factor
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ACKNOWLEDGMENTS
We thank José Roberto Lapa e Silva, MD, PhD for critical review of the manuscript.
SUBMISSION & CORRESPONDENCE INFORMATION
Submitted for publication October 19, 2017
Submitted in nal revised form February 14, 2018
Accepted for publication February 23, 2018
Address correspondence to: Ricardo L. M. Duarte. Sleep - Laboratório de Estudo
dos Distúrbios do Sono, Centro Médico BarraShopping, Avenida das Américas
4666, sala 309, Barra da Tijuca, 22649 -90 0, Rio de Janeiro, Brazil; Tel: 55 21 2430-
9222; Fax: 55 21 2430-9220; Email: rlmduarte@gmail.com
DISCLOSURE STATEMENT
All authors have seen and approved the manuscript. The authors report no conicts
of interest.
