Predicting Obstructive Sleep ApneaAmong Women Candidates for Bariatric Surgery

Katherine M. Sharkey, M.D., Ph.D.,1,2 Jason T. Machan, Ph.D.,3,4,5 Christine Tosi, B.A.,1

G. Dean Roye, M.D.,3 David Harrington, M.D.,3 and Richard P. Millman, M.D.1

Abstract

Background: More women than men pursue bariatric surgery for treatment of obesity. Untreated obstructivesleep apnea (OSA) in bariatric patients increases perioperative morbidity and mortality, and, therefore, mostbariatric surgeons screen for OSA with polysomnography (PSG). We sought to develop a model for predictingOSA in women seeking bariatric surgery in order to use this diagnostic resource most efficiently.Methods: We identified 296 women who had PSG in preparation for bariatric surgery. Regression and logisticregression analyses were used to assess the relationship between history and physical examination findings andOSA severity. After developing best statistical models, we constructed a summary index to identify patients ex-ceeding clinical thresholds for mild (apnea-hypopnea index [AHI] �5) and moderate to severe disease (AHI �15).Results: In our sample, most women (86%) had OSA, and more than half (53%) had moderate to severe disease.Multiple logistic regression showed that age, body mass index (BMI), neck circumference, hypertension, wit-nessed apneas, and snoring predicted AHI. Diabetes mellitus and daytime sleepiness measured with the Ep-worth Sleepiness Scale (ESS) were not significant predictors of OSA. Prediction models were statisticallysignificant but had poor specificity for predicting OSA severity.Conclusions: OSA is highly prevalent in symptomatic and asymptomatic women planning bariatric surgery forobesity. Best prediction models based on clinical characteristics did not predict disease severity under conditionssuperior to those in which they might be applied. In light of the perioperative risks associated with OSA inbariatric patients, all women considering bariatric surgery for obesity should be evaluated for OSA with PSG.

Introduction

Obstructive sleep apnea (OSA) is a form of sleep-disordered breathing that causes sleep fragmentation

and nocturnal oxygen desaturation. In addition to experi-encing such consequences as disrupted sleep and daytimesleepiness, women with OSA are at higher risk for coronaryartery disease (CAD),1 hypertension,2 and stroke.3 In womenof childbearing age, OSA is often comorbid with conditions,such as polycystic ovary syndrome (POS)4,5 and pregnancy-induced hypertension (PIH).6

OSA is common in the general population. For instance,Young et al.7 studied a random sample of 626 adults aged 30–60 years and found that 9% of women and 24% of men had atleast mild OSA (defined as apnea-hypopnea index [AHI] �5),with moderate to severe disease (AHI �15) observed in 4% of

women and 9% of men. Several studies have shown thatwomen have a lower prevalence of OSA than men, but amongwomen, OSA is underdiagnosed.7,8 The knowledge of this sexdifference in prevalence may underlie observed gender dis-parities in diagnosis of OSA in women.7,9,10 Underdiagnosis inwomen has also been attributed to differing clinical presenta-tions in men and women11,12 and disregard of typical symp-toms (such as snoring) in women.13 In addition to male sex,other well-documented risk factors for OSA include older age,obesity, large neck circumference, narrow airway configura-tion, presence of snoring, and complaints of daytime sleepiness.

Given the importance of obesity as a risk factor for OSA, itis not surprising that OSA is frequently observed in patientsplanning bariatric surgery. Bariatric surgery is an increasinglycommon treatment for obesity, with more women than menpursuing weight loss surgery.14 In addition, bariatric patients

1Department of Medicine, Rhode Island Hospital=Alpert Medical School of Brown University, Providence, Rhode Island.2Department of Psychiatry & Human Behavior, Alpert Medical School of Brown University, Providence, Rhode Island.3Department of Surgery and 4Department of Orthopedics, Rhode Island Hospital=Alpert Medical School of Brown University, Providence,

Rhode Island.5Department of Research (Biostatistics), Rhode Island Hospital, Providence, Rhode Island.

JOURNAL OF WOMEN’S HEALTHVolume 19, Number 10, 2010ª Mary Ann Liebert, Inc.DOI: 10.1089=jwh.2009.1859

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represent a population where typical sex differences in OSAare less robust. For example, Frey and Pilcher15 performeddiagnostic polysomnography (PSG) in 41 patients (34 women)as part of a bariatric surgery evaluation and found OSA (de-fined by AHI �5) in 68% of women and 86% of men. Serafiniet al.16 performed PSG in 23 women and 4 men who reportedsleepiness as part of bariatric surgery screening. They did notreport their results by gender, but only 1 of 27 patients did nothave OSA; thus, the prevalence of OSA in the women in theirsample was at least 96%. O’Keeffe and Patterson17 analyzeddata from 142 women and 21 men screened for bariatricsurgery and found OSA in 77% of women and 100% of men.Similarly, Sareli et al.18 examined 342 patients who under-went PSG as part of bariatric screening and reported sleepapnea in 74% of women and 94% of men. Therefore, com-pared to the male=female ratio of OSA in the general popu-lation of approximately 2.4:1, the ratio in patients with obesitywho are planning bariatric surgery is lower, at approximately1.3:1, an increased risk of approximately 1.8 times. This in-creased risk, coupled with a tendency for underdiagnosis inwomen, poses a particular risk to the population of womenseeking weight loss surgery.

The implications for underdiagnosis of OSA are profound.Patients with OSA are more prone to postoperative respiratorycomplications than patients without sleep-disordered breath-ing,19,20 and untreated OSA in bariatric patients increasesperioperative morbidity and mortality.21 Therefore, most bar-iatric surgeons routinely employ PSG for all patients beingconsidered for weight loss surgery to screen for OSA. Per-forming PSG on all patients planning bariatric surgery may notbe practical at all centers because of the cost and somewhatlimited availability of PSG. Sleep medicine physicians com-monly use the presence of symptoms, such as snoring, wit-nessed apneas during sleep, and daytime sleepiness, to decidewhether or not to perform PSG on nonbariatric patients. Wehypothesized that these clinical correlates could predict thepresence or severity of sleep-disordered breathing in womenwith obesity. Thus, we sought to develop a model for pre-dicting the presence, absence, and severity of OSA using his-tory, symptoms, and physical findings in women seekingbariatric surgery in order to use this diagnostic resource mostefficiently.

Materials and Methods

Patient selection

Using retrospective chart review, we identified 342 adultpatients (296 women) who were evaluated for bariatric sur-gery at Rhode Island Hospital between January 2003 andDecember 2005. As part of preoperative screening, all patientshad PSG evaluation for OSA and were seen by a sleep med-icine specialist (R.P.M. or a colleague). None of the patientswas preselected for sleep evaluation based on symptoms. Aninvestigator (C.T.) with no knowledge of the clinical status ofthe patients or if they eventually had bariatric surgery re-viewed the Sleep Disorders Center and sleep physician officecharts of the 296 female patients. Age, height, weight, neckcircumference, report of snoring, witnessed apneas duringsleep, and the presence of comorbid hypertension and dia-betes mellitus were extracted for each patient. Daytimesleepiness was measured with the Epworth Sleepiness Scale(ESS), an 8-item questionnaire where participants rate their

likelihood of dozing in various situations.22 The ESS is in-cluded among the basic questionnaires that all patients com-plete for the Sleep Center and was not used to screen forwhich bariatric patients would undergo PSG.

Polysomnography

Standard overnight PSG was performed at one of three sitesof the Sleep Disorders Center of Lifespan Hospitals (TheMiriam Hospital and Rhode Island Hospital in Providence,RI, and Newport Hospital in Newport, RI). All studieswere performed using Viasys equipment (Yorba Linda, CA).Sleep staging was monitored using central and occipitalelectroencephalographic leads, bilateral electro-oculograms,and a submental electromyelogram (EMG). Respiration wasmonitored using continuous pulse oximetry, a snoring mi-crophone, nasal and oral thermistors, a nasal pressure trans-ducer, and chest and abdominal piezo electrodes. In addition,the heart rate was continually monitored using a modified V2lead, and bilateral tibialis EMG leads were placed to detectperiodic limb movements.

All records were visually scored by the technologists usingRechtshaffen and Kales criteria,23 with the Viasys software totabulate indices. The same technologists did all scoring andhad periodic concordance checks performed. Respiratoryevents were scored according to consensus critera.24 Apneaswere defined as an absence of airflow lasting >10 seconds.Hypopneas were scored if there was a clear decrease in thenasal pressure transducer signal of >50% from baseline am-plitude lasting at least 10 seconds with either an oxygen de-saturation>3% or an arousal. Events were classified as centralin the absence of any respiratory effort, mixed if there wasinitially no respiratory effort followed by progressive evi-dence of ineffective respiration, and obstructive if there waspersistent respiratory effort despite an absence of airflow.Arousals were determined using established criteria.25 Thepresence of OSA was determined using the AHI, which isdefined as the total number of apneas and hypopneas dividedby the total number of hours of sleep. Mild OSA was definedas AHI�5 and<15. Moderate OSA was noted if the AHI was�15 and <30, and severe OSA was noted if the AHI was �30episodes per hour.24

This study was approved by the Rhode Island HospitalInstitutional Review Board.

Statistical methods

Data screening and preparation. Analyses were carriedout using SAS version 9.2 (SAS Institute, Cary, NC), SPSS 17.0(SPSS, Inc., Chicago, IL), and Matlab r2008b (The MathworksInc., Lowell, MA). The distributions of continuous variableswere checked for symmetry and normality based on theskewness coefficient (criteria set to exceeding� 2) andShapiro-Wilk statistic ( p< 0.01). Variables that violated bothcriteria were logarithmically transformed. A constant wasadded to those with ranges including zero such that the dis-tribution was shifted to right of zero before transformation(to avoid undefined numerical values). This transformationreduced the skewness coefficient in each instance, indicatingbetter symmetry (not reported). The transformed variable wasthen used in any subsequent parametric analyses (as indi-cated). Such transformations are generally thought to result inbetter representation of the measure, as they often better ap-

1834 SHARKEY ET AL.

proximate an underlying linear aspect of the mechanism andreduce the influence of values appearing to be outliers on onescale but not on another.26,27

We used analysis of variance (ANOVA) and chi-square an-alyses to compare means and distributions of women withoutOSA and those with mild and moderate=severe disease.

Predictive models of OSA severity (AHI). Ordinary least-squares linear regression and multiple linear regressions wereused to assess the strength of the relationship between patientcharacteristics and the apnea severity as estimated by the AHI(individually and additively, respectively), as well as to de-velop predictive models. We used characteristics that wereindividually predictive in the least-squares linear regression(with alpha set at 0.05) as eligible predictors in the multipleregression using a stepwise-selection method (entry=exit atp<=�0.15).

Because two of our statistically significant independent pre-dictors from the least-squares regression (body mass index[BMI] and neck circumference) were strongly correlated witheach other, we chose to construct two independent multipleregression models. The first model excluded neck circumfer-ence from the pool of predictors to avoid multicolinearity withBMI. In addition, neck circumference was missing in manypatients, so the first model included more participants. Despitethe missing neck circumference data, we were interested inexamining this variable, particularly because of the likelihoodof neck circumference being the more proximal risk factor forOSA. Thus, the second multiple regression model included bothneck circumference and BMI in the variable pool and permittedthem to compete for entry=exit into and out of the model.

Clinical utility of best AHI prediction models. To assessthe utility of the best statistical models constructed, the betasfrom each were used to construct summary indices, whichrepresent the AHI for each patient as predicted by the model.The predicted AHIs were used to attempt to identify patients

exceeding clinical thresholds for mild (AHI�5) and moderateto severe (AHI �15) apnea using the models with a signaldetection theory approach. Plots of sensitivity=specificity as afunction of threshold were produced, as well as the receiveroperator characteristic (ROC) curves. The area under the ROCcurve was calculated based on the Wilcoxon score along withthe risk ratio associated with crossing an optimized thresholdselected as the point of intersection for sensitivity and speci-ficity (using linear interpolation where necessary).

Results

Patient characteristics and demographics

Table 1 presents patient demographics for the sample di-vided into three groups: women without OSA, women withmild OSA (5�AHI< 15) and women with moderate=severeOSA (AHI >15). The mean age of the entire sample was 42years (range 19–61 years), and average BMI was 50.1 kg=m2.Two hundred fifty-five women (86%) had an AHI�5, and 158(53%) had an AHI �15. There was a high prevalence of hy-pertension (44%) and of diabetes (20%). Although the major-ity of the sample reported snoring, only a small percentagereported witnessed apneas while sleeping or had ESS scoresindicative of excessive daytime sleepiness.

ANOVA and chi-square analyses showed significant ef-fects of OSA severity for age, BMI, neck circumference, historyof any symptoms, history of witnessed apneas, and historyof snoring. Post hoc analyses performed with Tukey honestlysignificant differences (HSD) test revealed that comparedwith women without OSA, women with moderate=severeOSA were older, had higher BMI, and had larger neck cir-cumferences. Post hoc Holm procedure confirmed that womenwith moderate=severe disease were also more likely to reportsymptoms, particularly snoring and witnessed apneas, thanwomen without OSA. Women with moderate=severe diseasealso had larger neck circumferences than women with mildOSA. Women with mild OSA had significantly higher BMIs

Table 1. Patient Characteristics

OSA Negative, n¼ 41(AHI< 5 events=hr)

Mild OSA, n¼ 97(5 �AHI< 15)

Moderate=severe OSA,n¼ 158 (AHI �15)

Variable (sample size) Mean SD Mean SD Mean SD F(df ) p value

Age in years (296)a 38 10 41 10 44 9 6.92 (2,293) .001BMI in kg=m2* (296)a,b 45.4 5.0 49.6 7.4 51.7 8.3 12.02 (2,293) .000Neck Circ. in inches* (112)a,c 16.1 1.2 16.4 1.3 17.1 1.3 6.01 (2,109) .003Epworth* (245) 5.9 4.4 6.4 4.3 6.5 4.8 0.21 (2,242) ns

Frequency Percent Frequency Percent Frequency Percent Chi-square p value

Hypertension (295) 14 34.1% 42 43.8% 74 46.8% 2.13 nsDiabetes (295) 7 17.1% 23 24.0% 29 18.4% 1.43 nsRetrognathia (102) 8 42.1% 6 17.1% 17 35.4% 4.71 nsAny Symptoms (296)a 33 80.5% 84 86.6% 148 93.7% 7.35 .03Witnessed Apneas (294)a 5 12.2% 22 22.9% 48 30.6% 6.28 .04Epworth� 10 (245) 6 16.2% 13 16.3% 34 26.6% 3.84 nsSnoring (296)a 33 80.5% 84 86.6% 147 93.0% 6.32 .04

*Variable logarithmically transformed for analyses; raw means and standard deviations reported in table.asignificant difference between OSA negative group and moderate=severe OSA group.bsignificant difference between OSA negative group and mild OSA group.csignificant difference between mild OSA group and moderate=severe OSA group.OSA, obstructive sleep apnea; AHI, apnea-hypopnea index; F, F-statistic; df, degrees of freedom.

OBSTRUCTIVE SLEEP APNEA IN WOMEN SEEKING BARIATRIC SURGERY 1835

and lower oxygen saturation (SaO2) nadirs than womenwithout OSA (Table 1).

Prediction of apnea severity

Table 2 summarizes the linear regressions used to predictapnea severity. Age, log BMI, log neck circumference, hy-pertension, report of at least one symptom, witnessed apneas,and snoring each individually predicted relative apnea se-verity (log AHI). Diabetes and daytime sleepiness (log ESS)did not predict severity of OSA.

The statistically significant predictors were entered into astepwise multiple linear regression, excluding only neck cir-cumference. Although neck circumference may be consideredmore proximal to OSA, it was excluded to avoid multi-colinearity with BMI (r¼ 0.44, p< 0.0001) and because it wasnot recorded in a high proportion of the sample. Age, BMI, andwitnessed apneas were selected for the final model. Each ofthese was positively related to the AHI and remained statisti-cally significant after adjusting for each other’s effects. A secondstepwise multiple linear regression included neck circumfer-ence in the list of potential predictors, lowering the sample sizeconsiderably and permitting BMI and neck circumference tocompete for variability. Age, neck circumference, and snoringwere selected for the final model, whereas witnessed apneaswere not. Both the smaller sample size and any systematicdifferences between patients with neck circumference mea-

surements and those without may have contributed to thevariation between variables selected in the final models.

Prediction of apnea severity clinical thresholds

The final models from the two stepwise multiple regressionspredicting log AHI severity were used to create two differentindices, which were in effect predicted values for log AHI.These were then evaluated for clinical utility by calculatingtheir sensitivity and specificity for identifying patients withAHI �5 or AHI �15 at all possible thresholds. An ROC curvewas created, and optimized thresholds were determined basedon maximizing sensitivity and specificity (the point of inter-section for their two functions with threshold).

Figure 1 illustrates the diagnostic parameters for identifyingpatients having an AHI of �5 (top) and �15 (bottom) using amodel that excluded patient neck size. For each, sensitivity andspecificity were plotted as a function of thresholds in predictedAHI. The vertical lines represent the optimized threshold atwhich the sensitivity and specificity functions intersect. AnROC curve is inset in each graph, with the asterisks re-presenting the point in the ROC at the optimized threshold.

Although areas under the ROC curve of 0.76 (any OSA) and0.67 (moderate to severe OSA) seem encouraging, the opti-mized sensitivities and specificities (i.e., the point at whichsensitivity and specificity are the same) were approximately66% and 64% for AHI of �5 and �15, respectively. In our

Table 2. Predictors of Apnea Severity

Predictor dfN dfD F p r2

Simple predictors of AHIa

Age 1 294 20.25 <0.0001BMIa 1 294 32.73 <0.0001Neck circumferencea 1 110 19.70 <0.0001Hypertension 1 293 5.26 0.0225Diabetes 1 293 0.30 0.5865Any symptoms 1 290 8.00 0.0050

ESSa 1 243 1.40 0.2378Witnessed apneas 1 292 14.96 0.0001Snoring 1 294 5.39 0.0210

Multiple predictor models

Model without neck size (n¼ 278) 3 241 14.20 <0.0001 15.03%

Eligible variables b SE t p Semipartial r2

Age 0.021 0.006 3.29 0.0012 4.85%BMIa 1.773 0.413 4.29 <0.0001 7.08%Hypertension Not selectedESSa Not selectedWitnessed apneas 0.414 0.140 2.97 0.0033 3.10%Snoring Not selected

Model with neck size (n¼ 116) 3 92 17.05 <0.0001 35.74%

Eligible variables b SE t p Semipartial r2

Age 0.040 0.009 4.36 <0.0001 20.27%BMIa Not selectedNeck circumferencea 4.064 1.041 3.90 0.0002 12.16%Hypertension Not selectedESSa Not selectedWitnessed apneas Not selectedSnoring 0.492 0.226 2.18 0.0320 3.31%

aVariable logarithmically transformed for analysis.ESS, Epworth Sleepiness Scale; df, degrees of freedom; N, numerator; D, denominator; F, F-statistic; r2, the proportion of variance

accounted for or r-square.

1836 SHARKEY ET AL.

sample of 294 patients, 253 had an AHI�5, but our best modelat our best threshold missed 86 patients, or 34%. Similarly, 157patients had an AHI �15, but our best model with an opti-mized threshold missed 57 patients, or 36%. These translatedto negative predictive values of 24% and 60% for AHI of �5and �15, respectively.

Figure 2 similarly illustrates the diagnostic parameters foridentifying patients having AHI of�5 (top) and�15 (bottom),including neck circumference as a predictor (note smallersample size). Again, sensitivity and specificity are plotted as afunction of thresholds in predicted AHI. The vertical linesrepresent the optimized threshold at which the sensitivity andspecificity functions with threshold intersect. An ROC curve isinset in each graph, with the asterisks representing the pointin the ROC at the optimized threshold.

There were 112 patients in this analysis, and 93 had an AHI�5. The optimized sensitivity and specificity for distinguish-ing patients with AHI �5 was approximately 76%, with 22patients missed. The corresponding negative predictive valuewas 39%. There were 60 patients who had an AHI �15. The

optimized sensitivity and specificity for distinguishing thesepatients was approximately 72%, with 16 patients missed. Thecorresponding negative predictive value was 68%.

We also note that as the models produce predicted AHI,one would expect the optimized thresholds in the predictionto be at or near the clinical thresholds. However, the opti-mized thresholds for identifying an actual AHI of �5, basedon a predicted AHI, were much higher than 5 for both models.If the model predictions were perfect (or even unbiased), thethreshold would have been close to 5. These results furtherindicate that the model fails to predict mild OSA despitestatistically significant effects. The model’s optimized thresh-olds for predicting an actual AHI of �15 were closer, indi-cating less bias, although performance was still poor.

Discussion

Prevalence of OSA

The major finding of our study is the high prevalence ofOSA in this large sample of women planning bariatric surgery

FIG. 1. This illustrates thediagnostic parameters foridentifying patients with AHI�5 (top) and �15 (bottom)using the model that ex-cluded patient neck size asa predictor. For each, sensi-tivity and specificity wereplotted as a function ofthresholds in predicted AHI.The vertical lines representthe optimized threshold atwhich the sensitivity andspecificity functions intersect.An ROC curve was inset ineach graph with the asterisksrepresenting the point in theROC at the optimized thresh-old. AHI, apnea-hypopneaindex; ROC, receiver operatorcurve; AUROC, area underthe receiver operator curve;CI, confidence interval.

OBSTRUCTIVE SLEEP APNEA IN WOMEN SEEKING BARIATRIC SURGERY 1837

for treatment of obesity. Using a cutoff of an AHI �5, 86% ofour participants had at least mild OSA, and 53% had mod-erate to severe disease (AHI �15). Furthermore, average AHIin our sample was in the severe range, at 30.0 events per hourof sleep. Our findings are similar to those of other studies ofthe prevalence of OSA in bariatric patients, where percentagesof 68%–96% have been observed in female patients.15–18 Theseresults highlight the fact that although OSA is more commonin men than in women in the general population, this sexdifference is overshadowed by extremely high rates of OSA ina population with obesity.

Clinical characteristics related to OSA

Our analyses showed that older age, higher BMI, largerneck circumference, presence of hypertension, report of wit-nessed apneas, and report of snoring were related to OSAseverity in our sample. Age is a known risk factor for OSA,although in midlife women, the age-related risk for OSA does

not increase until women are older than 50 years.7 Further-more, several studies have demonstrated that sleep apnea ismore prevalent in postmenopausal rather than premeno-pausal women.28,29 The average age of our participants was42 years, and the youngest woman evaluated for surgery wasonly 19 years of age. Thus, although we did not measuremenopausal status per se, there were a high number ofyounger women in our sample who most likely were pre-menopausal. This finding implies that obesity itself plays asignificant role in the development of OSA independent ofhormonal state. This is consistent with our previous work in anonbariatric sample showing that hormonal factors may beless important than weight and facial morphology in the de-velopment of OSA in women.30

The mean neck circumference in our participants was 16.75inches, and the BMI was 50.1 kg=m2. Previous work hasshown that women with a neck circumference >16 inches areat increased risk of OSA.31 The current finding is also con-sistent with a previous study from our laboratory on women

FIG. 2. This similarly illus-trates the diagnostic parame-ters for identifying patientswith AHI �5 (top) and �15(bottom) but including neckcircumference as a predictor(note smaller sample size).Again, sensitivity and speci-ficity were plotted as a func-tion of thresholds in predictedAHI. The vertical lines repre-sent the optimized thresholdat which the sensitivity andspecificity functions intersect.An ROC curve was inset ineach graph, with the asterisksrepresenting the point inthe ROC at the optimizedthreshold.

1838 SHARKEY ET AL.

with OSA that showed that the AHI correlated with BMI andmeasurements of upper body obesity, including triceps andsubscapular skin folds and neck skin fold.32

Neither diabetes mellitus nor daytime sleepiness was asignificant predictor of OSA. We were surprised that sleepi-ness was not associated with OSA in this sample, as ESS scorehas been associated with OSA severity in other studies of menand women from the general population33 and from a pop-ulation with obesity planning bariatric surgery.18 For exam-ple, in a nonbariatric sample of men and women with OSA,patients with AHIs in the severe range (like our participants)had a mean ESS score (�SD) of 16.2� 3.3.33 In women withobesity and severe OSA, Sareli et al.18 reported a mean ESS(�SD) score of 9.9� 5.4, but ESS was not related to OSA se-verity once they controlled for gender and menopausal status.Average ESS (�SD) was 6.4� 4.5 in our sample. A clinicalcutoff of ESS �10 is typically used for screening for excessivedaytime sleepiness,22 although a threshold of ESS >6 has beenused in the bariatric population.16,34 Although the ESS has beenused frequently to subjectively assess daytime sleepiness inpatients with OSA, recent studies have questioned its reliabilityin known clinical populations.35 In addition, it has beensuggested that bariatric patients may be motivated to under-report symptoms, particularly psychiatric symptoms, duringbariatric screening.36 Thus, ESS scores in our sample may below because of participants’ belief that surgery may be deniedif they endorse symptoms. Other possible factors explainingthe lack of a relationship between ESS and OSA severity in oursample include inability to recognize impairment or trueabsence of sleepiness. Further work is needed to understand ifthere are gender differences in sleepiness reported in bariatricpatients. Future investigations of this issue should also includeobjective measures of sleepiness and performance.

Predictive models of OSA

In an effort to make efficient use of PSG, researchers haveattempted to create predictive models for assessing bariatricpatients’ risk for OSA based on symptoms consistent withOSA.16,18,37–40 Two of the most consistently identified factorsin patients with known OSA are snoring and observed ap-neas.41,42 Our study confirmed that virtually all of our patientswho reported observed apneas while sleeping had an AHI�5 and an AHI �15 on PSG. We were unable to separatewomen without OSA from those with OSA or women withmild OSA from patients with moderate=severe disease basedon our models.

Ultimately, however, these models are based on the rela-tively low bar of statistical significance. This should be viewedas a necessary but not sufficient criterion. A predictive modelshould be assessed based on performance in the context inwhich it would be used. For example, although symptoms ofsnoring and observed apneas were clearly (and significantly)related to OSA, they could not be used effectively to identifypatients without OSA, as there were many patients found tohave OSA without either of these symptoms.

The current study sought to augment the approach takenby others16–18 by similarly constructing the best possible sta-tistical models for predicting AHI, then using the algorithmdefined by the model to create an index score (predicted AHI),which was submitted to a battery of evaluations for diagnosticutility. This approach provides a best-case scenario, as the

models are fit for the same set of patients. The results using thesame algorithm on different patients (i.e., a validation study)would almost certainly be inferior to those of the current an-alyses. Thus, this analytical strategy highlights the distinctionbetween statistical significance and precision in predictionmodels.

There was unequivocal statistical significance for our fac-tors, but our models failed to provide the precision necessaryfor clinical use. The implication considering our design is thata model with fixed parameters applied to other populationswould exhibit further limitations. Our models failed to reachthe degree of precision necessary for clinical application, butthis does not mean other models suffer similarly. It does,however, suggest that the use for which other models aredeveloped should be rigorously specified and validated. Useshould not be based on solely statistical significancebut should be based on a study design that further tests usedirectly.

One methodological concern with our study is that symp-toms were based on patients’ self-report, which are subjectiveand can be influenced by various reporter biases. For exam-ple, snoring requires a bed partner or family member to hearthe snoring. Another limitation is absence of neck circumfer-ence data in many of the patients. This reduced our samplesize for certain analyses, although we do not believe thatfewer missing data points for neck circumference would havealtered the performance of our prediction models. Third, wedid not collect information about menopausal status in ourpatients, and this is certainly a measure of interest in thispopulation. Overall, the patients were young, however, sothis may not have affected the outcomes of our analyses. Fi-nally, we did not include race or ethnicity as a factor in theseanalyses, as this information was not routinely recorded forthe patients’ clinical evaluation.

Conclusions

Clinically significant OSA occurs in most women planningbariatric surgery as a treatment for obesity. Given the highprevalence of OSA in this population, predictive modelsbased on clinical characteristics are not effective for predictingAHI. The finding that the absence of symptoms did not pre-clude the presence of significant sleep apnea implies that it isdifficult for a healthcare provider to exclude the diagnosis ofOSA in women with obesity or distinguish between mild andmoderate=severe disease by history and physical examinationfindings alone. In light of the significant morbidity and mor-tality associated with untreated OSA, particularly in theperioperative period, women with obesity who are planningbariatric surgery should be evaluated for OSA with PSG untilbetter prediction models or alternate diagnostic technologiescan be developed to meet the needs of the many patients whorequire screening for sleep-disordered breathing.

Disclosure Statement

No competing financial interests exist.

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Address correspondence to:Richard P. Millman, M.D.

APC 701Rhode Island Hospital

593 Eddy StreetProvidence, RI 02903

E-mail: rmillman@lifespan.org

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