Simple Screen for Sleep-Disordered Screening
Simple Four-Variable Screening Tool for Identification of Patients with SleepDisordered Breathing Misa Takegami, RN, MPH1; Yasuaki Hayashino, MD, PhD1; Kazuo Chin, MD, PhD2; Shigeru Sokejima, MD, PhD3; Hiroshi Kadotani, MD, PhD4; Tsuneto Akashiba, MD, PhD5; Hiroshi Kimura, MD, PhD6; Motoharu Ohi, MD, PhD7; Shunichi Fukuhara, MD, DMSc, FACP1 Department of Epidemiology and Healthcare Research, Graduate School of Medicine and Public Health, Kyoto University, Kyoto, Japan; Department of Respiratory Care and Sleep Control Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan; 3Department of Public Health Policy, National Institute for Public Health, Japan; 4Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan; 5Department of Internal Medicine, Nihon University, School of Medicine, Tokyo, Japan; 6Second Department of Internal Medicine, Nara Medical University, Nara, Japan; 7Sleep Medical Center, Osaka Kaisei Hospital, Osaka, Japan 1 2
Objectives: To aid in the identification of patients with moderate-tosevere sleep-disordered breathing (SDB), we developed and validated a simple screening tool applicable to both clinical and community settings. Methods: Logistic regression analysis was used to develop an integerbased risk scoring system. The participants in this derivation study included 132 patients visiting one of 2 hospitals in Japan, and 175 residents of a rural town. The participants in the present validation study included 308 employees of a company in Japan who were undergoing a health check. Results: The screening tool consisted of only 4 variables: sex, blood pressure level, body mass index, and self-reported snoring. This tool (screening score) gave an area under the receiver operating characteristic curve (ROC) of 0.90, sensitivity of 0.93, and specificity of 0.66, using a cutoff point of 11. Predicted and observed prevalence proportions in the validation dataset were in close agreement across the en-
tire spectrum of risk scores. In the validation dataset, the area under the ROC for moderate-to-severe SDB and severe SDB were 0.78 and 0.85, respectively. The diagnostic performance of this tool did not significantly differ from that of previous, more complex tools. Conclusion: These findings suggest that our screening scoring system is a valid tool for the identification and assessment of moderate-tosevere SDB. With knowledge of only 4 easily ascertainable variables, which are routinely checked during daily clinical practice or mass health screening, moderate-to-severe SDB can be easily detected in clinical and public health settings. Keywords: Sleep-disordered breathing, screening, sensitivity, specificity, validation Citation: Takegami M; Hayashino Y; Chin K; Sokejima S; Kadotani H; Akashiba T; Kimura H; Ohi M; Fukuhara S. Simple four-variable screening tool for identification of patients with sleep-disordered breathing. SLEEP 2009;32(7):939-948.
SLEEP-DISORDERED BREATHING (SDB), INCLUDING OBSTRUCTIVE SLEEP APNEA, WAS INITIALLY CONSIDERED A RARE DISORDER; HOWEVER, RECENT epidemiologic studies have revealed that it is fairly prevalent in the general adult population.1,2 Apnea and hypopnea during sleep increase the risk of cardiovascular disease, including hypertension, arrhythmia, and myocardial infarction, as well as cerebrovascular disease.3 Moreover, because it may lead to motor vehicle and public transportation accidents, it is now also considered a serious social concern.4,5 SDB is therefore considered a problem requiring attention from both clinical and public health perspectives. Because SDB is rarely recognized as potentially fatal, however, and given the difficulty affected patients have in recognizing their condition, only a small proportion of those with moderateto-severe SDB receive appropriate therapy,6 notwithstanding the availability of several highly effective treatments.7 Regarding the diagnosis of SDB, polysomnography (PSG) has been used as a gold standard, and cardiorespiratory monitoring may be used for diagnosis.8 These machines require
overnight sleep testing and are thus time-consuming and burdensome, and neither is suitable for community-based screening. We therefore considered that a user-friendly screening tool may improve the identification of patients with moderate-tosevere SDB. To our knowledge, several questionnaires and prediction rules have been used for mass screening9-12; however, one includes numerous variables, and the others are not appropriate in occupational and community settings. Moreover, a comprehensive comparison of these questionnaires has yet to be conducted. Here, we sought to develop and validate a simple, userfriendly, integer-based, prediction rule with a relatively small number of variables to screen subjects for moderate-to-severe SDB. We also wanted to compare the predictive performance of this model with those previously developed. METHODS Subjects and Data Collection The derivation dataset used to derive the screening tool and the validation dataset used to test the external validity of this tool were collected separately. To ensure the generalizability of the screening tool, derivation data were gathered in 2 settings (university hospital and community settings). First, we included consecutive patients undergoing PSG testing in 2 medical university hospitals in Japan between July 1999 and December 2002. These patients underwent pulse oximetry as part of PSG testing,
Submitted for publication May, 2008 Submitted in final revised form February, 2009 Accepted for publication April, 2009 Address correspondence to: Misa Takegami, Graduate School of Medicine and Public Health, Department of Epidemiology and Health Care Research, Yoshida Konoe-cho, Sakyo-ku, Kyoto 606-8501 Japan; Tel: +81-075-7534646; Fax: +81-075-753-4644; E-mail:
[email protected] SLEEP, Vol. 32, No. 7, 2009
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Simple Four-Variable Screening Tool for SDB—Takegami et al
and, when diagnosed with SDB, completed a self-administered questionnaire. The physician who ordered the PSG also collected information on patient characteristics and clinical history. Second, we included a sample of subjects from a previous population-based survey. Of the 5,107 residents who had participated in the previous survey, we included those who consented to undergo pulse oximetry in the current study. This survey, originally conducted to clarify the impact of factors related to the subjects’ social and physical environment on health-related quality of life and/or sleep quality, has been described elsewhere.13 Briefly, the cohort consisted of all residents 20 years old or older living in Naie, Hokkaido Prefecture, a rural community in Japan. Participants in the original survey were invited to voluntarily undergo pulse oximetry for our study, and those who agreed were invited to participate in a subsequent overnight study. Public health nurses acquired the history of each participant. For the validation dataset, a cross-sectional survey was conducted among all employees of a wholesale company in Osaka, Japan between January 2004 and December 2005.14 The survey included a self-administered questionnaire and medical examination, along with sleep tests using a cardiorespiratory monitor (type 3 portable monitor) and an actigraph to diagnose SDB. Subjects with a history of treatment for SDB or other sleep disorders such as narcolepsy, were excluded, as were those who were unable to complete pulse oximetry or cardiorespiratory testing because of inappropriate operation of the testing machine or because it did not work well. The population-based study in Naie was approved by the Institutional Review Board of the Public Health Research Foundation, and the hospital-based and occupational studies were authorized by the Institutional Review Board of the Kyoto University Graduate School of Medicine.
sleep studies and an alternative to PSG in diagnosing SDB.19 Given that type 3 monitors are sometimes unable to measure sleep duration, however, this variable was measured using an actigraph on the basis of the similarity in findings between measurement of sleep duration in bed using an actigraph and PSG.20 Sleep duration at night was measured by wrist actigraph tracing21 in conjunction with a sleep diary, which documented when the participant went to bed at night and arose in the morning. Sleep onset was estimated by noting the sustained cessation of movement of the wrist on the actigraphy tracing, and rousing was noted by the appearance of wrist movements on the tracing. With regard to type 3 monitor analysis, sleep duration was defined as the estimated time duration between sleep onset and final rousing. SDB analyses incorporated the main segments of comprehensible type 3 monitor recordings within the estimated sleep spans. Apneas (cessation of breathing ≥ 10 sec) and hypopneas ( ≥ 50% reduction in respiratory effort with ≥ 3% oxygen desaturation for ≥ 10 sec) were visually scored by at least 2 medical doctors specializing in respiratory medicine. Subjects with an RDI of ≥ 15 and ≥ 30 were considered to have moderate-to-severe SDB or severe SDB, respectively. Measurement and Definition of Independent Variables Potential candidate variables for predictors were identified based on a review of the literature, clinical relevance, and routine availability; the selection of variables for abstraction was further guided by physicians specializing in sleep medicine. Potential candidate variables were classified as demographic characteristics, comorbid conditions, and clinical features (including symptoms). Comorbid conditions included diabetes mellitus, cardiovascular disease, and cerebrovascular disease. Clinical features included snoring, body mass index (BMI), and blood pressure. Laboratory features, such as blood tests and spirometry test data, were excluded because these are unsuitable for screening the general population. Symptoms were assessed using a self-administered questionnaire which asked about morning headaches, daytime sleepiness, vitality related to daytime sleepiness, and psychological well-being. Subjective daytime sleepiness was assessed using the Epworth Sleepiness Scale (ESS), a valid and reliable self-administered questionnaire instrument for measuring subjective daytime sleepiness.22,23 The ESS score can then help clinicians to separate patients into those who are clinically normal or those who are excessively sleepy and may have sleep disorders, using a cutoff point of 10. Vitality and psychological well-being was assessed using the Vitality (VT) and Mental Health (MH) subscales of the Medical Outcome Study Short-Form 36-Item Health Survey (SF36). The SF-36 is a valid and reliable instrument for measuring health-related quality of life.24,25
SDB Measure During the derivation process, we defined SDB in accordance with a guideline endorsed by the British Thoracic Society, namely: ≥ 10 respiratory events per hour and arterial oxygen desaturation ≥ 4% from the baseline saturation value obtained during the sleep study.15 It has been reported that, as the oxygen desaturation index (ODI; measured by overnight pulse oximetry) increases, the risk of cardiovascular disease increases, as it does with an increase in the apnea-hypopnea index (AHI; measured by PSG).15 The guideline uses an ODI of ≥ 10 to diagnose sleep apnea, without confirmation by PSG16,17 and recommends treatment at this level.15 For validation, the respiratory disturbance index (RDI: number of apneas and hypopneas per hour of sleep) was calculated from data obtained from both the actigraph and cardiorespiratory (type 3 portable monitor).14 To improve the accuracy of RDI measurement, the validation study used a type 3 portable monitor rather than a pulse oximeter. Type 3 monitors are defined as devices with a minimum of 4 monitored channels,18 including ventilation or airflow (with at least 2 channels of respiratory movement, or respiratory movement and airflow), heart rate or electrocardiogram, and oxygen saturation. In our validation study, a type 3 monitor recorded chest and abdominal respiratory movements, nasal pressure, oxygen saturation, heart rate, and body position. Type 3 monitors are considered standard in SLEEP, Vol. 32, No. 7, 2009
Statistical Analysis The association between SDB and candidate variables was determined using the t-test for continuous variables and Pearson’s χ2 test for categorical variables. BMI values were grouped into 6 categories, with the cutoff points modified using reported research data applicable to Japanese.26 Blood pressure values were categorized using the cutoff points described in the World 940
Simple Four-Variable Screening Tool for SDB—Takegami et al
Health Organization Hypertension guideline.27 We examined the strength and shape of the relationships of continuous variables with the log odds of SDB using cubic spline plots.28 These functions were then used to develop and refine the multivariable regression models reported previously.29 Candidate variables with P-values < 0.10 were included as covariates in a multiple logistic regression model,30 and those with P-values < 0.05 were retained in the final multivariable logistic regression model.31 Because our aim was to develop a simple and practical tool for screening, we excluded predictors determined on discussion with clinicians to be clinically unimportant. Model discrimination was assessed by the area under the receiver operating characteristic (ROC) curve,32 and calibration was assessed using the Hosmer-Lemeshow χ2 statistic.30 An integer score-based prediction rule for the prevalence of SDB was developed from the logistic regression model using a regression coefficient-based scoring method.33,34 To generate a simple integer-based point score for each predictor variable, the coefficient scores were assigned by dividing the β-coefficients by the criterion value, which is the sum of the smallest and second-smallest coefficients in the model multiplied by 0.4 and rounded up to the nearest integer. To achieve a value of 1 for the variable with the smallest coefficient score, the denominator was selected. Missing predictor variables were replaced with a zero. The overall risk score was calculated by summing all component scores for each participant.35,36 To assess the performance of the screening tool in the derivation dataset, we calculated the sensitivity, specificity, and likelihood ratios for positive and negative test results, post-test probability of positive and negative results, and area under the ROC curve by varying the positive threshold of our screening score (total risk score ≥ 9, ≥ 11, or ≥ 14). Screening scores were also assigned to different risk classes by quartile, and the prevalence of SDB observed in each was compared using the χ2 test for trend. We validated the screening tool internally using the leave-one-out cross-validation method, in which all cases but one were used to train the screening tool. The rule was then applied to the single excluded case.37-39 This procedure was repeated for every case, until each had been left out once.40 We externally validated the screening score by separately assessing model performance in the validation dataset in the same manner as in the derivation dataset for outcomes of moderate-to-severe SDB, or severe SDB. We also tested model performance for outcome of the definition of SDB using 4% desaturation with pulse oximetry. In addition, we used the hypothetical screening strategies proposed by Gurubhagavatula et al.41 in the validation dataset by combining our screening score with the pulse oximetry results to determine if the subject undergoes PSG or continuous positive airway pressure (CPAP) titration, thereby dividing the participants into 3 groups according to risk score. We evaluated 2 different strategies by varying the risk score threshold. For strategy 1, subjects in the highrisk group (scores ≥ 14 [upper threshold]) underwent PSG or CPAP titration, whereas subjects in the low-risk group (scores < 9 [lower threshold]) underwent no further testing. The remaining participants (intermediate group) were subjected to pulse oximetry, and, if their ODI was ≥ 10, they also underwent PSG or CPAP titration; otherwise, no further testing was done. In strategy 2, the upper and lower thresholds were set at 14 and 11, SLEEP, Vol. 32, No. 7, 2009
Patients with Suspected SDB (n=132) Residents in Naie (n=175)
Screening Score (n=307)
Positive Result, Screening Score, ≥11
Negative Result, Screening Score,
