Simple Four-Variable Screening Tool for Identification of Patients with Sleep-Disordered Breathing

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. 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: SLEEP, Vol. 32, No. 7, 2009 939 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, 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. F (...truncated)