This subproject is one of many research subprojects utilizing theresources provided by a Center grant funded by NIH/NCRR. The subproject andinvestigator (PI) may have received primary funding from another NIH source,and thus could be represented in other CRISP entries. The institution listed isfor the Center, which is not necessarily the institution for the investigator.It is estimated that 80% of persons with moderate to severe sleep apnea are not being identified. Polysomnography (PSG) is expensive, labor intensive, time-consuming and burdensome for many patients. Most of the available, clinical prediction models for sleep apnea have not been prospectively validated and have not been validated in patients identified by clinical parameters in non-sleep clinic settings. The primary purpose of this study is to validate an approach for the identification of patients with sleep apnea, based on easily acquired clinical information.The investigators have conducted a retrospective chart review of patients attending the Johns Hopkins Sleep Disorders Center from 11/95 through 11/97 to determine the clinical features that would be most predictive for the presence of sleep apnea. These included responses to a self-administered sleep symptom survey, demographics, including age, gender, race, weight, height, blood pressure, and sleep study results. Using this information, they created statistical models which should be predictive for the presence of sleep apnea.par par The Sleep Core was utilized for this protocol.par }
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