Study Objective: The Epworth Sleepiness Scale (ESS) has been used to detect patients with potential sleep disordered breathing (SDB). Recently, a 4-Variable screening tool was proposed to identify patients with SDB, in addition to the STOP and STOP-Bang questionnaires. This study evaluated the abilities of the 4-Variable screening tool, STOP, STOP-Bang, and ESS questionnaires in identifying subjects at risk for SDB. Methods: A total of 4,770 participants who completed polysomnograms in the baseline evaluation of the Sleep Heart Health Study (SHHS) were included. Subjects with RDIs ≥ 15 and ≥ 30 were considered to have moderate-to-severe or severe SDB, respectively. Variables were constructed to approximate those in the questionnaires. The risk of SDB was calculated by the 4-Variable screening tool according to Takegami et al. The STOP and STOP-Bang questionnaires were evaluated including variables for snoring, tiredness/sleepiness, observed apnea, blood pressure, body mass index, age, neck circumference, and gender. Sleepiness was evaluated using the ESS questionnaire and scores were dichotomized into < 11 and ≥ 11. Results:The STOP-Bang questionnaire had higher sensitivity to predict moderate-to-severe (87.0%) and severe (70.4%) SDB, while the 4-Variable screening tool had higher specifi city to predict moderate-to-severe and severe SDB (93.2% for both). Conclusions:In community populations such as the SHHS, high specifi cities may be more useful in excluding low-risk patients, while avoiding false positives. However, sleep clinicians may prefer to use screening tools with high sensitivities, like the STOP-Bang, in order to avoid missing cases that may lead to adverse health consequences and increased healthcare costs. S C I E N T I f I C I N V E S T I g A T I O N SP rimary care providers frequently decide whether or not patients are referred for obstructive sleep apnea evaluations. Due to fi nancial constraints, this decision must be made quickly and accurately during short patient visits. Accurate screening for sleep disordered breathing (SDB) is necessary to properly identify at-risk patients. Several tools have been proposed to rapidly identify these patients. Anecdotally, the Epworth Sleepiness Scale (ESS) has been used by primary care providers to identify patients with potential sleep disorders. However, the ESS was developed to measure propensity for sleep onset rather than the likelihood of SDB.1,2 Takegami et al. 3 proposed a 4-Variable screening tool with high sensitivity (0.93) and high specifi city (0.66) for determining SDB severity. This scale utilizes gender, blood pressure (BP), body mass index (BMI), and snoring. In addition, the STOP and STOP-Bang questionnaires, 4,5 two simple 4-and 8-item tools, also have been used to screen for SDB. However, these tools have been validated in different populations and clinical settings with differing results, leaving the clinician to wonder which tool best screens for SDB. We aimed to investigate this question by comparing the results of these 4 tools, ut...

Children with reduced amounts of sleep (≤ 7.5 h/night) had an increased risk for higher body weight in early adolescence. Similarly, children who slept ≤ 7.5 h/night had higher risk of being anxious or depressed or having learning problems in early adolescence.

Introduction The impact of sleep on quality of life (QoL) has been well documented; however, there is a great need for reliable QoL measures for persons with obstructive sleep apnea (OSA). We compared the QoL scores between the 36-Item Short Form of the Medical Outcomes Survey (SF-36), Calgary Sleep Apnea Quality of Life Index (SAQLI), and Functional Outcomes Sleep Questionnaire (FOSQ) in persons with OSA. Methods A total of 884 participants from the Sleep Heart Health Study second examination, who completed the SF-36, FOSQ, and SAQLI, and in-home polysomnograms, were included. The apnea hypopnea index (AHI) at 4% desaturation was categorized as no OSA (<5 /hour), mild to moderate OSA (5–30 /hour) and severe OSA (>30 /hour). QoL scores for each questionnaire were determined and compared by OSA severity category and by gender. Results Participants were 47.6% male, 49.2% (n=435) had no OSA, 43.2% (n=382) had mild to moderate OSA, and 7.6% (n=67) had severe OSA. Participants with severe OSA were significantly older (mean age = 63.7 years, p <.0001), had higher BMI (mean = 34.3 kg/m2, p <.0001) and had lower SF-36 Physical Component scores (PCS) (45.1) than participants with no OSA (48.5) or those with mild to moderate OSA (46.5, p= .006). When analyzed according to gender, no significant differences were found in males for QoL by OSA severity categories. However, females with severe OSA had significantly lower mean scores for the SAQLI (5.4, p= .006), FOSQ (10.9, p= .02), and SF-36 PCS (37.7, p<.0001) compared to females with no OSA (6.0, 11.5, 44.6) and those with mild to moderate OSA (5.9, 11.4, 48, respectively). Females with severe OSA also had significantly higher mean BMI (41.8 kg/m2,) than females with no OSA (26.5 kg/m2) or females with mild to moderate OSA (30.6 kg/m2, p<.0001). The SF-36 PCS and Mental Component Scores (MCS) were correlated with the FOSQ and SAQLI (r=.37 PCS vs FOSQ; r=.31 MCS vs FOSQ; r=.42 PCS vs SAQLI; r=.52 MCS vs SAQLI; and r=.66 FOSQ vs SAQLI, p<.001 for all correlations). Linear regression analyses, adjusting for potential confounders, indicated that the impact of OSA severity on QoL is largely explained by the presence of daytime sleepiness. Conclusion The impact of OSA on QoL differs between genders with a larger effect on females and is largely explained by the presence of daytime sleepiness. Correlations among QoL instruments are not high and various instruments may assess different aspects of QoL.

This study compared the predictive abilities of the STOP-Bang and Epworth Sleepiness Scale (ESS) for screening sleep clinic patients for obstructive sleep apnea (OSA) and sleep-disordered breathing (SDB). Forty-seven new adult patients without previous diagnoses of OSA or SDB were administered the STOP-Bang and ESS and were assigned to OSA or SDB risk groups based on their scores. STOP-Bang responses were scored with two Body Mass Index cut points of 35 and 30 kg/m(2) (SB35 and SB30). The tools' predictive abilities were determined by comparing patients' predicted OSA and SDB risks to their polysomnographic results. The SB30 correctly identified more patients with OSA and SDB than the ESS alone. The ESS had the highest specificity for OSA and SDB.