No-show rates to a sleep clinic: drivers and determinants.

STUDY OBJECTIVES: Attendance to sleep clinic appointments is imperative to diagnose sleep-related disorders and to offer appropriate treatment. As part of our quality assurance program, we assessed predictors of no-show rates at our sleep clinic. We hypothesize that no-show rates can be predicted by demographics, appointment type (new vs established) and timing, and insurance status. METHODS: We performed a 10-month, retrospective chart review of patients scheduled at Saint Louis University's SLUCare Sleep Disorders Center. Multivariable logistic regression was used to determine which factors were independently associated with no-show. RESULTS: A total of 2,532 clinical visits were reviewed, and the overall no-show rate was 21.2%. Factors associated with a higher incidence of no-show rates included younger age (17-40 years: 21.5%; 41-64 years: 23.5%; ≥65 years: 14.0%; P < .0001), appointment type (new: 30.5% vs established: 18.3%; P < .0001), and insurance status (no insurance: 24.6% vs public: 22.6% vs private: 15.9%; P < .0001). Multivariable logistic regression confirmed the independent association between no-show and age ≤ 40 years (adjusted odds ratio = 1.72; 95% confidence interval: 1.44, 2.20), new patient status (adjusted odds ratio = 1.78; 95% confidence interval: 1.44, 2.20), and absence of health insurance (adjusted odds ratio = 1.62; 95% confidence interval: 1.24, 2.11). Sex, appointment time, day of the week, and season did not significantly influence no-show rates. CONCLUSIONS: Independent predictors of no-show appointments included younger age, new patient status, and lack of health insurance. Our findings will aid future efforts to identify patients with high predictors of nonadherence. Further studies are needed to develop methods to decrease no-show rates once high-risk appointments have been identified.

Association between Body Mass Index (BMI) and fraction of exhaled nitric oxide (FeNO) levels in the National Health and Nutrition Examination Survey (NHANES) 2007-2010.

PURPOSE: Obesity is characterised by chronic, low-grade systemic inflammation. Elevated FeNO levels reflect airway inflammation in various lung diseases including asthma. METHODS: This is a cross-sectional analysis of data from NHANES 2007-2010. Participants younger than 20 years old with history of cough/cold symptoms in the past 7 days, smoking, exercise in the previous hour, consumption of nitric oxide rich meats/vegetables, or use of inhaled corticosteroids during the previous 2 days were excluded. BMI (in kg/m2) was divided in to 4 categories: underweight (UW) (0-18.5), Normal (N) (≥18.5 to <25), Overweight (OW) (≥25 and <30) and Obese (O) ≥30. RESULTS: There were a total of 149,629,652 weighted participants: UW (22,235,218), N (45,021,536), OW (5,1670,522) and O (50,199,974); 50.36% were men and 49.63% were women. The mean age increased with BMI category [p<.0001]. Mean FeNO levels (in ppb) increased with increasing BMI category: UW (12.52±1.05) N (16.25±0.64), OW (16.62±0.34), and O (16.78±0.39) [p=0.0035]. FEV1/FVC (%) decreased with increasing BMI category: UW (80.68) compared to N (78.51), OW (77.67) and O (78.72) [p=0.0014]. There is a weak yet statistically significant correlation between FeNO levels and both age, BMI. Multivariate analysis predicting FeNO based on BMI category, adjusting for age, gender, race and airway obstruction found age less than 60 years, male gender, certain races and UW BMI category were associated with statistically significantly lower FeNO levels. CONCLUSIONS: Older age and male gender are associated with increased FeNO levels. Controlling for age, gender, and race, obese individuals have a statistically significantly higher FENO than underweight individuals.