Calibration Model for @entity27 : Impact of Alternative Criteria for XXXX .

@entity1 @entity27 @entity387 @entity386
STUDY OBJECTIVE: To characterize the association among @entity27 indices (AHIs) determined using three common metrics for defining @entity387 , and to develop a model to calibrate between these AHIs. DESIGN: Cross-sectional analysis of Sleep Heart Health Study Data. SETTING: Community-based. @entity1 : There were 6,441 @entity1 and @entity1 age 40 y or older. MEASUREMENT AND RESULTS: Three separate AHIs have been calculated, using all @entity386 (defined as a decrease in airflow greater than 90% from baseline for >=  10 sec) @entity387 (defined as a decrease in airflow or chest wall or abdominal excursion greater than 30% from baseline, but not meeting @entity386 ) associated with either: (1) a 4% or greater fall in oxyhemoglobin saturation - AHI4; (2) a 3% or greater fall in oxyhemoglobin saturation - AHI3; or (3) a 3% or greater fall in oxyhemoglobin saturation or an event-related arousal - AHI3a. Median values were 5.4, 9.7, and 13.4 for AHI4, AHI3, and AHI3a, respectively (P < 0.0001). Penalized spline regression models were used to compare AHI values across the three metrics and to calculate prediction intervals. Comparison of regression models demonstrates divergence in AHI scores among the three methods at low AHI values and gradual convergence at higher levels of AHI. CONCLUSIONS: The three methods of scoring hypopneas yielded significantly different estimates of AHI, although the relative difference is reduced in severe disease. The regression models presented will enable clinicians and researchers to more appropriately compare AHI values obtained using differing metrics for @entity387 .
