BACKGROUND: The Hoffstein formula is the most widely used equation to predict optimal CPAP pressure in patients with obstructive sleep apnea, but it is based on data from white patients. The purpose of this study was to develop an equation for optimal CPAP based on data from Asian patients, and to compare this new formula with the Hoffstein formula. METHODS: We retrospectively reviewed the records of 356 Korean subjects with obstructive sleep apnea who had undergone successful CPAP titration, and randomly divided them into 2 groups, with 178 subjects per group. In group 1 we used stepwise multiple linear regression analysis to develop a predictive equation for optimal CPAP. Then we compared our equation with the Hoffstein equation in group 2. RESULTS: A predictive equation was: predicted pressure (cm H2O) = 6.656 + 0.156 × (body mass index [kg/m(2)]) - 0.071 × (minimal SpO2 [%]) + 0.041 × (respiratory disturbance index) + 0.094 × (score of Epworth Sleepiness Scale). This equation accounted for 38.9% of the total variance (P < .001). Proportions of optimal estimation of CPAP pressure were 38.8% and 36.5% in this equation and the Hoffstein equation, respectively. The Hoffstein formula significantly underestimated CPAP, compared with our formula. CONCLUSIONS: Although our equation was somewhat better to predict optimal CPAP level in Asian subjects than the Hoffstein equation, the CPAP prediction equation did not accurately predict the prescribed CPAP level. Therefore, the usefulness of the CPAP prediction formula is limited in some clinical settings.
BACKGROUND: The Hoffstein formula is the most widely used equation to predict optimal CPAP pressure in patients with obstructive sleep apnea, but it is based on data from white patients. The purpose of this study was to develop an equation for optimal CPAP based on data from Asian patients, and to compare this new formula with the Hoffstein formula. METHODS: We retrospectively reviewed the records of 356 Korean subjects with obstructive sleep apnea who had undergone successful CPAP titration, and randomly divided them into 2 groups, with 178 subjects per group. In group 1 we used stepwise multiple linear regression analysis to develop a predictive equation for optimal CPAP. Then we compared our equation with the Hoffstein equation in group 2. RESULTS: A predictive equation was: predicted pressure (cm H2O) = 6.656 + 0.156 × (body mass index [kg/m(2)]) - 0.071 × (minimal SpO2 [%]) + 0.041 × (respiratory disturbance index) + 0.094 × (score of Epworth Sleepiness Scale). This equation accounted for 38.9% of the total variance (P < .001). Proportions of optimal estimation of CPAP pressure were 38.8% and 36.5% in this equation and the Hoffstein equation, respectively. The Hoffstein formula significantly underestimated CPAP, compared with our formula. CONCLUSIONS: Although our equation was somewhat better to predict optimal CPAP level in Asian subjects than the Hoffstein equation, the CPAP prediction equation did not accurately predict the prescribed CPAP level. Therefore, the usefulness of the CPAP prediction formula is limited in some clinical settings.