Ming-Feng Wu1, Jeng-Yuan Hsu2, Wei-Chang Huang3, Gwan-Han Shen2, Jiunn-Min Wang4, Chih-Yu Wen5, Kang-Ming Chang6. 1. Division of Chest Medicine, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan, ROC; Department of Medical Laboratory Science and Biotechnology, Central Taiwan University of Science and Technology, Taichung, Taiwan, ROC. 2. Division of Chest Medicine, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan, ROC. 3. Division of Chest Medicine, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan, ROC. Electronic address: huangweichangtw@gmail.com. 4. Department of Medical Laboratory, Taichung Veterans General Hospital, Taichung, Taiwan, ROC; Department of Medical Technology, Jen-Teh Junior College of Medicine, Nursing and Management, Miaoli, Taiwan, ROC. 5. Department of Electrical Engineering, National Chung Hsing University, Taichung, Taiwan, ROC. 6. Department of Photonics and Communication Engineering, Asia University, Taichung, Taiwan, ROC.
Abstract
BACKGROUND: Several formulas predicting optimal continuous positive airway pressure (CPAP) for obstructive sleep apnea treatment have been developed and diverse parameters selected as predictive factors in different sleep laboratories using different ethnic groups. This study aimed to validate a constructed predictive formula for the study laboratory and to test the hypothesis that sleep laboratories should have their own predictive formulas. METHODS: Fifty-seven adult subjects with obstructive sleep apnea syndrome (OSAS) were enrolled in the model-building set and underwent two polysomnography (PSG) studies to diagnose OSAS and titrate for optimal CPAP. A predictive formula, derived from anthropometric and polysomnographic variables, was validated together with two other predictive formulas in 30 subjects by comparing the mean predictive CPAP values, rates of successful prediction, and agreements. RESULTS: Regression analysis showed that apnea-hypopnea index (AHI), SaO2nadir (nadir of arterial oxyhemoglobin saturation by pulse oximetry), and body mass index (BMI) strongly correlated with optimal CPAP. The derived predictive formula for the study laboratory was: CPAPpred (predictive CPAP) = 6.380 + 0.033 × AHI - 0.068 × SaO2nadir + 0.171 × BMI (R(2) = 0.335, adjusted R(2) = 0.298). In Taiwan, different predictive formulas used by different sleep laboratories with different independent predictors led to similar mean predictive CPAP values to the mean observed optimal CPAP values, rates of successful prediction, and agreements with the observed optimal CPAP. There were significant differences between the mean predictive CPAP values and mean observed optimal CPAP values, lower rates of successful prediction, and negatively skewed 95% confidence interval (CI) when using a predictive formula derived from different ethnic populations. CONCLUSION: A sleep laboratory may not need to have its own predictive formula for determining the optimal effective CPAP but should adopt the one derived from the same ethnicity of OSAS patients as the reference formula.
BACKGROUND: Several formulas predicting optimal continuous positive airway pressure (CPAP) for obstructive sleep apnea treatment have been developed and diverse parameters selected as predictive factors in different sleep laboratories using different ethnic groups. This study aimed to validate a constructed predictive formula for the study laboratory and to test the hypothesis that sleep laboratories should have their own predictive formulas. METHODS: Fifty-seven adult subjects with obstructive sleep apnea syndrome (OSAS) were enrolled in the model-building set and underwent two polysomnography (PSG) studies to diagnose OSAS and titrate for optimal CPAP. A predictive formula, derived from anthropometric and polysomnographic variables, was validated together with two other predictive formulas in 30 subjects by comparing the mean predictive CPAP values, rates of successful prediction, and agreements. RESULTS: Regression analysis showed that apnea-hypopnea index (AHI), SaO2nadir (nadir of arterial oxyhemoglobin saturation by pulse oximetry), and body mass index (BMI) strongly correlated with optimal CPAP. The derived predictive formula for the study laboratory was: CPAPpred (predictive CPAP) = 6.380 + 0.033 × AHI - 0.068 × SaO2nadir + 0.171 × BMI (R(2) = 0.335, adjusted R(2) = 0.298). In Taiwan, different predictive formulas used by different sleep laboratories with different independent predictors led to similar mean predictive CPAP values to the mean observed optimal CPAP values, rates of successful prediction, and agreements with the observed optimal CPAP. There were significant differences between the mean predictive CPAP values and mean observed optimal CPAP values, lower rates of successful prediction, and negatively skewed 95% confidence interval (CI) when using a predictive formula derived from different ethnic populations. CONCLUSION: A sleep laboratory may not need to have its own predictive formula for determining the optimal effective CPAP but should adopt the one derived from the same ethnicity of OSAS patients as the reference formula.