Clinical Studies Of The Harm And Screening Of OSAHS And The Effect Of The Metabolic Surgical Intervention

Part 1 The influence of OSAHS on all-cause mortality and the incidence of cancer I. OSAHS increases the risk of all-cause mortalityObjectives: To assess whether OSAHS is associated with increased risk for all-cause mortality.Methods: In this observational cohort study, consecutive subjects examined by PSG were followed up until subsequent death or until February 28, 2014. All the subjects were followed up through the outpatient visit, telephone interview or the Department of Vital Statistics of the Shanghai Municipal Center for Disease Control and Prevention. The Kaplan–Meier method and the Cox proportional hazards regression analysis were used to determine the effect of OSAHS on the composite outcome of death from any cause.Results: Among 5,650 enrolled subjects, 4,939(87.4%) had OSAHS. The median observation period was 5.3 yr or 31,555 person-yr. The mean AHI at baseline in the patients with OSAHS was 40.3, compared with 2.2 in the control group. The all-cause mortality risk adjusted for age, sex, and BMI was significantly associated with increased AHI(p=0.03). Among all enrolled subjects, the all-cause mortality risk for severe OSAHS(AHI>30) increased to 2.65-fold(95% confidence interval [CI], 1.59–4.41; p<0.001), compared with those with an AHI≤30, independent of age, sex, BMI, and the potential effect of OSAHS treatment. Similarly, after excluding participants who had reported being treated for OSAHS, the hazard ratio of severe OSAHS for all-cause mortality increased significantly to 2.54(95% CI, 1.51-4.27; p<0.001), compared with those with an AHI≤30.Conclusions: Increased OSAHS severity, particularly severe OSAHS, significantly increased the risk for death from any cause, and this association was independent of age, sex, and BMI.II. The influence of OSAHS on the incidence of cancerObjectives: To assess whether OSAHS is associated with increased risk for the incidence of any type of cancer.Methods: In this observational cohort study, consecutive subjects examined by PSG were followed up from August, 2001 until subsequent diagnosis of cancer, death or until February 28, 2014. All the subjects were followed up through the outpatient visit, telephone interview or the cancer registration system of the Shanghai Municipal Center for Disease Control and Prevention. The Kaplan–Meier method and the Cox proportional hazards regression analysis were used to determine the effect of OSAHS on the composite outcome of death from any cause.Results: Among 5,650 enrolled subjects, 4,939(87.4%) had OSAHS. The median observation period was 5.3 yr or 31,555 person-yr. In total, 171 patients have been registered for being diagnosed with cancer, with 63 patients diagnosed with cancers before the first PSG test and 108 patients after the first PSG test. When excluded the 63 patients who had sufferd cancers before the PSG test, the association between OSAHS and the incidence of cancer was analyzed in the remaining 5587 subjects. In the multivariable Cox regression models, the severity of OSAHS(expressed as AHI, minimum oxygen saturation, mean oxygen saturation, CT90%, ODI and so on) was not signifiantly associated with the incidence of cancer before and after adjustment for age, sex, body mass index and the treatment status(all p>0.05), while, in the middle aged group(aged 45-59 years), women had higher risk for cancer incidence than men(p<0.05).Conclusions: In our large scale cohort study, the severity of OSAHS was not significantly associated with the incidence of cancer. However, further studies are needed to be conducted in different populations and races.Part 2 The effect of ESS-based model and a two-channel portable device on the diagnosis of OSAHSI. The ESS-based model on screening OSAHSObjectives: To evaluate the efficiency of the ESS and ESS-based model in screening for OSAHS.Methods: The test cohort included 2032 Chinese adults over 20 years of age with suspected OSAHS who were consecutively recruited in January 2007–July 2011. The validation cohort included 784 Chinese adults over 20 years old with suspected OSAHS who were consecutively recruited in August 2011–July 2012. The ESS alone and ESS combined with other parameters were used to screen for OSAHS.Results: In the test cohort, the area under the ROC curve(AUC) was 0.774(95% CI, 0.743 to 0.805) for ESS alone. A high specificity of 82.77%(77.4 to 87.3%) and a moderate sensitivity of 61.65%(59.4 to 63.9%) were obtained at the ESS threshold of nine. Notably, sex-stratified analysis revealed different optimum cut-off points: nine for males and six for females. This result was confirmed in the validation cohort. Using forward conditional logistic regression analysis, we further generated a diagnostic model, including age, waist circumference, ESS and the minimum blood oxygen saturation as independent variables. An ROC curve was used to evaluate the efficiency of the diagnostic model. The relevant AUCs were 0.955(95% CI, 0.946 to 0.964) in the test cohort and 0.977(95% CI, 0.964 to 0.986) in the validation cohort. At the best cut-off point of ROC, this model revealed a sensitivity of 89.13%(95% CI, 87.6 to 90.5%) and specificity of 90.34%(95% CI, 85.9 to 93.8%) in the test cohort. These results were confirmed using the validation cohort.Conclusions: ESS alone showed moderate efficiency in screening OSAHS, and the results indicated that a sex-specified threshold should be considered in clinical practice. Additionally, the ESS-based diagnostic model facilitated identification of undiagnosed OSAHS.II. Evaluation of a two-channel portable device and a predictive model to screen for obstructive sleep apnea hypopnea syndrome in a laboratory environmentObjectives: Various portable monitors for identifying OSAHS have been investigated, and were reported to enable accurate recording of OSAHS severity. However, more information is needed from different population. This study was conducted to evaluate the efficiency of a portable two-channel sleep apnea device(Sleep View) for screening OSAHS.METHODS: Ninety-three consecutive subjects underwent simultaneous Sleep View test and laboratory PSG test. Data were collected and blindly analyzed. The efficiencies of the Sleep View device and a newly established predictive model for identifying OSA were evaluated in comparison with PSG.RESULTS: Good agreement was evident between SleepView and PSG based on the AHI(r2=0.837, p<0.01). The median AHI yielded by Sleep View was higher than that of PSG: 33.2(10.5–53.3) vs. 19.2(5.2–53.6). The sensitivity and specificity of Sleep View for a PSG AHI≥5 were 80.28% and 95.45%, respectively, and the cutoff point was 16.8. The areas under the ROC curves for PSG AHI≥5, >15, and >30 were 0.923, 0.924, and 0.979, respectively. When the AHI and the oxygen desaturation index calculated by Sleep View were combined with waist circumference, the new predictive model showed a higher sensitivity of 92.96% and a specificity of 95.45% for a PSG AHI≥5, and the corresponding area under the ROC curve was 0.983.CONCLUSIONS: The Sleep View device exhibited acceptable diagnostic accuracy for OSAHS, espically the severe group. A practical predictive model, comprising waist circumference, AHI and oxygen desaturation index, obtained from Sleep View, was highly effective for screening even mild OSAHS. This simple and practical device may serve as a useful tool to screen OSAHS. Further studies are required to validate the diagnostic efficiency of Sleep View in the home environment and different population.Part 3 Effect of metabolic surgery on obstructive sleep apnea hypopnea syndromeObjectives: We conducted a follow-up study to evaluate the effect of metabolic surgery on OSAHS in a Chinese population with obesity and type 2 diabetes mellitus(T2DM).Methods: From May 2011 to March 2014, consecutive subjects with obesity and T2 DM were recruited for this study. Before and at least 6 months after the laparoscopic Roux-en-Y gastric bypass(LYGB) surgery, all subjects were asked to undergo a polysomnography test. During the sleep centre visit, anthropometric characteristic data, blood samples, and sleep questionnaires were collected.Results: Initially, 72 subjects with obesity and T2 DM were recruited for this study, and 54 subjects were diagnosed as OSAHS by the first PSG test. Finally, 44 participants with OSAHS were followed-up in the study. Compared with baseline data, the postoperative anthropometric characteristics, blood measurements, and sleep recording data, such as weight, AHI and insulin resistance(IR) index, differed significantly(p<0.001). The change in AHI was correlated significantly with preoperative weight(r=0.298, p<0.05), preoperative AHI(r=0.729, p<0.001), preoperative waist circumference(r=0.307, p<0.05), and preoperative IR index(r=-0.301, p<0.05). Postoperative AHI was correlated significantly with age(r=0.039, p=0.039) and preoperative AHI(r=0.445, p=0.002), and the following prediction model was generated: log10(postoperative AHI)=0.626 × log10(preoperative AHI) + 0.010 × age- 0.581, which might predict the postoperative AHI and the change of AHI.Conclusion: Our findings indicate LYGB could be an effective therapeutic intervention in the management of OSAHS for patients with both obesity and T2 DM, and the preoperative AHI and age might be important factors that influence the effort of LYGB.