METHODS: A prospective 7-country clinical trial of 302 OSA patients, who met the selection criteria, and underwent nose, palate and/or tongue surgery. Pre- and post-operative data were recorded and analysed based on both the Sher criteria (apnoea hypopnea index, AHI reduction 50% and <20) and the SLEEP-GOAL.
RESULTS: There were 229 males and 73 females, mean age of 42.4±17.3 years, mean BMI 27.9±4.2. The mean VAS score improved from 7.7±1.4 to 2.5±1.7 (p<0.05), mean Epworth score (ESS) improved from 12.2±4.6 to 4.9±2.8 (p<0.05), mean body mass index (BMI) decreased from 27.9±4.2 to 26.1±3.7 (p>0.05), gross weight decreased from 81.9±14.3kg to 76.6±13.3kg. The mean AHI decreased 33.4±18.9 to 14.6±11.0 (p<0.05), mean lowest oxygen saturation (LSAT) improved 79.4±9.2% to 86.9±5.9% (p<0.05), and mean duration of oxygen <90% decreased from 32.6±8.9 minutes to 7.3±2.1 minutes (p<0.05). The overall success rate (302 patients) based on the Sher criteria was 66.2%. Crosstabulation of respective major/minor criteria fulfilment, based on fulfilment of two major and two minor or better, the success rate (based on SLEEP-GOAL) was 69.8%. Based solely on the Sher criteria, 63 patients who had significant blood pressure reduction, 29 patients who had BMI reduction and 66 patients who had clinically significant decrease in duration of oxygen <90% would have been misclassified as "failures".
CONCLUSION: AHI as a single parameter is unreliable. Assessing true success outcomes of OSA treatment, requires comprehensive and holistic parameters, reflecting true end-organ injury/function; the SLEEP-GOAL meets these requirements.
METHODS: This was a multicenter prospective cohort study involving patients with cardiovascular risk factors who were undergoing major noncardiac surgery. Patients underwent home sleep apnea testing. All patients completed the STOP-Bang questionnaire. The predictive parameters of STOP-Bang scores were calculated against the apnea-hypopnea index.
RESULTS: From 4 ethnic groups 1,205 patients (666 Chinese, 161 Indian, 195 Malay, and 183 Caucasian) were included in the study. The mean BMI ranged from 25 ± 4 to 30 ± 6 kg/m² and mean age ranged from 64 ± 8 to 71 ± 10 years. For the Chinese and Indian patients, diagnostic parameters are presented using BMI threshold of 27.5 kg/m² with the area under curve to predict moderate-to-severe OSA being 0.709 (0.665-0.753) and 0.722 (0.635-0.808), respectively. For the Malay and Caucasian, diagnostic parameters are presented using BMI threshold of 35 kg/m² with the area under curve for predicting moderate-to-severe OSA being 0.645 (0.572-0.720) and 0.657 (0.578-0.736), respectively. Balancing the sensitivity and specificity, the optimal STOP-Bang thresholds for the Chinese, Indian, Malay, and Caucasian groups were determined to be 4 or greater.
CONCLUSIONS: For predicting moderate-to-severe OSA, we recommend BMI threshold of 27.5 kg/m² for Chinese and Indian patients and 35 kg/m² for Malay and Caucasian patients. The optimal STOP-Bang threshold for the Chinese, Indian, Malay and Caucasian groups is 4 or greater.
CLINICAL TRIAL REGISTRATION: Registry: ClinicalTrials.gov; Name: Postoperative Vascular Events in Unrecognized Obstructive Sleep Apnea; URL: https://clinicaltrials.gov/ct2/show/study/NCT01494181; Identifier: NCT01494181.
METHODS: This article provides a comprehensive review of automated sleep stage scoring systems, which were created since the year 2000. The systems were developed for Electrocardiogram (ECG), Electroencephalogram (EEG), Electrooculogram (EOG), and a combination of signals.
RESULTS: Our review shows that all of these signals contain information for sleep stage scoring.
CONCLUSIONS: The result is important, because it allows us to shift our research focus away from information extraction methods to systemic improvements, such as patient comfort, redundancy, safety and cost.
OBJECTIVES: This study aimed to segment the breath cycles from pulmonary acoustic signals using the newly developed adaptive neuro-fuzzy inference system (ANFIS) based on breath phase detection and to subsequently evaluate the performance of the system.
METHODS: The normalised averaged power spectral density for each segment was fuzzified, and a set of fuzzy rules was formulated. The ANFIS was developed to detect the breath phases and subsequently perform breath cycle segmentation. To evaluate the performance of the proposed method, the root mean square error (RMSE) and correlation coefficient values were calculated and analysed, and the proposed method was then validated using data collected at KIMS Hospital and the RALE standard dataset.
RESULTS: The analysis of the correlation coefficient of the neuro-fuzzy model, which was performed to evaluate its performance, revealed a correlation strength of r = 0.9925, and the RMSE for the neuro-fuzzy model was found to equal 0.0069.
CONCLUSION: The proposed neuro-fuzzy model performs better than the fuzzy inference system (FIS) in detecting the breath phases and segmenting the breath cycles and requires less rules than FIS.