Detection and severity assessment of obstructive sleep apnea according to deep learning of single-lead electrocardiogram signals.

Developing a convenient detection method is important for diagnosing and treating obstructive sleep apnea. Considering availability and medical reliability, we established a deep-learning model that uses single-lead electrocardiogram signals for obstructive sleep apnea detection and severity assessment. The detection model consisted of signal preprocessing, feature extraction, time-frequency domain information fusion, and classification segments. A total of 375 patients who underwent polysomnography were included. The single-lead electrocardiogram signals obtained by polysomnography were used to train, validate and test the model. Moreover, the proposed model performance on a public dataset was compared with the findings of previous studies. In the test set, the accuracy of per-segment and per-recording detection were 82.55% and 85.33%, respectively. The accuracy values for mild, moderate and severe obstructive sleep apnea were 69.33%, 74.67% and 85.33%, respectively. In the public dataset, the accuracy of per-segment detection was 91.66%. A Bland-Altman plot revealed the consistency of true apnea-hypopnea index and predicted apnea-hypopnea index. We confirmed the feasibility of single-lead electrocardiogram signals and deep-learning model for obstructive sleep apnea detection and severity evaluation in both hospital and public datasets. The detection performance is high for patients with obstructive sleep apnea, especially those with severe obstructive sleep apnea.

Application and interpretation of machine learning models in predicting the risk of severe obstructive sleep apnea in adults.

BACKGROUND: Obstructive sleep apnea (OSA) is a globally prevalent disease with a complex diagnostic method. Severe OSA is associated with multi-system dysfunction. We aimed to develop an interpretable machine learning (ML) model for predicting the risk of severe OSA and analyzing the risk factors based on clinical characteristics and questionnaires. METHODS: This was a retrospective study comprising 1656 subjects who presented and underwent polysomnography (PSG) between 2018 and 2021. A total of 23 variables were included, and after univariate analysis, 15 variables were selected for further preprocessing. Six types of classification models were used to evaluate the ability to predict severe OSA, namely logistic regression (LR), gradient boosting machine (GBM), extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), bootstrapped aggregating (Bagging), and multilayer perceptron (MLP). All models used the area under the receiver operating characteristic curve (AUC) was calculated as the performance metric. We also drew SHapley Additive exPlanations (SHAP) plots to interpret predictive results and to analyze the relative importance of risk factors. An online calculator was developed to estimate the risk of severe OSA in individuals. RESULTS: Among the enrolled subjects, 61.47% (1018/1656) were diagnosed with severe OSA. Multivariate LR analysis showed that 10 of 23 variables were independent risk factors for severe OSA. The GBM model showed the best performance (AUC = 0.857, accuracy = 0.766, sensitivity = 0.798, specificity = 0.734). An online calculator was developed to estimate the risk of severe OSA based on the GBM model. Finally, waist circumference, neck circumference, the Epworth Sleepiness Scale, age, and the Berlin questionnaire were revealed by the SHAP plot as the top five critical variables contributing to the diagnosis of severe OSA. Additionally, two typical cases were analyzed to interpret the contribution of each variable to the outcome prediction in a single patient. CONCLUSIONS: We established six risk prediction models for severe OSA using ML algorithms. Among them, the GBM model performed best. The model facilitates individualized assessment and further clinical strategies for patients with suspected severe OSA. This will help to identify patients with severe OSA as early as possible and ensure their timely treatment. TRIAL REGISTRATION: Retrospectively registered.

The relationship of tongue fat content and efficacy of uvulopalatopharyngoplasty in Chinese patients with obstructive sleep apnea.

BACKGROUND: To investigate the relationship between tongue fat content and severity of obstructive sleep apnea (OSA) and its effects on the efficacy of uvulopalatopharyngoplasty (UPPP) in the Chinese group. METHOD: Fifty-two participants concluded to this study were diagnosed as OSA by performing polysomnography (PSG) then they were divided into moderate group and severe group according to apnea hypopnea index (AHI). All of them were also collected a series of data including age, BMI, height, weight, neck circumference, abdominal circumference, magnetic resonance imaging (MRI) of upper airway and the score of Epworth Sleepiness Scale (ESS) on the morning after they completed PSG. The relationship between tongue fat content and severity of OSA as well as the association between tongue fat content in pre-operation and surgical efficacy were analyzed.Participants underwent UPPP and followed up at 3rd month after surgery, and they were divided into two groups according to the surgical efficacy. RESULTS: There were 7 patients in the moderate OSA group and 45 patients in the severe OSA group. The tongue volume was significantly larger in the severe OSA group than that in the moderate OSA group. There was no difference in tongue fat volume and tongue fat rate between the two groups. There was no association among tongue fat content, AHI, obstructive apnea hypopnea index, obstructive apnea index and Epworth sleepiness scale (all P > 0.05), but tongue fat content was related to the lowest oxygen saturation (r=-0.335, P < 0.05). There was no significantly difference in pre-operative tongue fat content in two different surgical efficacy groups. CONCLUSIONS: This study didn't show an association between tongue fat content and the severity of OSA in the Chinese group, but it suggested a negative correlation between tongue fat content and the lowest oxygen saturation (LSaO2). Tongue fat content didn't influence surgical efficacy of UPPP in Chinese OSA patients. TRIAL REGISTRATION: This study didn't report on a clinical trial, it was retrospectively registered.

Expression and prognostic analyses of the significance of STEAP1 and STEAP2 in lung cancer.

PURPOSE: Lung cancer is the leading cause of cancer-related mortality. STEAP1 and STEAP2 are overexpressed in various cancers. The purpose of this study was to evaluate the expression and prognostic value of STEAP1 and STEAP2 in patients with lung cancer. METHODS: The mRNA expression and protein expression of STEAP1 and STEAP2 and their prognostic characteristics were examined using Oncomine, GEPIA, and Kaplan-Meier (KM) plotters. The correlation analysis of STEAP1 and STEAP2 gene and protein levels was conducted using GeneMANIA and STRING. KEGG pathway analysis was used to explore the related signal pathways of STEAP 1 and STEAP2. Immunohistochemical methods were used to compare the expression of STEAP2 in normal lung and non-small cell lung cancer (NSCLC) tissues. Real-time quantitative polymerase chain reaction, western blotting, and immunocytochemistry were used to evaluate the expression of STEAP1 and STEAP2 in three lung cancer cell lines and normal lung epithelial cell lines. RESULTS: Analysis of the Oncomine database and GEPIA showed that STEAP1 was upregulated and STEAP2 was downregulated in lung cancer tissue, and both expressions were related to the clinical stage of lung cancer. Immunohistochemical analysis showed that STEAP1 protein expression was significantly upregulated in lung cancer compared to that in adjacent tissues. The expression of STEAP1 was positively correlated with the migration and invasion abilities of lung cancer cells. Compared with paracancer tissues, the expression of STEAP2 protein in lung cancer was significantly downregulated and was correlated with the histological grade of squamous cell carcinoma, pathological classification of adenocarcinoma, tumor, lymph node, and metastasis clinical stage, and lymph node metastasis. The expression of STEAP2 was negatively correlated with the migration and invasion abilities of lung cancer cells. The KM curve showed that the downregulation of STEAP1 expression and upregulation of STEAP2 expression were related to a good lung cancer prognosis. CONCLUSION: STEAP1 and STEAP2 are expected to be potential diagnostic and prognostic markers for lung cancer, which may provide more accurate prognostic indicators for lung cancer.

Association between obstructive sleep apnea and thyroid function: A 10-year retrospective study.

PURPOSE: To explore whether the obstructive sleep apnea (OSA) has an impact on thyroid function in patients. METHOD: The data of 853 patients were retrospectively collected from the Second Affiliated Hospital of Xi'an Jiaotong University in recent ten years. All the objects were divided into the control group, mild-moderate and severe OSA groups according to the result of polysomnography. RESULTS: In the non-elderly population (age <60), there were significant differences in serum free triiodothyronine (FT3) and total triiodothyronine (TT3) between the mild-moderate and severe OSA groups (all p < 0.05). And there were differences in serum total thyroxine, anti-thyroid peroxidase, and antithyroglobulin between the control and mild-moderate OSA groups (all p < 0.05). Moreover, FT3 was associated with age (OR = 0.98, p < 0.05) and apnea-hypopnea index (OR = 1.01, p < 0.05). The occurrence of thyroid nodules was associated with average transcutaneous oxygen saturation (Mean SaO2) (OR = 0.97, p < 0.05). In the elderly (age ≥60), there was no difference in FT3 and TT3 between the mild-moderate and severe OSA. While the occurrence of thyroid nodules was also associated with Mean SaO2 (OR = 0.90, p < 0.05). CONCLUSION: In the non-elderly population, the progress of OSA may promote the increase in thyroid hormone (especially FT3) levels, while in the elderly population not. In the whole age population, Mean SaO 2 is associated with the occurrence of thyroid nodules. Future research on the relationship between OSA and thyroid function, and age stratification is necessary.