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Dai R, Yang K, Zhuang J, Yao L, Hu Y, Chen Q, Zheng H, Zhu X, Ke J, Zeng Y, Fan C, Chen X, Fan J, Zhang Y. Enhanced machine learning approaches for OSA patient screening: model development and validation study. Sci Rep 2024; 14:19756. [PMID: 39187569 PMCID: PMC11347604 DOI: 10.1038/s41598-024-70647-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 08/20/2024] [Indexed: 08/28/2024] Open
Abstract
Age, gender, body mass index (BMI), and mean heart rate during sleep were found to be risk factors for obstructive sleep apnea (OSA), and a variety of methods have been applied to predict the occurrence of OSA. This study aimed to develop and evaluate OSA prediction models using simple and accessible parameters, combined with multiple machine learning algorithms, and integrate them into a cloud-based mobile sleep medicine management platform for clinical use. The study data were obtained from the clinical records of 610 patients who underwent polysomnography (PSG) at the Sleep Medicine Center of the Second Affiliated Hospital of Fujian Medical University between January 2021 and December 2022. The participants were randomly divided into a training-test group (80%) and an independent validation group (20%). The logistic regression, artificial neural network, naïve Bayes, support vector machine, random forest, and decision tree algorithms were used with age, gender, BMI, and mean heart rate during sleep as predictors to build a risk prediction model for moderate-to-severe OSA. To evaluate the performance of the models, we calculated the area under the receiver operating curve (AUROC), accuracy, recall, specificity, precision, and F1-score for the independent validation set. In addition, the calibration curve, decision curve, and clinical impact curve were generated to determine clinical usefulness. Age, gender, BMI, and mean heart rate during sleep were significantly associated with OSA. The artificial neural network model had the best efficacy compared with the other prediction algorithms. The AUROC, accuracy, recall, specificity, precision, F1-score, and Brier score were 80.4% (95% CI 76.7-84.1%), 69.9% (95% CI 69.8-69.9%), 86.5% (95% CI 81.6-91.3%), 61.5% (95% CI 56.6-66.4%), 53.2% (95% CI 47.7-58.7%), 65.9% (95% CI 60.2-71.5%), and 0.165, respectively, for the artificial neural network model. The AUROCs for the LR, NB, SVM, RF, and DT models were 80.2%, 79.7%, 79.2%, 78.4%, and 70.4%, respectively. The six models based on four simple and easily accessible parameters effectively predicted moderate-to-severe OSA in patients with PSG screening, with the artificial neural network model having the best performance. These models can provide a reliable tool for early OSA diagnosis, and their integration into a cloud-based mobile sleep medicine management platform could improve clinical decision making.
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Affiliation(s)
- Rongrong Dai
- The Sleep Disorder Medicine Center of the Second Affiliated Hospital of Fujian Medical University, Quanzhou, 362000, Fujian, China
- The School of Public Health, Fujian Medical University, Fuzhou, 350108, Fujian, China
| | - Kang Yang
- The School of Clinical Medicine, Fujian Medical University, Fuzhou, 350108, Fujian, China
- Department of Neurosurgery, National Clinical Research Center of Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Jiajing Zhuang
- The School of Clinical Medicine, Fujian Medical University, Fuzhou, 350108, Fujian, China
| | - Ling Yao
- The School of Clinical Medicine, Fujian Medical University, Fuzhou, 350108, Fujian, China
| | - Yiming Hu
- The School of Public Health, Fujian Medical University, Fuzhou, 350108, Fujian, China
- National Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Qingquan Chen
- The School of Public Health, Fujian Medical University, Fuzhou, 350108, Fujian, China
| | - Huaxian Zheng
- The School of Clinical Medicine, Fujian Medical University, Fuzhou, 350108, Fujian, China
| | - Xi Zhu
- The School of Public Health, Fujian Medical University, Fuzhou, 350108, Fujian, China
| | - Jianfeng Ke
- The Second Affiliated Hospital of Fujian Medical University, Quanzhou, 362000, Fujian, China
| | - Yifu Zeng
- Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, 510030, Guangdong, China
| | - Chunmei Fan
- The Second Affiliated Hospital of Fujian Medical University, Quanzhou, 362000, Fujian, China
| | - Xiaoyang Chen
- The Sleep Disorder Medicine Center of the Second Affiliated Hospital of Fujian Medical University, Quanzhou, 362000, Fujian, China
- The Second Affiliated Hospital of Fujian Medical University, Quanzhou, 362000, Fujian, China
| | - Jimin Fan
- The Sleep Disorder Medicine Center of the Second Affiliated Hospital of Fujian Medical University, Quanzhou, 362000, Fujian, China
- The Second Affiliated Hospital of Fujian Medical University, Quanzhou, 362000, Fujian, China
| | - Yixiang Zhang
- The Sleep Disorder Medicine Center of the Second Affiliated Hospital of Fujian Medical University, Quanzhou, 362000, Fujian, China.
- The Second Affiliated Hospital of Fujian Medical University, Quanzhou, 362000, Fujian, China.
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Talukder A, Li Y, Yeung D, Shi M, Umbach DM, Fan Z, Li L. OSApredictor: A tool for prediction of moderate to severe obstructive sleep apnea-hypopnea using readily available patient characteristics. Comput Biol Med 2024; 178:108777. [PMID: 38901189 PMCID: PMC11265974 DOI: 10.1016/j.compbiomed.2024.108777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 05/25/2024] [Accepted: 06/15/2024] [Indexed: 06/22/2024]
Abstract
Sleep apnea is a common sleep disorder. The availability of an easy-to-use sleep apnea predictor would provide a public health benefit by promoting early diagnosis and treatment. Our goal was to develop a prediction tool that used commonly available variables and was accessible to the public through a web site. Using data from polysomnography (PSG) studies that measured the apnea-hypopnea index (AHI), we built a machine learning tool to predict the presence of moderate to severe obstructive sleep apnea (OSA) (defined as AHI ≥15). Our tool employs only seven widely available predictor variables: age, sex, weight, height, pulse oxygen saturation, heart rate and respiratory rate. As a preliminary step, we used 16,958 PSG studies to examine eight machine learning algorithms via five-fold cross validation and determined that XGBoost exhibited superior predictive performance. We then refined the XGBoost predictor by randomly partitioning the data into a training and a test set (13,566 and 3392 PSGs, respectively) and repeatedly subsampling from the training set to construct 1000 training subsets. We evaluated each of the resulting 1000 XGBoost models on the single set-aside test set. The resulting classification tool correctly identified 72.5 % of those with moderate to severe OSA as having the condition (sensitivity) and 62.8 % of those without moderate to-severe OSA as not having it (specificity); overall accuracy was 66 %. We developed a user-friendly publicly available website (https://manticore.niehs.nih.gov/OSApredictor). We hope that our easy-to-use tool will serve as a screening vehicle that enables more patients to be clinically diagnosed and treated for OSA.
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Affiliation(s)
- Amlan Talukder
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA
| | - Yuanyuan Li
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA
| | - Deryck Yeung
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA
| | - Min Shi
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA
| | - David M Umbach
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA
| | - Zheng Fan
- Division of Sleep Medicine, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Neurology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Leping Li
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA.
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Cheng HJ, Li CY, Lin CY. Inclusion of blood pressure parameter increases predictive capability of severe obstructive sleep apnea: A decision tree approach. J Clin Hypertens (Greenwich) 2024. [PMID: 39037154 DOI: 10.1111/jch.14871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Revised: 06/21/2024] [Accepted: 07/03/2024] [Indexed: 07/23/2024]
Abstract
Few studies included objective blood pressure (BP) to construct the predictive model of severe obstructive sleep apnea (OSA). This study used binary logistic regression model (BLRM) and the decision tree method (DTM) to constructed the predictive models for identifying severe OSA, and to compare the prediction capability between the two methods. Totally 499 adult patients with severe OSA and 1421 non-severe OSA controls examined at the Sleep Medicine Center of a tertiary hospital in southern Taiwan between October 2016 and April 2019 were enrolled. OSA was diagnosed through polysomnography. Data on BP, demographic characteristics, anthropometric measurements, comorbidity histories, and sleep questionnaires were collected. BLRM and DTM were separately applied to identify predictors of severe OSA. The performance of risk scores was assessed by area under the receiver operating characteristic curves (AUCs). In BLRM, body mass index (BMI) ≥27 kg/m2, and Snore Outcomes Survey score ≤55 were significant predictors of severe OSA (AUC 0.623). In DTM, mean SpO2 <96%, average systolic BP ≥135 mmHg, and BMI ≥39 kg/m2 were observed to effectively differentiate cases of severe OSA (AUC 0.718). The AUC for the predictive models produced by the DTM was higher in older adults than in younger adults (0.807 vs. 0.723) mainly due to differences in clinical predictive features. In conclusion, DTM, using a different set of predictors, seems more effective in identifying severe OSA than BLRM. Differences in predictors ascertained demonstrated the necessity for separately constructing predictive models for younger and older adults.
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Affiliation(s)
- Hsiang-Ju Cheng
- Department of Family Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Chung-Yi Li
- Department of Public Health, College of Medicine, National Cheng Kung University, Tainan, Taiwan
- Department of Public Health, College of Public Health, China Medical University, Taichung, Taiwan
- Department of Healthcare Administration, College of Medical and Health Science, Asia University, Taichung, Taiwan
| | - Cheng-Yu Lin
- Department of Otolaryngology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
- Sleep Medicine Center, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
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Bedoya O, Rodríguez S, Muñoz JP, Agudelo J. Application of Machine Learning Techniques for the Diagnosis of Obstructive Sleep Apnea/Hypopnea Syndrome. Life (Basel) 2024; 14:587. [PMID: 38792608 PMCID: PMC11122076 DOI: 10.3390/life14050587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Revised: 04/27/2024] [Accepted: 04/29/2024] [Indexed: 05/26/2024] Open
Abstract
Obstructive sleep apnea/hypopnea syndrome (OSAHS) is a condition linked to severe cardiovascular and neuropsychological consequences, characterized by recurrent episodes of partial or complete upper airway obstruction during sleep, leading to compromised ventilation, hypoxemia, and micro-arousals. Polysomnography (PSG) serves as the gold standard for confirming OSAHS, yet its extended duration, high cost, and limited availability pose significant challenges. In this paper, we employ a range of machine learning techniques, including Neural Networks, Decision Trees, Random Forests, and Extra Trees, for OSAHS diagnosis. This approach aims to achieve a diagnostic process that is not only more accessible but also more efficient. The dataset utilized in this study consists of records from 601 adults assessed between 2014 and 2016 at a specialized sleep medical center in Colombia. This research underscores the efficacy of ensemble methods, specifically Random Forests and Extra Trees, achieving an area under the Receiver Operating Characteristic (ROC) curve of 89.2% and 89.6%, respectively. Additionally, a web application has been devised, integrating the optimal model, empowering qualified medical practitioners to make informed decisions through patient registration, an input of 18 variables, and the utilization of the Random Forests model for OSAHS screening.
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Affiliation(s)
- Oscar Bedoya
- School of Systems Engineering and Computer Science, Universidad del Valle, Cali 760032, Colombia;
| | - Santiago Rodríguez
- School of Systems Engineering and Computer Science, Universidad del Valle, Cali 760032, Colombia;
| | | | - Jared Agudelo
- School of Internal Medicine, Universidad Libre—Seccional Cali, Cali 760032, Colombia;
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Vennard H, Buchan E, Davies P, Gibson N, Lowe D, Langley R. Paediatric sleep diagnostics in the 21st century: the era of "sleep-omics"? Eur Respir Rev 2024; 33:240041. [PMID: 38925792 PMCID: PMC11216690 DOI: 10.1183/16000617.0041-2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 04/16/2024] [Indexed: 06/28/2024] Open
Abstract
Paediatric sleep diagnostics is performed using complex multichannel tests in specialised centres, limiting access and availability and resulting in delayed diagnosis and management. Such investigations are often challenging due to patient size (prematurity), tolerability, and compliance with "gold standard" equipment. Children with sensory/behavioural issues, at increased risk of sleep disordered breathing (SDB), often find standard diagnostic equipment difficult.SDB can have implications for a child both in terms of physical health and neurocognitive development. Potential sequelae of untreated SDB includes failure to thrive, cardiopulmonary disease, impaired learning and behavioural issues. Prompt and accurate diagnosis of SDB is important to facilitate early intervention and improve outcomes.The current gold-standard diagnostic test for SDB is polysomnography (PSG), which is expensive, requiring the interpretation of a highly specialised physiologist. PSG is not feasible in low-income countries or outwith specialist sleep centres. During the coronavirus disease 2019 pandemic, efforts were made to improve remote monitoring and diagnostics in paediatric sleep medicine, resulting in a paradigm shift in SDB technology with a focus on automated diagnosis harnessing artificial intelligence (AI). AI enables interrogation of large datasets, setting the scene for an era of "sleep-omics", characterising the endotypic and phenotypic bedrock of SDB by drawing on genetic, lifestyle and demographic information. The National Institute for Health and Care Excellence recently announced a programme for the development of automated home-testing devices for SDB. Scorer-independent scalable diagnostic approaches for paediatric SDB have potential to improve diagnostic accuracy, accessibility and patient tolerability; reduce health inequalities; and yield downstream economic and environmental benefits.
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Affiliation(s)
- Hannah Vennard
- College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
- Department of Paediatric Respiratory and Sleep Medicine, Royal Hospital for Children, Glasgow, UK
| | - Elise Buchan
- Department of Paediatric Respiratory and Sleep Medicine, Royal Hospital for Children, Glasgow, UK
| | - Philip Davies
- Department of Paediatric Respiratory and Sleep Medicine, Royal Hospital for Children, Glasgow, UK
| | - Neil Gibson
- Department of Paediatric Respiratory and Sleep Medicine, Royal Hospital for Children, Glasgow, UK
| | - David Lowe
- College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
| | - Ross Langley
- College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
- Department of Paediatric Respiratory and Sleep Medicine, Royal Hospital for Children, Glasgow, UK
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Lee PL, Wu YW, Cheng HM, Wang CY, Chuang LP, Lin CH, Hang LW, Yu CC, Hung CL, Liu CL, Chou KT, Su MC, Cheng KH, Huang CY, Hou CJY, Chiu KL. Recommended assessment and management of sleep disordered breathing in patients with atrial fibrillation, hypertension and heart failure: Taiwan Society of Cardiology/Taiwan Society of sleep Medicine/Taiwan Society of pulmonary and Critical Care Medicine joint consensus statement. J Formos Med Assoc 2024; 123:159-178. [PMID: 37714768 DOI: 10.1016/j.jfma.2023.08.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Revised: 07/23/2023] [Accepted: 08/23/2023] [Indexed: 09/17/2023] Open
Abstract
Sleep disordered breathing (SDB) is highly prevalent and may be linked to cardiovascular disease in a bidirectional manner. The Taiwan Society of Cardiology, Taiwan Society of Sleep Medicine and Taiwan Society of Pulmonary and Critical Care Medicine established a task force of experts to evaluate the evidence regarding the assessment and management of SDB in patients with atrial fibrillation (AF), hypertension and heart failure with reduced ejection fraction (HFrEF). The GRADE process was used to assess the evidence associated with 15 formulated questions. The task force developed recommendations and determined strength (Strong, Weak) and direction (For, Against) based on the quality of evidence, balance of benefits and harms, patient values and preferences, and resource use. The resulting 11 recommendations are intended to guide clinicians in determining which the specific patient-care strategy should be utilized by clinicians based on the needs of individual patients.
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Affiliation(s)
- Pei-Lin Lee
- Center of Sleep Disorder, National Taiwan University Hospital, Taipei, Taiwan; School of Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Yen-Wen Wu
- Division of Cardiology, Cardiovascular Medical Center, Far Eastern Memorial Hospital, New Taipei City, Taiwan; School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Hao-Min Cheng
- Division of Faculty Development, Taipei Veterans General Hospital, Taipei, Taiwan; PhD Program of Interdisciplinary Medicine (PIM), National Yang Ming Chiao Tung University College of Medicine, Taipei, Taiwan
| | - Cheng-Yi Wang
- Department of Internal Medicine, Cardinal Tien Hospital and School of Medicine, College of Medicine, Fu Jen Catholic University, New Taipei City, Taiwan
| | - Li-Pang Chuang
- Sleep Center, Department of Thoracic Medicine, Chang Gung Memorial Hospital, Linkou, Taiwan; School of Medicine, Chang Gung University, Tauyan, Taiwan
| | - Chou-Han Lin
- Division of Respirology, Department of Internal Medicine, Far Eastern Memorial Hospital, New Taipei City, Taiwan
| | - Liang-Wen Hang
- School of Nursing & Graduate Institute of Nursing, China Medical University, Taichung, Taiwan; Sleep Medicine Center, China Medical University Hospital, Taichung, Taiwan
| | - Chih-Chieh Yu
- School of Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan; Division of Cardiology, Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Chung-Lieh Hung
- Cardiovascular Center, MacKay Memorial Hospital, Taipei, Taiwan; Institute of Biomedical Sciences, Mackay Medical College, Taipei, Taiwan
| | - Ching-Lung Liu
- Division of Chest, Departments of Internal Medicine, MacKay Memorial Hospital, Taipei, Taiwan; MacKay Medical College, New Taipei City, Taiwan
| | - Kun-Ta Chou
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Division of Clinical Respiratory Physiology, Department of Chest Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Mao-Chang Su
- Sleep Center, Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, Taiwan; Department of Respiratory Care, Chang Gung University of Science and Technology, Chiayi, Taiwan
| | - Kai-Hung Cheng
- Kao-Ho Hospital, Kaohsiung, Taiwan; Division of Cardiology, Department of Internal Medicine, E-Da Hospital, Kaohsiung, Taiwan
| | - Chun-Yao Huang
- Division of Cardiology, Department of Internal Medicine, Taipei Medical University Hospital, Taipei, Taiwan; Division of Cardiology, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Charles Jia-Yin Hou
- Cardiovascular Center, MacKay Memorial Hospital, Taipei, Taiwan; MacKay Medical College, New Taipei City, Taiwan.
| | - Kuo-Liang Chiu
- Division of Chest Medicine, Taichung Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Taichung, Taiwan; School of Post-Baccalaureate Chinese Medicine, Tzu Chi University, Hualien, Taiwan.
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Shi Y, Zhang Y, Cao Z, Ma L, Yuan Y, Niu X, Su Y, Xie Y, Chen X, Xing L, Hei X, Liu H, Wu S, Li W, Ren X. Application and interpretation of machine learning models in predicting the risk of severe obstructive sleep apnea in adults. BMC Med Inform Decis Mak 2023; 23:230. [PMID: 37858225 PMCID: PMC10585776 DOI: 10.1186/s12911-023-02331-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 10/10/2023] [Indexed: 10/21/2023] Open
Abstract
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.
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Affiliation(s)
- Yewen Shi
- Department of Otorhinolaryngology Head and Neck Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, NO. 157 Xi Wu Road, Xi'an, Shaan'xi Province, China
| | - Yitong Zhang
- Department of Otorhinolaryngology Head and Neck Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, NO. 157 Xi Wu Road, Xi'an, Shaan'xi Province, China
| | - Zine Cao
- Department of Otorhinolaryngology Head and Neck Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, NO. 157 Xi Wu Road, Xi'an, Shaan'xi Province, China
| | - Lina Ma
- Department of Otorhinolaryngology Head and Neck Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, NO. 157 Xi Wu Road, Xi'an, Shaan'xi Province, China
| | - Yuqi Yuan
- Department of Otorhinolaryngology Head and Neck Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, NO. 157 Xi Wu Road, Xi'an, Shaan'xi Province, China
| | - Xiaoxin Niu
- Department of Otorhinolaryngology Head and Neck Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, NO. 157 Xi Wu Road, Xi'an, Shaan'xi Province, China
| | - Yonglong Su
- Department of Otorhinolaryngology Head and Neck Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, NO. 157 Xi Wu Road, Xi'an, Shaan'xi Province, China
| | - Yushan Xie
- Department of Otorhinolaryngology Head and Neck Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, NO. 157 Xi Wu Road, Xi'an, Shaan'xi Province, China
| | - Xi Chen
- Department of Otorhinolaryngology Head and Neck Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, NO. 157 Xi Wu Road, Xi'an, Shaan'xi Province, China
| | - Liang Xing
- Department of Otorhinolaryngology Head and Neck Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, NO. 157 Xi Wu Road, Xi'an, Shaan'xi Province, China
| | - Xinhong Hei
- School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, Shaan'xi Province, China
| | - Haiqin Liu
- Department of Otorhinolaryngology Head and Neck Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, NO. 157 Xi Wu Road, Xi'an, Shaan'xi Province, China
| | - Shinan Wu
- School of Medicine, Eye Institute of Xiamen University, Xiamen University, Xiamen, Fujian Province, China.
| | - Wenle Li
- Molecular Imaging and Translational Medicine Research Center, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Xiamen University, Xiamen, Fujian Province, China.
| | - Xiaoyong Ren
- Department of Otorhinolaryngology Head and Neck Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, NO. 157 Xi Wu Road, Xi'an, Shaan'xi Province, China.
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Ye Y, Yan ZL, Huang Y, Li L, Wang S, Huang X, Zhou J, Chen L, Ou CQ, Chen H. A Novel Clinical Tool to Detect Severe Obstructive Sleep Apnea. Nat Sci Sleep 2023; 15:839-850. [PMID: 37869520 PMCID: PMC10590115 DOI: 10.2147/nss.s418093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 09/29/2023] [Indexed: 10/24/2023] Open
Abstract
Purpose Obstructive sleep apnea (OSA) is a disease with high morbidity and is associated with adverse health outcomes. Screening potential severe OSA patients will improve the quality of patient management and prognosis, while the accuracy and feasibility of existing screening tools are not so satisfactory. The purpose of this study is to develop and validate a well-feasible clinical predictive model for screening potential severe OSA patients. Patients and Methods We performed a retrospective cohort study including 1920 adults with overnight polysomnography among which 979 cases were diagnosed with severe OSA. Based on demography, symptoms, and hematological data, a multivariate logistic regression model was constructed and cross-validated and then a nomogram was developed to identify severe OSA. Moreover, we compared the performance of our model with the most commonly used screening tool, Stop-Bang Questionnaire (SBQ), among patients who completed the questionnaires. Results Severe OSA was associated with male, BMI≥ 28 kg/m2, high blood pressure, choke, sleepiness, apnea, white blood cell count ≥9.5×109/L, hemoglobin ≥175g/L, triglycerides ≥1.7 mmol/L. The AUC of the final model was 0.76 (95% CI: 0.74-0.78), with sensitivity and specificity under the optimal threshold selected by maximizing Youden Index of 73% and 66%. Among patients having the information of SBQ, the AUC of our model was statistically significantly greater than that of SBQ (0.78 vs 0.66, P = 0.002). Conclusion Based on common clinical examination of admission, we develop a novel model and a nomogram for identifying severe OSA from inpatient with suspected OSA, which provides physicians with a visual and easy-to-use tool for screening severe OSA.
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Affiliation(s)
- Yanqing Ye
- Department of Otorhinolaryngology-Head and Neck Surgery, Nan Fang Hospital, Southern Medical University, Guangzhou, People’s Republic of China
- Otolaryngology Department, Foshan Nan Hai District People’s Hospital, Foshan, People’s Republic of China
| | - Ze-Lin Yan
- State Key Laboratory of Organ Failure Research, Department of Biostatistics, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, People’s Republic of China
| | - Yuanshou Huang
- Otolaryngology Department, Foshan Nan Hai District People’s Hospital, Foshan, People’s Republic of China
| | - Li Li
- State Key Laboratory of Organ Failure Research, Department of Biostatistics, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, People’s Republic of China
| | - Shiming Wang
- Department of Otorhinolaryngology-Head and Neck Surgery, Nan Fang Hospital, Southern Medical University, Guangzhou, People’s Republic of China
| | - Xiaoxing Huang
- Department of Otorhinolaryngology-Head and Neck Surgery, Nan Fang Hospital, Southern Medical University, Guangzhou, People’s Republic of China
| | - Jingmeng Zhou
- Department of Otorhinolaryngology-Head and Neck Surgery, Nan Fang Hospital, Southern Medical University, Guangzhou, People’s Republic of China
| | - Liyi Chen
- Yidu Cloud Technology Ltd, Beijing, People’s Republic of China
| | - Chun-Quan Ou
- State Key Laboratory of Organ Failure Research, Department of Biostatistics, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, People’s Republic of China
| | - Huaihong Chen
- Department of Otorhinolaryngology-Head and Neck Surgery, Nan Fang Hospital, Southern Medical University, Guangzhou, People’s Republic of China
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Arslan RS. Sleep disorder and apnea events detection framework with high performance using two-tier learning model design. PeerJ Comput Sci 2023; 9:e1554. [PMID: 37810361 PMCID: PMC10557519 DOI: 10.7717/peerj-cs.1554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 08/04/2023] [Indexed: 10/10/2023]
Abstract
Sleep apnea is defined as a breathing disorder that affects sleep. Early detection of sleep apnea helps doctors to take intervention for patients to prevent sleep apnea. Manually making this determination is a time-consuming and subjectivity problem. Therefore, many different methods based on polysomnography (PSG) have been proposed and applied to detect this disorder. In this study, a unique two-layer method is proposed, in which there are four different deep learning models in the deep neural network (DNN), gated recurrent unit (GRU), recurrent neural network (RNN), RNN-based-long term short term memory (LSTM) architecture in the first layer, and a machine learning-based meta-learner (decision-layer) in the second layer. The strategy of making a preliminary decision in the first layer and verifying/correcting the results in the second layer is adopted. In the training of this architecture, a vector consisting of 23 features consisting of snore, oxygen saturation, arousal and sleep score data is used together with PSG data. A dataset consisting of 50 patients, both children and adults, is prepared. A number of pre-processing and under-sampling applications have been made to eliminate the problem of unbalanced classes. Proposed method has an accuracy of 95.74% and 99.4% in accuracy of apnea detection (apnea, hypopnea and normal) and apnea types detection (central, mixed and obstructive), respectively. Experimental results demonstrate that patient-independent consistent results can be produced with high accuracy. This robust model can be considered as a system that will help in the decisions of sleep clinics where it is expected to detect sleep disorders in detail with high performance.
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Bazoukis G, Bollepalli SC, Chung CT, Li X, Tse G, Bartley BL, Batool-Anwar S, Quan SF, Armoundas AA. Application of artificial intelligence in the diagnosis of sleep apnea. J Clin Sleep Med 2023; 19:1337-1363. [PMID: 36856067 PMCID: PMC10315608 DOI: 10.5664/jcsm.10532] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 02/21/2023] [Accepted: 02/21/2023] [Indexed: 03/02/2023]
Abstract
STUDY OBJECTIVES Machine learning (ML) models have been employed in the setting of sleep disorders. This review aims to summarize the existing data about the role of ML techniques in the diagnosis, classification, and treatment of sleep-related breathing disorders. METHODS A systematic search in Medline, EMBASE, and Cochrane databases through January 2022 was performed. RESULTS Our search strategy revealed 132 studies that were included in the systematic review. Existing data show that ML models have been successfully used for diagnostic purposes. Specifically, ML models showed good performance in diagnosing sleep apnea using easily obtained features from the electrocardiogram, pulse oximetry, and sound signals. Similarly, ML showed good performance for the classification of sleep apnea into obstructive and central categories, as well as predicting apnea severity. Existing data show promising results for the ML-based guided treatment of sleep apnea. Specifically, the prediction of outcomes following surgical treatment and optimization of continuous positive airway pressure therapy can be guided by ML models. CONCLUSIONS The adoption and implementation of ML in the field of sleep-related breathing disorders is promising. Advancements in wearable sensor technology and ML models can help clinicians predict, diagnose, and classify sleep apnea more accurately and efficiently. CITATION Bazoukis G, Bollepalli SC, Chung CT, et al. Application of artificial intelligence in the diagnosis of sleep apnea. J Clin Sleep Med. 2023;19(7):1337-1363.
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Affiliation(s)
- George Bazoukis
- Department of Cardiology, Larnaca General Hospital, Larnaca, Cyprus
- Department of Basic and Clinical Sciences, University of Nicosia Medical School, Nicosia, Cyprus
| | | | - Cheuk To Chung
- Cardiac Electrophysiology Unit, Cardiovascular Analytics Group, China-UK Collaboration, Hong Kong
| | - Xinmu Li
- Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular disease, Department of Cardiology, Tianjin Institute of Cardiology, the Second Hospital of Tianjin Medical University, Tianjin, China
| | - Gary Tse
- Cardiac Electrophysiology Unit, Cardiovascular Analytics Group, China-UK Collaboration, Hong Kong
- Kent and Medway Medical School, Canterbury, Kent, United Kingdom
| | - Bethany L. Bartley
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Salma Batool-Anwar
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Stuart F. Quan
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, Massachusetts
- Asthma and Airway Disease Research Center, University of Arizona College of Medicine, Tucson, Arizona
| | - Antonis A. Armoundas
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts
- Broad Institute, Massachusetts Institute of Technology, Cambridge, Massachusetts
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11
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Chen T, Li F, Xi Y, Deng Y, Chen S, Tao Z. Association between sleep-disordered breathing and self-reported sinusitis in adults in the United States: NHANES 2005-2006. EAR, NOSE & THROAT JOURNAL 2023:1455613231167884. [PMID: 37097775 DOI: 10.1177/01455613231167884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/26/2023] Open
Abstract
OBJECTIVES The association between sleep-disordered breathing (SDB) and sinusitis has been widely studied; however, research on SDB-related sleep problems and sinusitis are limited. This study aims to determine the relationship between SDB-related sleep problems, SDB symptom score, and sinusitis. METHODS After the screening, data were analyzed from 3414 individuals (≥20 years) from the 2005-2006 National Health and Nutrition Examination Survey questionnaire. Data on snoring, daytime sleepiness, obstructive sleep apnea (snorting, gasping, or cessation of breathing while sleeping), and sleep duration were analyzed. The SDB symptom score was determined based on a summary of the scores of the above four parameters. Pearson chi-square test and logistic regression analysis were used in statistical analyses. RESULTS After adjusting for confounders, self-reported sinusitis was strongly correlated with frequent apneas (OR: 1.950; 95% CI: 1.349-2.219), excessive daytime sleepiness (OR: 1.880; 95% CI: 1.504-2.349), and frequent snoring (OR: 1.481; 95% CI: 1.097-2.000). Compared to an SDB symptom score of 0, the higher the SDB symptom score, the higher the risk of self-reported sinusitis. For the subgroup analyses, this association was significant in females and across ethnic groups. CONCLUSION In the United States, SDB is significantly associated with self-reported sinusitis in adults. In addition, our study suggests that patients with SDB should be aware of the risk of developing sinusitis.
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Affiliation(s)
- Tian Chen
- Department of Otolaryngology, Head and Neck Surgery, Renmin Hospital of Wuhan University, Wuhan, China
| | - Fen Li
- Institute of Otolaryngology, Head and Neck Surgery, Renmin Hospital of Wuhan University, Wuhan, China
| | - Yang Xi
- Department of Otolaryngology, Head and Neck Surgery, Renmin Hospital of Wuhan University, Wuhan, China
| | - Yuqin Deng
- Department of Otolaryngology, Head and Neck Surgery, Renmin Hospital of Wuhan University, Wuhan, China
| | - Shiming Chen
- Department of Otolaryngology, Head and Neck Surgery, Renmin Hospital of Wuhan University, Wuhan, China
- Institute of Otolaryngology, Head and Neck Surgery, Renmin Hospital of Wuhan University, Wuhan, China
| | - Zezhang Tao
- Department of Otolaryngology, Head and Neck Surgery, Renmin Hospital of Wuhan University, Wuhan, China
- Institute of Otolaryngology, Head and Neck Surgery, Renmin Hospital of Wuhan University, Wuhan, China
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12
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Bandyopadhyay A, Goldstein C. Clinical applications of artificial intelligence in sleep medicine: a sleep clinician's perspective. Sleep Breath 2023; 27:39-55. [PMID: 35262853 PMCID: PMC8904207 DOI: 10.1007/s11325-022-02592-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 01/25/2022] [Accepted: 03/02/2022] [Indexed: 12/27/2022]
Abstract
BACKGROUND The past few years have seen a rapid emergence of artificial intelligence (AI)-enabled technology in the field of sleep medicine. AI refers to the capability of computer systems to perform tasks conventionally considered to require human intelligence, such as speech recognition, decision-making, and visual recognition of patterns and objects. The practice of sleep tracking and measuring physiological signals in sleep is widely practiced. Therefore, sleep monitoring in both the laboratory and ambulatory environments results in the accrual of massive amounts of data that uniquely positions the field of sleep medicine to gain from AI. METHOD The purpose of this article is to provide a concise overview of relevant terminology, definitions, and use cases of AI in sleep medicine. This was supplemented by a thorough review of relevant published literature. RESULTS Artificial intelligence has several applications in sleep medicine including sleep and respiratory event scoring in the sleep laboratory, diagnosing and managing sleep disorders, and population health. While still in its nascent stage, there are several challenges which preclude AI's generalizability and wide-reaching clinical applications. Overcoming these challenges will help integrate AI seamlessly within sleep medicine and augment clinical practice. CONCLUSION Artificial intelligence is a powerful tool in healthcare that may improve patient care, enhance diagnostic abilities, and augment the management of sleep disorders. However, there is a need to regulate and standardize existing machine learning algorithms prior to its inclusion in the sleep clinic.
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Affiliation(s)
- Anuja Bandyopadhyay
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, USA.
| | - Cathy Goldstein
- Department of Neurology, University of Michigan, Ann Arbor, MI, USA
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Casal-Guisande M, Torres-Durán M, Mosteiro-Añón M, Cerqueiro-Pequeño J, Bouza-Rodríguez JB, Fernández-Villar A, Comesaña-Campos A. Design and Conceptual Proposal of an Intelligent Clinical Decision Support System for the Diagnosis of Suspicious Obstructive Sleep Apnea Patients from Health Profile. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:3627. [PMID: 36834325 PMCID: PMC9963107 DOI: 10.3390/ijerph20043627] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 02/16/2023] [Accepted: 02/17/2023] [Indexed: 06/18/2023]
Abstract
Obstructive Sleep Apnea (OSA) is a chronic sleep-related pathology characterized by recurrent episodes of total or partial obstruction of the upper airways during sleep. It entails a high impact on the health and quality of life of patients, affecting more than one thousand million people worldwide, which has resulted in an important public health concern in recent years. The usual diagnosis involves performing a sleep test, cardiorespiratory polygraphy, or polysomnography, which allows characterizing the pathology and assessing its severity. However, this procedure cannot be used on a massive scale in general screening studies of the population because of its execution and implementation costs; therefore, causing an increase in waiting lists which would negatively affect the health of the affected patients. Additionally, the symptoms shown by these patients are often unspecific, as well as appealing to the general population (excessive somnolence, snoring, etc.), causing many potential cases to be referred for a sleep study when in reality are not suffering from OSA. This paper proposes a novel intelligent clinical decision support system to be applied to the diagnosis of OSA that can be used in early outpatient stages, quickly, easily, and safely, when a suspicious OSA patient attends the consultation. Starting from information related to the patient's health profile (anthropometric data, habits, comorbidities, or medications taken), the system is capable of determining different alert levels of suffering from sleep apnea associated with different apnea-hypopnea index (AHI) levels to be studied. To that end, a series of automatic learning algorithms are deployed that, working concurrently, together with a corrective approach based on the use of an Adaptive Neuro-Based Fuzzy Inference System (ANFIS) and a specific heuristic algorithm, allow the calculation of a series of labels associated with the different levels of AHI previously indicated. For the initial software implementation, a data set with 4600 patients from the Álvaro Cunqueiro Hospital in Vigo was used. The results obtained after performing the proof tests determined ROC curves with AUC values in the range 0.8-0.9, and Matthews correlation coefficient values close to 0.6, with high success rates. This points to its potential use as a support tool for the diagnostic process, not only from the point of view of improving the quality of the services provided, but also from the best use of hospital resources and the consequent savings in terms of costs and time.
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Affiliation(s)
- Manuel Casal-Guisande
- Department of Design in Engineering, University of Vigo, 36208 Vigo, Spain
- Design, Expert Systems and Artificial Intelligent Solutions Group (DESAINS), Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, 36213 Vigo, Spain
| | - María Torres-Durán
- Pulmonary Department, Hospital Álvaro Cunqueiro, 36213 Vigo, Spain
- NeumoVigo I+i Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, 36213 Vigo, Spain
| | - Mar Mosteiro-Añón
- Pulmonary Department, Hospital Álvaro Cunqueiro, 36213 Vigo, Spain
- NeumoVigo I+i Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, 36213 Vigo, Spain
| | - Jorge Cerqueiro-Pequeño
- Department of Design in Engineering, University of Vigo, 36208 Vigo, Spain
- Design, Expert Systems and Artificial Intelligent Solutions Group (DESAINS), Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, 36213 Vigo, Spain
| | - José-Benito Bouza-Rodríguez
- Department of Design in Engineering, University of Vigo, 36208 Vigo, Spain
- Design, Expert Systems and Artificial Intelligent Solutions Group (DESAINS), Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, 36213 Vigo, Spain
| | - Alberto Fernández-Villar
- Pulmonary Department, Hospital Álvaro Cunqueiro, 36213 Vigo, Spain
- NeumoVigo I+i Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, 36213 Vigo, Spain
| | - Alberto Comesaña-Campos
- Department of Design in Engineering, University of Vigo, 36208 Vigo, Spain
- Design, Expert Systems and Artificial Intelligent Solutions Group (DESAINS), Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, 36213 Vigo, Spain
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Liang Z. Novel method combining multiscale attention entropy of overnight blood oxygen level and machine learning for easy sleep apnea screening. Digit Health 2023; 9:20552076231211550. [PMID: 37936958 PMCID: PMC10627021 DOI: 10.1177/20552076231211550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 10/16/2023] [Indexed: 11/09/2023] Open
Abstract
Objective Sleep apnea is a common sleep disorder affecting a significant portion of the population, but many apnea patients remain undiagnosed because existing clinical tests are invasive and expensive. This study aimed to develop a method for easy sleep apnea screening. Methods Three supervised machine learning algorithms, including logistic regression, support vector machine, and light gradient boosting machine, were applied to develop apnea screening models at two apnea-hypopnea index cutoff thresholds: ≥ 5 and ≥ 30 events/hours. The SpO2 recordings of the Sleep Heart Health Study database (N = 5786) were used for model training, validation, and test. Multiscale entropy analysis was performed to derive a set of multiscale attention entropy features from the SpO2 recordings. Demographic features including age, sex, body mass index, and blood pressure were also used. The dependency among the multiscale attention entropy features were handled with the independent component analysis. Results For cutoff ≥ 5/hours, logistic regression model achieved the highest Matthew's correlation coefficient (0.402) and area under the curve (0.747), and reasonably good sensitivity (75.38%), specificity (74.02%), and positive predictive value (92.94%). For cutoff ≥ 30/hours, support vector machine model achieved the highest Matthew's correlation coefficient (0.545) and area under the curve (0.823), and good sensitivity (82.00%), specificity (82.69%), and negative predictive value (95.53%). Conclusions Our models achieved better performance than existing methods and have the potential to be integrated with home-use pulse oximeters.
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Affiliation(s)
- Zilu Liang
- Kyoto University of Advanced Science (KUAS), Japan
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15
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Aiyer I, Shaik L, Sheta A, Surani S. Review of Application of Machine Learning as a Screening Tool for Diagnosis of Obstructive Sleep Apnea. MEDICINA (KAUNAS, LITHUANIA) 2022; 58:1574. [PMID: 36363530 PMCID: PMC9696886 DOI: 10.3390/medicina58111574] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 10/27/2022] [Indexed: 07/30/2023]
Abstract
Obstructive sleep apnea syndrome (OSAS) is a pervasive disorder with an incidence estimated at 5-14 percent among adults aged 30-70 years. It carries significant morbidity and mortality risk from cardiovascular disease, including ischemic heart disease, atrial fibrillation, and cerebrovascular disease, and risks related to excessive daytime sleepiness. The gold standard for diagnosis of OSAS is the polysomnography (PSG) test which requires overnight evaluation in a sleep laboratory and expensive infrastructure, which renders it unsuitable for mass screening and diagnosis. Alternatives such as home sleep testing need patients to wear diagnostic instruments overnight, but accuracy continues to be suboptimal while access continues to be a barrier for many. Hence, there is a continued significant underdiagnosis and under-recognition of sleep apnea in the community, with at least one study suggesting that 80-90% of middle-aged adults with moderate to severe sleep apnea remain undiagnosed. Recently, we have seen a surge in applications of artificial intelligence and neural networks in healthcare diagnostics. Several studies have attempted to examine its application in the diagnosis of OSAS. Signals included in data analytics include Electrocardiogram (ECG), photo-pletysmography (PPG), peripheral oxygen saturation (SpO2), and audio signals. A different approach is to study the application of machine learning to use demographic and standard clinical variables and physical findings to try and synthesize predictive models with high accuracy in assisting in the triage of high-risk patients for sleep testing. The current paper will review this latter approach and identify knowledge gaps that may serve as potential avenues for future research.
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Affiliation(s)
| | - Likhita Shaik
- Department of Medicine, Hennepin Healthcare, Minneapolis, MN 55404, USA
| | - Alaa Sheta
- Department of Computer Science, Southern Connecticut University, New Haven, CT 06515, USA
| | - Salim Surani
- Department of Medicine, Texas A&M University, College Station, TX 77843, USA
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16
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Liu X, Shu Y, Yu P, Li H, Duan W, Wei Z, Li K, Xie W, Zeng Y, Peng D. Classification of severe obstructive sleep apnea with cognitive impairment using degree centrality: A machine learning analysis. Front Neurol 2022; 13:1005650. [PMID: 36090863 PMCID: PMC9453022 DOI: 10.3389/fneur.2022.1005650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 08/11/2022] [Indexed: 11/24/2022] Open
Abstract
In this study, we aimed to use voxel-level degree centrality (DC) features in combination with machine learning methods to distinguish obstructive sleep apnea (OSA) patients with and without mild cognitive impairment (MCI). Ninety-nine OSA patients were recruited for rs-MRI scanning, including 51 MCI patients and 48 participants with no mild cognitive impairment. Based on the Automated Anatomical Labeling (AAL) brain atlas, the DC features of all participants were calculated and extracted. Ten DC features were screened out by deleting variables with high pin-correlation and minimum absolute contraction and performing selective operator lasso regression. Finally, three machine learning methods were used to establish classification models. The support vector machine method had the best classification efficiency (AUC = 0.78), followed by random forest (AUC = 0.71) and logistic regression (AUC = 0.77). These findings demonstrate an effective machine learning approach for differentiating OSA patients with and without MCI and provide potential neuroimaging evidence for cognitive impairment caused by OSA.
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Affiliation(s)
- Xiang Liu
- Department of Radiology, the First Affiliated Hospital of Nanchang University, Jiangxi, China
| | - Yongqiang Shu
- Department of Radiology, the First Affiliated Hospital of Nanchang University, Jiangxi, China
| | - Pengfei Yu
- Big Data Center, the Second Affiliated Hospital of Nanchang University, Jiangxi, China
| | - Haijun Li
- Department of PET Center, the First Affiliated Hospital of Nanchang University, Jiangxi, China
| | - Wenfeng Duan
- Department of Radiology, the First Affiliated Hospital of Nanchang University, Jiangxi, China
| | - Zhipeng Wei
- Department of Radiology, the First Affiliated Hospital of Nanchang University, Jiangxi, China
| | - Kunyao Li
- Department of Radiology, the First Affiliated Hospital of Nanchang University, Jiangxi, China
| | - Wei Xie
- Department of Radiology, the First Affiliated Hospital of Nanchang University, Jiangxi, China
| | - Yaping Zeng
- Department of Radiology, the First Affiliated Hospital of Nanchang University, Jiangxi, China
| | - Dechang Peng
- Department of Radiology, the First Affiliated Hospital of Nanchang University, Jiangxi, China
- *Correspondence: Dechang Peng
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17
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S ME, K B, H D, Ludwig K, N K, T H, H G, M M. A Novel Quantitative Arousal-Associated EEG-Metric to Predict Severity of Respiratory Distress in Obstructive Sleep Apnea Patients. Front Physiol 2022; 13:885270. [PMID: 35812317 PMCID: PMC9257225 DOI: 10.3389/fphys.2022.885270] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Accepted: 05/31/2022] [Indexed: 11/25/2022] Open
Abstract
Respiratory arousals (RA) on polysomnography (PSG) are an important predictor of obstructive sleep apnea (OSA) disease severity. Additionally, recent reports suggest that more global indices of desaturation such as the hypoxic burden, namely the area under the curve (AUC) of the oxygen saturation (SaO2) PSG trace may better depict the desaturation burden in OSA. Here we investigated possible associations between a new metric, namely the AUC of the respiratory arousal electroencephalographic (EEG) recording, and already established parameters as the apnea/hypopnea index (AHI), arousal index and hypoxic burden in patients with OSA. In this data-driven study, polysomnographic data from 102 patients with OSAS were assessed (32 female; 70 male; mean value of age: 52 years; mean value of Body-Mass-Index-BMI: 31 kg/m2). The marked arousals from the pooled EEG signal (C3 and C4) were smoothed and the AUC was estimated. We used a support vector regressor (SVR) analysis to predict AHI, arousal index and hypoxic burden as captured by the PSG. The SVR with the arousal-AUC metric could quite reliably predict the AHI with a high correlation coefficient (0,58 in the training set, 0,65 in the testing set and 0,64 overall), as well as the hypoxic burden (0,62 in the training set, 0,58 in the testing set and 0,59 overall) and the arousal index (0,58 in the training set, 0,67 in the testing set and 0,66 overall). This novel arousal-AUC metric may predict AHI, hypoxic burden and arousal index with a quite high correlation coefficient and therefore could be used as an additional quantitative surrogate marker in the description of obstructive sleep apnea disease severity.
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Affiliation(s)
- Malatantis-Ewert S
- Department of Otorhinolaryngology, Sleep Medicine Center, Medical Center of the University of Mainz, Mainz, Germany
- Movement Disorders and Neurostimulation, Department of Neurology, Biomedical Statistics and Multimodal Signal Processing Unit, Medical Center of the University of Mainz, Mainz, Germany
| | - Bahr K
- Department of Otorhinolaryngology, Sleep Medicine Center, Medical Center of the University of Mainz, Mainz, Germany
| | - Ding H
- Movement Disorders and Neurostimulation, Department of Neurology, Biomedical Statistics and Multimodal Signal Processing Unit, Medical Center of the University of Mainz, Mainz, Germany
| | - Katharina Ludwig
- Department of Otorhinolaryngology, Sleep Medicine Center, Medical Center of the University of Mainz, Mainz, Germany
| | - Koirala N
- Haskins Laboratories, Yale University, New Haven, CT, United States
| | - Huppertz T
- Department of Otorhinolaryngology, Sleep Medicine Center, Medical Center of the University of Mainz, Mainz, Germany
| | - Gouveris H
- Department of Otorhinolaryngology, Sleep Medicine Center, Medical Center of the University of Mainz, Mainz, Germany
| | - Muthuraman M
- Movement Disorders and Neurostimulation, Department of Neurology, Biomedical Statistics and Multimodal Signal Processing Unit, Medical Center of the University of Mainz, Mainz, Germany
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Iwasaki A, Fujiwara K, Nakayama C, Sumi Y, Kano M, Nagamoto T, Kadotani H. R-R interval-based sleep apnea screening by a recurrent neural network in a large clinical polysomnography dataset. Clin Neurophysiol 2022; 139:80-89. [DOI: 10.1016/j.clinph.2022.04.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 03/10/2022] [Accepted: 04/12/2022] [Indexed: 11/03/2022]
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19
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Yin T, Zheng H, Ma T, Tian X, Xu J, Li Y, Lan L, Liu M, Sun R, Tang Y, Liang F, Zeng F. Predicting acupuncture efficacy for functional dyspepsia based on routine clinical features: a machine learning study in the framework of predictive, preventive, and personalized medicine. EPMA J 2022; 13:137-147. [PMID: 35273662 PMCID: PMC8897529 DOI: 10.1007/s13167-022-00271-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 01/17/2022] [Indexed: 12/12/2022]
Abstract
Background Acupuncture is safe and effective for functional dyspepsia (FD), while its efficacy varies among individuals. Predicting the response of different FD patients to acupuncture treatment in advance and therefore administering the tailored treatment to the individual is consistent with the principle of predictive, preventive, and personalized medicine (PPPM/3PM). In the current study, the individual efficacy prediction models were developed based on the support vector machine (SVM) algorithm and routine clinical features, aiming to predict the efficacy of acupuncture in treating FD and identify the FD patients who were appropriate to acupuncture treatment. Methods A total of 745 FD patients were collected from two clinical trials. All the patients received a 4-week acupuncture treatment. Based on the demographic and baseline clinical features of 80% of patients in trial 1, the SVM models were established to predict the acupuncture response and improvements of symptoms and quality of life (QoL) at the end of treatment. Then, the left 20% of patients in trial 1 and 193 patients in trial 2 were respectively applied to evaluate the internal and external generalizations of these models. Results These models could predict the efficacy of acupuncture successfully. In the internal test set, models achieved an accuracy of 0.773 in predicting acupuncture response and an R 2 of 0.446 and 0.413 in the prediction of QoL and symptoms improvements, respectively. Additionally, these models had well generalization in the independent validation set and could also predict, to a certain extent, the long-term efficacy of acupuncture at the 12-week follow-up. The gender, subtype of disease, and education level were finally identified as the critical predicting features. Conclusion Based on the SVM algorithm and routine clinical features, this study established the models to predict acupuncture efficacy for FD patients. The prediction models developed accordingly are promising to assist doctors in judging patients' responses to acupuncture in advance, so that they could tailor and adjust acupuncture treatment plans for different patients in a prospective rather than the reactive manner, which could greatly improve the clinical efficacy of acupuncture treatment for FD and save medical expenditures. Supplementary Information The online version contains supplementary material available at 10.1007/s13167-022-00271-8.
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Affiliation(s)
- Tao Yin
- Acupuncture and Tuina School/The 3rd Teaching Hospital, Chengdu University of Traditional Chinese Medicine, Chengdu, 610075 Sichuan China ,Acupuncture-Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, 610075 Sichuan China
| | - Hui Zheng
- Acupuncture and Tuina School/The 3rd Teaching Hospital, Chengdu University of Traditional Chinese Medicine, Chengdu, 610075 Sichuan China
| | - Tingting Ma
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 610075 Sichuan China
| | - Xiaoping Tian
- Acupuncture and Tuina School/The 3rd Teaching Hospital, Chengdu University of Traditional Chinese Medicine, Chengdu, 610075 Sichuan China
| | - Jing Xu
- Acupuncture and Tuina School/The 3rd Teaching Hospital, Chengdu University of Traditional Chinese Medicine, Chengdu, 610075 Sichuan China
| | - Ying Li
- Graduate School, Chengdu University of Traditional Chinese Medicine, Chengdu, 610075 Sichuan China
| | - Lei Lan
- Acupuncture and Tuina School/The 3rd Teaching Hospital, Chengdu University of Traditional Chinese Medicine, Chengdu, 610075 Sichuan China ,Acupuncture-Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, 610075 Sichuan China
| | - Mailan Liu
- Acupuncture and Tuina School, Hunan University of Chinese Medicine, Changsha, 410208 Hunan China
| | - Ruirui Sun
- Acupuncture and Tuina School/The 3rd Teaching Hospital, Chengdu University of Traditional Chinese Medicine, Chengdu, 610075 Sichuan China ,Acupuncture-Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, 610075 Sichuan China
| | - Yong Tang
- Acupuncture and Tuina School/The 3rd Teaching Hospital, Chengdu University of Traditional Chinese Medicine, Chengdu, 610075 Sichuan China ,Key Laboratory of Sichuan Province for Acupuncture and Chronobiology, Chengdu, 610075 Sichuan China
| | - Fanrong Liang
- Acupuncture and Tuina School/The 3rd Teaching Hospital, Chengdu University of Traditional Chinese Medicine, Chengdu, 610075 Sichuan China
| | - Fang Zeng
- Acupuncture and Tuina School/The 3rd Teaching Hospital, Chengdu University of Traditional Chinese Medicine, Chengdu, 610075 Sichuan China ,Acupuncture-Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, 610075 Sichuan China
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20
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Yang L, Jiang H, Ding X, Liao Z, Wei M, Li J, Wu T, Li C, Fang Y. Modulation of Sleep Architecture by Whole-Body Static Magnetic Exposure: A Study Based on EEG-Based Automatic Sleep Staging. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:741. [PMID: 35055561 PMCID: PMC8775472 DOI: 10.3390/ijerph19020741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 01/04/2022] [Accepted: 01/07/2022] [Indexed: 12/10/2022]
Abstract
A steady increase in sleep problems has been observed along with the development of society. Overnight exposure to a static magnetic field has been found to improve sleep quality; however, such studies were mainly based on subjective evaluation. Thus, the presented data cannot be used to infer sleep architecture in detail. In this study, the subjects slept on a magneto-static mattress for four nights, and self-reported scales and electroencephalogram (EEG) were used to determine the effect of static magnetic field exposure (SMFE) on sleep. Machine learning operators, i.e., decision tree and supporting vector machine, were trained and optimized with the open access sleep EEG dataset to automatically discriminate the individual sleep stages, determined experimentally. SMEF was found to decrease light sleep duration (N2%) by 3.51%, and sleep onset latency (SOL) by 15.83%, while it increased deep sleep duration (N3%) by 8.43%, compared with the sham SMFE group. Further, the overall sleep efficiency (SE) was also enhanced by SMFE. It is the first study, to the best of our knowledge, where the change in sleep architecture was explored by SMFE. Our findings will be useful in developing a non-invasive sleep-facilitating instrument.
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Affiliation(s)
- Lei Yang
- China Academy of Information and Communications Technology, Beijing 100191, China; (L.Y.); (H.J.); (X.D.); (J.L.); (T.W.)
| | - Haoyu Jiang
- China Academy of Information and Communications Technology, Beijing 100191, China; (L.Y.); (H.J.); (X.D.); (J.L.); (T.W.)
| | - Xiaotong Ding
- China Academy of Information and Communications Technology, Beijing 100191, China; (L.Y.); (H.J.); (X.D.); (J.L.); (T.W.)
| | - Zhongcai Liao
- Zhejiang Heye Health Technology, Anji 313300, China; (Z.L.); (M.W.)
| | - Min Wei
- Zhejiang Heye Health Technology, Anji 313300, China; (Z.L.); (M.W.)
| | - Juan Li
- China Academy of Information and Communications Technology, Beijing 100191, China; (L.Y.); (H.J.); (X.D.); (J.L.); (T.W.)
| | - Tongning Wu
- China Academy of Information and Communications Technology, Beijing 100191, China; (L.Y.); (H.J.); (X.D.); (J.L.); (T.W.)
| | - Congsheng Li
- China Academy of Information and Communications Technology, Beijing 100191, China; (L.Y.); (H.J.); (X.D.); (J.L.); (T.W.)
| | - Yanwen Fang
- Zhejiang Heye Health Technology, Anji 313300, China; (Z.L.); (M.W.)
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21
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Ramesh J, Keeran N, Sagahyroon A, Aloul F. Towards Validating the Effectiveness of Obstructive Sleep Apnea Classification from Electronic Health Records Using Machine Learning. Healthcare (Basel) 2021; 9:healthcare9111450. [PMID: 34828496 PMCID: PMC8622500 DOI: 10.3390/healthcare9111450] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 10/23/2021] [Accepted: 10/25/2021] [Indexed: 11/20/2022] Open
Abstract
Obstructive sleep apnea (OSA) is a common, chronic, sleep-related breathing disorder characterized by partial or complete airway obstruction in sleep. The gold standard diagnosis method is polysomnography, which estimates disease severity through the Apnea-Hypopnea Index (AHI). However, this is expensive and not widely accessible to the public. For effective screening, this work implements machine learning algorithms for classification of OSA. The model is trained with routinely acquired clinical data of 1479 records from the Wisconsin Sleep Cohort dataset. Extracted features from the electronic health records include patient demographics, laboratory blood reports, physical measurements, habitual sleep history, comorbidities, and general health questionnaire scores. For distinguishing between OSA and non-OSA patients, feature selection methods reveal the primary important predictors as waist-to-height ratio, waist circumference, neck circumference, body-mass index, lipid accumulation product, excessive daytime sleepiness, daily snoring frequency and snoring volume. Optimal hyperparameters were selected using a hybrid tuning method consisting of Bayesian Optimization and Genetic Algorithms through a five-fold cross-validation strategy. Support vector machines achieved the highest evaluation scores with accuracy: 68.06%, sensitivity: 88.76%, specificity: 40.74%, F1-score: 75.96%, PPV: 66.36% and NPV: 73.33%. We conclude that routine clinical data can be useful in prioritization of patient referral for further sleep studies.
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22
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Hsu YC, Wang JD, Huang PH, Chien YW, Chiu CJ, Lin CY. Integrating domain knowledge with machine learning to detect obstructive sleep apnea: Snore as a significant bio-feature. J Sleep Res 2021; 31:e13487. [PMID: 34549473 DOI: 10.1111/jsr.13487] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Revised: 08/21/2021] [Accepted: 09/07/2021] [Indexed: 11/30/2022]
Abstract
Our study's main purpose is to emphasise the significance of medical knowledge of pathophysiology before machine learning. We investigated whether combining domain knowledge with machine learning results might increase accuracy and minimise the number of bio-features used to detect obstructive sleep apnea (OSA). The present study analysed data on 36 self-reported symptoms and 24 clinical features obtained from 3,495 patients receiving polysomnography at a regional hospital and a medical centre. The area under the receiver operating characteristic (AUC) curve was used to evaluate patients with and without moderate or severe OSA using three prediction models on the basis of various estimation methods: the multiple logistic regression (MLR), support vector machine (SVM), and neural network (NN) methods. Odds ratios stratified by gender and age were also measured to account for clinicians' common sense. We discovered that adding the self-reported snoring item improved the AUC by 0.01-0.10 and helped us to rapidly achieve the optimum level. The performance of four items (gender, age, body mass index [BMI], and snoring) was comparable with that of adding two or more items (neck and waist circumference) for predicting moderate to severe OSA (Apnea-Hypopnea Index ≥15 events/hr) in all three prediction models, demonstrating the medical knowledge value of pathophysiology. The four-item test sample AUCs were 0.83, 0.84, and 0.83 for MLR, SVM, and NN, respectively. Participants with regular snoring and a BMI of ≥25 kg/m2 had a greater chance of moderate to severe OSA according to the stratified adjusted odds ratios. Combining domain knowledge into machine learning could increase efficiency and enable primary care physicians to refer for an OSA diagnosis earlier.
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Affiliation(s)
- Yu-Ching Hsu
- Department of Public Health, College of Medicine, National Cheng Kung University, Tainan, Taiwan.,Sleep Medicine Center, Tainan Hospital, Ministry of Health and Welfare, Tainan, Taiwan.,Department of Chinese medicine, Tainan Hospital, Ministry of Health and Welfare, Tainan, Taiwan
| | - Jung-Der Wang
- Department of Public Health, College of Medicine, National Cheng Kung University, Tainan, Taiwan.,Department of Occupational and Environmental Medicine, National Cheng Kung University Hospital, Tainan, Taiwan
| | - Po-Hsien Huang
- Department of Psychology, National Chengchi University, Taiwan
| | - Yu-Wen Chien
- Department of Public Health, College of Medicine, National Cheng Kung University, Tainan, Taiwan.,Department of Occupational and Environmental Medicine, National Cheng Kung University Hospital, Tainan, Taiwan
| | - Ching-Ju Chiu
- Institute of Gerontology, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Cheng-Yu Lin
- Department of Otolaryngology, College of Medicine, National Cheng Kung University Hospital, National Cheng Kung University, Tainan, Taiwan.,Sleep Medicine Center, College of Medicine, National Cheng Kung University Hospital, National Cheng Kung University, Tainan, Taiwan
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23
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Abudoureyimu R, Heizhati M, Wang L, Li M, Zhang D, Wang Z, Yang Z, Hong J, Li N. Lower 24-h urinary potassium excretion is negatively associated with excessive daytime sleepiness in the general population. Sleep Breath 2021; 26:733-741. [PMID: 34331198 DOI: 10.1007/s11325-021-02444-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2021] [Revised: 07/11/2021] [Accepted: 07/14/2021] [Indexed: 10/20/2022]
Abstract
OBJECTIVES Uncertainty remains about the association of potassium (K) intake and sleepiness. Therefore, we aimed to explore the relationship between K excretion using 24-h urine samples and excessive daytime sleepiness (EDS) in the general population. METHODS In a cross-sectional study, we used multi-stage proportional random sampling to obtain a study sample aged ≥ 18 years from Emin, China between March and June 2019. We collected timed 24-h urine specimens and conducted EDS assessments using the Epworth Sleepiness Scale (ESS) questionnaire. Subjects were divided into two groups by the median of 24-h urinary potassium (24-h UK). EDS was defined as ESS score ≥ 10. Multi-variable linear regression was used to examine the association between the 24-h UK and the odds of prevalent EDS. We performed a sensitivity analysis by excluding subjects under anti-hypertensive treatment and those with sleep disordered breathing by the NoSAS scale. RESULTS A total of 470 participants with complete 24-h urine samples and ESS data (62% women, mean age 49.6 years, mean ESS score of 9.0 ± 5.2) were enrolled. The mean ESS score was significantly lower in the upper half of 24-h UK group than in the lower half (9.5 ± 5.3 vs 8.5 ± 5.1, P = 0.044), and accordingly, prevalent EDS was significantly greater in the lower half than in the higher half (49% vs 40%, P = 0.039). In further improving the propensity matching score, the results remained consistent with the overall results. In multiple linear regression, 24-h UK was negatively correlated with ESS score (β = - 0.180 (- 0.276, - 0.085), < 0.001). Sensitivity analysis demonstrated augmented results in those without anti-hypertensive treatment. CONCLUSION Lower potassium intake, as suggested by lower UK excretion, may be implicated in the presence of EDS in the general population.
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Affiliation(s)
- Reyila Abudoureyimu
- Hypertension Center of People's Hospital of Xinjiang Uygur Autonomous Region, National Health Committee Key Laboratory of Hypertension Clinical Research China, No. 91 Tianchi Road, Urumqi, 830001, Xinjiang, China
| | - Mulalibieke Heizhati
- Hypertension Center of People's Hospital of Xinjiang Uygur Autonomous Region, National Health Committee Key Laboratory of Hypertension Clinical Research China, No. 91 Tianchi Road, Urumqi, 830001, Xinjiang, China
| | - Lin Wang
- Hypertension Center of People's Hospital of Xinjiang Uygur Autonomous Region, National Health Committee Key Laboratory of Hypertension Clinical Research China, No. 91 Tianchi Road, Urumqi, 830001, Xinjiang, China
| | - Mei Li
- Hypertension Center of People's Hospital of Xinjiang Uygur Autonomous Region, National Health Committee Key Laboratory of Hypertension Clinical Research China, No. 91 Tianchi Road, Urumqi, 830001, Xinjiang, China
| | - Delian Zhang
- Hypertension Center of People's Hospital of Xinjiang Uygur Autonomous Region, National Health Committee Key Laboratory of Hypertension Clinical Research China, No. 91 Tianchi Road, Urumqi, 830001, Xinjiang, China
| | - Zhongrong Wang
- Hypertension Center of People's Hospital of Xinjiang Uygur Autonomous Region, National Health Committee Key Laboratory of Hypertension Clinical Research China, No. 91 Tianchi Road, Urumqi, 830001, Xinjiang, China
| | - Zhikang Yang
- Hypertension Center of People's Hospital of Xinjiang Uygur Autonomous Region, National Health Committee Key Laboratory of Hypertension Clinical Research China, No. 91 Tianchi Road, Urumqi, 830001, Xinjiang, China
| | - Jing Hong
- Hypertension Center of People's Hospital of Xinjiang Uygur Autonomous Region, National Health Committee Key Laboratory of Hypertension Clinical Research China, No. 91 Tianchi Road, Urumqi, 830001, Xinjiang, China
| | - Nanfang Li
- Hypertension Center of People's Hospital of Xinjiang Uygur Autonomous Region, National Health Committee Key Laboratory of Hypertension Clinical Research China, No. 91 Tianchi Road, Urumqi, 830001, Xinjiang, China.
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Abstract
Interest in telemedicine has increased exponentially. There is a growing body of published evidence on the use of telemedicine for patients using continuous positive airway pressure. Telemedicine-ready devices can support the transmission on use time, apnea-hypopnea index, and leakage. This approach enables early activation of troubleshooting. Automated, personalized feedback for patients and patient access to their own data provide unprecedented opportunities for integrating comanagement approaches, multiactor interactions, and patient empowerment. Telemedicine is likely cost effective, but requires better evidence. Notwithstanding barriers for implementation that remain, telemedicine has to be embraced, leaving the physician and patient to accept it or not.
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Affiliation(s)
- Johan Verbraecken
- Department of Pulmonary Medicine and Multidisciplinary Sleep Disorders Centre, Antwerp University Hospital, University of Antwerp, Drie Eikenstraat 655, Edegem, Antwerp 2650, Belgium.
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25
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Redline S, Purcell SM. Sleep and Big Data: harnessing data, technology, and analytics for monitoring sleep and improving diagnostics, prediction, and interventions-an era for Sleep-Omics? Sleep 2021; 44:6248627. [PMID: 33893509 DOI: 10.1093/sleep/zsab107] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Affiliation(s)
- Susan Redline
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Shaun M Purcell
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
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26
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Kim YJ, Jeon JS, Cho SE, Kim KG, Kang SG. Prediction Models for Obstructive Sleep Apnea in Korean Adults Using Machine Learning Techniques. Diagnostics (Basel) 2021; 11:diagnostics11040612. [PMID: 33808100 PMCID: PMC8066462 DOI: 10.3390/diagnostics11040612] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Revised: 03/24/2021] [Accepted: 03/26/2021] [Indexed: 12/01/2022] Open
Abstract
This study aimed to investigate the applicability of machine learning to predict obstructive sleep apnea (OSA) among individuals with suspected OSA in South Korea. A total of 92 clinical variables for OSA were collected from 279 South Koreans (OSA, n = 213; no OSA, n = 66), from which seven major clinical indices were selected. The data were randomly divided into training data (OSA, n = 149; no OSA, n = 46) and test data (OSA, n = 64; no OSA, n = 20). Using the seven clinical indices, the OSA prediction models were trained using four types of machine learning models—logistic regression, support vector machine (SVM), random forest, and XGBoost (XGB)—and each model was validated using the test data. In the validation, the SVM showed the best OSA prediction result with a sensitivity, specificity, and area under curve (AUC) of 80.33%, 86.96%, and 0.87, respectively, while the XGB showed the lowest OSA prediction performance with a sensitivity, specificity, and AUC of 78.69%, 73.91%, and 0.80, respectively. The machine learning algorithms showed high OSA prediction performance using data from South Koreans with suspected OSA. Hence, machine learning will be helpful in clinical applications for OSA prediction in the Korean population.
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Affiliation(s)
- Young Jae Kim
- Department of Biomedical Engineering, Gil Medical Center, Gachon University College of Medicine, Incheon 21565, Korea; (Y.J.K.); (J.S.J.)
| | - Ji Soo Jeon
- Department of Biomedical Engineering, Gil Medical Center, Gachon University College of Medicine, Incheon 21565, Korea; (Y.J.K.); (J.S.J.)
| | - Seo-Eun Cho
- Department of Psychiatry, Gil Medical Center, Gachon University College of Medicine, Incheon 21565, Korea;
| | - Kwang Gi Kim
- Department of Biomedical Engineering, Gil Medical Center, Gachon University College of Medicine, Incheon 21565, Korea; (Y.J.K.); (J.S.J.)
- Correspondence: (K.G.K.); (S.-G.K.); Tel.: +82-32-458-2818 (S.-G.K.)
| | - Seung-Gul Kang
- Department of Psychiatry, Gil Medical Center, Gachon University College of Medicine, Incheon 21565, Korea;
- Correspondence: (K.G.K.); (S.-G.K.); Tel.: +82-32-458-2818 (S.-G.K.)
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27
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Chen L, Tang W, Wang C, Chen D, Gao Y, Ma W, Zha P, Lei F, Tang X, Ran X. Diagnostic Accuracy of Oxygen Desaturation Index for Sleep-Disordered Breathing in Patients With Diabetes. Front Endocrinol (Lausanne) 2021; 12:598470. [PMID: 33767667 PMCID: PMC7985532 DOI: 10.3389/fendo.2021.598470] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Accepted: 02/01/2021] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND Polysomnography (PSG) is the gold standard for diagnosis of sleep-disordered breathing (SDB). But it is impractical to perform PSG in all patients with diabetes. The objective was to develop a clinically easy-to-use prediction model to diagnosis SDB in patients with diabetes. METHODS A total of 440 patients with diabetes were recruited and underwent overnight PSG at West China Hospital. Prediction algorithms were based on oxygen desaturation index (ODI) and other variables, including sex, age, body mass index, Epworth score, mean oxygen saturation, and total sleep time. Two phase approach was employed to derivate and validate the models. RESULTS ODI was strongly correlated with apnea-hypopnea index (AHI) (rs = 0.941). In the derivation phase, the single cutoff model with ODI was selected, with area under the receiver operating characteristic curve (AUC) of 0.956 (95%CI 0.917-0.994), 0.962 (95%CI 0.943-0.981), and 0.976 (95%CI 0.956-0.996) for predicting AHI ≥5/h, ≥15/h, and ≥30/h, respectively. We identified the cutoff of ODI 5/h, 15/h, and 25/h, as having important predictive value for AHI ≥5/h, ≥15/h, and ≥30/h, respectively. In the validation phase, the AUC of ODI was 0.941 (95%CI 0.904-0.978), 0.969 (95%CI 0.969-0.991), and 0.949 (95%CI 0.915-0.983) for predicting AHI ≥5/h, ≥15/h, and ≥30/h, respectively. The sensitivity of ODI ≥5/h, ≥15/h, and ≥25/h was 92%, 90%, and 93%, respectively, while the specificity was 73%, 89%, and 85%, respectively. CONCLUSIONS ODI is a sensitive and specific tool to predict SDB in patients with diabetes.
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Affiliation(s)
- Lihong Chen
- Diabetic Foot Care Center, Department of Endocrinology and Metabolism, West China Hospital, Sichuan University, Chengdu, China
| | - Weiwei Tang
- Diabetic Foot Care Center, Department of Endocrinology and Metabolism, West China Hospital, Sichuan University, Chengdu, China
| | - Chun Wang
- Diabetic Foot Care Center, Department of Endocrinology and Metabolism, West China Hospital, Sichuan University, Chengdu, China
| | - Dawei Chen
- Diabetic Foot Care Center, Department of Endocrinology and Metabolism, West China Hospital, Sichuan University, Chengdu, China
| | - Yun Gao
- Diabetic Foot Care Center, Department of Endocrinology and Metabolism, West China Hospital, Sichuan University, Chengdu, China
| | - Wanxia Ma
- Diabetic Foot Care Center, Department of Endocrinology and Metabolism, West China Hospital, Sichuan University, Chengdu, China
| | - Panpan Zha
- Diabetic Foot Care Center, Department of Endocrinology and Metabolism, West China Hospital, Sichuan University, Chengdu, China
| | - Fei Lei
- Sleep Medicine Center, Mental Health Center, Translational Neuroscience Center, and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Xiangdong Tang
- Sleep Medicine Center, Mental Health Center, Translational Neuroscience Center, and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Xingwu Ran
- Diabetic Foot Care Center, Department of Endocrinology and Metabolism, West China Hospital, Sichuan University, Chengdu, China
- *Correspondence: Xingwu Ran,
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