{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T16:30:57Z","timestamp":1792168257967,"version":"4.4.0"},"reference-count":37,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2022,12,5]],"date-time":"2022-12-05T00:00:00Z","timestamp":1670198400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Cardiovasc. Med."],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Obstructive sleep apnea (OSA) is a globally prevalent disease closely associated with hypertension. To date, no predictive model for OSA-related hypertension has been established. We aimed to use machine learning (ML) to construct a model to analyze risk factors and predict OSA-related hypertension.<\/jats:p><\/jats:sec><jats:sec><jats:title>Materials and methods<\/jats:title><jats:p>We retrospectively collected the clinical data of OSA patients diagnosed by polysomnography from October 2019 to December 2021 and randomly divided them into training and validation sets. A total of 1,493 OSA patients with 27 variables were included. Independent risk factors for the risk of OSA-related hypertension were screened by the multifactorial logistic regression models. Six ML algorithms, including the logistic regression (LR), the gradient boosting machine (GBM), the extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), bootstrapped aggregating (Bagging), and the multilayer perceptron (MLP), were used to develop the model on the training set. The validation set was used to tune the model hyperparameters to determine the final prediction model. We compared the accuracy and discrimination of the models to identify the best machine learning algorithm for predicting OSA-related hypertension. In addition, a web-based tool was developed to promote its clinical application. We used permutation importance and Shapley additive explanations (SHAP) to determine the importance of the selected features and interpret the ML models.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>A total of 18 variables were selected for the models. The GBM model achieved the most extraordinary discriminatory ability (area under the receiver operating characteristic curve = 0.873, accuracy = 0.885, sensitivity = 0.713), and on the basis of this model, an online tool was built to help clinicians optimize OSA-related hypertension patient diagnosis. Finally, age, family history of hypertension, minimum arterial oxygen saturation, body mass index, and percentage of time of SaO<jats:sub>2<\/jats:sub> &amp;lt; 90% were revealed by the SHAP method as the top five critical variables contributing to the diagnosis of OSA-related hypertension.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>We established a risk prediction model for OSA-related hypertension patients using the ML method and demonstrated that among the six ML models, the gradient boosting machine model performs best. This prediction model could help to identify high-risk OSA-related hypertension patients, provide early and individualized diagnoses and treatment plans, protect patients from the serious consequences of OSA-related hypertension, and minimize the burden on society.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fcvm.2022.1042996","type":"journal-article","created":{"date-parts":[[2022,12,5]],"date-time":"2022-12-05T06:02:15Z","timestamp":1670220135000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":25,"title":["Prediction model of obstructive sleep apnea\u2013related hypertension: Machine learning\u2013based development and interpretation study"],"prefix":"10.3389","volume":"9","author":[{"given":"Yewen","family":"Shi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lina","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xi","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenle","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yani","family":"Feng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yitong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zine","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuqi","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yushan","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haiqin","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Libo","family":"Yin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Changying","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shinan","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoyong","family":"Ren","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2022,12,5]]},"reference":[{"key":"B1","doi-asserted-by":"crossref","first-page":"687","DOI":"10.1016\/S2213-2600(19)30198-5","article-title":"Estimation of the global prevalence and burden of obstructive sleep apnoea: a literature-based analysis.","volume":"7","author":"Benjafield","year":"2019","journal-title":"Lancet Respir Med."},{"key":"B2","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1016\/j.ijporl.2018.10.015","article-title":"Related biomarkers of neurocognitive impairment in children with obstructive sleep apnea.","volume":"116","author":"Shi","year":"2019","journal-title":"Int J Pediatr Otorhinolaryngol."},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.1186\/s12887-022-03514-9","article-title":"Serum neurofilament light reflects cognitive dysfunctions in children with obstructive sleep apnea.","volume":"22","author":"Shi","year":"2022","journal-title":"BMC Pediatr."},{"key":"B4","doi-asserted-by":"publisher","first-page":"e56","DOI":"10.1161\/CIR.0000000000000988","article-title":"Obstructive sleep apnea and cardiovascular disease: a scientific statement from the American heart association.","volume":"144","author":"Yeghiazarians","year":"2021","journal-title":"Circulation."},{"key":"B5","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1034\/j.1399-3003.2000.16a23.x","article-title":"Cardiovascular risk factors in patients with obstructive sleep apnoea syndrome.","volume":"16","author":"Kiely","year":"2000","journal-title":"Eur Respir J."},{"key":"B6","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1378\/chest.99.4.861","article-title":"Daytime hypertension in obstructive sleep apnea. Prevalence and contributing risk factors.","volume":"99","author":"Millman","year":"1991","journal-title":"Chest."},{"key":"B7","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1164\/ajrccm.157.1.9609063","article-title":"The prevalence of obstructive sleep apnea in hypertensives.","volume":"157","author":"Worsnop","year":"1998","journal-title":"Am J Respir Crit Care Med."},{"key":"B8","article-title":"Medical association expert consensus on clinical diagnosis and treatment of obstructive sleep apnea-related hypertension.","year":"2013","journal-title":"Chin J Pract Intern Med."},{"key":"B9","doi-asserted-by":"publisher","first-page":"1047","DOI":"10.1161\/HYPERTENSIONAHA.120.16378","article-title":"Obstructive sleep apnea-induced neurogenic nocturnal hypertension: a potential role of renal denervation?","volume":"77","author":"Kario","year":"2021","journal-title":"Hypertension."},{"key":"B10","doi-asserted-by":"publisher","DOI":"10.1155\/2017\/1848375","article-title":"Review of and updates on hypertension in obstructive sleep apnea.","volume":"2017","author":"Ahmad","year":"2017","journal-title":"Int J Hypertens."},{"key":"B11","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1038\/s41371-020-00470-8","article-title":"Obstructive sleep apnea and ambulatory blood pressure monitoring: current evidence and research gaps.","volume":"35","author":"Pio-Abreu","year":"2021","journal-title":"J Hum Hypertens."},{"key":"B12","doi-asserted-by":"publisher","first-page":"885","DOI":"10.1097\/HJH.0b013e3282f55049","article-title":"Masked hypertension in obstructive sleep apnea syndrome.","volume":"26","author":"Baguet","year":"2008","journal-title":"J Hypertens."},{"key":"B13","doi-asserted-by":"publisher","first-page":"817","DOI":"10.1378\/chest.125.3.817","article-title":"White coat hypertension in patients with obstructive sleep apnea-hypopnea syndrome.","volume":"125","author":"Garc\u00eda-R\u00edo","year":"2004","journal-title":"Chest."},{"key":"B14","doi-asserted-by":"publisher","first-page":"523","DOI":"10.1183\/09031936.00226711","article-title":"Recommendations for the management of patients with obstructive sleep apnoea and hypertension.","volume":"41","author":"Parati","year":"2013","journal-title":"Eur Respir J."},{"key":"B15","doi-asserted-by":"publisher","first-page":"1579","DOI":"10.1016\/S0140-6736(20)30226-9","article-title":"Artificial intelligence and the future of global health.","volume":"395","author":"Schwalbe","year":"2020","journal-title":"Lancet."},{"key":"B16","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-020-00331-1","article-title":"Machine learning and atherosclerotic cardiovascular disease risk prediction in a multi-ethnic population.","volume":"3","author":"Ward","year":"2020","journal-title":"NPJ Digit Med."},{"key":"B17","author":"Molnar","year":"2020","journal-title":"Interpretable Machine Learning."},{"key":"B18","article-title":"A unified approach to interpreting model predictions.","author":"Lundberg","year":"2017","journal-title":"Advances in Neural Information Processing Systems 30."},{"key":"B19","doi-asserted-by":"publisher","first-page":"1387","DOI":"10.1378\/chest.14-0970","article-title":"International classification of sleep disorders-third edition: highlights and modifications.","volume":"146","author":"Sateia","year":"2014","journal-title":"Chest."},{"key":"B20","doi-asserted-by":"publisher","first-page":"540","DOI":"10.1093\/sleep\/14.6.540","article-title":"A new method for measuring daytime sleepiness: the epworth sleepiness scale.","volume":"14","author":"Johns","year":"1991","journal-title":"Sleep."},{"key":"B21","doi-asserted-by":"publisher","first-page":"p. 785","DOI":"10.1145\/2939672.2939785","article-title":"X: a scalable tree boosting system.","author":"Chen","year":"2016","journal-title":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining-KDD 2016."},{"key":"B22","first-page":"p. 1310","article-title":"A review of supervised machine learning algorithms.","author":"Singh","year":"2016","journal-title":"2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom)."},{"key":"B23","doi-asserted-by":"publisher","first-page":"e262","DOI":"10.1016\/S1470-2045(19)30149-4","article-title":"Big data and machine learning algorithms for health-care delivery.","volume":"20","author":"Ngiam","year":"2019","journal-title":"Lancet Oncol."},{"key":"B24","doi-asserted-by":"publisher","DOI":"10.21037\/atm.2019.03.29","article-title":"Predictive analytics with gradient boosting in clinical medicine.","volume":"7","author":"Zhang","year":"2019","journal-title":"Ann Transl Med."},{"key":"B25","doi-asserted-by":"publisher","first-page":"8761","DOI":"10.1021\/acs.jmedchem.9b01101","article-title":"Interpretation of compound activity predictions from complex machine learning models using local approximations and shapley values.","volume":"63","author":"Rodr\u00edguez-P\u00e9rez","year":"2020","journal-title":"J Med Chem."},{"key":"B26","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1038\/s42256-019-0138-9","article-title":"From local explanations to global understanding with explainable AI for trees.","volume":"2","author":"Lundberg","year":"2020","journal-title":"Nat Mach Intell."},{"key":"B27","doi-asserted-by":"publisher","first-page":"2353","DOI":"10.1021\/acs.jcim.6b00591","article-title":"Extreme gradient boosting as a method for quantitative structure-activity relationships.","volume":"56","author":"Sheridan","year":"2016","journal-title":"J Chem Inf Model."},{"key":"B28","doi-asserted-by":"publisher","first-page":"4169","DOI":"10.21037\/jtd.2019.09.83","article-title":"Risk factors for obstructive sleep apnea-related hypertension in police officers in Southern China.","volume":"11","author":"Pan","year":"2019","journal-title":"J Thorac Dis."},{"key":"B29","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1111\/jch.12645","article-title":"Age, body mass index, and daytime and nocturnal hypoxia as predictors of hypertension in patients with obstructive sleep apnea.","volume":"18","author":"Natsios","year":"2016","journal-title":"J Clin Hypertens."},{"key":"B30","doi-asserted-by":"publisher","DOI":"10.3389\/fneur.2022.956208","article-title":"Associations of sleep apnea risk and oxygen desaturation indices with cerebral small vessel disease burden in patients with stroke.","volume":"13","author":"Liu","year":"2022","journal-title":"Front Neurol."},{"key":"B31","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1007\/s12012-020-09593-3","article-title":"Hypoxemia during sleep and the progression of coronary artery calcium.","volume":"21","author":"Seo","year":"2021","journal-title":"Cardiovasc Toxicol."},{"key":"B32","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1089\/dia.2015.0433","article-title":"Association of diabetic microvascular complications and parameters of obstructive sleep apnea in patients with type 2 diabetes.","volume":"18","author":"Zhang","year":"2016","journal-title":"Diabetes Technol Ther."},{"key":"B33","doi-asserted-by":"publisher","DOI":"10.1007\/s11886-020-1257-y","article-title":"Obstructive sleep apnea, hypertension, and cardiovascular risk: epidemiology, pathophysiology, and management.","volume":"22","author":"Salman","year":"2020","journal-title":"Curr Cardiol Rep."},{"key":"B34","doi-asserted-by":"publisher","DOI":"10.3389\/fendo.2019.00752","article-title":"Sympathetic hyperactivity and sleep disorders in individuals with type 2 diabetes.","volume":"10","author":"L\u00f3pez-Cano","year":"2019","journal-title":"Front Endocrinol."},{"key":"B35","doi-asserted-by":"publisher","first-page":"1378","DOI":"10.1056\/NEJM200005113421901","article-title":"Prospective study of the association between sleep-disordered breathing and hypertension.","volume":"342","author":"Peppard","year":"2000","journal-title":"N Engl J Med."},{"key":"B36","doi-asserted-by":"publisher","first-page":"1159","DOI":"10.1164\/rccm.200712-1809OC","article-title":"Prospective study of sleep-disordered breathing and hypertension: the Sleep Heart Health Study.","volume":"179","author":"O\u2019Connor","year":"2009","journal-title":"Am J Respir Crit Care Med."},{"key":"B37","doi-asserted-by":"publisher","first-page":"1299","DOI":"10.1164\/rccm.201101-0130OC","article-title":"Obstructive sleep apnea and systemic hypertension: longitudinal study in the general population: the Vitoria sleep cohort.","volume":"184","author":"Cano-Pumarega","year":"2011","journal-title":"Am J Respir Crit Care Med."}],"container-title":["Frontiers in Cardiovascular Medicine"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fcvm.2022.1042996\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,5]],"date-time":"2022-12-05T06:02:19Z","timestamp":1670220139000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fcvm.2022.1042996\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,5]]},"references-count":37,"alternative-id":["10.3389\/fcvm.2022.1042996"],"URL":"https:\/\/doi.org\/10.3389\/fcvm.2022.1042996","relation":{},"ISSN":["2297-055X"],"issn-type":[{"value":"2297-055X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,5]]},"article-number":"1042996"}}