{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T16:58:23Z","timestamp":1776445103412,"version":"3.51.2"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,1,31]],"date-time":"2025-01-31T00:00:00Z","timestamp":1738281600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,1,31]],"date-time":"2025-01-31T00:00:00Z","timestamp":1738281600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62171471"],"award-info":[{"award-number":["62171471"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:sec>\n            <jats:title>Background<\/jats:title>\n            <jats:p>Postoperative pulmonary complications (PPCs) following cardiac valvular surgery are characterized by high morbidity, mortality, and economic cost. This study leverages wearable technology and machine learning algorithms to preoperatively identify high-risk individuals, thereby enhancing clinical decision-making for the mitigation of PPCs.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Methods<\/jats:title>\n            <jats:p>A prospective study was conducted at the Department of Cardiovascular Surgery of West China Hospital, Sichuan University, from August 2021 to December 2022. We examined 100 cardiac valvular surgery patients, where wearable technology was utilized to collect and analyze nocturnal physiological data at the 24-hour admission, in conjunction with clinical data extraction from the Hospital Information System\u2019s electronic records. We systematically evaluated three different input types (physiological, clinical, and both) and five classifiers (XGB, LR, RF, SVM, KNN) to identify the combination with strong predictive performance for PPCs. Feature selection was conducted using Recursive Feature Elimination with Cross-Validated (RFECV) for each model, yielding an optimal feature subset for each, followed by a grid search to tune hyperparameters. Stratified 5-fold cross-validation was used to evaluate the generalization performance. The significance of AUC differences between models was tested using the DeLong test to determine the optimal prognostic model comprehensively. Additionally, univariate logistic regression analysis was conducted on the features of the best-performing model to understand the impact of individual feature on PPCs.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>In this study, 22 patients (22%) developed PPCs. Across classifiers, models combining both physiological and clinical features performed better than physiological or clinical features alone. Specifically, including physiological data in the classification model improved AUC, ACC, F1, and precision by an average of 8.32%, 1.80%, 3.28% and 6.06% compared to using clinical data only. The XGB classifier, utilizing both dataset, achieved the highest performance with an AUC of 0.82 (\u00b1\u20090.08) and identified eight significant features. The DeLong test indicated that the XGB model utilizing the both dataset significantly outperformed the XGB models trained on the physiological or clinical datasets alone. Univariate logistic regression analysis suggested that surgical methods, age, nni_50, and min_ven_in_mean are significantly associated with the occurrence of PPCs.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusion<\/jats:title>\n            <jats:p>The integration of continuous wearable physiological and clinical data significantly improves preoperative risk assessment for PPCs, which helps to optimize surgical management and reduce PPCs morbidity and mortality.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/s12911-025-02875-2","type":"journal-article","created":{"date-parts":[[2025,1,31]],"date-time":"2025-01-31T15:54:25Z","timestamp":1738338865000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Exploring the assessment of post-cardiac valve surgery pulmonary complication risks through the integration of wearable continuous physiological and clinical data"],"prefix":"10.1186","volume":"25","author":[{"given":"Lixuan","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuekong","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhicheng","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zeruxin","family":"Luo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiachen","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenqing","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoli","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuqiang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Fan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pengming","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhengbo","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,1,31]]},"reference":[{"key":"2875_CR1","unstructured":"Weissman C. Pulmonary complications after cardiac surgery. in Seminars in cardiothoracic and vascular anesthesia. 2004. Westminster Publications, Inc. 708 Glen Cove Avenue, Glen Head, NY 11545, USA."},{"issue":"24","key":"2875_CR2","doi-asserted-by":"publisher","first-page":"2480","DOI":"10.1001\/jama.2021.2133","volume":"325","author":"LJ Davidson","year":"2021","unstructured":"Davidson LJ, Davidson CJ. Transcatheter treatment of valvular heart disease: a review. JAMA. 2021;325(24):2480\u201394.","journal-title":"JAMA"},{"issue":"3","key":"2875_CR3","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1093\/bja\/aex002","volume":"118","author":"A Miskovic","year":"2017","unstructured":"Miskovic A, Lumb A. Postoperative pulmonary complications. BJA: Br J Anaesth. 2017;118(3):317\u201334.","journal-title":"BJA: Br J Anaesth"},{"issue":"4","key":"2875_CR4","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1007\/s44254-023-00034-2","volume":"1","author":"ME Tuna","year":"2023","unstructured":"Tuna ME, Akg\u00fcn M. Preoperative pulmonary evaluation to prevent postoperative pulmonary complications. Anesthesiology Perioperative Sci. 2023;1(4):34.","journal-title":"Anesthesiology Perioperative Sci"},{"issue":"2","key":"2875_CR5","doi-asserted-by":"publisher","first-page":"242","DOI":"10.1097\/00000658-200008000-00015","volume":"232","author":"AM Arozullah","year":"2000","unstructured":"Arozullah AM, et al. Multifactorial risk index for predicting postoperative respiratory failure in men after major noncardiac surgery. Ann Surg. 2000;232(2):242.","journal-title":"Ann Surg"},{"issue":"10","key":"2875_CR6","doi-asserted-by":"publisher","first-page":"847","DOI":"10.7326\/0003-4819-135-10-200111200-00005","volume":"135","author":"AM Arozullah","year":"2001","unstructured":"Arozullah AM, et al. Development and validation of a multifactorial risk index for predicting postoperative pneumonia after major noncardiac surgery. Ann Intern Med. 2001;135(10):847\u201357.","journal-title":"Ann Intern Med"},{"issue":"1","key":"2875_CR7","first-page":"117","volume":"115","author":"DJ Kor","year":"2011","unstructured":"Kor DJ, et al. Derivation and diagnostic accuracy of the surgical lung injury prediction model. J Am Soc Anesthesiologists. 2011;115(1):117\u201328.","journal-title":"J Am Soc Anesthesiologists"},{"issue":"6","key":"2875_CR8","first-page":"1338","volume":"113","author":"J Canet","year":"2010","unstructured":"Canet J, et al. Prediction of postoperative pulmonary complications in a population-based surgical cohort. J Am Soc Anesthesiologists. 2010;113(6):1338\u201350.","journal-title":"J Am Soc Anesthesiologists"},{"issue":"9","key":"2875_CR9","doi-asserted-by":"publisher","first-page":"691","DOI":"10.1097\/EJA.0000000000000845","volume":"35","author":"AS Neto","year":"2018","unstructured":"Neto AS, et al. The LAS VEGAS risk score for prediction of postoperative pulmonary complications:: an observational study. Eur J Anaesthesiol. 2018;35(9):691.","journal-title":"Eur J Anaesthesiol"},{"issue":"3","key":"2875_CR10","doi-asserted-by":"publisher","first-page":"e212240","DOI":"10.1001\/jamanetworkopen.2021.2240","volume":"4","author":"B Xue","year":"2021","unstructured":"Xue B, et al. Use of machine learning to develop and evaluate models using preoperative and intraoperative data to identify risks of postoperative complications. JAMA Netw open. 2021;4(3):e212240\u2013212240.","journal-title":"JAMA Netw open"},{"issue":"1","key":"2875_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12931-021-01690-3","volume":"22","author":"C Chen","year":"2021","unstructured":"Chen C, et al. Development and performance assessment of novel machine learning models to predict pneumonia after liver transplantation. Respir Res. 2021;22(1):1\u201312.","journal-title":"Respir Res"},{"issue":"6","key":"2875_CR12","doi-asserted-by":"publisher","first-page":"1105","DOI":"10.1038\/s41591-021-01339-0","volume":"27","author":"J Dunn","year":"2021","unstructured":"Dunn J, et al. Wearable sensors enable personalized predictions of clinical laboratory measurements. Nat Med. 2021;27(6):1105\u201312.","journal-title":"Nat Med"},{"issue":"8","key":"2875_CR13","doi-asserted-by":"publisher","first-page":"581","DOI":"10.1038\/s41569-021-00522-7","volume":"18","author":"K Bayoumy","year":"2021","unstructured":"Bayoumy K, et al. Smart wearable devices in cardiovascular care: where we are and how to move forward. Nat Reviews Cardiol. 2021;18(8):581\u201399.","journal-title":"Nat Reviews Cardiol"},{"issue":"8","key":"2875_CR14","doi-asserted-by":"publisher","first-page":"2344","DOI":"10.1053\/j.jvca.2021.12.024","volume":"36","author":"M-O Fischer","year":"2022","unstructured":"Fischer M-O, et al. Postoperative pulmonary complications after cardiac surgery: the VENICE International Cohort Study. J Cardiothorac Vasc Anesth. 2022;36(8):2344\u201351.","journal-title":"J Cardiothorac Vasc Anesth"},{"issue":"2","key":"2875_CR15","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1016\/j.bpa.2020.04.011","volume":"34","author":"D Chandler","year":"2020","unstructured":"Chandler D, et al. Perioperative strategies for the reduction of postoperative pulmonary complications. Best Pract Res Clin Anaesthesiol. 2020;34(2):153\u201366.","journal-title":"Best Pract Res Clin Anaesthesiol"},{"issue":"1","key":"2875_CR16","doi-asserted-by":"publisher","first-page":"e50983","DOI":"10.2196\/50983","volume":"11","author":"T Lee","year":"2023","unstructured":"Lee T, et al. Accuracy of 11 wearable, nearable, and airable consumer sleep trackers: prospective multicenter validation study. JMIR mHealth uHealth. 2023;11(1):e50983.","journal-title":"JMIR mHealth uHealth"},{"issue":"11","key":"2875_CR17","doi-asserted-by":"publisher","first-page":"1268","DOI":"10.1111\/anae.15834","volume":"77","author":"P Chan","year":"2022","unstructured":"Chan P, et al. Novel wearable and contactless heart rate, respiratory rate, and oxygen saturation monitoring devices: a systematic review and meta-analysis. Anaesthesia. 2022;77(11):1268\u201380.","journal-title":"Anaesthesia"},{"issue":"20","key":"2875_CR18","doi-asserted-by":"publisher","first-page":"2191","DOI":"10.1001\/jama.2013.281053","volume":"310","author":"WM Association","year":"2013","unstructured":"Association WM. World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects. JAMA. 2013;310(20):2191\u20134.","journal-title":"JAMA"},{"issue":"1","key":"2875_CR19","first-page":"121","volume":"36","author":"D Cao","year":"2019","unstructured":"Cao D, et al. Design and preliminary validation of a ubiquitous and wearable physiological monitoring system. J Biomed Eng. 2019;36(1):121\u201330.","journal-title":"J Biomed Eng"},{"key":"2875_CR20","doi-asserted-by":"crossref","unstructured":"Wang Z et al. Development and Validation of Algorithms for Sleep Stage Classification and Sleep Apnea\/Hypopnea Event Detection Using a Medical-Grade Wearable Physiological Monitoring System. in International Conference on Wireless Mobile Communication and Healthcare. 2021. Springer.","DOI":"10.1007\/978-3-031-06368-8_12"},{"key":"2875_CR21","doi-asserted-by":"publisher","first-page":"887954","DOI":"10.3389\/fphys.2022.887954","volume":"13","author":"J Wang","year":"2022","unstructured":"Wang J, et al. Predicting adverse events during six-minute walk test using continuous physiological signals. Front Physiol. 2022;13:887954.","journal-title":"Front Physiol"},{"issue":"5","key":"2875_CR22","doi-asserted-by":"publisher","first-page":"1158","DOI":"10.1016\/j.ejcts.2009.12.011","volume":"37","author":"JC Reeve","year":"2010","unstructured":"Reeve JC, et al. Does physiotherapy reduce the incidence of postoperative pulmonary complications following pulmonary resection via open thoracotomy? A preliminary randomised single-blind clinical trial. Eur J Cardiothorac Surg. 2010;37(5):1158\u201366.","journal-title":"Eur J Cardiothorac Surg"},{"issue":"12","key":"2875_CR23","doi-asserted-by":"publisher","first-page":"1157","DOI":"10.1109\/TBME.1986.325695","volume":"33","author":"PS Hamilton","year":"1986","unstructured":"Hamilton PS, Tompkins WJ. Quantitative investigation of QRS detection rules using the MIT\/BIH arrhythmia database. IEEE Trans Bio Med Eng. 1986;33(12):1157\u201365.","journal-title":"IEEE Trans Bio Med Eng"},{"issue":"9","key":"2875_CR24","doi-asserted-by":"publisher","first-page":"094001","DOI":"10.1088\/1361-6579\/aad7e6","volume":"39","author":"D Khodadad","year":"2018","unstructured":"Khodadad D, et al. Optimized breath detection algorithm in electrical impedance tomography. Physiol Meas. 2018;39(9):094001.","journal-title":"Physiol Meas"},{"issue":"8","key":"2875_CR25","doi-asserted-by":"publisher","first-page":"e25415","DOI":"10.2196\/25415","volume":"9","author":"H Xu","year":"2021","unstructured":"Xu H, et al. Assessing electrocardiogram and respiratory signal quality of a wearable device (sensecho): semisupervised machine learning-based validation study. JMIR mHealth uHealth. 2021;9(8):e25415.","journal-title":"JMIR mHealth uHealth"},{"issue":"8","key":"2875_CR26","first-page":"5","volume":"6","author":"ZA Denu","year":"2015","unstructured":"Denu ZA, et al. Postoperative pulmonary complications and associated factors among surgical patients. J Anesth Clin Res. 2015;6(8):5.","journal-title":"J Anesth Clin Res"},{"issue":"11","key":"2875_CR27","doi-asserted-by":"publisher","first-page":"1578","DOI":"10.7150\/ijms.6904","volume":"10","author":"Q Ji","year":"2013","unstructured":"Ji Q, et al. Risk factors for pulmonary complications following cardiac surgery with cardiopulmonary bypass. Int J Med Sci. 2013;10(11):1578.","journal-title":"Int J Med Sci"},{"issue":"5","key":"2875_CR28","doi-asserted-by":"publisher","first-page":"792","DOI":"10.1038\/s41591-019-0414-6","volume":"25","author":"SM Sch\u00fcssler-Fiorenza Rose","year":"2019","unstructured":"Sch\u00fcssler-Fiorenza Rose SM, et al. A longitudinal big data approach for precision health. Nat Med. 2019;25(5):792\u2013804.","journal-title":"Nat Med"},{"key":"2875_CR29","doi-asserted-by":"publisher","first-page":"205520762311876","DOI":"10.1177\/20552076231187605","volume":"9","author":"T Dong","year":"2023","unstructured":"Dong T, et al. Cardiac surgery risk prediction using ensemble machine learning to incorporate legacy risk scores: a benchmarking study. Digit Health. 2023;9:20552076231187605.","journal-title":"Digit Health"},{"key":"2875_CR30","doi-asserted-by":"publisher","first-page":"m540","DOI":"10.1136\/bmj.m540","volume":"368","author":"PM Odor","year":"2020","unstructured":"Odor PM, et al. Perioperative interventions for prevention of postoperative pulmonary complications: systematic review and meta-analysis. BMJ. 2020;368:m540.","journal-title":"BMJ"},{"issue":"1","key":"2875_CR31","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1016\/S0749-0690(02)00065-4","volume":"19","author":"JE Sevransky","year":"2003","unstructured":"Sevransky JE, Haponik EF. Respiratory failure in elderly patients. Clin Geriatr Med. 2003;19(1):205\u201324.","journal-title":"Clin Geriatr Med"},{"key":"2875_CR32","doi-asserted-by":"publisher","first-page":"904961","DOI":"10.3389\/fcvm.2022.904961","volume":"9","author":"P-M Yu","year":"2022","unstructured":"Yu P-M, et al. Postoperative pulmonary complications in patients with transcatheter tricuspid valve implantation\u2014implications for physiotherapists. Front Cardiovasc Med. 2022;9:904961.","journal-title":"Front Cardiovasc Med"},{"issue":"1","key":"2875_CR33","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1111\/jocs.14355","volume":"35","author":"ER Winkelmann","year":"2020","unstructured":"Winkelmann ER, et al. Preoperative expiratory and inspiratory muscle weakness to predict postoperative outcomes in patients undergoing elective cardiac surgery. J Card Surg. 2020;35(1):128\u201334.","journal-title":"J Card Surg"},{"issue":"5","key":"2875_CR34","doi-asserted-by":"publisher","first-page":"426","DOI":"10.1177\/0269215514545350","volume":"29","author":"CM Mans","year":"2015","unstructured":"Mans CM, Reeve JC, Elkins MR. Postoperative outcomes following preoperative inspiratory muscle training in patients undergoing cardiothoracic or upper abdominal surgery: a systematic review and meta analysis. Clin Rehabil. 2015;29(5):426\u201338.","journal-title":"Clin Rehabil"},{"issue":"10","key":"2875_CR35","doi-asserted-by":"publisher","first-page":"1020","DOI":"10.33963\/KP.a2022.0173","volume":"80","author":"\u0141 Kali\u0144czuk","year":"2022","unstructured":"Kali\u0144czuk \u0141, et al. Prognostic value of computed tomography derived measurements of pulmonary artery diameter for long-term outcomes after transcatheter aortic valve replacement. Kardiologia Polska (Polish Heart Journal). 2022;80(10):1020\u20136.","journal-title":"Kardiologia Polska (Polish Heart Journal)"},{"issue":"18","key":"2875_CR36","doi-asserted-by":"publisher","first-page":"1788","DOI":"10.1001\/jama.2019.4783","volume":"321","author":"MT Chan","year":"2019","unstructured":"Chan MT, et al. Association of unrecognized obstructive sleep apnea with postoperative cardiovascular events in patients undergoing major noncardiac surgery. JAMA. 2019;321(18):1788\u201398.","journal-title":"JAMA"},{"issue":"10","key":"2875_CR37","doi-asserted-by":"publisher","first-page":"1083","DOI":"10.5664\/jcsm.5076","volume":"11","author":"N Foldvary-Schaefer","year":"2015","unstructured":"Foldvary-Schaefer N, et al. Prevalence of undetected sleep apnea in patients undergoing cardiovascular surgery and impact on postoperative outcomes. J Clin Sleep Med. 2015;11(10):1083\u20139.","journal-title":"J Clin Sleep Med"},{"issue":"9","key":"2875_CR38","doi-asserted-by":"publisher","first-page":"2764","DOI":"10.1016\/j.clnu.2019.12.002","volume":"39","author":"HY Woo","year":"2020","unstructured":"Woo HY, et al. Evaluation of the association between decreased skeletal muscle mass and extubation failure after long-term mechanical ventilation. Clin Nutr. 2020;39(9):2764\u201370.","journal-title":"Clin Nutr"},{"issue":"5","key":"2875_CR39","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1007\/s00408-021-00469-z","volume":"199","author":"J Yu","year":"2021","unstructured":"Yu J, et al. Predictors of successful weaning from noninvasive ventilation in patients with acute exacerbation of chronic obstructive pulmonary disease: a single-center retrospective cohort study. Lung. 2021;199(5):457\u201366.","journal-title":"Lung"},{"issue":"2","key":"2875_CR40","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1378\/chest.117.2.447","volume":"117","author":"G Misuri","year":"2000","unstructured":"Misuri G, et al. Mechanism of CO2 retention in patients with neuromuscular disease. Chest. 2000;117(2):447\u201353.","journal-title":"Chest"},{"issue":"5","key":"2875_CR41","doi-asserted-by":"publisher","first-page":"471","DOI":"10.1016\/j.jcin.2022.01.016","volume":"15","author":"S Kodali","year":"2022","unstructured":"Kodali S, et al. Transfemoral tricuspid valve replacement in patients with tricuspid regurgitation: TRISCEND study 30-day results. Cardiovasc Interventions. 2022;15(5):471\u201380.","journal-title":"Cardiovasc Interventions"},{"key":"2875_CR42","doi-asserted-by":"publisher","first-page":"531","DOI":"10.1007\/s10741-020-10051-z","volume":"26","author":"S Shah","year":"2021","unstructured":"Shah S, et al. Impact of atrial fibrillation on the outcomes of transcatheter mitral valve repair using MitraClip: a systematic review and meta-analysis. Heart Fail Rev. 2021;26:531\u201343.","journal-title":"Heart Fail Rev"}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-025-02875-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12911-025-02875-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-025-02875-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,31]],"date-time":"2025-01-31T15:54:32Z","timestamp":1738338872000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedinformdecismak.biomedcentral.com\/articles\/10.1186\/s12911-025-02875-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,31]]},"references-count":42,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["2875"],"URL":"https:\/\/doi.org\/10.1186\/s12911-025-02875-2","relation":{},"ISSN":["1472-6947"],"issn-type":[{"value":"1472-6947","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,31]]},"assertion":[{"value":"19 August 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 January 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 January 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All participants provided written informed consent. This study was approved by the Ethics Committee of the West China Hospital of Sichuan University, Ethics No.20211023, Clinical Registration No. ChiCTR2100050005 ().","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"This work was done during ZY\u2019s internship at Beijing SensEcho Science and Technology Co., Ltd., Beijing, China, when he was a Ph.D. candidate at University of California, Davis, CA, United states, and now he works in PAII Inc., Palo Alto, CA, United States. The authors declared that the sleep feature calculation algorithm described in this article is covered by a patent (Patent Title: Sleep Apnoea Event Detection Method, Patent Number: ZL 202110167659.6X, Holder: Chinese PLA General Hospital). Zhengbo Zhang is named as inventors on this patent. The remaining authors declared that they have no conflict of interest.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"47"}}