{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,21]],"date-time":"2025-12-21T01:36:54Z","timestamp":1766281014959},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,9,23]],"date-time":"2021-09-23T00:00:00Z","timestamp":1632355200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,9,23]],"date-time":"2021-09-23T00:00:00Z","timestamp":1632355200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Critically ill patients affected by atrial fibrillation are at high risk of adverse events: however, the actual risk stratification models for haemorrhagic and thrombotic events are not validated in a critical care setting. With this paper we aimed to identify, adopting topological data analysis, the risk factors for therapeutic failure (in-hospital death or intensive care unit transfer), the in-hospital occurrence of stroke\/TIA and major bleeding in a cohort of critically ill patients with pre-existing atrial fibrillation admitted to a stepdown unit; to engineer newer prediction models based on machine learning in the same cohort. We selected all medical patients admitted for critical illness and a history of pre-existing atrial fibrillation in the timeframe 01\/01\/2002\u201303\/08\/2007. All data regarding patients\u2019 medical history, comorbidities, drugs adopted, vital parameters and outcomes (therapeutic failure, stroke\/TIA and major bleeding) were acquired from electronic medical records. Risk factors for each outcome were analyzed adopting topological data analysis. Machine learning was used to generate three different predictive models. We were able to identify specific risk factors and to engineer dedicated clinical prediction models for therapeutic failure (AUC: 0.974, 95%CI: 0.934\u20130.975), stroke\/TIA (AUC: 0.931, 95%CI: 0.896\u20130.940; Brier score: 0.13) and major bleeding (AUC: 0.930:0.911\u20130.939; Brier score: 0.09) in critically-ill patients, which were able to predict accurately their respective clinical outcomes. Topological data analysis and machine learning techniques represent a concrete viewpoint for the physician to predict the risk at the patients\u2019 level, aiding the selection of the best therapeutic strategy in critically ill patients affected by pre-existing atrial fibrillation.<\/jats:p>","DOI":"10.1038\/s41598-021-97218-2","type":"journal-article","created":{"date-parts":[[2021,9,23]],"date-time":"2021-09-23T10:57:49Z","timestamp":1632394669000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Risk prediction of clinical adverse outcomes with machine learning in a cohort of critically ill patients with atrial fibrillation"],"prefix":"10.1038","volume":"11","author":[{"given":"Lorenzo","family":"Falsetti","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Matteo","family":"Rucco","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marco","family":"Proietti","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Giovanna","family":"Viticchi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vincenzo","family":"Zaccone","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mattia","family":"Scarponi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Laura","family":"Giovenali","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gianluca","family":"Moroncini","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cinzia","family":"Nitti","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aldo","family":"Salvi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,9,23]]},"reference":[{"key":"97218_CR1","doi-asserted-by":"publisher","first-page":"1424","DOI":"10.1016\/j.chest.2018.03.040","volume":"154","author":"NA Bosch","year":"2018","unstructured":"Bosch, N. A., Cimini, J. & Walkey, A. J. Atrial fibrillation in the ICU. Chest 154, 1424\u20131434 (2018).","journal-title":"Chest"},{"key":"97218_CR2","doi-asserted-by":"publisher","first-page":"2893","DOI":"10.1093\/eurheartj\/ehw210","volume":"37","author":"P Kirchhof","year":"2016","unstructured":"Kirchhof, P. et al. 2016 ESC Guidelines for the management of atrial fibrillation developed in collaboration with EACTS. Eur. Heart J. 37, 2893\u20132962 (2016).","journal-title":"Eur. Heart J."},{"key":"97218_CR3","doi-asserted-by":"publisher","first-page":"619","DOI":"10.1016\/j.cjca.2019.02.009","volume":"35","author":"M Proietti","year":"2019","unstructured":"Proietti, M., Lane, D. A., Boriani, G. & Lip, G. Y. H. Stroke prevention, evaluation of bleeding risk, and anticoagulant treatment management in atrial fibrillation contemporary international guidelines. Can. J. Cardiol. 35, 619\u2013633 (2019).","journal-title":"Can. J. Cardiol."},{"key":"97218_CR4","doi-asserted-by":"publisher","first-page":"693","DOI":"10.1007\/s11739-016-1580-x","volume":"12","author":"G Costantino","year":"2017","unstructured":"Costantino, G. et al. Guidelines on the management of atrial fibrillation in the emergency department: A critical appraisal. Intern. Emerg. Med. 12, 693\u2013703 (2017).","journal-title":"Intern. Emerg. Med."},{"key":"97218_CR5","doi-asserted-by":"publisher","DOI":"10.1111\/eci.13317","author":"L Falsetti","year":"2020","unstructured":"Falsetti, L. et al. Impact of atrial fibrillation in critically-ill patients admitted to a stepdown unit. Eur. J. Clin. Invest. https:\/\/doi.org\/10.1111\/eci.13317 (2020).","journal-title":"Eur. J. Clin. Invest."},{"key":"97218_CR6","doi-asserted-by":"publisher","DOI":"10.1093\/europace\/euy110","author":"G Boriani","year":"2018","unstructured":"Boriani, G. et al. European Heart Rhythm Association (EHRA) consensus document on management of arrhythmias and cardiac electronic devices in the critically ill and post-surgery patient, endorsed by Heart Rhythm Society (HRS), Asia Pacific Heart Rhythm Society (APHRS), Cardiac Arrhythmia Society of Southern Africa (CASSA), and Latin American Heart Rhythm Society (LAHRS). EP Eur. https:\/\/doi.org\/10.1093\/europace\/euy110 (2018).","journal-title":"EP Eur."},{"key":"97218_CR7","doi-asserted-by":"publisher","first-page":"368","DOI":"10.1186\/cc1071","volume":"5","author":"DJ Cook","year":"2001","unstructured":"Cook, D. J. et al. The attribute mortality and length of intensive care unit stay of clinically important gastrointestinal bleeding in critically ill patients. Crit. Care 5, 368\u2013375 (2001).","journal-title":"Crit. Care"},{"key":"97218_CR8","doi-asserted-by":"publisher","first-page":"1524","DOI":"10.1007\/s00134-017-5034-3","volume":"44","author":"S Bailly","year":"2018","unstructured":"Bailly, S., Meyfroidt, G. & Timsit, J.-F. What\u2019s new in ICU in 2050: Big data and machine learning. Intensive Care Med. 44, 1524\u20131527 (2018).","journal-title":"Intensive Care Med."},{"key":"97218_CR9","doi-asserted-by":"publisher","first-page":"770","DOI":"10.3390\/math8050770","volume":"8","author":"M Rucco","year":"2020","unstructured":"Rucco, M., Viticchi, G. & Falsetti, L. Towards personalized diagnosis of glioblastoma in fluid-attenuated inversion recovery (FLAIR) by topological interpretable machine learning. Mathematics 8, 770 (2020).","journal-title":"Mathematics"},{"key":"97218_CR10","doi-asserted-by":"publisher","first-page":"617","DOI":"10.1186\/s13104-015-1554-5","volume":"8","author":"M Rucco","year":"2015","unstructured":"Rucco, M. et al. Neural hypernetwork approach for pulmonary embolism diagnosis. BMC Res. Notes 8, 617 (2015).","journal-title":"BMC Res. Notes"},{"key":"97218_CR11","doi-asserted-by":"publisher","first-page":"e66506","DOI":"10.1371\/journal.pone.0066506","volume":"8","author":"G Petri","year":"2013","unstructured":"Petri, G., Scolamiero, M., Donato, I. & Vaccarino, F. Topological strata of weighted complex networks. PLoS ONE 8, e66506 (2013).","journal-title":"PLoS ONE"},{"key":"97218_CR12","doi-asserted-by":"publisher","first-page":"e0237747","DOI":"10.1371\/journal.pone.0237747","volume":"15","author":"CS Pun","year":"2020","unstructured":"Pun, C. S., Yong, B. Y. S. & Xia, K. Weighted-persistent-homology-based machine learning for RNA flexibility analysis. PLoS ONE 15, e0237747 (2020).","journal-title":"PLoS ONE"},{"key":"97218_CR13","doi-asserted-by":"publisher","first-page":"e0126383","DOI":"10.1371\/journal.pone.0126383","volume":"10","author":"CM Topaz","year":"2015","unstructured":"Topaz, C. M., Ziegelmeier, L. & Halverson, T. Topological data analysis of biological aggregation models. PLoS ONE 10, e0126383 (2015).","journal-title":"PLoS ONE"},{"key":"97218_CR14","doi-asserted-by":"publisher","first-page":"e0213679","DOI":"10.1371\/journal.pone.0213679","volume":"14","author":"M Ulmer","year":"2019","unstructured":"Ulmer, M., Ziegelmeier, L. & Topaz, C. M. A topological approach to selecting models of biological experiments. PLoS ONE 14, e0213679 (2019).","journal-title":"PLoS ONE"},{"key":"97218_CR15","doi-asserted-by":"publisher","first-page":"e0225577","DOI":"10.1371\/journal.pone.0225577","volume":"14","author":"E Cawi","year":"2019","unstructured":"Cawi, E., La Rosa, P. S. & Nehorai, A. Designing machine learning workflows with an application to topological data analysis. PLoS ONE 14, e0225577 (2019).","journal-title":"PLoS ONE"},{"key":"97218_CR16","doi-asserted-by":"publisher","first-page":"104274","DOI":"10.1016\/j.ijmedinf.2020.104274","volume":"143","author":"A Higaki","year":"2020","unstructured":"Higaki, A., Uetani, T., Ikeda, S. & Yamaguchi, O. Co-authorship network analysis in cardiovascular research utilizing machine learning (2009\u20132019). Int. J. Med. Inform. 143, 104274 (2020).","journal-title":"Int. J. Med. Inform."},{"key":"97218_CR17","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1016\/j.jcmg.2018.11.025","volume":"12","author":"G Casaclang-Verzosa","year":"2019","unstructured":"Casaclang-Verzosa, G. et al. Network tomography for understanding phenotypic presentations in aortic stenosis. JACC. Cardiovasc. Imaging 12, 236\u2013248 (2019).","journal-title":"JACC. Cardiovasc. Imaging"},{"key":"97218_CR18","doi-asserted-by":"publisher","first-page":"2118","DOI":"10.1161\/circ.104.17.2118","volume":"104","author":"V Fuster","year":"2001","unstructured":"Fuster, V. et al. ACC\/AHA\/ESC guidelines for the management of patients with atrial fibrillation: Executive summary a report of the American College of Cardiology\/American Heart Association task force on practice guidelines and the European Society of Cardiology Committee. Circulation 104, 2118\u20132150 (2001).","journal-title":"Circulation"},{"key":"97218_CR19","unstructured":"Falsetti, L. AFICILL database. Mendeley Data 4 (2019)."},{"key":"97218_CR20","doi-asserted-by":"publisher","first-page":"2119","DOI":"10.1111\/jth.13140","volume":"13","author":"S Kaatz","year":"2015","unstructured":"Kaatz, S., Ahmad, D., Spyropoulos, A. C. & Schulman, S. Definition of clinically relevant non-major bleeding in studies of anticoagulants in atrial fibrillation and venous thromboembolic disease in non-surgical patients: Communication from the SSC of the ISTH. J. Thromb. Haemost. 13, 2119\u20132126 (2015).","journal-title":"J. Thromb. Haemost."},{"key":"97218_CR21","doi-asserted-by":"publisher","first-page":"818","DOI":"10.1097\/00003246-198510000-00009","volume":"13","author":"WA Knaus","year":"1985","unstructured":"Knaus, W. A., Draper, E. A., Wagner, D. P. & Zimmerman, J. E. APACHE II: A severity of disease classification system. Crit. Care Med. 13, 818\u2013829 (1985).","journal-title":"Crit. Care Med."},{"key":"97218_CR22","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1378\/chest.09-1584","volume":"137","author":"GYH Lip","year":"2010","unstructured":"Lip, G. Y. H., Nieuwlaat, R., Pisters, R., Lane, D. A. & Crijns, H. J. G. M. Refining clinical risk stratification for predicting stroke and thromboembolism in atrial fibrillation using a novel risk factor-based approach. Chest 137, 263\u2013272 (2010).","journal-title":"Chest"},{"key":"97218_CR23","doi-asserted-by":"publisher","first-page":"1093","DOI":"10.1378\/chest.10-0134","volume":"138","author":"R Pisters","year":"2010","unstructured":"Pisters, R. et al. A novel user-friendly score (HAS-BLED) to assess 1-year risk of major bleeding in patients with atrial fibrillation: The Euro heart survey. Chest 138, 1093\u20131100 (2010).","journal-title":"Chest"},{"key":"97218_CR24","doi-asserted-by":"publisher","unstructured":"Saul, N. & van Veen, H. J. MLWave\/kepler-mapper: 186f. https:\/\/kepler-mapper.scikit-tda.org\/index.html# (2017). https:\/\/doi.org\/10.5281\/ZENODO.1054444","DOI":"10.5281\/ZENODO.1054444"},{"key":"97218_CR25","unstructured":"Ester, Martin; Kriegel, Hans-Peter; Sander, J\u00f6rg; Xu, X. A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining 226\u2013231 (1996)."},{"key":"97218_CR26","doi-asserted-by":"crossref","unstructured":"Chen, T. & Guestrin, C. XGBoost: A scalable tree boosting system. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 13\u201317-August-2016, 785\u2013794 (Association for Computing Machinery, 2016).","DOI":"10.1145\/2939672.2939785"},{"key":"97218_CR27","doi-asserted-by":"publisher","DOI":"10.6092\/unibo\/amsdottorato\/8767","author":"L Falsetti","year":"2019","unstructured":"Falsetti, L. AFICILL: A single-cohort, retrospective study on atrial fibrillation in critically ILL patients admitted to a medical sub-intensive care unit: implications for clinical management, outcomes and elaboration of new data-driven models. AMS Dottorato Univ. Bologna https:\/\/doi.org\/10.6092\/unibo\/amsdottorato\/8767 (2019).","journal-title":"AMS Dottorato Univ. Bologna"},{"key":"97218_CR28","first-page":"2826","volume":"12","author":"F Pedregosa FABIANPEDREGOSA","year":"2011","unstructured":"Pedregosa FABIANPEDREGOSA, F. et al. Scikit-learn: Machine learning in Python Ga\u00ebl Varoquaux Bertrand Thirion Vincent Dubourg Alexandre Passos PEDREGOSA, VAROQUAUX, GRAMFORT ET AL. Matthieu Perrot. J. Mach. Learn. Res. 12, 2826\u20132830 (2011).","journal-title":"J. Mach. Learn. Res."},{"key":"97218_CR29","doi-asserted-by":"crossref","unstructured":"Fr\u00f6hlich, H. & Zell, A. Efficient parameter selection for support vector machines in classification and regression via model-based global optimization. In Proceedings of the International Joint Conference on Neural Networks 3, 1431\u20131436 (Institute of Electrical and Electronics Engineers Inc., 2005).","DOI":"10.1109\/IJCNN.2005.1556085"},{"key":"97218_CR30","doi-asserted-by":"publisher","first-page":"2131","DOI":"10.1109\/TCBB.2019.2911071","volume":"17","author":"A Ogunleye","year":"2020","unstructured":"Ogunleye, A. & Wang, Q.-G. XGBoost model for chronic kidney disease diagnosis. IEEE\/ACM Trans. Comput. Biol. Bioinform. 17, 2131\u20132140 (2020).","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform."},{"key":"97218_CR31","volume-title":"XGBoost with Python: Gradient Boosted Trees with XGBoost and Scikit-Learn","author":"J Brownlee","year":"2019","unstructured":"Brownlee, J. XGBoost with Python: Gradient Boosted Trees with XGBoost and Scikit-Learn (Machine Learning Mastery, 2019)."},{"key":"97218_CR32","doi-asserted-by":"publisher","first-page":"839","DOI":"10.1148\/radiology.148.3.6878708","volume":"148","author":"JA Hanley","year":"1983","unstructured":"Hanley, J. A. & McNeil, B. J. A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology 148, 839\u2013843 (1983).","journal-title":"Radiology"},{"key":"97218_CR33","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-28954-6","volume-title":"Explainable AI: Interpreting, Explaining and Visualizing Deep Learning","author":"W Samek","year":"2019","unstructured":"Samek, W., Montavon, G., Vedaldi, A., Hansen, L. K. & M\u00fcller, K.-R. Explainable AI: Interpreting, Explaining and Visualizing Deep Learning (Springer, 2019)."},{"key":"97218_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/cc6810","volume":"12","author":"D Cook","year":"2008","unstructured":"Cook, D. et al. Venous thromboembolism and bleeding in critically ill patients with severe renal insufficiency receiving dalteparin thromboprophylaxis: Prevalence, incidence and risk factors. Crit. Care 12, 1\u20139 (2008).","journal-title":"Crit. Care"},{"key":"97218_CR35","first-page":"840615","volume":"2014","author":"M Arrigo","year":"2014","unstructured":"Arrigo, M., Bettex, D. & Rudiger, A. Management of atrial fibrillation in critically ill patients. Crit. Care Res Pr. 2014, 840615 (2014).","journal-title":"Crit. Care Res Pr."},{"key":"97218_CR36","doi-asserted-by":"publisher","first-page":"358","DOI":"10.1002\/clc.23147","volume":"42","author":"F Vitali","year":"2019","unstructured":"Vitali, F. et al. CHA2DS2-VASc score predicts atrial fibrillation recurrence after cardioversion: Systematic review and individual patient pooled meta-analysis. Clin. Cardiol. 42, 358\u2013364 (2019).","journal-title":"Clin. Cardiol."},{"key":"97218_CR37","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1016\/j.ahj.2016.08.011","volume":"181","author":"M Proietti","year":"2016","unstructured":"Proietti, M. et al. Impact of chronic obstructive pulmonary disease on prognosis in atrial fibrillation: A report from the EURObservational Research Programme Pilot Survey on Atrial Fibrillation (EORP-AF) General Registry. Am. Heart J. 181, 83\u201391 (2016).","journal-title":"Am. Heart J."},{"key":"97218_CR38","doi-asserted-by":"publisher","first-page":"1314","DOI":"10.2215\/CJN.04060318","volume":"13","author":"JJ Carrero","year":"2018","unstructured":"Carrero, J. J. et al. Incident atrial fibrillation and the risk of stroke in adults with chronic kidney disease: The Stockholm CREAtinine measurements (SCREAM) project. Clin. J. Am. Soc. Nephrol. 13, 1314\u20131320 (2018).","journal-title":"Clin. J. Am. Soc. Nephrol."},{"key":"97218_CR39","doi-asserted-by":"publisher","unstructured":"Subat, Y. et al. Risk of major bleeding associated with aspirin use in critically ill medical patients receiving therapeutic anticoagulation. In A2477\u2013A2477 (American Thoracic Society, 2019). https:\/\/doi.org\/10.1164\/ajrccm-conference.2019.199.1_meetingabstracts.a2477","DOI":"10.1164\/ajrccm-conference.2019.199.1_meetingabstracts.a2477"},{"key":"97218_CR40","doi-asserted-by":"publisher","first-page":"2171","DOI":"10.1055\/s-0038-1675400","volume":"118","author":"ED Borre","year":"2018","unstructured":"Borre, E. D. et al. Predicting thromboembolic and bleeding event risk in patients with non-valvular atrial fibrillation: A systematic review. Thromb. Haemost. 118, 2171\u20132187 (2018).","journal-title":"Thromb. Haemost."},{"key":"97218_CR41","doi-asserted-by":"publisher","first-page":"2014","DOI":"10.1055\/s-0038-1676074","volume":"118","author":"M Proietti","year":"2018","unstructured":"Proietti, M., Mujovic, N. & Potpara, T. S. Optimizing stroke and bleeding risk assessment in patients with atrial fibrillation: A balance of evidence, practicality and precision. Thromb. Haemost. 118, 2014\u20132017 (2018).","journal-title":"Thromb. Haemost."},{"key":"97218_CR42","unstructured":"High-Level Expert Group on Artificial Intelligence. Ethics Guidelines for Thrustworthy AI. (Accessed 21 August 2021); https:\/\/ai.bsa.org\/wp-content\/uploads\/2019\/09\/AIHLEG_EthicsGuidelinesforTrustworthyAI-ENpdf.pdf (2019)."},{"key":"#cr-split#-97218_CR43.1","unstructured":"Grimme, T. & Hohma, E. The use of AI to analyze process-based data in hospitals: Opportunities, limits and ethical considerations. (Accessed 21 August 2021)"},{"key":"#cr-split#-97218_CR43.2","unstructured":"https:\/\/ieai.mcts.tum.de\/wp-content\/uploads\/2021\/06\/ResearchBrief_June2021_Useof-AI-Prozess-Data-in-Hospitals_FINAL.pdf (2021)."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-021-97218-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-021-97218-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-021-97218-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,8]],"date-time":"2024-09-08T18:59:25Z","timestamp":1725821965000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-021-97218-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,23]]},"references-count":44,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["97218"],"URL":"https:\/\/doi.org\/10.1038\/s41598-021-97218-2","relation":{},"ISSN":["2045-2322"],"issn-type":[{"type":"electronic","value":"2045-2322"}],"subject":[],"published":{"date-parts":[[2021,9,23]]},"assertion":[{"value":"29 March 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 August 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 September 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"18925"}}