{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T04:20:12Z","timestamp":1772166012480,"version":"3.50.1"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T00:00:00Z","timestamp":1756857600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T00:00:00Z","timestamp":1756857600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Universit\u00e4tsklinikum Augsburg"}],"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 and Objectives<\/jats:title>\n                    <jats:p>\n                      Brain tissue oxygenation is usually inferred from arterial partial pressure of oxygen (paO\n                      <jats:sub>2<\/jats:sub>\n                      ), which is in turn often inferred from pulse oximetry measurements or other non-invasive proxies. Our aim was to evaluate the feasibility of continuous paO\n                      <jats:sub>2<\/jats:sub>\n                      prediction in an intraoperative setting among neurosurgical patients undergoing craniotomies with modern machine learning methods.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Data from routine clinical care of lung-healthy neurosurgical patients were extracted from databases of the respective clinical systems and normalized. We used recursive feature elimination to identify relevant features for the prediction of paO\n                      <jats:sub>2<\/jats:sub>\n                      . Six machine learning regression algorithms (gradient boosting, k-nearest neighbors, random forest, support vector, neural network, linear model with stochastic gradient descent) and a multivariable linear regression were then tuned and fitted to the selected features. A performance matrix consisting of standard deviation of absolute errors (\n                      <jats:bold>\u03c3<\/jats:bold>\n                      <jats:sub>ae<\/jats:sub>\n                      ), mean absolute percentage error (MAPE), adjusted R\n                      <jats:sup>2<\/jats:sup>\n                      , root mean squared error (RMSE), mean absolute error (MAE) and Spearman\u2019s\n                      <jats:bold>\u03c1<\/jats:bold>\n                      was finally computed based on the test set, and used to compare and rank each algorithm.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      We analyzed\n                      <jats:italic>N<\/jats:italic>\n                      \u2009=\u20094,581 patients with\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200917,821 observations. Between 5 and 22 features were selected from the analysis of the training dataset comprising 3,436 patients with 13,257 observations. The best algorithm, a regularized linear model with stochastic gradient descent, could predict paO\n                      <jats:sub>2<\/jats:sub>\n                      values with \u03c3\n                      <jats:sub>ae<\/jats:sub>\n                      \u2009=\u200986.4\u2009mmHg, MAPE\u2009=\u200916 %, adjusted R\n                      <jats:sup>2<\/jats:sup>\n                      \u2009=\u20090.77, RMSE\u2009=\u200944\u2009mmHg and Spearman\u2019s \u03c1\u2009=\u20090.83. Further improvement was possible by calibrating the algorithm with the first measured paO\n                      <jats:sub>2<\/jats:sub>\n                      \/FiO\n                      <jats:sub>2<\/jats:sub>\n                      (p\/F) ratio during surgery.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>\n                      PaO\n                      <jats:sub>2<\/jats:sub>\n                      can be predicted by perioperative routine data in neurosurgical patients even before blood gas analysis. The prediction improves further when including the first measured p\/F ratio, realizing quasi-continuous paO\n                      <jats:sub>2<\/jats:sub>\n                      monitoring.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12911-025-03148-8","type":"journal-article","created":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T03:59:43Z","timestamp":1756871983000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Comparing supervised machine learning algorithms for the prediction of partial arterial pressure of oxygen during craniotomy"],"prefix":"10.1186","volume":"25","author":[{"given":"Andrea S.","family":"Gutmann","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maximilian M.","family":"Mandl","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Clemens","family":"Rieder","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dominik J.","family":"Hoechter","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Konstantin","family":"Dietz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Benjamin P.","family":"Geisler","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anne-Laure","family":"Boulesteix","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Roland","family":"Tomasi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ludwig C.","family":"Hinske","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,3]]},"reference":[{"key":"3148_CR1","doi-asserted-by":"crossref","unstructured":"Busl KM, Greer DM. Hypoxic-ischemic brain injury: Pathophysiology, neuropathology and mechanisms. NeuroRehabilitation. 2010;26:5\u201313.","DOI":"10.3233\/NRE-2010-0531"},{"key":"3148_CR2","doi-asserted-by":"crossref","unstructured":"Mach WJ, Thimmesch AR, Pierce JT, Pierce JD. Consequences of hyperoxia and the toxicity of oxygen in the lung. Nurs Res Pract. 2011;2011, 260482 PMC3169834.","DOI":"10.1155\/2011\/260482"},{"key":"3148_CR3","doi-asserted-by":"crossref","unstructured":"McIlroy, et al. Oxygen administration during surgery and postoperative organ injury: observational cohort study. BMJ. 2022;379:e070941. PMC9710248.","DOI":"10.1136\/bmj-2022-070941"},{"key":"3148_CR4","doi-asserted-by":"crossref","unstructured":"Weenink, et al. Perioperative hyperoxyphobia: Justified or not? benefits and harms of hyperoxia during surgery. J Clin Med Res. 2020;9. PMC7141263.","DOI":"10.3390\/jcm9030642"},{"key":"3148_CR5","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1097\/ALN.0000000000002181","volume":"129","author":"Suzuki","year":"2018","unstructured":"Suzuki, et al. Current ventilator and oxygen management during general anesthesia: a multileft, cross-sectional observational study. Anesthesiology. 2018;129:67\u201376.","journal-title":"Anesthesiology"},{"key":"3148_CR6","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1186\/s13613-015-0084-6","volume":"5","author":"S Hafner","year":"2015","unstructured":"Hafner S, Beloncle F, Koch A, Radermacher P, Asfar P. Hyperoxia in intensive care, emergency, and peri-operative medicine: Dr. jekyll or mr. hyde? a 2015 update. Ann Intensive Care. 2015;5:42). PMC4653126.","journal-title":"Ann Intensive Care"},{"key":"3148_CR7","first-page":"73","volume":"13","author":"WA Altemeier","year":"2007","unstructured":"Altemeier WA, Sinclair SE. Hyperoxia in the intensive care unit: Why more is not always better. Curr Opin Crit Care. 2007;13:73\u201378.","journal-title":"Curr Opin Crit Care"},{"key":"3148_CR8","doi-asserted-by":"publisher","first-page":"73","DOI":"10.12968\/hmed.2005.66.2.17548","volume":"66","author":"M Singer","year":"2005","unstructured":"Singer M. Give oxygen, get a blood pressure\u2026 but don\u2019t overdo it. Br J Hosp Med. 2005;66:73\u201375.","journal-title":"Br J Hosp Med"},{"key":"3148_CR9","doi-asserted-by":"crossref","unstructured":"Bitterman H. Bench-to-bedside review: Oxygen as a drug. critical care \/ the society of critical care medicine. 2009;13:205). PMC2688103.","DOI":"10.1186\/cc7151"},{"key":"3148_CR10","doi-asserted-by":"publisher","first-page":"S559","DOI":"10.1097\/CCM.0b013e3181f1fe70","volume":"38","author":"E Calzia","year":"2010","unstructured":"Calzia E, et al. Hyperoxia may be beneficial. Crit Care Med. 2010;38:S559\u201368.","journal-title":"Crit Care Med"},{"key":"3148_CR11","doi-asserted-by":"publisher","first-page":"1317","DOI":"10.1097\/CCM.0000000000002514","volume":"45","author":"Brown","year":"2017","unstructured":"Brown, et al. Nonlinear imputation of pao2\/fio2 from spo2\/fio2 among mechanically ventilated patients in the icu: A prospective, observational study. Crit Care Med. 2017;45:1317\u201324). PMC5511089.","journal-title":"Crit Care Med"},{"key":"3148_CR12","doi-asserted-by":"publisher","first-page":"999","DOI":"10.1007\/s10877-016-9941-6","volume":"31","author":"Bou-Khalil","year":"2017","unstructured":"Bou-Khalil, et al. Prediction of inspired oxygen fraction for targeted arterial oxygen tension following open heart surgery in non-smoking and smoking patients. J Clin Monit Comput. 2017;31:999\u20131008.","journal-title":"J Clin Monit Comput"},{"key":"3148_CR13","doi-asserted-by":"publisher","first-page":"813","DOI":"10.1111\/resp.12560","volume":"20","author":"F Sanz","year":"2015","unstructured":"Sanz F, et al. Accuracy of pao2 \/fio2 calculated from spo2 for severity assessment in ed patients with pneumonia. Respirology. 2015;20:813\u201318.","journal-title":"Respirology"},{"key":"3148_CR14","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1177\/026765919400900108","volume":"9","author":"DW Fried","year":"1994","unstructured":"Fried DW, Mohamed H. Use of the oxygen transfer slope and estimated membrane oxygen transfer to predict pao2. Perfusion. 1994;9:49\u201355.","journal-title":"Perfusion"},{"key":"3148_CR15","unstructured":"Sharma S, Hashmi MF, Burns B. Alveolar gas equation. (StatPearls (StatPearls Publishing, Treasure Island (FL), 2019). https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/29489223"},{"key":"3148_CR16","first-page":"246","volume":"21","author":"H Bahmani Bohloli","year":"2018","unstructured":"Bahmani Bohloli H, et al. Prediction of arterial blood gas factors from venous blood gas factors in intensive care unit admitted patients. Arch Iran Med. 2018;21:246\u201350. https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/29940743","journal-title":"Arch Iran Med"},{"key":"3148_CR17","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1093\/bja\/aew261","volume":"117","author":"T Boulain","year":"2016","unstructured":"Boulain T, et al. Predicting arterial blood gas and lactate from central venous blood analysis in critically ill patients: A multicentre, prospective, diagnostic accuracy study. Br J Anaesth. 2016;117:341\u201349.","journal-title":"Br J Anaesth"},{"key":"3148_CR18","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1378\/chest.79.3.311","volume":"79","author":"R Gross","year":"1981","unstructured":"Gross R, Israel RH. A graphic approach for prediction of arterial oxygen tension at different concentrations of inspired oxygen. Chest. 1981;79:311\u201315.","journal-title":"Chest"},{"key":"3148_CR19","doi-asserted-by":"publisher","first-page":"180","DOI":"10.4103\/2228-7477.120993","volume":"3","author":"K Tavakol","year":"2013","unstructured":"Tavakol K, Ghahramanpoori B, Fararouei M. Prediction of arterial blood Ph and partial pressure of carbon dioxide from venous blood samples in patients receiving Mechanical ventilation. J Med Signals And Sensors. 2013;3:180\u201384. https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/24672766. PMC3959008","journal-title":"J Med Signals And Sensors"},{"key":"3148_CR20","doi-asserted-by":"publisher","first-page":"483","DOI":"10.1007\/s10916-009-9384-4","volume":"35","author":"MR Raoufy","year":"2011","unstructured":"Raoufy MR, Eftekhari P, Gharibzadeh S, Masjedi MR. Predicting arterial blood gas values from venous samples in patients with acute exacerbation chronic obstructive pulmonary disease using artificial neural network. J Educ Chang Medical Systems. 2011;35:483\u201388.","journal-title":"J Educ Chang Medical Systems"},{"key":"3148_CR21","doi-asserted-by":"publisher","first-page":"476","DOI":"10.1016\/j.wneu.2017.09.149","volume":"109","author":"JT Senders","year":"2018","unstructured":"Senders JT, et al. Machine learning and neurosurgical outcome prediction: a systematic review. World Neurosurg. 2018;109:476\u201386.e1.","journal-title":"World Neurosurg"},{"key":"3148_CR22","doi-asserted-by":"publisher","first-page":"285","DOI":"10.4097\/kja.20124","volume":"73","author":"D Chae","year":"2020","unstructured":"Chae D. Data science and machine learning in anesthesiology. Korean J Anesthesiol. 2020;73:285\u201395. PMC7403106.","journal-title":"Korean J Anesthesiol"},{"key":"3148_CR23","doi-asserted-by":"publisher","first-page":"1263","DOI":"10.1161\/STROKEAHA.118.024293","volume":"50","author":"Heo","year":"2019","unstructured":"Heo, et al. Machine learning-based model for prediction of outcomes in acute stroke. Stroke; a J Cereb Circ. 2019;50:1263\u201365.","journal-title":"Stroke; a J Cereb Circ"},{"key":"3148_CR24","first-page":"744","volume":"115","author":"J Stausberg","year":"1998","unstructured":"Stausberg J. Classification of surgical interventions in Germany. Langenbecks Arch Chir Suppl Kongressbd. 1998;115:744\u201347. https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/9931716","journal-title":"Langenbecks Arch Chir Suppl Kongressbd"},{"key":"3148_CR25","doi-asserted-by":"publisher","DOI":"10.4324\/9780203774441","volume-title":"Applied multiple Regression\/Correlation analysis for the behavioral sciences","author":"J Cohen","year":"2013","unstructured":"Cohen J, Cohen, West SG, Aiken LS. Applied multiple Regression\/Correlation analysis for the behavioral sciences. Routledge; 2013."},{"key":"3148_CR26","doi-asserted-by":"publisher","DOI":"10.4324\/9780203771587","volume-title":"Statistical Power analysis for the behavioral sciences","author":"J Cohen","year":"2013","unstructured":"Cohen J. Statistical Power analysis for the behavioral sciences. Routledge; 2013. https:\/\/play.google.com\/store\/books\/details?id=cIJH0lR33bgC"},{"key":"3148_CR27","doi-asserted-by":"crossref","unstructured":"Al Enezi F, et al. Arterial and venous estimation of hemoglobin: A correlational study. journal of hematology (Brossard, Quebec). 2015;4:187\u201392.","DOI":"10.14740\/jh224e"},{"key":"3148_CR28","unstructured":"Theodore AC. Venous blood gases and alternatives to arterial carbon dioxide measurement in adults. 2025\/1\/26. https:\/\/www.uptodate.com\/contents\/venous-blood-gases-and-alternatives-to-arterial-carbon-dioxide-measurement-in-adults?csi=386454e5-8e9a-4cb4-a42a-5b3030f2c6fb&26source=contentShare"},{"key":"3148_CR29","volume-title":"Pulmonary compliance","author":"JP Desai","year":"2019","unstructured":"Desai JP, Moustarah F. Pulmonary compliance. StatPearls (StatPearls Publishing, Treasure Island (FL); 2019. https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/30855908"},{"key":"3148_CR30","doi-asserted-by":"crossref","unstructured":"Gadrey, et al. Imputation of partial pressures of arterial oxygen using oximetry and its impact on sepsis diagnosis. Physiol Meas. 2019;40, 115008.","DOI":"10.1088\/1361-6579\/ab5154"},{"key":"3148_CR31","doi-asserted-by":"publisher","first-page":"1198","DOI":"10.1080\/01621459.1988.10478722","volume":"83","author":"RJA Little","year":"1988","unstructured":"Little RJA. A test of missing completely at random for multivariate data with missing values. J Am Stat Assoc. 1988;83:1198\u2013202.","journal-title":"J Am Stat Assoc"},{"key":"3148_CR32","doi-asserted-by":"crossref","unstructured":"Ross RK, Breskin A, Westreich D. When is a complete-case approach to missing data valid? the importance of effect-measure modification. 189:1583\u201389.","DOI":"10.1093\/aje\/kwaa124"},{"key":"3148_CR33","unstructured":"Hornung R, et al. Evaluating machine learning models in non-standard settings: An overview and new findings."},{"key":"3148_CR34","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1016\/j.chest.2016.01.003","volume":"150","author":"SM Brown","year":"2016","unstructured":"Brown SM, et al. Nonlinear imputation of pao2\/fio2 from spo2\/fio2 among patients with acute respiratory distress syndrome. Chest. 2016;150:307\u201313). PMC4980543.","journal-title":"Chest"},{"key":"3148_CR35","unstructured":"Wu S. 3 best metrics to evaluate regression model? 2020. 2022\/12\/20. https:\/\/towardsdatascience.com\/what-are-the-best-metrics-to-evaluate-your-regression-model-418ca481755b"},{"key":"3148_CR36","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, et al. Scikit-learn: Machine learning in python. J Retailing Machine Learning Research: JMLR. 2011;12:2825\u201330.","journal-title":"J Retailing Machine Learning Research: JMLR"},{"key":"3148_CR37","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1016\/S0140-6736(86)90837-8","volume":"327","author":"JM Bland","year":"1986","unstructured":"Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 1986;327:307\u201310.","journal-title":"Lancet"},{"key":"3148_CR38","unstructured":"Lundberg S, Lee S-I. A unified approach to interpreting model predictions. (2017). http:\/\/arxiv.org\/abs\/1705.07874 arXiv:1705.07874."},{"key":"3148_CR39","volume-title":"17. A value for n-person Games, 307\u2013318. Contributions to the theory of games (AM-28)","author":"LS Shapley","year":"1953","unstructured":"Shapley LS. 17. A value for n-person Games, 307\u2013318. Contributions to the theory of games (AM-28). Vol. II. Princeton University Press, Princeton; 1953."},{"key":"3148_CR40","doi-asserted-by":"publisher","first-page":"64","DOI":"10.2307\/2065930","volume":"7","author":"JR Beniger","year":"1978","unstructured":"Beniger JR, Tukey JW. Exploratory data analysis. Contemp Sociol. 1978;7:64.","journal-title":"Contemp Sociol"},{"key":"3148_CR41","doi-asserted-by":"publisher","first-page":"806","DOI":"10.1093\/bja\/aer219","volume":"107","author":"HM Al-Otaibi","year":"2011","unstructured":"Al-Otaibi HM, Hardman JG. Prediction of arterial oxygen partial pressure after changes in fio2: validation and clinical application of a novel formula. Br J Anaesth. 2011;107:806\u201312.","journal-title":"Br J Anaesth"},{"key":"3148_CR42","first-page":"63","volume":"133","author":"J Brockway","year":"1998","unstructured":"Brockway J, Hay WW Jr. Prediction of arterial partial pressure of oxygen with pulse oxygen saturation measurements. J Sport Hist Of Pediatrics. 1998;133:63\u201366.","journal-title":"J Sport Hist Of Pediatrics"},{"key":"3148_CR43","doi-asserted-by":"publisher","first-page":"e0250740","DOI":"10.1371\/journal.pone.0250740","volume":"16","author":"JG R\u00f6ttgering","year":"2021","unstructured":"R\u00f6ttgering JG, et al. Determining a target spo2 to maintain pao2 within a physiological range. PLoS One. 2021;16:e0250740. PMC8118260.","journal-title":"PLoS One"},{"key":"3148_CR44","doi-asserted-by":"crossref","unstructured":"Benkendorf DJ, Hawkins CP. Effects of sample size and network depth on a deep learning approach to species distribution modeling. Ecol Inf. 2020;60, 101137.","DOI":"10.1016\/j.ecoinf.2020.101137"},{"key":"3148_CR45","doi-asserted-by":"crossref","unstructured":"Liu B, Wei Y, Zhang Y, Yang Q. Deep neural networks for high dimension, low sample size data. International Joint Conference on Artificial Intelligence. 2017 2287\u201393. https:\/\/www.ijcai.org\/proceedings\/2017\/318","DOI":"10.24963\/ijcai.2017\/318"}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-025-03148-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12911-025-03148-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-025-03148-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T05:14:42Z","timestamp":1757481282000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedinformdecismak.biomedcentral.com\/articles\/10.1186\/s12911-025-03148-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,3]]},"references-count":45,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["3148"],"URL":"https:\/\/doi.org\/10.1186\/s12911-025-03148-8","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-6455053\/v1","asserted-by":"object"}]},"ISSN":["1472-6947"],"issn-type":[{"value":"1472-6947","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,3]]},"assertion":[{"value":"15 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 August 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 September 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":"The study was conducted as a single-left retrospective cohort study. Before accessing the data, our protocol (submission 19\u2013539) received approval from the University of Munich\u2019s institutional review board. The requirement for informed consent was waived from the Ethics Committee of University of Munich\u2019s institutional review board because of the retrospective nature of the study. All procedures performed in this study involving human participants were in accordance with the ethical standards of the institutional and\/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.","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":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"326"}}