{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T08:11:36Z","timestamp":1767859896409,"version":"3.49.0"},"reference-count":33,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,4,15]],"date-time":"2022-04-15T00:00:00Z","timestamp":1649980800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JPM"],"abstract":"<jats:p>The complications of thoracic aortic disease include aortic dissection and aneurysm. The risks are frequently compounded by many cardiovascular comorbidities, which makes the process of clinical decision making complicated. The purpose of this study is to develop risk predictive models for patients after thoracic aneurysm surgeries, using integrated data from different medical institutions. Seven risk features were formulated for prediction. The CatBoost classifier performed best and provided an ROC AUC of 0.94\u20130.98 and an F-score of 0.95\u20130.98. The obtained results are widely in line with the current literature. The obtained findings provide additional support for clinical decision making, guiding a patient care team prior to surgical treatment, and promoting a safe postoperative period.<\/jats:p>","DOI":"10.3390\/jpm12040637","type":"journal-article","created":{"date-parts":[[2022,4,18]],"date-time":"2022-04-18T04:21:28Z","timestamp":1650255688000},"page":"637","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Aortic Risks Prediction Models after Cardiac Surgeries Using Integrated Data"],"prefix":"10.3390","volume":"12","author":[{"given":"Iuliia","family":"Lenivtceva","sequence":"first","affiliation":[{"name":"National Center for Cognitive Research, ITMO University, 49 Kronverskiy Prospect, 197101 Saint-Petersburg, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dmitri","family":"Panfilov","sequence":"additional","affiliation":[{"name":"Cardiology Research Institute, Tomsk National Research Medical Center of the Russian Academy of Science, 634012 Tomsk, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6231-8036","authenticated-orcid":false,"given":"Georgy","family":"Kopanitsa","sequence":"additional","affiliation":[{"name":"National Center for Cognitive Research, ITMO University, 49 Kronverskiy Prospect, 197101 Saint-Petersburg, Russia"},{"name":"Almazov National Medical Research Center, 2 Akkuratova Street, 197341 Saint-Petersburg, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0217-7737","authenticated-orcid":false,"given":"Boris","family":"Kozlov","sequence":"additional","affiliation":[{"name":"Cardiology Research Institute, Tomsk National Research Medical Center of the Russian Academy of Science, 634012 Tomsk, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11748-017-0874-x","article-title":"Thoracic aortic aneurysm: Unlocking the \u201csilent killer\u201d secrets","volume":"67","author":"Saeyeldin","year":"2019","journal-title":"Gen. Thorac. Cardiovasc. Surg."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1112","DOI":"10.1016\/j.jvs.2008.12.024","article-title":"A population-based analysis of endovascular versus open thoracic aortic aneurysm repair","volume":"49","author":"Orandi","year":"2009","journal-title":"J. Vasc. Surg."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"700","DOI":"10.1093\/ejcts\/ezaa156","article-title":"Prediction of mortality rate in acute type A dissection: The German Registry for Acute Type A Aortic Dissection score","volume":"58","author":"Czerny","year":"2020","journal-title":"Eur. J. Cardiothorac. Surg."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"795","DOI":"10.1016\/j.athoracsur.2018.09.052","article-title":"Risk Prediction Model for Major Adverse Outcome in Proximal Thoracic Aortic Surgery","volume":"107","author":"Wagner","year":"2019","journal-title":"Ann. Thorac. Surg."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Weng, S.F., Reps, J., Kai, J., Garibaldi, J.M., and Qureshi, N. (2017). Can Machine-learning improve cardiovascular risk prediction using routine clinical data?. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0174944"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"767","DOI":"10.1177\/0003319720916300","article-title":"Prediction of Abdominal Aortic Aneurysm Growth and Risk of Rupture in the Era of Machine Learning","volume":"71","author":"Lareyre","year":"2020","journal-title":"Angiology"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1097\/RCT.0000000000000958","article-title":"Machine Learning to Predict the Rapid Growth of Small Abdominal Aortic Aneurysm","volume":"44","author":"Hirata","year":"2020","journal-title":"J. Comput. Assist. Tomogr."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.ejvssr.2018.03.004","article-title":"Applied Machine Learning for the Prediction of Growth of Abdominal Aortic Aneurysm in Humans","volume":"39","author":"Lee","year":"2018","journal-title":"EJVES Short Rep."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"S160","DOI":"10.1161\/CIRCULATIONAHA.107.757419","article-title":"Ascending and transverse aortic arch repair: The impact of retrograde cerebral perfusion","volume":"118","author":"Estrera","year":"2008","journal-title":"Circulation"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.jss.2019.03.055","article-title":"Predictors of Acute Kidney Injury Following Aortic Arch Surgery","volume":"242","author":"Ghincea","year":"2019","journal-title":"J. Surg. Res."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1016\/j.jtcvs.2019.01.127","article-title":"Aortic arch surgery with hypothermic circulatory arrest and unilateral antegrade cerebral perfusion: Perioperative outcomes","volume":"159","author":"Wang","year":"2020","journal-title":"J. Thorac. Cardiovasc. Surg."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Lee, H., Yoon, H., Nam, K., Cho, Y., Kim, T., Kim, W., and Bahk, J. (2018). Derivation and Validation of Machine Learning Approaches to Predict Acute Kidney Injury after Cardiac Surgery. J. Clin. Med., 7.","DOI":"10.3390\/jcm7100322"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3384","DOI":"10.1038\/s41598-021-83020-7","article-title":"Machine learning prediction models for prognosis of critically ill patients after open-heart surgery","volume":"11","author":"Zhong","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Allyn, J., Allou, N., Augustin, P., Philip, I., Martinet, O., Belghiti, M., Provenchere, S., Montravers, P., and Ferdynus, C. (2017). A comparison of a machine learning model with EuroSCORE II in predicting mortality after elective cardiac surgery: A decision curve analysis. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0169772"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"857","DOI":"10.1053\/j.jvca.2020.07.029","article-title":"Machine Learning Models with Preoperative Risk Factors and Intraoperative Hypotension Parameters Predict Mortality After Cardiac Surgery","volume":"35","author":"Fernandes","year":"2021","journal-title":"J. Cardiothorac. Vasc. Anesth."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"866","DOI":"10.1053\/j.jvca.2020.06.072","article-title":"Predicting Acute Kidney Injury After Cardiac Surgery Using a Simpler Model","volume":"35","author":"Coulson","year":"2021","journal-title":"J. Cardiothorac. Vasc. Anesth."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1148\/radiology.143.1.7063747","article-title":"The meaning and use of the area under a receiver operating characteristic (ROC) curve","volume":"143","author":"Hanley","year":"1982","journal-title":"Radiology"},{"key":"ref_18","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Michel","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_19","unstructured":"Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A.V., and Gulin, A. (2018, January 3\u20138). CatBoost: Unbiased boosting with categorical features. Proceedings of the NIPS\u201918: Proceedings of the 32nd International Conference on Neural Information Processing Systems, Montr\u00e9al, QC, Canada."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3021","DOI":"10.21105\/joss.03021","article-title":"seaborn: Statistical data visualization","volume":"6","author":"Waskom","year":"2021","journal-title":"J. Open Source Softw."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1109\/MCSE.2007.55","article-title":"Matplotlib: A 2D graphics environment","volume":"9","author":"Hunter","year":"2007","journal-title":"Comput. Sci. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic minority over-sampling technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"ref_23","unstructured":"Lundberg, S.M., and Lee, S.-I. (2017, January 4\u20139). A Unified Approach to Interpreting Model Predictions. Proceedings of the NIPS\u201917: Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"394","DOI":"10.5606\/tgkdc.dergisi.2018.15559","article-title":"Machine learning techniques in cardiac risk assessment","volume":"26","author":"Kartal","year":"2018","journal-title":"Turkish J. Thorac. Cardiovasc. Surg."},{"key":"ref_25","first-page":"88","article-title":"Predicting the Aortic Aneurysm Postoperative Risks Based on Russian Integrated Data","volume":"285","author":"Lenivtceva","year":"2021","journal-title":"Stud. Health Technol. Inform."},{"key":"ref_26","unstructured":"Cabitza, F., and Campagner, A. (2019). Who wants accurate models? Arguing for a different metrics to take classification models seriously. arXiv."},{"key":"ref_27","first-page":"5979","article-title":"On evaluation metrics for medical applications of artificial intelligence","volume":"12","author":"Hicks","year":"2021","journal-title":"medRxiv"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"e273","DOI":"10.1097\/MD.0000000000000273","article-title":"Potentially modifiable risk factors for acute kidney injury after surgery on the thoracic aorta: A propensity score matched case-control study","volume":"94","author":"Kim","year":"2015","journal-title":"Medicine"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2210","DOI":"10.1053\/j.jvca.2018.02.026","article-title":"Acute Kidney Injury After Total Arch Replacement Combined With Frozen Elephant Trunk Implantation: Incidence, Risk Factors, and Outcome","volume":"32","author":"Zhou","year":"2018","journal-title":"J. Cardiothorac. Vasc. Anesth."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"955","DOI":"10.1093\/icvts\/ivu241","article-title":"Risk factors for acute kidney injury in aortic arch surgery with selective cerebral perfusion and mild hypothermic lower body circulatory arrest","volume":"19","author":"Nota","year":"2014","journal-title":"Interact. Cardiovasc. Thorac. Surg."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"692","DOI":"10.1016\/j.jvs.2018.05.245","article-title":"The distance of the primary intimal tear from the left subclavian artery predicts aortic growth in uncomplicated type B aortic dissection","volume":"69","author":"Codner","year":"2019","journal-title":"J. Vasc. Surg."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"486","DOI":"10.1016\/j.athoracsur.2018.07.054","article-title":"Acute Kidney Injury in Acute Type B Aortic Dissection: Outcomes Over 20 Years","volume":"107","author":"Hoogmoed","year":"2019","journal-title":"Ann. Thorac. Surg."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1037","DOI":"10.1093\/ejcts\/ezy427","article-title":"Locational impact of luminal communication on aortic diameter changes and reintervention in acute type I aortic dissection","volume":"55","author":"Heo","year":"2019","journal-title":"Eur. J. Cardiothorac. Surg."}],"container-title":["Journal of Personalized Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4426\/12\/4\/637\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:54:33Z","timestamp":1760136873000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4426\/12\/4\/637"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,15]]},"references-count":33,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2022,4]]}},"alternative-id":["jpm12040637"],"URL":"https:\/\/doi.org\/10.3390\/jpm12040637","relation":{},"ISSN":["2075-4426"],"issn-type":[{"value":"2075-4426","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,15]]}}}