{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T01:37:20Z","timestamp":1789609040737,"version":"build-2803163510"},"reference-count":24,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,26]],"date-time":"2022-12-26T00:00:00Z","timestamp":1672012800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Grant of China","award":["82072225"],"award-info":[{"award-number":["82072225"]}]},{"name":"National Natural Science Grant of China","award":["62276146"],"award-info":[{"award-number":["62276146"]}]},{"name":"National Natural Science Grant of China","award":["202206010044"],"award-info":[{"award-number":["202206010044"]}]},{"name":"National Natural Science Grant of China","award":["2021J011380"],"award-info":[{"award-number":["2021J011380"]}]},{"name":"National Natural Science Grant of China","award":["2020J01923"],"award-info":[{"award-number":["2020J01923"]}]},{"name":"National Natural Science Grant of China","award":["2021J011111"],"award-info":[{"award-number":["2021J011111"]}]},{"name":"National Natural Science Grant of China","award":["2020GGA079"],"award-info":[{"award-number":["2020GGA079"]}]},{"name":"National Natural Science Grant of China","award":["2020GP003"],"award-info":[{"award-number":["2020GP003"]}]},{"name":"Science and Technology Program of Guangzhou, China","award":["82072225"],"award-info":[{"award-number":["82072225"]}]},{"name":"Science and Technology Program of Guangzhou, China","award":["62276146"],"award-info":[{"award-number":["62276146"]}]},{"name":"Science and Technology Program of Guangzhou, China","award":["202206010044"],"award-info":[{"award-number":["202206010044"]}]},{"name":"Science and Technology Program of Guangzhou, China","award":["2021J011380"],"award-info":[{"award-number":["2021J011380"]}]},{"name":"Science and Technology Program of Guangzhou, China","award":["2020J01923"],"award-info":[{"award-number":["2020J01923"]}]},{"name":"Science and Technology Program of Guangzhou, China","award":["2021J011111"],"award-info":[{"award-number":["2021J011111"]}]},{"name":"Science and Technology Program of Guangzhou, China","award":["2020GGA079"],"award-info":[{"award-number":["2020GGA079"]}]},{"name":"Science and Technology Program of Guangzhou, China","award":["2020GP003"],"award-info":[{"award-number":["2020GP003"]}]},{"name":"Natural Science Foundation of Fujian Province","award":["82072225"],"award-info":[{"award-number":["82072225"]}]},{"name":"Natural Science Foundation of Fujian Province","award":["62276146"],"award-info":[{"award-number":["62276146"]}]},{"name":"Natural Science Foundation of Fujian Province","award":["202206010044"],"award-info":[{"award-number":["202206010044"]}]},{"name":"Natural Science Foundation of Fujian Province","award":["2021J011380"],"award-info":[{"award-number":["2021J011380"]}]},{"name":"Natural Science Foundation of Fujian Province","award":["2020J01923"],"award-info":[{"award-number":["2020J01923"]}]},{"name":"Natural Science Foundation of Fujian Province","award":["2021J011111"],"award-info":[{"award-number":["2021J011111"]}]},{"name":"Natural Science Foundation of Fujian Province","award":["2020GGA079"],"award-info":[{"award-number":["2020GGA079"]}]},{"name":"Natural Science Foundation of Fujian Province","award":["2020GP003"],"award-info":[{"award-number":["2020GP003"]}]},{"name":"Fujian provincial health technology project","award":["82072225"],"award-info":[{"award-number":["82072225"]}]},{"name":"Fujian provincial health technology project","award":["62276146"],"award-info":[{"award-number":["62276146"]}]},{"name":"Fujian provincial health technology project","award":["202206010044"],"award-info":[{"award-number":["202206010044"]}]},{"name":"Fujian provincial health technology project","award":["2021J011380"],"award-info":[{"award-number":["2021J011380"]}]},{"name":"Fujian provincial health technology project","award":["2020J01923"],"award-info":[{"award-number":["2020J01923"]}]},{"name":"Fujian provincial health technology project","award":["2021J011111"],"award-info":[{"award-number":["2021J011111"]}]},{"name":"Fujian provincial health technology project","award":["2020GGA079"],"award-info":[{"award-number":["2020GGA079"]}]},{"name":"Fujian provincial health technology project","award":["2020GP003"],"award-info":[{"award-number":["2020GP003"]}]},{"name":"Putian technology planning project","award":["82072225"],"award-info":[{"award-number":["82072225"]}]},{"name":"Putian technology planning project","award":["62276146"],"award-info":[{"award-number":["62276146"]}]},{"name":"Putian technology planning project","award":["202206010044"],"award-info":[{"award-number":["202206010044"]}]},{"name":"Putian technology planning project","award":["2021J011380"],"award-info":[{"award-number":["2021J011380"]}]},{"name":"Putian technology planning project","award":["2020J01923"],"award-info":[{"award-number":["2020J01923"]}]},{"name":"Putian technology planning project","award":["2021J011111"],"award-info":[{"award-number":["2021J011111"]}]},{"name":"Putian technology planning project","award":["2020GGA079"],"award-info":[{"award-number":["2020GGA079"]}]},{"name":"Putian technology planning project","award":["2020GP003"],"award-info":[{"award-number":["2020GP003"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>Logistic regression (LR) and artificial intelligence algorithms were used to analyze the risk factors for the early rupture of acute type A aortic dissection (ATAAD). Data from electronic medical records of 200 patients diagnosed with ATAAD from the Department of Emergency of Guangdong Provincial People\u2019s Hospital from April 2012 to March 2017 were collected. Logistic regression and artificial intelligence algorithms were used to establish prediction models, and the prediction effects of four models were analyzed. According to the LR models, we elucidated independent risk factors for ATAAD rupture, which included age &gt; 63 years (odds ratio (OR) = 1.69), female sex (OR = 1.77), ventilator assisted ventilation (OR = 3.05), AST &gt; 80 U\/L (OR = 1.59), no distortion of the inner membrane (OR = 1.57), the diameter of the aortic sinus &gt; 41 mm (OR = 0.92), maximum aortic diameter &gt; 48 mm (OR = 1.32), the ratio of false lumen area to true lumen area &gt; 2.12 (OR = 1.94), lactates &gt; 1.9 mmol\/L (OR = 2.28), and white blood cell &gt; 14.2 \u00d7 109 \/L (OR = 1.23). The highest sensitivity and accuracy were found with the convolutional neural network (CNN) model. Its sensitivity was 0.93, specificity was 0.90, and accuracy was 0.90. In this present study, we found that age, sex, select biomarkers, and select morphological parameters of the aorta are independent predictors for the rupture of ATAAD. In terms of predicting the risk of ATAAD, the performance of random forests and CNN is significantly better than LR, but the performance of the support vector machine (SVM) is worse than LR.<\/jats:p>","DOI":"10.3390\/jcm12010179","type":"journal-article","created":{"date-parts":[[2022,12,27]],"date-time":"2022-12-27T02:55:07Z","timestamp":1672109707000},"page":"179","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Application of Logistic Regression and Artificial Intelligence in the Risk Prediction of Acute Aortic Dissection Rupture"],"prefix":"10.3390","volume":"12","author":[{"given":"Yanya","family":"Lin","sequence":"first","affiliation":[{"name":"Critical Care Medicine, Affiliated Hospital of Putian University, Putian 351100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianxiong","family":"Hu","sequence":"additional","affiliation":[{"name":"Critical Care Medicine, Affiliated Hospital of Putian University, Putian 351100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rongbin","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Mechanical, Electrical & Information Engineering, Putian University, Putian 351100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaocong","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Mechanical, Electrical & Information Engineering, Putian University, Putian 351100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fei","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Emergency Medicine, Guangdong Provincial People\u2019s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Radiology, Guangdong Provincial People\u2019s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Mechanical, Electrical & Information Engineering, Putian University, Putian 351100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Emergency Medicine, Guangdong Provincial People\u2019s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1016\/j.ccl.2017.03.004","article-title":"Acute type a aortic dissection","volume":"35","author":"Elsayed","year":"2017","journal-title":"Cardiol. Clin."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2873","DOI":"10.1093\/eurheartj\/ehu281","article-title":"2014 esc guidelines on the diagnosis and treatment of aortic diseases: Document covering acute and chronic aortic diseases of the thoracic and abdominal aorta of the adult. The task force for the diagnosis and treatment of aortic diseases of the european society of cardiology (esc)","volume":"35","author":"Erbel","year":"2014","journal-title":"Eur. Heart J."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1009","DOI":"10.1001\/jamacardio.2022.2718","article-title":"Early mortality in type a acute aortic dissection: Insights from the international registry of acute aortic dissection","volume":"7","author":"Harris","year":"2022","journal-title":"JAMA Cardiol."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Kuang, J., Yang, J., Wang, Q., Yu, C., Li, Y., and Fan, R. (2020). A preoperative mortality risk assessment model for stanford type a acute aortic dissection. BMC Cardiovasc. Disord., 20.","DOI":"10.1186\/s12872-020-01802-9"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"4634","DOI":"10.21037\/jtd.2019.10.82","article-title":"Predicting in-hospital rupture of type a aortic dissection using random forest","volume":"11","author":"Wu","year":"2019","journal-title":"J. Thorac. Dis."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1232","DOI":"10.2174\/1381612827999210111181554","article-title":"Prediction models and scores in adult congenital heart disease","volume":"27","author":"Arvanitaki","year":"2021","journal-title":"Curr. Pharm. Des."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"5245","DOI":"10.1038\/s41598-020-62133-5","article-title":"Study of cardiovascular disease prediction model based on random forest in eastern China","volume":"10","author":"Yang","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.jcrc.2019.08.010","article-title":"Comparison between logistic regression and machine learning algorithms on survival prediction of traumatic brain injuries","volume":"54","author":"Feng","year":"2019","journal-title":"J. Crit. Care"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"31254","DOI":"10.1109\/ACCESS.2020.2973188","article-title":"Deep learning from limited training data: Novel segmentation and ensemble algorithms applied to automatic melanoma diagnosis","volume":"8","author":"Albert","year":"2020","journal-title":"IEEE Access"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1016\/j.ajo.2018.10.007","article-title":"Using deep learning and transfer learning to accurately diagnose early-onset glaucoma from macular optical coherence tomography images","volume":"198","author":"Asaoka","year":"2019","journal-title":"Am. J. Ophthalmol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"e266","DOI":"10.1161\/CIR.0b013e3181d47d48","article-title":"2010 ACCF\/AHA\/AATS\/ACR\/ASA\/SCA\/SCAI\/SIR\/STS\/SVM guidelines for the diagnosis and management of patients with Thoracic Aortic Disease: A report of the American College of Cardiology Foundation\/American Heart Association Task Force on Practice Guidelines, American Association for Thoracic Surgery, American College of Radiology, American Stroke Association, Society of Cardiovascular Anesthesiologists, Society for Cardiovascular Angiography and Interventions, Society of Interventional Radiology, Society of Thoracic Surgeons, and Society for Vascular Medicine","volume":"121","author":"Hiratzka","year":"2010","journal-title":"Circulation"},{"key":"ref_12","first-page":"239","article-title":"Application of support vector machine in predicting in-hospital mortality risk of patients with acute kidney injury in ICU","volume":"50","author":"Lin","year":"2018","journal-title":"J. Peking Univ. Health Sci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"5185","DOI":"10.1109\/TNNLS.2018.2791507","article-title":"F-svm: Combination of feature transformation and svm learning via convex relaxation","volume":"29","author":"Wu","year":"2018","journal-title":"IEEE Trans. Neural. Netw. Learn Syst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"748","DOI":"10.1007\/s10278-018-0052-4","article-title":"Automatic organ segmentation for ct scans based on super-pixel and convolutional neural networks","volume":"31","author":"Liu","year":"2018","journal-title":"J. Digit. Imaging"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2977","DOI":"10.1001\/archinte.160.19.2977","article-title":"Clinical prediction of acute aortic dissection","volume":"160","author":"Schwartz","year":"2000","journal-title":"Arch. Intern. Med."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Casanova, R., Saldana, S., Simpson, S.L., Lacy, M.E., Subauste, A.R., Blackshear, C., Wagenknecht, L., and Bertoni, A.G. (2016). Prediction of incident diabetes in the jackson heart study using high-dimensional machine learning. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0163942"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2031","DOI":"10.1161\/CIRCULATIONAHA.112.000483","article-title":"Population-based study of incidence and outcome of acute aortic dissection and premorbid risk factor control: 10-year results from the oxford vascular study","volume":"127","author":"Howard","year":"2013","journal-title":"Circulation"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/j.hrtlng.2015.01.006","article-title":"Potential biomarkers for early diagnosis of acute aortic dissection","volume":"44","author":"Peng","year":"2015","journal-title":"Heart Lung"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1016\/j.ajem.2016.11.003","article-title":"Association of platelet to lymphocyte ratio and risk of in-hospital mortality in patients with type b acute aortic dissection","volume":"35","author":"Du","year":"2017","journal-title":"Am. J. Emerg. Med."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1177\/1538574419858161","article-title":"Prognostic role of admission c-reactive protein level as a predictor of in-hospital mortality in type-a acute aortic dissection: A meta-analysis","volume":"53","author":"Hsieh","year":"2019","journal-title":"Vasc. Endovasc. Surg."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1007\/s00217-019-03419-5","article-title":"Identification of rice flour types with near-infrared spectroscopy associated with pls-da and svm methods","volume":"246","author":"Sampaio","year":"2020","journal-title":"Eur. Food Res. Technol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3072959.3073592","article-title":"Deep bilateral learning for real-time image enhancement","volume":"36","author":"Gharbi","year":"2017","journal-title":"ACM Trans. Graph."},{"key":"ref_24","first-page":"977","article-title":"Research on convolutional neural network and its application on medical image","volume":"35","author":"Liang","year":"2018","journal-title":"J. Biomed. Eng."}],"container-title":["Journal of Clinical Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2077-0383\/12\/1\/179\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:51:31Z","timestamp":1760147491000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2077-0383\/12\/1\/179"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,26]]},"references-count":24,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["jcm12010179"],"URL":"https:\/\/doi.org\/10.3390\/jcm12010179","relation":{},"ISSN":["2077-0383"],"issn-type":[{"value":"2077-0383","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,26]]}}}