{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T13:29:40Z","timestamp":1789046980550,"version":"build-2803163510"},"reference-count":46,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2022,9,21]],"date-time":"2022-09-21T00:00:00Z","timestamp":1663718400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Cardiovasc. Med."],"abstract":"<jats:sec><jats:title>Objective<\/jats:title><jats:p>A clinical prediction model for postoperative combined Acute kidney injury (AKI) in patients with Type A acute aortic dissection (TAAAD) and Type B acute aortic dissection (TBAAD) was constructed by using Machine Learning (ML).<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Baseline data was collected from Acute aortic division (AAD) patients admitted to First Affiliated Hospital of Xinjiang Medical University between January 1, 2019 and December 31, 2021. (1) We identified baseline Serum creatinine (SCR) estimation methods and used them as a basis for diagnosis of AKI. (2) Divide their total datasets randomly into Training set (70%) and Test set (30%), Bootstrap modeling and validation of features using multiple ML methods in the training set, and select models corresponding to the largest Area Under Curve (AUC) for follow-up studies. (3) Screening of the best ML model variables through the model visualization tools Shapley Addictive Explanations (SHAP) and Recursive feature reduction (REF). (4) Finally, the pre-screened prediction models were evaluated using test set data from three aspects: discrimination, Calibration, and clinical benefit.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The final incidence of AKI was 69.4% (120\/173) in 173 patients with TAAAD and 28.6% (81\/283) in 283 patients with TBAAD. For TAAAD-AKI, the Random Forest (RF) model showed the best prediction performance in the training set (AUC = 0.760, 95% CI:0.630\u20130.881); while for TBAAD-AKI, the Light Gradient Boosting Machine (LightGBM) model worked best (AUC = 0.734, 95% CI:0.623\u20130.847). Screening of the characteristic variables revealed that the common predictors among the two final prediction models for postoperative AKI due to AAD were baseline SCR, Blood urea nitrogen (BUN) and Uric acid (UA) at admission, Mechanical ventilation time (MVT). The specific predictors in the TAAAD-AKI model are: White blood cell (WBC), Platelet (PLT) and D dimer at admission, Plasma The specific predictors in the TBAAD-AKI model were N-terminal pro B-type natriuretic peptide (BNP), Serum kalium, Activated partial thromboplastin time (APTT) and Systolic blood pressure (SBP) at admission, Combined renal arteriography in surgery. Finally, we used in terms of Discrimination, the ROC value of the RF model for TAAAD was 0.81 and the ROC value of the LightGBM model for TBAAD was 0.74, both with good accuracy. In terms of calibration, the calibration curve of TAAAD-AKI's RF fits the ideal curve the best and has the lowest and smallest Brier score (0.16). Similarly, the calibration curve of TBAAD-AKI's LightGBM model fits the ideal curve the best and has the smallest Brier score (0.15). In terms of Clinical benefit, the best ML models for both types of AAD have good Net benefit as shown by Decision Curve Analysis (DCA).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>We successfully constructed and validated clinical prediction models for the occurrence of AKI after surgery in TAAAD and TBAAD patients using different ML algorithms. The main predictors of the two types of AAD-AKI are somewhat different, and the strategies for early prevention and control of AKI are also different and need more external data for validation.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fcvm.2022.984772","type":"journal-article","created":{"date-parts":[[2022,9,22]],"date-time":"2022-09-22T07:50:09Z","timestamp":1663833009000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":20,"title":["Prediction model of acute kidney injury after different types of acute aortic dissection based on machine learning"],"prefix":"10.3389","volume":"9","author":[{"given":"Li","family":"Xinsai","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wang","family":"Zhengye","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huang","family":"Xuan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chu","family":"Xueqian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng","family":"Kai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chen","family":"Sisi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiang","family":"Xuyan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Suhua","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2022,9,21]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"e008834","DOI":"10.1161\/JAHA.118.008834","article-title":"Cardiac and Vascular Surgery-Associated Acute Kidney Injury: The 20th International Consensus Conference of the Adqi (Acute Disease Quality Initiative) Group","volume":"7","author":"Nadim","year":"2018","journal-title":"J. Am. Heart Assoc."},{"key":"B2","doi-asserted-by":"publisher","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":"B3","doi-asserted-by":"publisher","first-page":"2590","DOI":"10.21037\/jtd.2018.05.47","article-title":"Independent risk factors for postoperative aki and the impact of the Aki on 30-day postoperative outcomes in patients with type a acute aortic dissection: an updated meta-analysis and meta-regression","volume":"10","author":"Wang","year":"2018","journal-title":"J Thorac Dis."},{"key":"B4","doi-asserted-by":"publisher","first-page":"2727","DOI":"10.1093\/eurheartj\/ehz153","article-title":"Differential clinical features and long-term prognosis of acute aortic syndrome according to disease entity","volume":"40","author":"Ahn","year":"2019","journal-title":"Eur Heart J."},{"key":"B5","doi-asserted-by":"publisher","first-page":"1023","DOI":"10.2147\/TCRM.S131456","article-title":"Risk factors and early outcomes of acute renal injury after thoracic aortic endograft repair for type B aortic dissection","volume":"13","author":"Luo","year":"2017","journal-title":"Ther Clin Risk Manag."},{"key":"B6","doi-asserted-by":"publisher","first-page":"302","DOI":"10.1093\/ejcts\/ezaa063","article-title":"Preoperative renal malperfusion is an independent predictor for acute kidney injury and operative death but not associated with late mortality after surgery for acute type a aortic dissection","volume":"58","author":"Nishigawa","year":"2020","journal-title":"Eur J Cardiothorac Surg."},{"key":"B7","doi-asserted-by":"publisher","first-page":"612","DOI":"10.1016\/j.jfma.2014.01.017","article-title":"Acute renal injury after thoracic endovascular aortic repair of stanford type B aortic dissection: incidence, risk factors, and prognosis","volume":"113","author":"Zhu","year":"2014","journal-title":"J Formos Med Assoc."},{"key":"B8","doi-asserted-by":"publisher","first-page":"1292","DOI":"10.1016\/j.athoracsur.2020.07.019","article-title":"Acute kidney injury after acute repair of type A aortic dissection","volume":"111","author":"Helgason","year":"2021","journal-title":"Ann Thorac Surg."},{"key":"B9","doi-asserted-by":"publisher","first-page":"557044","DOI":"10.3389\/fmed.2020.557044","article-title":"The incidence, risk factors and in-hospital mortality of acute kidney injury in patients after surgery for acute type a aortic dissection: a single-center retrospective analysis of 335 patients","volume":"7","author":"Li","year":"2020","journal-title":"Front Med."},{"key":"B10","doi-asserted-by":"publisher","first-page":"314","DOI":"10.1093\/ejcts\/ezab044","article-title":"Does deeper hypothermia reduce the risk of acute kidney injury after circulatory arrest for aortic arch surgery?","volume":"60","author":"Vekstein","year":"2021","journal-title":"Eur J Cardiothorac Surg."},{"key":"B11","doi-asserted-by":"publisher","first-page":"697","DOI":"10.1093\/icvts\/ivaa164","article-title":"Acute kidney injury in patients operated on for type a acute aortic dissection: incidence, risk factors and short-term outcomes","volume":"31","author":"Wang","year":"2020","journal-title":"Interact Cardiovasc Thorac Surg."},{"key":"B12","doi-asserted-by":"publisher","first-page":"766","DOI":"10.1016\/j.athoracsur.2012.04.057","article-title":"Incidence and risk factors of acute kidney injury after thoracic aortic surgery for acute dissection","volume":"94","author":"Roh","year":"2012","journal-title":"Ann Thorac Surg."},{"key":"B13","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1016\/j.hlc.2021.05.087","article-title":"Blood transfusion and acute kidney injury after total aortic arch replacement for acute stanford type A aortic dissection","volume":"31","author":"Li","year":"2022","journal-title":"Heart Lung Circ."},{"key":"B14","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1186\/s13019-019-0907-x","article-title":"Cardiopulmonary bypass time is an independent risk factor for acute kidney injury in emergent thoracic aortic surgery: a retrospective cohort study","volume":"14","author":"Xu","year":"2019","journal-title":"J Cardiothorac Surg."},{"key":"B15","doi-asserted-by":"publisher","first-page":"91","DOI":"10.12945\/j.aorta.2015.14.022","article-title":"Prognostic implications of acute renal failure after surgery for type A acute aortic dissection","volume":"3","author":"Sansone","year":"2015","journal-title":"Aorta (Stamford, Conn)."},{"key":"B16","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1080\/09537104.2020.1847266","article-title":"Association between platelet counts and morbidity and mortality after endovascular repair for type B aortic dissection","volume":"33","author":"Xie","year":"2022","journal-title":"Platelets."},{"key":"B17","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1681\/ASN.2004040331","article-title":"A Clinical score to predict acute renal failure after cardiac surgery","volume":"16","author":"Thakar","year":"2005","journal-title":"J Am Soc Nephrol."},{"key":"B18","doi-asserted-by":"publisher","first-page":"2208","DOI":"10.1161\/CIRCULATIONAHA.106.635573","article-title":"Bedside tool for predicting the risk of postoperative dialysis in patients undergoing cardiac surgery","volume":"114","author":"Mehta","year":"2006","journal-title":"Circulation."},{"key":"B19","doi-asserted-by":"publisher","first-page":"1076","DOI":"10.1503\/cmaj.151447","article-title":"A new model to predict acute kidney injury requiring renal replacement therapy after cardiac surgery","volume":"188","author":"Pannu","year":"2016","journal-title":"CMAJ."},{"key":"B20","doi-asserted-by":"publisher","first-page":"733","DOI":"10.1161\/CIRCULATIONAHA.106.623538","article-title":"Risk index for perioperative renal dysfunction\/failure: critical dependence on pulse pressure hypertension","volume":"115","author":"Aronson","year":"2007","journal-title":"Circulation."},{"key":"B21","doi-asserted-by":"publisher","first-page":"e003754","DOI":"10.1161\/JAHA.116.003754","article-title":"Dynamic predictive scores for cardiac surgery\u2013associated acute kidney injury","volume":"5","author":"Jiang","year":"2016","journal-title":"J Am Heart Assoc."},{"key":"B22","doi-asserted-by":"publisher","first-page":"1347","DOI":"10.1056\/NEJMra1814259","article-title":"Machine learning in medicine","volume":"380","author":"Rajkomar","year":"2019","journal-title":"N Engl J Med."},{"key":"B23","doi-asserted-by":"publisher","first-page":"c179","DOI":"10.1159\/000339789","article-title":"Kdigo clinical practice guidelines for acute kidney injury","volume":"120","author":"Khwaja","year":"2012","journal-title":"Nephron Clinical Practice."},{"key":"B24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/cc2872","article-title":"Acute renal failure\u2013definition, outcome measures, animal models, fluid therapy and information technology needs: the second international consensus conference of the acute dialysis quality initiative (Adqi) group","volume":"8","author":"Bellomo","year":"2004","journal-title":"Critical care."},{"key":"B25","doi-asserted-by":"publisher","first-page":"930","DOI":"10.1089\/end.2016.0143","article-title":"Use of the modification of diet in renal disease equation for estimating glomerular filtration rate in the urologic literature","volume":"30","author":"Zabell","year":"2016","journal-title":"J Endourol."},{"key":"B26","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1186\/s13054-016-1218-4","article-title":"Creatinine-based definitions: from baseline creatinine to serum creatinine adjustment in intensive care","volume":"20","author":"De Rosa","year":"2016","journal-title":"Critical care (London, England)."},{"key":"B27","doi-asserted-by":"publisher","first-page":"536","DOI":"10.1038\/ki.2009.479","article-title":"Commonly used surrogates for baseline renal function affect the classification and prognosis of acute kidney injury","volume":"77","author":"Siew","year":"2010","journal-title":"Kidney Int."},{"key":"B28","doi-asserted-by":"publisher","first-page":"712","DOI":"10.2215\/CJN.10821011","article-title":"Estimating baseline kidney function in hospitalized patients with impaired kidney function","volume":"7","author":"Siew","year":"2012","journal-title":"Clin J Am Soc Nephrol."},{"key":"B29","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1002\/widm.8","article-title":"Classification and regression trees","volume":"1","author":"Loh","year":"2011","journal-title":"Wiley Interdiscip Rev Data Min Knowl Discov."},{"key":"B30","doi-asserted-by":"publisher","first-page":"1811","DOI":"10.1109\/TPAMI.2016.2618118","article-title":"Learn on source, refine on target: a model transfer learning framework with random forests","volume":"39","author":"Segev","year":"2016","journal-title":"IEEE Trans Pattern Anal Mach Intell."},{"key":"B31","first-page":"651","article-title":"Experimenting Xgboost algorithm for prediction and classification of different datasets","volume":"9","author":"Ramraj","year":"2016","journal-title":"Int J Control Theor Appl."},{"key":"B32","first-page":"3149","article-title":"Lightgbm: a highly efficient gradient boosting decision tree","volume":"30","author":"Ke","year":"2017","journal-title":"Adv Neural Inf Process Syst."},{"key":"B33","article-title":"Consistent individualized feature attribution for tree ensembles","author":"Lundberg","year":"2018","journal-title":"arXiv preprint arXiv:180203888"},{"key":"B34","doi-asserted-by":"publisher","first-page":"1385","DOI":"10.3978\/j.issn.2072-1439.2015.07.19","article-title":"Risk factors for acute kidney injury in overweight patients with acute type a aortic dissection: a retrospective study","volume":"7","author":"Zhao","year":"2015","journal-title":"J Thorac Dis."},{"key":"B35","doi-asserted-by":"publisher","first-page":"1279","DOI":"10.1007\/s11255-021-02993-6","article-title":"Development and validation of a simple equation to evaluate dietary protein intake using the blood urea nitrogen\/serum creatinine ratio in patients with stage 3 chronic kidney disease","volume":"54","author":"Wang","year":"2022","journal-title":"Int Urol Nephrol."},{"key":"B36","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1093\/ajh\/hpaa044","article-title":"Uric acid and hypertension: an update with recommendations","volume":"33","author":"Sanchez-Lozada","year":"2020","journal-title":"Am J Hypertens."},{"key":"B37","doi-asserted-by":"publisher","first-page":"718","DOI":"10.1111\/nep.13559","article-title":"Hyperuricemia is associated with acute kidney injury and all-cause mortality in hospitalized patients","volume":"24","author":"Kang","year":"2019","journal-title":"Nephrology."},{"key":"B38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13019-017-0613-5","article-title":"Consumption coagulopathy in acute aortic dissection: principles of management","volume":"12","author":"Liu","year":"2017","journal-title":"J Cardiothorac Surg."},{"key":"B39","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1097\/ALN.0000000000001461","article-title":"Effects of cardiopulmonary bypass on renal perfusion, filtration, and oxygenation in patients undergoing cardiac surgery","volume":"126","author":"Lannemyr","year":"2017","journal-title":"Anesthesiology."},{"key":"B40","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1002\/cphy.c160006","article-title":"Ischemia\/reperfusion","volume":"7","author":"Kalogeris","year":"2016","journal-title":"Compr Physiol."},{"key":"B41","doi-asserted-by":"publisher","first-page":"1904","DOI":"10.1016\/j.amjcard.2014.03.023","article-title":"Impact of acute kidney injury on in-hospital outcomes in patients with debakey type III acute aortic dissection","volume":"113","author":"Takahashi","year":"2014","journal-title":"Am J Cardiol."},{"key":"B42","doi-asserted-by":"publisher","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":"B43","doi-asserted-by":"publisher","first-page":"997","DOI":"10.1016\/j.jcrc.2014.06.011","article-title":"Goal-directed therapy after cardiac surgery and the incidence of acute kidney injury","volume":"29","author":"Thomson","year":"2014","journal-title":"J Crit Care."},{"key":"B44","doi-asserted-by":"publisher","first-page":"382","DOI":"10.1053\/j.ackd.2018.08.004","article-title":"Cardiorenal syndrome: an overview","volume":"25","author":"Ronco","year":"2018","journal-title":"Adv Chronic Kidney Dis."},{"key":"B45","doi-asserted-by":"publisher","first-page":"1294","DOI":"10.1056\/NEJMc1908879","article-title":"Contrast-associated acute kidney injury","volume":"381","author":"Swenson","year":"2019","journal-title":"N Engl J Med."},{"key":"B46","doi-asserted-by":"publisher","first-page":"612","DOI":"10.1016\/j.jfma.2014.01.017","article-title":"Acute renal injury after thoracic endovascular aortic repair of stanford type B aortic dissection: incidence, risk factors, and prognosis","volume":"113","author":"Zhu","year":"2014","journal-title":"J Formosan Med Assoc."}],"container-title":["Frontiers in Cardiovascular Medicine"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fcvm.2022.984772\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,22]],"date-time":"2022-09-22T07:50:20Z","timestamp":1663833020000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fcvm.2022.984772\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,21]]},"references-count":46,"alternative-id":["10.3389\/fcvm.2022.984772"],"URL":"https:\/\/doi.org\/10.3389\/fcvm.2022.984772","relation":{},"ISSN":["2297-055X"],"issn-type":[{"value":"2297-055X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,21]]},"article-number":"984772"}}