AUTHOR=Xinsai Li , Zhengye Wang , Xuan Huang , Xueqian Chu , Kai Peng , Sisi Chen , Xuyan Jiang , Suhua Li 

TITLE=Prediction model of acute kidney injury after different types of acute aortic dissection based on machine learning

JOURNAL=Frontiers in Cardiovascular Medicine

VOLUME=Volume 9 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2022.984772

DOI=10.3389/fcvm.2022.984772

ISSN=2297-055X

ABSTRACT=Objective: 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). Methods: 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 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. Results: A total of 456 clinical data of AAD patients were included, of which the incidence of TAAAD-AKI was 69.4% (120/173); the incidence of TBAAD-AKI was 28.6% (81/283). For TAAAD-AKI, the Random Forest model showed the best prediction performance in the training set (AUC=0.760, 95% CI:0.630-0.881); while for TBAAD-AKI, the LightGBM model worked best (AUC=0.734, 95% CI:0.623-0.847). 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, BUN and UA at admission, Mechanical ventilation time. The specific predictors in the TAAAD-AKI model are: WBC, PLT and D dimer at admission, Plasma The specific predictors in the TBAAD-AKI model were BNP, Serum kalium, APTT and SBP at admission, Combined renal arteriography in surgery. Conclusion: 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.