Machine learning-based model to predict severe acute kidney injury after total aortic arch replacement for acute type A aortic dissection

Liu, Xiaolong1; Fang, Miaoxian2; Wang, Kai1,3; Zhu, Junjiang1; Chen, Zeling1; He, Linling4; Liang, Silin4; Deng, Yiyu1; Chen, Chunbo2,5

2024-07-15

10.1016/j.heliyon.2024.e34171

HELIYON   影响因子和分区

2405-8440

Background: Severe acute kidney injury (AKI) after total aortic arch replacement (TAAR) is related to adverse outcomes in patients with acute type A aortic dissection (ATAAD). However, the early prediction of severe AKI remains a challenge. This study aimed to develop a novel model to predict severe AKI after TAAR in ATAAD patients using machine learning algorithms.
Methods: A total of 572 ATAAD patients undergoing TAAR were enrolled in this retrospective study, and randomly divided into a training set (70 %) and a validation set (30 %). Lasso regression, support vector machine-recursive feature elimination and random forest algorithms were used to screen indicators for severe AKI (defined as AKI stage III) in the training set, respectively. Then the intersection indicators were selected to construct models through artificial neural network (ANN) and logistic regression. The AUC-ROC curve was employed to ascertain the prediction efficacy of the ANN and logistic regression models.
Results: The incidence of severe AKI after TAAR was 22.9 % among ATAAD patients. The intersection predictors identified by different machine learning algorithms were baseline serum creatinine and ICU admission variables, including serum cystatin C, procalcitonin, aspartate transaminase, platelet, lactic dehydrogenase, urine N-acetyl-beta-D-glucosidase and Acute Physiology and Chronic Health Evaluation II score. The ANN model showed a higher AUC-ROC than logistic regression (0.938 vs 0.908, p < 0.05). Furthermore, the ANN model could predict 89.1 % of severe AKI cases beforehand. In the validation set, the superior performance of the ANN model was further confirmed in terms of discrimination ability (AUC = 0.916), calibration curve analysis and decision curve analysis. Conclusion: This study developed a novel and reliable clinical prediction model for severe AKI after TAAR in ATAAD patients using machine learning algorithms. Importantly, the ANN model showed a higher predictive ability for severe AKI than logistic regression.

Acute kidney injury Total aortic arch replacement Machine learning Artificial neural network Prediction

SCI

英语

通讯

National Natural Science Foundation of China[82172162] ; Office of Talent Work Leading Group in Maoming of China[[2020] 24]

Science & Technology - Other Topics

Multidisciplinary Sciences

WOS:001266842100001

CELL PRESS

Web of Science

1.Southern Med Univ, Guangdong Prov Peoples Hosp, Guangdong Acad Med Sci, Dept Crit Care Med, Guangzhou 510080, Peoples R China
2.Southern Med Univ, Guangdong Prov Peoples Hosp, Guangdong Cardiovasc Inst, Guangdong Acad Med Sci,Dept Intens Care Unit Cardi, Guangzhou 510080, Peoples R China
3.South China Univ Technol, Sch Med, Guangzhou 510000, Peoples R China
4.Southern Univ Sci & Technol, Clin Med Coll 2, Shenzhen Peoples Hosp, Dept Crit Care,Affiliated Hosp 1,Jinan Univ, Shenzhen 518020, Peoples R China
5.Jinan Univ, Southern Univ Sci & Technol, Shenzhen Peoples Hosp, Dept Emergency,Clin Med Coll 2,Affiliated Hosp 1, Shenzhen 518020, Peoples R China

Liu, Xiaolong,Fang, Miaoxian,Wang, Kai,et al. Machine learning-based model to predict severe acute kidney injury after total aortic arch replacement for acute type A aortic dissection[J]. HELIYON,2024,10(13).

Liu, Xiaolong.,Fang, Miaoxian.,Wang, Kai.,Zhu, Junjiang.,Chen, Zeling.,...&Chen, Chunbo.(2024).Machine learning-based model to predict severe acute kidney injury after total aortic arch replacement for acute type A aortic dissection.HELIYON,10(13).

Liu, Xiaolong,et al."Machine learning-based model to predict severe acute kidney injury after total aortic arch replacement for acute type A aortic dissection".HELIYON 10.13(2024).