| Application of Bayesian network model in the study of influencing factors of acute renal injury related to cardiac surgery |
| Received:August 29, 2019 Revised:March 19, 2020 Click here to download the full text |
| Citation of this paper:LI Yang,JIANG Wu-hua,XU Jia-rui,SHEN Bo,SHEN Zi-yan,YU Jia-wei,LIN Jing,DING Xiao-qiang.Application of Bayesian network model in the study of influencing factors of acute renal injury related to cardiac surgery[J].Chinese Journal of Clinical Medicine,2020,27(3):465-471 |
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| Author Name | Affiliation | E-mail | | LI Yang | Department of Nephrology, Zhongshan Hospital, Fudan University, Shanghai 200032, China | | | JIANG Wu-hua | Department of Nephrology, Zhongshan Hospital, Fudan University, Shanghai 200032, China | | | XU Jia-rui | Department of Nephrology, Zhongshan Hospital, Fudan University, Shanghai 200032, China | | | SHEN Bo | Department of Nephrology, Zhongshan Hospital, Fudan University, Shanghai 200032, China | | | SHEN Zi-yan | Department of Nephrology, Zhongshan Hospital, Fudan University, Shanghai 200032, China | | | YU Jia-wei | Department of Nephrology, Zhongshan Hospital, Fudan University, Shanghai 200032, China | | | LIN Jing | Department of Nephrology, Zhongshan Hospital, Fudan University, Shanghai 200032, China | lin.jing@zs-hospital.sh.cn | | DING Xiao-qiang | Department of Nephrology, Zhongshan Hospital, Fudan University, Shanghai 200032, China | ding.xiaoqiang@zs-hospital.sh.cn |
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| Abstract:Objective: To analyze the relevant risk factors and the interactions between variables affecting the incidence of cardiac surgery associated acute kidney injury (CSA-AKI), and explore the Bayesian network in clinical applicability in etiology analysis and disease prediction by Bayesian network (BN) model.Methods: 1 778 inpatients who underwent cardiac surgery at Zhongshan Hospital, Fudan University from May 2015 to December 2015 were recruited. The age, sex, body mass index, previous medical history, and information related to cardiac surgery were extracted from the electronic medical record system and laboratory testing database. BN analysis was used to construct the CSA-AKI incidence factor network and evaluate the model prediction effectiveness.Results: The incidence of CSA-AKI was 34.6% (615/1 778). BN revealed that age, estimated glomerular filtration rate (eGFR), left ventricular ejection fraction (LVEF), type of cardiac surgery, estimated circulation time and intraoperative blood transfusion were directly related to the occurrence of CSA-AKI. Preoperative serum uric acid, diabetes and angiography dosage had indirect connections with CSA-AKI through eGFR; New York Heart Association(NYHA)classification grade was linked with CSA-AKI by affecting CPB time and LVEF. The AUC value of BNs model was 0.758, higher than that in logistic model and logistic score model.Conclusions: BN has good applicability in describing the interaction among risk factors and predicting the risk of AKI, which is helpful for early detection of high-risk population of CSA-AKI in clinical practice, so as to prevent the occurrence of disease and improve the prognosis of patients. |
| keywords:Bayesian network logistic regression cardiac surgery associated acute kidney injury risk factors |
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