Artificial intelligence algorithms permits rapid acute kidney injury risk classification of patients with acute myocardial infarction
Jun Wei,
Dabei Cai,
Tingting Xiao,
Qianwen Chen,
Wenwu Zhu,
Qingqing Gu,
Yu Wang,
Qingjie Wang,
Xin Chen,
Shenglin Ge,
Ling Sun
Affiliations
Jun Wei
Department of Cardiovascular Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China; Department of Cardiovascular Surgery, the Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221006, Jiangsu, China
Dabei Cai
Department of Cardiology, the Affiliated Changzhou Second People's Hospital of Nanjing Medical University, Changzhou, 213000, Jiangsu, China; Graduate School of Dalian Medical University, Dalian Medical University, Dalian, 116000, Liaoning, China
Tingting Xiao
Department of Cardiology, the Affiliated Changzhou Second People's Hospital of Nanjing Medical University, Changzhou, 213000, Jiangsu, China
Qianwen Chen
Department of Cardiology, the Affiliated Changzhou Second People's Hospital of Nanjing Medical University, Changzhou, 213000, Jiangsu, China
Wenwu Zhu
Department of Cardiology, Xuzhou Central Hospital, Xuzhou Clinical School of Nanjing Medical University, Xuzhou Institute of Cardiovascular Disease, Xuzhou, 221006, Jiangsu, China
Qingqing Gu
Department of Cardiology, the Affiliated Changzhou Second People's Hospital of Nanjing Medical University, Changzhou, 213000, Jiangsu, China
Yu Wang
Department of Cardiology, the Affiliated Changzhou Second People's Hospital of Nanjing Medical University, Changzhou, 213000, Jiangsu, China
Qingjie Wang
Department of Cardiology, the Affiliated Changzhou Second People's Hospital of Nanjing Medical University, Changzhou, 213000, Jiangsu, China; Graduate School of Dalian Medical University, Dalian Medical University, Dalian, 116000, Liaoning, China
Xin Chen
Department of Cardiology, the Affiliated Changzhou Second People's Hospital of Nanjing Medical University, Changzhou, 213000, Jiangsu, China; Corresponding author. Department of Cardiology, Affiliated Changzhou No.2 people's Hospital of Nanjing Medical University, Changzhou, Jiangsu, 213003, China.
Shenglin Ge
Department of Cardiovascular Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China; Corresponding author. Department of Cardiovascular surgery, First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230000, China.
Ling Sun
Department of Cardiology, the Affiliated Changzhou Second People's Hospital of Nanjing Medical University, Changzhou, 213000, Jiangsu, China; Graduate School of Dalian Medical University, Dalian Medical University, Dalian, 116000, Liaoning, China; Corresponding author. Department of Cardiology, the Affiliated Changzhou No.2 people's Hospital of Nanjing Medical University, Changzhou, Jiangsu, 213003, China.
Objective: This study aimed to develop and validate several artificial intelligence (AI) models to identify acute myocardial infarction (AMI) patients at an increased risk of acute kidney injury (AKI) during hospitalization. Methods: Included were patients diagnosed with AMI from the Medical Information Mart for Intensive Care (MIMIC) III and IV databases. Two cohorts of AMI patients from Changzhou Second People's Hospital and Xuzhou Center Hospital were used for external validation of the models. Patients' demographics, vital signs, clinical characteristics, laboratory results, and therapeutic measures were extracted. Totally, 12 AI models were developed. The area under the receiver operating characteristic curve (AUC) were calculated and compared. Results: AKI occurred during hospitalization in 1098 (28.3 %) of the 3882 final enrolled patients, split into training (3105) and test (777) sets randomly. Among them, Random Forest (RF), C5.0 and Bagged CART models outperformed the other models in both the training and test sets. The AUCs for the test set were 0.754, 0.734 and 0.730, respectively. The incidence of AKI was 9.8 % and 9.5 % in 2202 patients in the Changzhou cohort and 807 patients in the Xuzhou cohort with AMI, respectively. The AUCs for patients in the Changzhou cohort were RF, 0.761; C5.0, 0.733; and bagged CART, 0.725, respectively, and Xuzhou cohort were RF, 0.799; C5.0, 0.808; and bagged CART, 0.784, respectively. Conclusion: Several machines learning-based prediction models for AKI after AMI were developed and validated. The RF, C5.0 and Bagged CART model performed robustly in identifying high-risk patients earlier. Clinical trial approval statement: This Trial was registered in the Chinese clinical trials registry: ChiCTR1800014583. Registered January 22, 2018 (http://www.chictr.org.cn/searchproj.aspx).
