Predicting acute kidney injury risk in acute myocardial infarction patients: An artificial intelligence model using medical information mart for intensive care databases
BackgroundPredictive models based on machine learning have been widely used in clinical practice. Patients with acute myocardial infarction (AMI) are prone to the risk of acute kidney injury (AKI), which results in a poor prognosis for the patient. The aim of this study was to develop a machine lear...
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2022-09-01
|
| Series: | Frontiers in Cardiovascular Medicine |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fcvm.2022.964894/full |
| _version_ | 1828109003217960960 |
|---|---|
| author | Dabei Cai Dabei Cai Tingting Xiao Ailin Zou Lipeng Mao Lipeng Mao Boyu Chi Boyu Chi Yu Wang Qingjie Wang Qingjie Wang Yuan Ji Ling Sun Ling Sun |
| author_facet | Dabei Cai Dabei Cai Tingting Xiao Ailin Zou Lipeng Mao Lipeng Mao Boyu Chi Boyu Chi Yu Wang Qingjie Wang Qingjie Wang Yuan Ji Ling Sun Ling Sun |
| author_sort | Dabei Cai |
| collection | DOAJ |
| description | BackgroundPredictive models based on machine learning have been widely used in clinical practice. Patients with acute myocardial infarction (AMI) are prone to the risk of acute kidney injury (AKI), which results in a poor prognosis for the patient. The aim of this study was to develop a machine learning predictive model for the identification of AKI in AMI patients.MethodsPatients with AMI who had been registered in the Medical Information Mart for Intensive Care (MIMIC) III and IV database were enrolled. The primary outcome was the occurrence of AKI during hospitalization. We developed Random Forests (RF) model, Naive Bayes (NB) model, Support Vector Machine (SVM) model, eXtreme Gradient Boosting (xGBoost) model, Decision Trees (DT) model, and Logistic Regression (LR) models with AMI patients in MIMIC-IV database. The importance ranking of all variables was obtained by the SHapley Additive exPlanations (SHAP) method. AMI patients in MIMIC-III databases were used for model evaluation. The area under the receiver operating characteristic curve (AUC) was used to compare the performance of each model.ResultsA total of 3,882 subjects with AMI were enrolled through screening of the MIMIC database, of which 1,098 patients (28.2%) developed AKI. We randomly assigned 70% of the patients in the MIMIC-IV data to the training cohort, which is used to develop models in the training cohort. The remaining 30% is allocated to the testing cohort. Meanwhile, MIMIC-III patient data performs the external validation function of the model. 3,882 patients and 37 predictors were included in the analysis for model construction. The top 5 predictors were serum creatinine, activated partial prothrombin time, blood glucose concentration, platelets, and atrial fibrillation, (SHAP values are 0.670, 0.444, 0.398, 0.389, and 0.381, respectively). In the testing cohort, using top 20 important features, the models of RF, NB, SVM, xGBoost, DT model, and LR obtained AUC of 0.733, 0.739, 0.687, 0.689, 0.663, and 0.677, respectively. Placing RF models of number of different variables on the external validation cohort yielded their AUC of 0.711, 0.754, 0.778, 0.781, and 0.777, respectively.ConclusionMachine learning algorithms, particularly the random forest algorithm, have improved the accuracy of risk stratification for AKI in AMI patients and are applied to accurately identify the risk of AKI in AMI patients. |
| first_indexed | 2024-04-11T10:55:54Z |
| format | Article |
| id | doaj.art-ecea451b0e1f44a59ff3bc7aed373f29 |
| institution | Directory Open Access Journal |
| issn | 2297-055X |
| language | English |
| last_indexed | 2024-04-11T10:55:54Z |
| publishDate | 2022-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Cardiovascular Medicine |
| spelling | doaj.art-ecea451b0e1f44a59ff3bc7aed373f292022-12-22T04:28:46ZengFrontiers Media S.A.Frontiers in Cardiovascular Medicine2297-055X2022-09-01910.3389/fcvm.2022.964894964894Predicting acute kidney injury risk in acute myocardial infarction patients: An artificial intelligence model using medical information mart for intensive care databasesDabei Cai0Dabei Cai1Tingting Xiao2Ailin Zou3Lipeng Mao4Lipeng Mao5Boyu Chi6Boyu Chi7Yu Wang8Qingjie Wang9Qingjie Wang10Yuan Ji11Ling Sun12Ling Sun13Department of Cardiology, The Affiliated Changzhou No. 2 People’s Hospital of Nanjing Medical University, Changzhou, ChinaGraduate School of Dalian Medical University, Dalian Medical University, Dalian, ChinaDepartment of Cardiology, The Affiliated Changzhou No. 2 People’s Hospital of Nanjing Medical University, Changzhou, ChinaDepartment of Cardiology, The Affiliated Changzhou No. 2 People’s Hospital of Nanjing Medical University, Changzhou, ChinaDepartment of Cardiology, The Affiliated Changzhou No. 2 People’s Hospital of Nanjing Medical University, Changzhou, ChinaGraduate School of Dalian Medical University, Dalian Medical University, Dalian, ChinaDepartment of Cardiology, The Affiliated Changzhou No. 2 People’s Hospital of Nanjing Medical University, Changzhou, ChinaGraduate School of Dalian Medical University, Dalian Medical University, Dalian, ChinaDepartment of Cardiology, The Affiliated Changzhou No. 2 People’s Hospital of Nanjing Medical University, Changzhou, ChinaDepartment of Cardiology, The Affiliated Changzhou No. 2 People’s Hospital of Nanjing Medical University, Changzhou, ChinaGraduate School of Dalian Medical University, Dalian Medical University, Dalian, ChinaDepartment of Cardiology, The Affiliated Changzhou No. 2 People’s Hospital of Nanjing Medical University, Changzhou, ChinaDepartment of Cardiology, The Affiliated Changzhou No. 2 People’s Hospital of Nanjing Medical University, Changzhou, ChinaGraduate School of Dalian Medical University, Dalian Medical University, Dalian, ChinaBackgroundPredictive models based on machine learning have been widely used in clinical practice. Patients with acute myocardial infarction (AMI) are prone to the risk of acute kidney injury (AKI), which results in a poor prognosis for the patient. The aim of this study was to develop a machine learning predictive model for the identification of AKI in AMI patients.MethodsPatients with AMI who had been registered in the Medical Information Mart for Intensive Care (MIMIC) III and IV database were enrolled. The primary outcome was the occurrence of AKI during hospitalization. We developed Random Forests (RF) model, Naive Bayes (NB) model, Support Vector Machine (SVM) model, eXtreme Gradient Boosting (xGBoost) model, Decision Trees (DT) model, and Logistic Regression (LR) models with AMI patients in MIMIC-IV database. The importance ranking of all variables was obtained by the SHapley Additive exPlanations (SHAP) method. AMI patients in MIMIC-III databases were used for model evaluation. The area under the receiver operating characteristic curve (AUC) was used to compare the performance of each model.ResultsA total of 3,882 subjects with AMI were enrolled through screening of the MIMIC database, of which 1,098 patients (28.2%) developed AKI. We randomly assigned 70% of the patients in the MIMIC-IV data to the training cohort, which is used to develop models in the training cohort. The remaining 30% is allocated to the testing cohort. Meanwhile, MIMIC-III patient data performs the external validation function of the model. 3,882 patients and 37 predictors were included in the analysis for model construction. The top 5 predictors were serum creatinine, activated partial prothrombin time, blood glucose concentration, platelets, and atrial fibrillation, (SHAP values are 0.670, 0.444, 0.398, 0.389, and 0.381, respectively). In the testing cohort, using top 20 important features, the models of RF, NB, SVM, xGBoost, DT model, and LR obtained AUC of 0.733, 0.739, 0.687, 0.689, 0.663, and 0.677, respectively. Placing RF models of number of different variables on the external validation cohort yielded their AUC of 0.711, 0.754, 0.778, 0.781, and 0.777, respectively.ConclusionMachine learning algorithms, particularly the random forest algorithm, have improved the accuracy of risk stratification for AKI in AMI patients and are applied to accurately identify the risk of AKI in AMI patients.https://www.frontiersin.org/articles/10.3389/fcvm.2022.964894/fullacute myocardia infarctionacute kidney injurymachine learningrandom forestarea under the receiver operating characteristic curve |
| spellingShingle | Dabei Cai Dabei Cai Tingting Xiao Ailin Zou Lipeng Mao Lipeng Mao Boyu Chi Boyu Chi Yu Wang Qingjie Wang Qingjie Wang Yuan Ji Ling Sun Ling Sun Predicting acute kidney injury risk in acute myocardial infarction patients: An artificial intelligence model using medical information mart for intensive care databases Frontiers in Cardiovascular Medicine acute myocardia infarction acute kidney injury machine learning random forest area under the receiver operating characteristic curve |
| title | Predicting acute kidney injury risk in acute myocardial infarction patients: An artificial intelligence model using medical information mart for intensive care databases |
| title_full | Predicting acute kidney injury risk in acute myocardial infarction patients: An artificial intelligence model using medical information mart for intensive care databases |
| title_fullStr | Predicting acute kidney injury risk in acute myocardial infarction patients: An artificial intelligence model using medical information mart for intensive care databases |
| title_full_unstemmed | Predicting acute kidney injury risk in acute myocardial infarction patients: An artificial intelligence model using medical information mart for intensive care databases |
| title_short | Predicting acute kidney injury risk in acute myocardial infarction patients: An artificial intelligence model using medical information mart for intensive care databases |
| title_sort | predicting acute kidney injury risk in acute myocardial infarction patients an artificial intelligence model using medical information mart for intensive care databases |
| topic | acute myocardia infarction acute kidney injury machine learning random forest area under the receiver operating characteristic curve |
| url | https://www.frontiersin.org/articles/10.3389/fcvm.2022.964894/full |
| work_keys_str_mv | AT dabeicai predictingacutekidneyinjuryriskinacutemyocardialinfarctionpatientsanartificialintelligencemodelusingmedicalinformationmartforintensivecaredatabases AT dabeicai predictingacutekidneyinjuryriskinacutemyocardialinfarctionpatientsanartificialintelligencemodelusingmedicalinformationmartforintensivecaredatabases AT tingtingxiao predictingacutekidneyinjuryriskinacutemyocardialinfarctionpatientsanartificialintelligencemodelusingmedicalinformationmartforintensivecaredatabases AT ailinzou predictingacutekidneyinjuryriskinacutemyocardialinfarctionpatientsanartificialintelligencemodelusingmedicalinformationmartforintensivecaredatabases AT lipengmao predictingacutekidneyinjuryriskinacutemyocardialinfarctionpatientsanartificialintelligencemodelusingmedicalinformationmartforintensivecaredatabases AT lipengmao predictingacutekidneyinjuryriskinacutemyocardialinfarctionpatientsanartificialintelligencemodelusingmedicalinformationmartforintensivecaredatabases AT boyuchi predictingacutekidneyinjuryriskinacutemyocardialinfarctionpatientsanartificialintelligencemodelusingmedicalinformationmartforintensivecaredatabases AT boyuchi predictingacutekidneyinjuryriskinacutemyocardialinfarctionpatientsanartificialintelligencemodelusingmedicalinformationmartforintensivecaredatabases AT yuwang predictingacutekidneyinjuryriskinacutemyocardialinfarctionpatientsanartificialintelligencemodelusingmedicalinformationmartforintensivecaredatabases AT qingjiewang predictingacutekidneyinjuryriskinacutemyocardialinfarctionpatientsanartificialintelligencemodelusingmedicalinformationmartforintensivecaredatabases AT qingjiewang predictingacutekidneyinjuryriskinacutemyocardialinfarctionpatientsanartificialintelligencemodelusingmedicalinformationmartforintensivecaredatabases AT yuanji predictingacutekidneyinjuryriskinacutemyocardialinfarctionpatientsanartificialintelligencemodelusingmedicalinformationmartforintensivecaredatabases AT lingsun predictingacutekidneyinjuryriskinacutemyocardialinfarctionpatientsanartificialintelligencemodelusingmedicalinformationmartforintensivecaredatabases AT lingsun predictingacutekidneyinjuryriskinacutemyocardialinfarctionpatientsanartificialintelligencemodelusingmedicalinformationmartforintensivecaredatabases |