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.
Objective. Small extracellular vesicles derived from mesenchymal stem cells (MSCs) play important roles in cardiac protection. Studies have shown that the cardiovascular protection of sodium-glucose cotransporter 2 inhibitor (SGLT2i) is independent of its hypoglycemic effect. This study is aimed at investigating whether small extracellular vesicles derived from MSCs pretreated with empagliflozin (EMPA) has a stronger cardioprotective function after myocardial infarction (MI) and to explore the underlying mechanisms. Methods and Results. We evaluated the effects of EMPA on MSCs and the effects of EMPA-pretreated MSCs-derived small extracellular vesicles (EMPA-sEV) on myocardial apoptosis, angiogenesis, and cardiac function after MI in vitro and in vivo. The small extracellular vesicles of control MSCs (MSC-sEV) and EMPA-pretreated MSCs were extracted, respectively. Small extracellular vesicles were cocultured with apoptotic H9c2 cells induced by H2O2 or injected into the infarcted area of the Sprague-Dawley (SD) rat myocardial infarction model. EMPA increased the cell viability, migration ability, and inhibited apoptosis and senescence of MSCs. In vitro, EMPA-sEV inhibited apoptosis of H9c2 cells compared with the control group (MSC-sEV). In the SD rat model of MI, EMPA-sEV inhibited myocardial apoptosis and promoted angiogenesis in the infarct marginal areas compared with the MSC-sEV. Meanwhile, EMPA-sEV reduced infarct size and improved cardiac function. Through small extracellular vesicles (miRNA) sequencing, we found several differentially expressed miRNAs, among which miR-214-3p was significantly elevated in EMPA-sEV. Coculture of miR-214-3p high expression MSC-derived small extracellular vesicles with H9c2 cells produced similar protective effects. In addition, miR-214-3p was found to promote AKT phosphorylation in H9c2 cells. Conclusions. Our data suggest that EMPA-sEV significantly improve cardiac repair after MI by inhibiting myocardial apoptosis. miR-214-3p at least partially mediated the myocardial protection of EMPA-sEV through the AKT signaling pathway.
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