Artificial intelligence algorithms permits rapid acute kidney injury risk classification of patients with acute myocardial infarction.

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).

Plasma triglyceride is associated with the recurrence of atrial fibrillation after radiofrequency catheter ablation: A retrospective study.

BACKGROUND: The purpose of this study was to explore the association between triglycerides (TGs) and the risk of atrial fibrillation (AF) recurrence. METHODS AND RESULTS: Included were adult patients with AF who underwent radiofrequency catheter ablation in the Affiliated Changzhou Second People's Hospital of Nanjing Medical University. The enrolled patients were divided into the AF recurrence group and the sinus rhythm (SR) maintenance group. The univariate Cox regression analysis and Kaplan-Meier survival curve were performed estimate the association between TG and the risk of AF recurrence. Of the 402 patients, 79 (19.7%) experienced recurrence of AF after ablation. The TG level was significantly higher in the AF recurrence group than in the SR-maintaining group. Patients were grouped by quartile of TG levels, with Quartile 1 and Quartile 2 defined as the low concentration group, Quartile 3 as the moderate concentration group, and Quartile 4 as the high concentration group. Multivariate Cox regression analysis showed that the moderate concentration group (p = .02, hazard ratio [HR]: 2.331, 95% confidence interval [CI]: 1.141-4.762) and high concentration group (p = .007, HR: 2.873, 95% CI: 1.332-6.199) were associated with an increased risk of AF recurrence compared with the low concentration group. The median follow-up was 1.17 years, it is indicated that a higher risk of recurrent AF was observed in the moderate concentration and high concentration group (log-rank: χ2 = 7.540, p = .023). CONCLUSION: Our data suggest that an elevated TG level measured before catheter ablation is associated with an increased risk of AF recurrence.

Engineered Exosomes with Growth Differentiation Factor-15 Overexpression Enhance Cardiac Repair After Myocardial Injury.

Background: Cardiac repair remains a thorny issue for survivors of acute myocardial infarction (AMI), due to the regenerative inertia of myocardial cells. Cell-free therapies, such as exosome transplantation, have become a potential strategy for myocardial injury. The aim of this study was to investigate the role of engineered exosomes in overexpressing Growth Differentiation Factor-15 (GDF-15) (GDF15-EVs) after myocardial injury, and their molecular mechanisms in cardiac repair. Methods: H9C2 cells were transfected with GDF-15 lentivirus or negative control. The exosomes secreted from H9C2 cells were collected and identified. The cellular apoptosis and autophagy of H2O2-injured H9C2 cells were assessed by Western blotting, TUNEL assay, electron microscopy, CCK-8 and caspase 3/7 assay. A rat model of AMI was constructed by ligating the left anterior descending artery. The anti-apoptotic, pro-angiogenic effects of GDF15-EVs treatment, as well as ensuing functional and histological recovery were evaluated. Then, mRNA sequencing was performed to identify the differentially expressed mRNAs after GDF15-EVs treatment. Results: GDF15-EVs inhibited apoptosis and promoted autophagy in H2O2 injured H9C2 cells. GDF15-EVs effectively decreased the infarct area and enhanced the cardiac function in rats with AMI. Moreover, GDF15-EVs hindered inflammatory cell infiltration, inhibited cell apoptosis, and promoted cardiac angiogenesis in rats with AMI. RNA sequence showed that telomerase reverse transcriptase (TERT) mRNA was upregulated in GDF15-EVs-treated H9C2 cells. AMPK signaling was activated after GDF15-EVs. Silencing TERT impaired the protective effects of GDF15-EVs on H2O2-injured H9C2 cells. Conclusion: GDF15-EVs could fulfil their protective effects against myocardial injury by upregulating the expression of TERT and activating the AMPK signaling pathway. GDF15-EVs might be exploited to design new therapies for AMI.

Association between triglyceride glucose and acute kidney injury in patients with acute myocardial infarction: a propensity score­matched analysis.

BACKGROUND: Acute kidney injury (AKI) in patients with acute myocardial infarction (AMI) often indicates a poor prognosis. OBJECTIVE: This study aimed to investigate the association between the TyG index and the risk of AKI in patients with AMI. METHODS: Data were taken from the Medical Information Mart for Intensive Care (MIMIC) database. A 1:3 propensity score (PS) was set to match patients in the AKI and non-AKI groups. Multivariate logistic regression analysis, restricted cubic spline (RCS) regression and subgroup analysis were performed to assess the association between TyG index and AKI. RESULTS: Totally, 1831 AMI patients were included, of which 302 (15.6%) had AKI. The TyG level was higher in AKI patients than in non-AKI patients (9.30 ± 0.71 mg/mL vs. 9.03 ± 0.73 mg/mL, P < 0.001). Compared to the lowest quartile of TyG levels, quartiles 3 or 4 had a higher risk of AKI, respectively (Odds Ratiomodel 4 = 2.139, 95% Confidence Interval: 1.382-3.310, for quartile 4 vs. quartile 1, Ptrend < 0.001). The risk of AKI increased by 34.4% when the TyG level increased by 1 S.D. (OR: 1.344, 95% CI: 1.150-1.570, P < 0.001). The TyG level was non-linearly associated with the risk of AKI in the population within a specified range. After 1:3 propensity score matching, the results were similar and the TyG level remained a risk factor for AKI in patients with AMI. CONCLUSION: High levels of TyG increase the risk of AKI in AMI patients. The TyG level is a predictor of AKI risk in AMI patients, and can be used for clinical management.

Neutrophil Percentage as a Potential Biomarker of Acute Kidney Injury Risk and Short-Term Prognosis in Patients with Acute Myocardial Infarction in the Elderly.

Objective: This study aimed to explore the association of preoperative neutrophil percentage (NEUT%) with the risk of acute kidney injury (AKI) in patients with acute myocardial infarction (AMI) having undergone coronary interventional therapy. Methods: A single-center, retrospective and observational study was conducted. From December 2012 to June 2021, patients with AMI were enrolled and divided into AKI group and non-AKI group. The NEUT% in the two groups was compared. The association between NEUT% with the risk of post-AMI AKI was analyzed by univariate and multivariable logistic regression. Kaplan-Meier survival curve was drawn to evaluate the prognostic ability of NEUT% for short-term all-cause death following AMI. Results: A total of 3001 consecutive patients were enrolled with an average age of 64.38 years. AKI occurred in 327 (10.9%) patients. The NEUT% was higher in the AKI group than in the non-AKI group ([76.65±11.43]% versus [73.22±11.83]%, P<0.001). NEUT% was also identified as an independent risk factor for AKI in AMI patients after adjustment (OR=1.021, 95% CI: 1.010-1.033, P < 0.001). Compared with those at the lowest quartile of NEUT%, the patients at quartiles 2-4 had a higher risk of AKI (P for trend = 0.003). The odds of AKI increased by 29.0% as NEUT% increased by 1 standard deviation (OR=1.290, 95% CI: 1.087-1.531, P = 0.004). After a median of 35 days follow-up, 93 patients died. Patients with a higher NEUT% presented a higher risk of all-cause death after AMI (Log rank: χ2 =24.753, P<0.001). Conclusion: In AMI patients, the peripheral blood NEUT% was positively associated with the odds of AKI and short-term all-cause mortality. NEUT% may provide physicians with more information about disease development and prognosis.

Association of SGLT2 inhibitor dapagliflozin with risks of acute kidney injury and all-cause mortality in acute myocardial infarction patients.

OBJECTIVE: Sodium-glucose cotransporter 2 (SGLT2) inhibitors have well-documented effects in reducing hospitalization or cardiovascular mortality, while the association of SGLT2 inhibitor dapagliflozin (DAPA) and the risk of acute kidney injury (AKI) in acute myocardial infarction (AMI) patients has not been comprehensively investigated. Therefore, we aimed to assess the association between DAPA and AKI risk in AMI patients after percutaneous coronary intervention (PCI) therapy. METHODS: Using the Changzhou Acute Myocardial Infarction Registry database, we retrospectively included AMI patients from January 2017 to August 2021 and analyzed the risk of AKI and all-cause mortality after PCI therapy. The patients were divided into two groups according to the use of DAPA (DAPA group and Ctrl group). Patients in the DAPA group started to use DAPA after admission and continued its use during hospitalization and follow-up period. Baseline characteristics were balanced between the two groups with a propensity score matching (PSM) analysis. The outcome was AKI within 7 days after PCI and all-cause mortality during a follow-up of 2 years. Univariate and multivariate logistic regression analyses were used to assess the association between DAPA and AKI risk. RESULTS: A total of 1839 AMI patients undergoing PCI were enrolled. DAPA was used in 278 (15.1%) patients. Postoperative AKI occurred in 351 (19.1%) cases. A 1:1 PSM analysis was used to reduce confounding factors. The multivariate stepwise regression analysis showed that DAPA (odds ratio, OR 0.66; 95% confidence interval, CI 0.44-0.97; P = 0.036) was an independent protective factor in the entire cohort. After matching, the use of DAPA in AMI patients was independently associated with a decline of AKI risk (OR 0.32; 95% CI, 0.19-0.53; P < 0.001) after hospital admission. Meanwhile, there were significant differences in mortality between the DAPA group and Ctrl group (2.5% vs. 7.6%, P = 0.012). CONCLUSION: SGLT2 inhibitor DAPA was associated with lower risks of incident AKI and all-cause mortality in AMI patients after PCI therapy.

Extracellular vesicle mediated targeting delivery of growth differentiation factor-15 improves myocardial repair by reprogramming macrophages post myocardial injury.

OBJECTIVE: Extracellular vesicles (EVs) have garnered considerable attention among researchers as candidates for natural drug delivery systems. This study aimed to investigate whether extracellular vesicle mediated targeting delivery of growth differentiation factor-15 (GDF15) improves myocardial repair by reprogramming macrophages post myocardial injury. METHODS: EVs were isolated from macrophages transfected with GDF15 (EXO-GDF15) and control macrophages (EXO-NC). In vitro and vivo experiments, we compared their reprogram ability of macrophages and regeneration activity. Furthermore, proteomic analysis were employed to determine the specific mechanism by which GDF15 repairs the myocardium. RESULTS: Compared with EXO-NC, EXO-GDF15 significantly regulated macrophage phenotypic shift, inhibited cardiomyocyte apoptosis, and enhanced endothelial cell angiogenesis. Moreover, EXO-GDF15 also significantly regulated macrophage heterogeneity and inflammatory cytokines, reduced fibrotic area, and enhanced cardiac function in infarcted rats. Proteomic analysis revealed a decrease in fatty acid-binding protein 4 (FABP4) protein expression following treatment with EXO-GDF15. Mechanistically, the reprogramming of macrophages by EXO-GDF15 is accomplished through the activation of Smad2/3 phosphorylation, which subsequently inhibits the production of FABP4. CONCLUSIONS: Extracellular vesicle mediated targeting delivery of growth differentiation factor-15 improves myocardial repair by reprogramming macrophages post myocardial injury via down-regulating the expression of FABP4. EXO-GDF15 may serve as a promising approach of immunotherapy.

Development and validation of a novel combinatorial nomogram model to predict in-hospital deaths in heart failure patients.

BACKGROUND: The purpose of this study was to develop a Nomogram model to identify the risk of all-cause mortality during hospitalization in patients with heart failure (HF). METHODS: HF patients who had been registered in the Medical Information Mart for Intensive Care (MIMIC) III and IV databases were included. The primary outcome was the occurrence of all-cause mortality during hospitalization. Two Logistic Regression models (LR1 and LR2) were developed to predict in-hospital death for HF patients from the MIMIC-IV database. The MIMIC-III database were used for model validation. The area under the receiver operating characteristic curve (AUC) was used to compare the discrimination of each model. Calibration curve was used to assess the fit of each developed models. Decision curve analysis (DCA) was used to estimate the net benefit of the predictive model. RESULTS: A total of 16,908 HF patients were finally enrolled through screening, of whom 2,283 (13.5%) presented with in-hospital death. Totally, 48 variables were included and analyzed in the univariate and multifactorial regression analysis. The AUCs for the LR1 and LR2 models in the test cohort were 0.751 (95% CI: 0.735∼0.767) and 0.766 (95% CI: 0.751-0.781), respectively. Both LR models performed well in the calibration curve and DCA process. Nomogram and online risk assessment system were used as visualization of predictive models. CONCLUSION: A new risk prediction tool and an online risk assessment system were developed to predict mortality in HF patients, which performed well and might be used to guide clinical practice.

Dapagliflozin reduces risk of heart failure rehospitalization in diabetic acute myocardial infarction patients: a propensity score-matched analysis.

OBJECTIVE: The aim of this study was to investigate the effect of dapagliflozin (DAPA) on the rate of heart failure rehospitalization in patients with acute myocardial infarction (AMI) and type 2 diabetes mellitus (T2DM). METHODS: AMI patients with T2DM from CZ-AMI registry between January 2017 and January 2021 were enrolled in this study. Patients were stratified into DAPA users and non-DAPA users. The primary outcome was the incidence of heart failure rehospitalization. Kaplan-Meier analysis and Cox regressions were performed to evaluate the prognostic significance of DAPA. Propensity score matching (PSM) was performed to minimize the bias of confounding factors and facilitate the comparability between groups. The enrolled patients were matched with a propensity score of 1:1. RESULTS: A total of 961 patients were included, and 132 (13.74%) heart failure rehospitalizations occurred during a median follow-up of 540 days. In the Kaplan-Meier analysis, DAPA users had a statistically significantly lower rate of heart failure rehospitalization than non-DAPA users (p < 0.0001). Multivariate Cox analysis showed that DAPA was an independent protective factor for heart failure rehospitalization risk after discharge (HR = 0.498, 95% CI = 0.296 ~ 0.831, p = 0.001). After 1:1 propensity score matching, survival analysis showed a lower cumulative risk of heart failure rehospitalization in DAPA users than in non-DAPA users (p = 0.0007). In-hospital and continued use of DAPA remained significantly associated with a reduced risk of heart failure rehospitalization (HR = 0.417, 95% CI = 0.417 ~ 0.838, p = 0.001). Results were consistent across sensitivity and subgroup analyses. CONCLUSION: In patients with diabetic AMI, in-hospital and continued use of DAPA after discharge were associated with a significant lower risk of heart failure rehospitalization.

A 3D culture system improves the yield of MSCs-derived extracellular vesicles and enhances their therapeutic efficacy for heart repair.

BACKGROUND: Extracellular vesicles (EVs) derived from mesenchymal stem cells (MSCs), due to their inner functional substances, have shown great value in treating acute myocardial infarction (AMI). However, their clinical application is limited by a low yield. In the present study, we cultured EVs using a hollow fiber bioreactor-based three-dimensional (3D) system, and assessed their therapeutic effectiveness on AMI. METHODS: The MSCs separated from fresh human umbilical cord were planted into the flasks of two systems: two-dimensional (2D) culture and hollow-fiber-bioreactor based 3D culture. EVs were extracted from the culture supernatants. Characteristics and yields of EVs from two culture systems, namely 2D-EVs and 3D-EVs, were compared. A rat model of AMI was built up to assess their therapeutic efficacy on AMI. RESULTS: The yield of 3D-EVs was higher, with biofunctions similar to those of 2D-EVs. 3D-EVs repressed the apoptosis of cardiomyocytes, facilitated angiogenesis, and regulated the transition of macrophage subpopulations after myocardial infarction, and eventually improved cardiac function in the AMI rats. CONCLUSIONS: The hollow fiber 3D culture system can increase the yield of MSCs-derived EVs to render a strong cardioprotective effect in AMI rats.

Empagliflozin-Pretreated Mesenchymal Stem Cell-Derived Small Extracellular Vesicles Attenuated Heart Injury.

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.

Predicting acute kidney injury risk in acute myocardial infarction patients: An artificial intelligence model using medical information mart for intensive care databases.

Background: Predictive 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. Methods: Patients 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. Results: A 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. Conclusion: Machine 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.