Objective:To explore and model risk factors in patients with major adverse cardiovascular events (MACEs) after acute type A aortic dissection (ATAAD), and to develop and validate a personalized machine learning model to assess risk factors and predict MACEs in these patients.Methods:Clinical data of patients who attended Beijing Anzhen Hospital and underwent surgical treatment for ATAAD from January 2018 to October 2022 were retrospectively analyzed. Using MACEs as the endpoint, 70% of these patients were randomly divided into the training set and the remaining 30% into the validation set. LASSO regression was applied to explore key clinical variables in the training set. The optimal predictive model was selected from nine machine learning algorithms based on area under the curve. And Shapley Additive explanations was used to elucidate the predictive model. Results:Of the 481 patients included in this study, 135 (35.6%) patients experienced an endpoint event. By combining the results of the training and validation sets, when assessing the validity of the single model with the highest predictive accuracy for the outcome, it was shown that the logistic model (0.774, 95% CI: 0.717-0.830) was the most effective in the combined effect and had a high model accuracy (0.743, 95% CI: 0.720-0.766). According to the results of the LASSO, the factors most associated with postoperative MACEs were history of cerebrovascular disease, coronary artery involvement, shock status on admission to the operating room, FDP, PLT, CPB, ascending aortic clamping, and age. Conclusion:In this study, nine machine learning models were developed to predict the occurrence of postoperative MACEs in patients with acute type A aortic dissection. The logistic model performed significantly better compared to other algorithms. Our study successfully predicted postoperative MACES and identified the factors most associated with MACEs.

Objective:To evaluate the correlation between preoperative platelet function and in-hospital mortality in patients with acute Stanford A aortic dissection(ATAAD) undergoing emergency surgery.Methods:ATAAD emergency surgical patients who underwent preoperative thromboelastography(TEG) examination at three cardiovascular disease hospitals from January 2018 to December 2023 were consecutively selected in this study. The patients were divided into two groups according to whether the patient survived to discharge.Results:867 patients were included in this study. The in-hospital mortality was 11.2%(97 cases). Compared with the DG group(97 cases), the age, pericardial effusion volume, lactate concentration, and MA value of patients in the SG group(770 cases) were higher( P<0.001). Factors independently associated with in-hospital mortality were age(>60 years old), coronary hypoperfusion, pericardial effusion volume(>200.00 ml), and MA value(<60.6 mm). There was a correlation between MA value and in-hospital mortality( P=0.012), and the mediating effect between MA value and platelet count was not significant. Conclusion:There is a correlation between preoperative platelet function and in-hospital in ATAAD patients, and improving platelet function may be one of the important ways to improve the clinical prognosis of those patients.

Objective:To investigate the dynamic changes of inflammatory biomarkers in patients receiving venoarterial extracorporeal membrane oxygenation (VA-ECMO) support after cardiac surgery, and evaluate their predictive value for in-hospital mortality.Methods:The retrospective study included 212 patients who underwent VA-ECMO support following cardiac surgery at Beijing Anzhen Hospital, Capital Medical University, from January 2021 to May 2024. Baseline characteristics and inflammatory markers during ECMO support including procalcitonin (PCT), C-reactive protein (CRP), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and fibrinogen (FBG) were collected. Univariate analysis, ROC curves, and multivariate logistic regression were performed to assess the association of these indicators with outcomes. Results:On day 3 post-ECMO, mean PCT and CRP levels were significantly higher in the deceased group (87 cases) than in the survival group (125 cases). PCT demonstrated an area under the curve ( AUC) of 0.750 (95% CI: 0.680-0.819) for predicting mortality, while CRP had an AUC of 0.701(95% CI: 0.625-0.778). No significant differences were observed in FBG, NLR, or PLR between the two groups. Lactate levels at 24 h post-ECMO ( AUC=0.723) and SOFA scores ( OR=2.511, AUC=0.713) were also significantly associated with mortality risk in the deceased group ( P<0.05). Conclusion:Dynamic increases in PCT and CRP are independent predictors of in-hospital mortality in cardiac surgery patients supported by ECMO. Elevated lactate levels and SOFA scores, aligning with previous studies, reflect severe tissue hypoperfusion and multi-organ dysfunction in non-survivors, underscoring the necessity of dynamic monitoring of inflammatory and organ function markers for prognosis assessment.

The important studies in the field of extracorporeal life support (ECLS) in 2024 focused on the application of cardiac support technologies in acute myocardial infarction (AMI) with cardiogenic shock (CS): veno-arterial extracorporeal membrane oxygenation (V-A ECMO) has not shown advantages in either short- or long-term outcomes and may increase the risk of bleeding and vascular complications; in contrast, micro-axial flow pumps demonstrate potential in improving mortality. The effects of veno-venous extracorporeal membrane oxygenation (V-V ECMO) combined with prone positioning on severe acute respiratory distress syndrome (ARDS) remain uncertain. The survival benefit of extracorporeal cardiopulmonary resuscitation (ECPR) in out-of-hospital cardiac arrest (OHCA) patients has been further validated. The potential benefits of extracorporeal carbon dioxide removal (ECCO2R) require further investigation. Additionally, new guidelines released in 2024 focus on Neurological monitoring and management during ECMO, as well as the Definition and management of right ventricular injury during veno-venous ECMO. ECMO management requires more refined strategies, including optimized oxygenation targets, anticoagulation, blood transfusion, and weaning strategies to improve patient outcomes.
Humans ; Extracorporeal Membrane Oxygenation/methods* ; Shock, Cardiogenic/therapy* ; Cardiopulmonary Resuscitation ; Myocardial Infarction/therapy*

Acute myocardial infarction complicated by cardiogenic shock(AMI-CS)is a clinical syndrome characterized by decreased cardiac output and inadequate tissue perfusion due to acute myocardial ischemia and necrosis.Despite advances in early revascularization and mechanical circulatory support,the hospital mortality rate remains high.To address this challenge,Chinese Society of Cardiology(Chinese Medical Association),the Chinese Society of Extracorporeal Life Support(Chinese Medical Doctor Association)and Chinese Society of Extracorporeal Circulation(Chinese Society of Biomedical Engineering)organized multidisciplinary experts to formulate the"Chinese Expert Consensus on Establishing a Comprehensive Rescue and Treatment System for Patients With Acute Myocardial Infarction Complicated by Cardiogenic Shock".This consensus proposes the establishment of hub-and-spoke model,with spoke hospitals as branches in the region around the hub hospital for AMI-CS rescue and treatment network.Detailed recommendations are provided on issues such as the implementation of multi-directional transfer strategies,the responsibilities among hospitals at different levels,standardization of patient transfer processes,the application of advanced extracorporeal life support technologies,the cultivation of multidisciplinary comprehensive rescue and treatment teams,and the establishment of medical big data.The goal of this consensus is to further promote the development of the rescue and treatment system for AMI-CS patient in China,optimize the allocation of medical resources,enhance patient rescue and treatment efficiency and quality,and improve their outcomes.

Acute myocardial infarction complicated by cardiogenic shock(AMI-CS)is a clinical syndrome characterized by decreased cardiac output and inadequate tissue perfusion due to acute myocardial ischemia and necrosis.Despite advances in early revascularization and mechanical circulatory support,the hospital mortality rate remains high.To address this challenge,Chinese Society of Cardiology(Chinese Medical Association),the Chinese Society of Extracorporeal Life Support(Chinese Medical Doctor Association)and Chinese Society of Extracorporeal Circulation(Chinese Society of Biomedical Engineering)organized multidisciplinary experts to formulate the"Chinese Expert Consensus on Establishing a Comprehensive Rescue and Treatment System for Patients With Acute Myocardial Infarction Complicated by Cardiogenic Shock".This consensus proposes the establishment of hub-and-spoke model,with spoke hospitals as branches in the region around the hub hospital for AMI-CS rescue and treatment network.Detailed recommendations are provided on issues such as the implementation of multi-directional transfer strategies,the responsibilities among hospitals at different levels,standardization of patient transfer processes,the application of advanced extracorporeal life support technologies,the cultivation of multidisciplinary comprehensive rescue and treatment teams,and the establishment of medical big data.The goal of this consensus is to further promote the development of the rescue and treatment system for AMI-CS patient in China,optimize the allocation of medical resources,enhance patient rescue and treatment efficiency and quality,and improve their outcomes.

Objective:To explore and model risk factors in patients with major adverse cardiovascular events (MACEs) after acute type A aortic dissection (ATAAD), and to develop and validate a personalized machine learning model to assess risk factors and predict MACEs in these patients.Methods:Clinical data of patients who attended Beijing Anzhen Hospital and underwent surgical treatment for ATAAD from January 2018 to October 2022 were retrospectively analyzed. Using MACEs as the endpoint, 70% of these patients were randomly divided into the training set and the remaining 30% into the validation set. LASSO regression was applied to explore key clinical variables in the training set. The optimal predictive model was selected from nine machine learning algorithms based on area under the curve. And Shapley Additive explanations was used to elucidate the predictive model. Results:Of the 481 patients included in this study, 135 (35.6%) patients experienced an endpoint event. By combining the results of the training and validation sets, when assessing the validity of the single model with the highest predictive accuracy for the outcome, it was shown that the logistic model (0.774, 95% CI: 0.717-0.830) was the most effective in the combined effect and had a high model accuracy (0.743, 95% CI: 0.720-0.766). According to the results of the LASSO, the factors most associated with postoperative MACEs were history of cerebrovascular disease, coronary artery involvement, shock status on admission to the operating room, FDP, PLT, CPB, ascending aortic clamping, and age. Conclusion:In this study, nine machine learning models were developed to predict the occurrence of postoperative MACEs in patients with acute type A aortic dissection. The logistic model performed significantly better compared to other algorithms. Our study successfully predicted postoperative MACES and identified the factors most associated with MACEs.

Objective:To evaluate the correlation between preoperative platelet function and in-hospital mortality in patients with acute Stanford A aortic dissection(ATAAD) undergoing emergency surgery.Methods:ATAAD emergency surgical patients who underwent preoperative thromboelastography(TEG) examination at three cardiovascular disease hospitals from January 2018 to December 2023 were consecutively selected in this study. The patients were divided into two groups according to whether the patient survived to discharge.Results:867 patients were included in this study. The in-hospital mortality was 11.2%(97 cases). Compared with the DG group(97 cases), the age, pericardial effusion volume, lactate concentration, and MA value of patients in the SG group(770 cases) were higher( P<0.001). Factors independently associated with in-hospital mortality were age(>60 years old), coronary hypoperfusion, pericardial effusion volume(>200.00 ml), and MA value(<60.6 mm). There was a correlation between MA value and in-hospital mortality( P=0.012), and the mediating effect between MA value and platelet count was not significant. Conclusion:There is a correlation between preoperative platelet function and in-hospital in ATAAD patients, and improving platelet function may be one of the important ways to improve the clinical prognosis of those patients.

Objective:To investigate the dynamic changes of inflammatory biomarkers in patients receiving venoarterial extracorporeal membrane oxygenation (VA-ECMO) support after cardiac surgery, and evaluate their predictive value for in-hospital mortality.Methods:The retrospective study included 212 patients who underwent VA-ECMO support following cardiac surgery at Beijing Anzhen Hospital, Capital Medical University, from January 2021 to May 2024. Baseline characteristics and inflammatory markers during ECMO support including procalcitonin (PCT), C-reactive protein (CRP), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and fibrinogen (FBG) were collected. Univariate analysis, ROC curves, and multivariate logistic regression were performed to assess the association of these indicators with outcomes. Results:On day 3 post-ECMO, mean PCT and CRP levels were significantly higher in the deceased group (87 cases) than in the survival group (125 cases). PCT demonstrated an area under the curve ( AUC) of 0.750 (95% CI: 0.680-0.819) for predicting mortality, while CRP had an AUC of 0.701(95% CI: 0.625-0.778). No significant differences were observed in FBG, NLR, or PLR between the two groups. Lactate levels at 24 h post-ECMO ( AUC=0.723) and SOFA scores ( OR=2.511, AUC=0.713) were also significantly associated with mortality risk in the deceased group ( P<0.05). Conclusion:Dynamic increases in PCT and CRP are independent predictors of in-hospital mortality in cardiac surgery patients supported by ECMO. Elevated lactate levels and SOFA scores, aligning with previous studies, reflect severe tissue hypoperfusion and multi-organ dysfunction in non-survivors, underscoring the necessity of dynamic monitoring of inflammatory and organ function markers for prognosis assessment.

AIM:To investigate the impact of celastrol intervention on excitatory amino acid transporter 2(EAAT2)and its neuroprotective role in subarachnoid hemorrhage(SAH).METHODS:Western blot analysis was uti-lized to assess the EAAT2 expression level within 72 h after SAH,while glutamate concentration in cortical brain tissues was measured.Computational simulation was employed to explore the binding of celastrol with EAAT2.Seventy SD rats were randomly assigned to sham,model,model+GT949(an EAAT2 agonist),model+dihydrokainic acid(DHK;an EAAT2 inhibitor),and model+celastrol groups.Glutamate concentration in cortical brain tissues was quantified,and brain edema was assessed by dry-wet weight method.Western blot analysis was conducted to evaluate the expression of EAAT2,aquaporin 4 and apoptosis-related proteins(Bax,Bcl-2,caspase-3 and caspase-9),and TUNEL staining was employed to assess the apoptotic cell count in each group.RESULTS:(1)EAAT2 level decreased while glutamate con-centration increased.(2)Celastrol was found to directly bind to EAAT2,enhancing EAAT2 expression and reducing glu-tamate concentration after SAH.(3)Celastrol demonstrated the ability to inhibit brain edema after SAH.(4)Celastrol was effective in reducing neuronal apoptosis after SAH.CONCLUSION:Celastrol has the potential to up-regulate EAAT2 expression,lower glutamate level,mitigate brain edema,and decrease neuronal apoptosis after SAH.