The Essential of ACC/AHA Guidelines for the Management of Patients with ST-Elevation Myocardial Infarction was introduced, including epidemology, initial management in the emergency department and hospital management.
ObjectiveTo investigate the effectiveness of establishment of chest pain center and optimized process in the diagnostic and treatment progress and short-term prognostic value of acute non-ST segment elevation myocardial infarction (NSTEMI) patients. MethodsThis was a retrospective study. We included NSTEMI patients admitted in the Emergency Department in our hospital, 41 patients admitted before the establishment of the chest pain center (April 2015) were included as group A (30 males and 11 females at age of 64.7±11.8 years), 42 patients after the establishment of the chest pain center (April 2016) as group B (31 males and 11 females at age of 64.6±11.8 years), and 38 patients after the establishment of the chest pain center (April 2017) as group C (30 males and 8 females at age of 62.6±10.0 years). The clinical outcomes of the three groups were compared.ResultsThe time from admission to electrocardiogram was 20.0 (17.0, 25.5) min in the group A, 4.0 (2.8, 5.0) min in the group B, and 3.0 (2.0, 4.0) min in the group C (P<0.001). The first doctor's non-electrocardiogram advice time was 13.0 (10.0, 18.0) min, 9.5 (6.8, 15.3) min, and 9.0 (7.0, 12.0) min (P=0.001) in the three groups, respectively. The diagnostic confirmed time was 139.4±48.5 min, 71.1±51.5 min, 63.9±41.9 min (P<0.001). The proportion of patients receiving emergency dual anti-platelet load dose treatment was 53.1%, 70.0%, 100.0% (P=0.001), respectively. The time of receiving emergency dual anti-platelet load dose treatment was 208.0 (72.0, 529.0) min, 259.0 (91.0, 340.0) min, and 125.0 (86.0, 170.0) min (P=0.044) in the three groups, respectively. Emergency percutaneous coronary artery intervention (PCI) start time was 60.9 (42.1, 95.8) hours, 61.3 (43.3, 92.2) hours, 30.5 (2.8, 44.1) hours (P<0.001) in the three groups, respectively. Among them, the moderate risk patients’ PCI starting time was 63.0 (48.1, 94.2) hours, 62.3 (42.1, 116.2) hours, and 40.1 (17.2, 60.4) hours (P>0.05), respectively. The high risk patients’ PCI starting time was 47.9 (23.7, 102.4) hours, 55.2 (44.0, 89.6) hours, 23.2 (1.7, 41.8) hours in the three groups, respectively (P<0.001). The hospitalization time of the patients was 7.0 (5.4, 9.4) days, 5.9 (4.9, 8.7) days, 4.7 (3.1, 6.2) days in the three groups (P<0.001), respectively. The hospitalization time of the moderate risk patients was 6.9 (4.9, 8.8) days, 6.4 (4.9, 8.0) days, 4.8 (3.2, 6.5) days in the three groups (P>0.05), respectively. The hospitalization time of the high risk patients was 7.1 (5.5, 9.9) days, 5.9 (4.6, 9.8) days, and 4.4 (3.0, 6.1) days, respectively (P<0.001). The fatality rate of inpatients was 4.9%, 0.0%, and 0.0%, respectively (P>0.05). The correlation coefficient of hospitalization time, diagnosis confirmed time and PCI starting time was 0.219 and 0.456 (P<0.05), respectively.ConclusionThe establishment and optimized process of chest pain center can accelerate the time of early diagnosis of NSTEMI, which is helpful to obtain stratified and graded standardized treatment for patients according to their conditions, to accelerate the specific treatment process of high risk NSTEMI patients, and shorten the hospitalization time.
ObjectiveTo analyze prognostic ability of inflammation-based Glasgow prognostic score (GPS) in patients with ST-segment elevation myocardial infarction (STEMI).MethodsWe retrospectively analyzed the clinical data of 289 patients with STEMI admitted to the Department of Emergency in West China Hospital from April 2015 to January 2016. All study subjects were divided into three groups: a group of GPS 0 (190 patients including 150 males and 40 females aged 62.63±12.98 years), a group of GPS 1 (78 patients including 58 males and 20 females aged 66.57±15.25 years), and a group of GPS 2 (21 patients including 16 males and 5 females aged 70.95±9.58 years). Cox regression analysis was conducted to analyze the independent risk factors of predicting long-term mortality of patients with STEMI.ResultsThere was a statistical difference in long-term mortality (9.5% vs. 23.1% vs. 61.9%, P<0.001) and in-hospital mortality (3.7% vs. 7.7% vs. 23.8%, P<0.001) among the three groups. The Global Registry of Acute Coronary Events (GRACE) scores and Gensini scores increased in patients with higher GPS scores, and the differences were statistically different (P<0.001). Multivariable Cox regression analysis showed that the GPS was independently associated with STEMI long-term all-cause mortality (1 vs. 0, HR: 2.212, P=0.037; 2 vs. 0, HR: 8.286, P<0.001).ConclusionGPS score is helpful in predicting the long-term and in-hospital prognosis of STEMI patients, and thus may guide clinical precise intervention by early risk stratification.
The purpose of this study is to investigate the effects of self-assembling peptide GFS-4 on three-dimen-sional myocardial cell culture and tissue repair of myocardial infarction. The circular dichroism (CD) spectrum was used to detect secondary structure of GFS-4, and atomic force microscope (AFM) was used to analyze the microstructure of self-assembly. The nanofiber scaffolds self-assembled by GFS-4 were used as the three-dimensional culture material to observe the growth effect of cardiomyocytes. The model of myocardial infarction was established and the effect of GFS-4 on myocardial infarction was studied. The results indicated that self-assembling peptide GFS-4 could form mainly β-sheet structure that can form dense nanofiber scaffolds after 24 hours’ self-assembling. The myocardial cells had a favorable growth status in GFS-4 nanofiber scaffold hydrogel when cells treated in three-dimen-sional cell culture. The experiment of repairing myocardial infarction in vitro proved that peptide GFS-4 hydrogel scaffold could alleviate tissue necrosis in a myocardial infarction area. As a new nanofiber scaffold material, self-assembling peptide GFS-4 can be used for three-dimensional cell culture and tissue repairing in myocardial infarction area.
Objective To detect the bile acid profile in serum based on liquid chromatography-tandem mass spectrometry, and construct a combined biomarker diagnostic model for differentiating acute myocardial infarction (AMI) from unstable angina (UA). Methods A total of 180 patients with acute coronary syndrome who visited Huludao Central Hospital between August 2023 and February 2024 were randomly selected, and there were 117 patients with UA and 63 patients with AMI. Using liquid chromatography-tandem mass spectrometry, 15 bile acid subtypes in serum were detected. Orthogonal partial least squares discriminant analysis was used to compare the serum bile acid metabolic profiles of the subjects. Differences in metabolites were screened based on a significance level of P<0.05 and variable importance in projection (VIP)>1. Multiple logistic regression analysis was performed to construct a diagnostic model for differentiating AMI from UA, and the diagnostic performance of the model was evaluated using receiver operating characteristic (ROC) curve and other statistical methods. Results The differential bile acid biomarkers in the serum of UA and AMI patients included glycodeoxycholic acid, glycochenodeoxycholic acid (GCDCA), deoxycholic acid (DCA), glycocholic acid, and aurodeoxycholic acid (TDCA) (P<0.05, VIP>1). A binary logistic stepwise regression analysis showed that three bile acid biomarkers (GCDCA, DCA, and TDCA) and three common biochemical indicators (aspartate aminotransferase, creatine kinase, and total bile acid) were factors differentiating AMI from UA (P<0.05). The area under the ROC curve of the model was 0.986 [95% confidence interval (0.973, 0.999), P<0.001], demonstrating a good diagnostic performance. Conclusions GCDCA, DCA, and TDCA can serve as potential biomarkers for distinguishing AMI from UA. The model combining these three bile acids with aspartate aminotransferase, creatine kinase, and total bile acid can effectively identify AMI.
摘要:目的:探讨基层医院开展急诊经皮冠状动脉支架植入术(PCI)治疗急性心肌梗死(AMI)的可行性、安全性。方法:回顾分析2002年11月~2009年4月我院41例AMI患者的急诊PCI资料。结果:41例AMI患者,急诊开通梗死相关动脉(IRA)39例(即时成功率95.1%),开通IRA者中术后死亡2例(死亡率4.9%),总成功率90.2％。结论:在有条件的基层医院开展急诊PCI安全有效。Abstract: Objective: To explore the feasibility and safety of primary percutaneous coronary intervention in patients with acute myocardial infarction in elementary hospital. Methods: The clinical data of 41 AMI patients who underwent emergent PCI from November 2002 to April 2009 were retrospectively analyzed. Results: Among the 41 AMI patients referred to PCI, infarctrelated arteries were recanalized in 39 cases. The immediate success rate was 95.1%. 2 cases of them died. The total success rate was 90.2%.Conclusion: Emergent PCI is safe and effective in the hospitals which could carry out PCI.
ObjectiveTo investigate the influencing factors of serum NT-proBNP level in elderly patients with acute myocardial infarction (AMI) after PCI, and to analyze its predictive value for the short-term prognosis of patients. MethodsA total of 98 elderly patients with AMI in Zhengzhou central hospital from May 2020 to August 2022 were selected, all of whom underwent PCI. The level of serum NT-proBNP before and after PCI was detected. The level of serum NT-probNP after PCI was ≥125 pg/mL, and the level of serum NT-probNP after PCI was normal. Univariate analysis of the general data of the elevated NT-proBNP group and the normal group, Lasso regression model was used to screen the screening variables, and Logistic regression was used to analyze the influencing factors of serum NT-proBNP level in elderly AMI patients after PCI. The prognosis recovery of patients with different NT-proBNP and the level of NT-proBNP in patients with different prognosis were compared and analyzed. ROC curve was drawn to analyze the predictive value of NT-proBNP level in patients with short-term prognosis after PCI. ResultsLogistic regression analysis showed that the time from onset to PCI, age, left ventricular ejection fraction (LVEF), stroke, number of stents implanted, no recirculation and stent diameter were the influencing factors of serum NT-proBNP level in elderly AMI patients after PCI. The incidence of adverse cardiovascular events (MACE) was 21.43% (21/98) in 98 patients followed up 6 months after surgery, and the incidence of NT-proBNP increased group was 68.00% (17/25), which was significantly higher than that of normal group (5.48% (4/73) (P<0.05). The level of NT-proBNP in the group with MACE was significantly higher than that in the group without MACE (P<0.05). ROC curve showed that AUC was 0.813 (95%CI 0.721 to0.884), sensitivity and specificity were 80.95% and 79.22%, respectively, suggesting that serum NT-proBNP level after PCI had certain predictive value for short-term prognosis of patients. ConclusionSerum NT-proBNP level in elderly AMI patients after PCI has a good ability to predict the short-term prognosis of patients. Comprehensive consideration of the number of stents inserted, the presence of stroke, the presence of reflow and age and other factors to strengthen the monitoring of NT-proBNP level is helpful to prevent and control the occurrence of MACE, so as to improve the prognosis of patients.
ObjectiveTo establish and validate the diagnostic model of ferroptosis genes for acute myocardial infarction (AMI) based on bioinformatics. MethodsFive AMI gene expression data were obtained from Gene Expression Omnibus (GEO), namely GSE66360, GSE48060, GSE60993, GSE83500, GSE34198. Among them, GSE66360 was used as the training set to perform differential analysis, and intersection of differential genes and ferroptosis genes was taken to obtain differentially expressed ferroptosis genes in AMI. GO and KEGG enrichment analysis was performed using Metascape website. Subsequently, random forest (RF) algorithm was used to screen out key genes with high classification performance according to the Keeny coefficient score, and artificial neural network (ANN) diagnostic model of AMI ferroptosis feature gene was constructed by model group GSE83500. The area under the receiver operating characteristic curve (AUC) of 10-fold cross-validation was used to evaluate the performance and generalization ability of the model, and 3 external independent datasets were used to verify the diagnostic performance of this model. The single sample gene setenrichment analysis was used to explore the difference in immune cell infiltration between infarcted myocardium and normal myocardium after AMI. In addition, correlation analysis between immune cells and key genes was also conducted. Finally, potential drugs that would prevent and treat AMI by regulating ferroptosis were screened out from the Coremin Medical platform. ResultsA total of 16 differentially expressed ferroptosis genes were obtained in the training set, GO enrichment analysis showed that they mainly participated in biological functions such as cellular response to biological stimuli and chemical stress, regulation of interleukin 17, etc. KEGG enrichment analysis showed that these genes were significantly enriched in NOD-like receptor signaling pathway, programmed cell necrosis, Leishmaniasis and other pathways. Four genes with good classification performance were screened out using RF algorithm, namely EPAS1, SLC7A5, FTH1, and ZFP36. The results of 10-fold cross-validation showed that the minimum AUC value was 0.746, the maximum value was 0.906, and the average value was 0.805. The AUC of the ANN model was 0.859, and the AUC values of the three independent validation sets were 0.763 (GSE48060), 0.673 (GSE60993), 0.698 (GSE34198). Immune cell infiltration found that macrophages, mast cells and monocytes were significantly active after AMI. Correlation analysis found that there were positive correlations between 4 key genes and activated dendritic cells, eosinophils and γδT cells. A total of 20 potential western medicines were predicted which could prevent and treat AMI by regulating ferroptosis, and the predicted potential Chinese medicine was mainly heat-clearing and detoxifying and blood-activating and removing blood stasis drugs. ConclusionThe identified AMI ferroptosis genes by bioinformatics method have certain diagnostic significance, which provides a reference for disease diagnosis and treatment.
ObjectiveTo systematically evaluate prediction models for in-hospital mortality risk in patients with acute myocardial infarction (AMI). MethodsA comprehensive search was conducted in PubMed, Embase, Web of Science, Cochrane Library, and CNKI databases from inception to May 30, 2025, to identify studies related to AMI in-hospital mortality prediction models. Risk of bias and applicability were assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Relevant data were extracted for model quality assessment. ResultsA total of 29 studies involving 75 AMI in-hospital mortality prediction models were included. Key predictive factors identified included Killip classification, neutrophil count, renal insufficiency, age, systolic blood pressure, and left ventricular ejection fraction. The area under the receiver operating characteristic curve (AUC) ranged from 0.580 to 0.998. Internal validation was reported in 21 studies, external validation in 4, and both in 4 studies. Model calibration was evaluated in 23 studies. Most models were presented as nomograms. All studies demonstrated good applicability, though 25 were rated as high risk of bias overall. ConclusionCurrent AMI in-hospital mortality prediction models show generally good predictive performance, with some variables exhibiting stable predictive effects. However, the lack of external validation and high risk of bias remain prevalent issues. Future studies should focus on prospective, multicenter, high-quality designs to enhance the practical and clinical value of these models.