Association between inflammation and left ventricular thrombus formation following ST-elevation myocardial infarction

Factors predicting resolution of left ventricular thrombus in different time windows after myocardial infarction

BACKGROUND

Left ventricular thrombus (LVT) is a serious complication after myocardial infarction. However, due to its asymptomatic nature, early detection is challenging. We aimed to explore the differences in clinical correlates of LVT found in acute to subacute and chronic phases of myocardial infarction.

METHODS

We collected data from 153 patients who were diagnosed with LVT after myocardial infarction at the Affiliated Hospital of Qingdao University from January 2013 to December 2022. Baseline information, inflammatory markers, transthoracic echocardiograph (TTE) data and other clinical correlates were collected. Patients were categorized into acute to subacute phase group (< 30 days) and chronic phase group (30 days and after) according to the time at which echocardiograph was performed. The resolution of thrombus within 90 days is regarded as the primary endpoint event. We fitted logistic regression models to relating clinical correlates with phase-specific thrombus resolution.

RESULTS

For acute to subacute phase thrombus patients: C-reactive protein levels (OR: 0.95, 95% CI: 0.918-0.983, p = 0.003) were significantly associated with thrombus resolution. For chronic phase thrombus patients: anticoagulant treatment was associated with 5.717-fold odds of thrombus resolution (OR: 5.717, 95% CI: 1.543-21.18, p = 0.009).

CONCLUSIONS

Higher levels of CRP were associated with lower likelihood of LVT resolution in acute phase myocardial infarction; Anticoagulant therapy is still needed for thrombus in the chronic stage of myocardial infarction.

Differential gene expression patterns in ST-elevation Myocardial Infarction and Non-ST-elevation Myocardial Infarction

The ST-elevation Myocardial Infarction (STEMI) and Non-ST-elevation Myocardial Infarction (NSTEMI) might occur because of coronary artery stenosis. The gene biomarkers apply to the clinical diagnosis and therapeutic decisions in Myocardial Infarction. The aim of this study was to introduce, enrich and estimate timely the blood gene profiles based on the high-throughput data for the molecular distinction of STEMI and NSTEMI. The text mining data (50 genes) annotated with DisGeNET data (144 genes) were merged with the GEO gene expression data (5 datasets) using R software. Then, the STEMI and NSTEMI networks were primarily created using the STRING server, and improved using the Cytoscape software. The high-score genes were enriched using the KEGG signaling pathways and Gene Ontology (GO). Furthermore, the genes were categorized to determine the NSTEMI and STEMI gene profiles. The time cut-off points were identified statistically by monitoring the gene profiles up to 30 days after Myocardial Infarction (MI). The gene heatmaps were clearly created for the STEMI (high-fold genes 69, low-fold genes 45) and NSTEMI (high-fold genes 68, low-fold genes 36). The STEMI and NSTEMI networks suggested the high-score gene profiles. Furthermore, the gene enrichment suggested the different biological conditions for STEMI and NSTEMI. The time cut-off points for the NSTEMI (4 genes) and STEMI (13 genes) gene profiles were established up to three days after Myocardial Infarction. The study showed the different pathophysiologic conditions for STEMI and NSTEMI. Furthermore, the high-score gene profiles are suggested to measure up to 3 days after MI to distinguish the STEMI and NSTEMI.

High CRP-albumin ratio is associated high thrombus burden in patients with newly diagnosed STEMI

In patients undergoing primary percutaneous coronary intervention (pPCI) due to ST-segment elevation myocardial infarction (STEMI), an increased intracoronary thrombus burden is a strong predictive factor for adverse cardiovascular events. The C-reactive protein (CRP)-serum albumin (SA) ratio (CAR), used as an inflammatory marker, is closely associated with thrombogenicity. In this study, we investigated the relationship between coronary thrombus burden and CAR in patients undergoing pPCI due to newly diagnosed STEMI. A total of 216 patients who underwent pPCI due to STEMI were retrospectively included for the study. Angiographic thrombus burden was assessed according to thrombolysis in myocardial infarction (TIMI) grading, and those with grade 1, 2, 3 were classified as low thrombus burden (n = 120) and those with grade 4, 5 were classified as high thrombus burden (HTB) (n = 96). CAR was calculated as the ratio of CRP to SA. The average age of the patients was 60 ± 9.8, and the male ratio was 61.1. Compared to the LTB group, the HTB group had higher CAR, age, SYNTAX score, baseline cTnT, peak cTnT, CRP, glucose, WBC, and NLR while the LVEF and SA levels were lower (P < .05). Spearman's correlation analysis revealed a significant correlation between thrombus burden and CAR. The multivariable logistic regression analysis revealed that CAR (odds ratio: 10.206; 95% confidence interval: 2.987-34.872, P < .001) was a independent risk factor for HTB. According to the receiver operating characteristic (ROC) analysis, when the cutoff value for CAR was taken as ≥1.105 CAR could predict HTB with a sensitivity of 70.8% and specificity of 67.7%. Our data indicate that CAR an independent risk factor for thrombus burden.

The Systemic Immune Inflammatory Index Predicts No-Reflow Phenomenon after Primary Percutaneous Coronary Intervention in Older Patients with STEMI

Purpose: Coronary no-reflow phenomenon (NRP), a common adverse complication in patients with ST-segment elevation myocardial infarction (STEMI) treated by percutaneous coronary intervention (PCI), is associated with poor patient prognosis. In this study, the correlation between the systemic immune-inflammation index (SII) and NRP in older patients with STEMI was studied, to provide a basis for early identification of high-risk patients and improve their prognosis.

Materials and methods: Between January 2017 and June 2020, 578 older patients with acute STEMI admitted to the Department of Cardiology of Hebei General Hospital for direct PCI treatment were selected for this retrospective study. Patients were divided into an NRP group and normal-flow group according to whether NRP occurred during the operation. Clinical data and the examination indexes of the two groups were collected. Logistic regression was used to analyze the independent predictors of NRP, and the receiver operating characteristic curve was used to further analyze the ability of SII to predict NRP in older patients with STEMI.

Results: Multivariate logistic analysis indicated that hypertension (OR=2.048, 95% CI:1.252–3.352, P=0.004), lymphocyte count (OR=0.571, 95% CI:0.368–0.885, P=0.012), platelet count (OR=1.009, 95% CI:1.005–1.013, P<0.001), hemoglobin (OR=1.015, 95% CI:1.003–1.028, P=0.018), multivessel disease (OR=2.237, 95% CI:1.407–3.558, P=0.001), and SII≥1814 (OR=3.799, 95% CI:2.190–6.593, P<0.001) were independent predictors of NRP after primary PCI in older patients with STEMI. Receiver operating characteristic curve analysis demonstrated that SII had a high predictive value for NRP (AUC=0.738; 95% CI:0.686–0.790), with the best cut-off value of 1814, a sensitivity of 52.85% and a specificity of 85.71%.

Conclusion: For older patients with STEMI undergoing primary PCI, SII is a valid predictor of NRP.

Effects of opium use on one-year major adverse cardiovascular events (MACE) in the patients with ST-segment elevation MI undergoing primary PCI: a propensity score matched - machine learning based study

BACKGROUND

Considerable number of people still use opium worldwide and many believe in opium's health benefits. However, several studies proved the detrimental effects of opium on the body, especially the cardiovascular system. Herein, we aimed to provide the first evidence regarding the effects of opium use on one-year major adverse cardiovascular events (MACE) in the patients with ST-elevation MI (STEMI) who underwent primary PCI.

METHODS

We performed a propensity score matching of 2:1 (controls: opium users) that yielded 518 opium users and 1036 controls. Then, we performed conventional statistical and machine learning analyses on these matched cohorts. Regarding the conventional analysis, we performed multivariate analysis for hazard ratio (HR) of different variables and MACE and plotted Kaplan Meier curves. In the machine learning section, we used two tree-based ensemble algorithms, Survival Random Forest and XGboost for survival analysis. Variable importance (VIMP), tree minimal depth, and variable hunting were used to identify the importance of opium among other variables.

RESULTS

Opium users experienced more one-year MACE than their counterparts, although it did not reach statistical significance (Opium: 72/518 (13.9%), Control: 112/1036 (10.8%), HR: 1.27 (95% CI: 0.94-1.71), adjusted p-value = 0.136). Survival random forest algorithm ranked opium use as 13th, 13th, and 12th among 26 variables, in variable importance, minimal depth, and variable hunting, respectively. XGboost revealed opium use as the 12th important variable. Partial dependence plot demonstrated that opium users had more one-year MACE compared to non-opium-users.

CONCLUSIONS

Opium had no protective effects on one-year MACE after primary PCI on patients with STEMI. Machine learning and one-year MACE analysis revealed some evidence of its possible detrimental effects, although the evidence was not strong and significant. As we observed no strong evidence on protective or detrimental effects of opium, future STEMI guidelines may provide similar strategies for opium and non-opium users, pending the results of forthcoming studies. Governments should increase the public awareness regarding the evidence for non-beneficial or detrimental effects of opium on various diseases, including the outcomes of primary PCI, to dissuade many users from relying on false beliefs about opium's benefits to continue its consumption.