Derivation and validation of a simple inflammation-based risk score system for predicting in-hospital mortality in acute coronary syndrome patients

Inflammation and renal function decline in chronic coronary syndrome: a prospective multicenter cohort study

BACKGROUND

Renal function decline is a frequently encountered complication in patients with chronic coronary syndrome. Aside from traditional cardiovascular risk factors, the inflammatory burden emerged as the novel phenotype that compromised renal prognosis in such population.

METHODS

A cohort with chronic coronary syndrome was enrolled to investigate the association between inflammatory status and renal dysfunction. Levels of inflammatory markers, including high-sensitivity C-reactive protein (hs-CRP), tumour necrosis factor-α (TNF-α), adiponectin, matrix metalloproteinase-9, interleukin-6, lipoprotein-associated phospholipase A2, were assessed. Renal event was defined as > 25% decline in estimated glomerular filtration rate (eGFR). Inflammatory scores were calculated based on the aggregate of hs-CRP, TNF-α, and adiponectin levels.

RESULTS

Among the 850 enrolled subjects, 145 patients sustained a renal event during an averaged 3.5 years follow-up. Multivariate analysis with Cox regression suggested elevations in hs-CRP, TNF-α, and adiponectin levels were independent risk factors for the occurrence of a renal event. Whereas, Kaplan-Meier curve illustrated significant correlation between high TNF-α (P = 0.005), adiponectin (P < 0.001), but not hs-CRP (P = 0.092), and eGFR decline. The aggregative effect of these biomarkers was also distinctly correlated with renal events (score 2: P = 0.042; score 3: P < 0.001).

CONCLUSIONS

Inflammatory burden was associated with eGFR decline in patients with chronic coronary syndrome.

Prognostic Value of GRACE Risk Score Combined With Systemic Immune-Inflammation Index in Patients With ST-Segment Elevation Myocardial Infarction After Percutaneous Coronary Intervention

The Global Registry of Acute Coronary Events (GRACE) score and the systemic immune-inflammation index (SII) were used independently to predict adverse outcomes in patients with ST-elevation myocardial infarction (STEMI). In this study, 1041 patients with STEMI were divided into 4 groups based on GRACE scores and optimal cutoff values for SII. SII was positively correlated with GRACE score (r = 0.164; P < .001). SII (HR, hazard ratio: 2.051; 95%CI: 1.249-3.368; P = .005) and GRACE score (HR: 7.625; 95%CI: 3.692-15.746; P < .001) were independent risk predictors of short-term major adverse cardiovascular events (MACEs). Taking the low SII+low GRACE group as the reference group, the short-term MACE HR of the high SII+high GRACE group was 40.470 (95%CI: 5.547-295.253). Comparing the area under the curve, the combined use of SII and GRACE scores can significantly improve the prediction efficiency of short-term MACE compared with the single use of SII and GRACE scores. In conclusion, SII may be positively correlated with GRACE score, and the combination of the two accurately predicted the occurrence of short-term MACE in STEMI patients after percutaneous coronary intervention (PCI).

C-Reactive Protein: The Quintessential Marker of Systemic Inflammation in Coronary Artery Disease-Advancing toward Precision Medicine

Atherosclerotic cardiovascular disease (CVD) remains the leading cause of mortality worldwide. While conventional risk factors have been studied and managed, CVD continues to pose a global threat. Risk scoring systems based on these factors have been developed to predict acute coronary syndromes and guide therapeutic interventions. However, traditional risk algorithms may not fully capture the complexities of individual patients. Recent research highlights the role of inflammation, particularly chronic low-grade inflammation, in the pathogenesis of coronary artery disease (CAD). C-reactive protein (CRP) is an inflammatory molecule that has demonstrated value as a predictive marker for cardiovascular risk assessment, both independently and in conjunction with other parameters. It has been incorporated into risk assessment algorithms, enhancing risk prediction and guiding therapeutic decisions. Pharmacological interventions with anti-inflammatory properties, such as statins, glucagon-like peptide-1 agonists, and interleukin-1 inhibitors, have shown promising effects in reducing both cardiovascular risks and CRP levels. This manuscript provides a comprehensive review of CRP as a marker of systemic inflammation in CAD. By exploring the current knowledge surrounding CRP and its implications for risk prediction and therapeutic interventions, this review contributes to the advancement of personalized cardiology and the optimization of patient care.

Diagnostic Performance of Serum MicroRNAs for ST-Segment Elevation Myocardial Infarction in the Emergency Department

Prompt diagnosis of ST-segment elevation myocardial infarction (STEMI) is essential for initiating timely treatment. MicroRNAs have recently emerged as biomarkers in cardiovascular diseases. This study aimed to evaluate the discriminatory capacity of serum microRNAs in identifying an ischemic origin in patients presenting with chest discomfort to the Emergency Department. The study included 98 participants (78 with STEMI and 20 with nonischemic chest discomfort). Significant differences in the expression levels of miR-133b, miR-126, and miR-155 (but not miR-1, miR-208, and miR-208b) were observed between groups. miR-133b and miR-155 exhibited 97% and 93% sensitivity in identifying STEMI patients, respectively. miR-126 demonstrated a specificity of 90% in identifying STEMI patients. No significant associations were found between microRNAs and occurrence of major adverse cardiovascular events (MACE). However, patients with MACE had higher levels of interleukin (IL)-15, IL-21, IFN-γ-induced protein-10, and N-terminal pro B-type natriuretic peptide compared to non-MACE patients. Overall, there were significant associations among the expression levels of microRNAs. However, microRNAs did not demonstrate associations with either inflammatory markers or cardiovascular risk scores. This study highlights the potential of microRNAs, particularly miR-133b and miR-126, as diagnostic biomarkers for distinguishing patients with STEMI from those presenting with nonischemic chest discomfort to the Emergency Department.

Comparative characterization of inflammatory profile and oral microbiome according to an inflammation-based risk score in ST-segment elevation myocardial infarction

Ischemic heart disease considers the myocardial infarction (MI), either non-ST-segment elevation (non-STEMI) or ST-segment elevation myocardial infarction (STEMI); this represents the main cause of mortality in Mexican population. Regarding to the inflammatory state, this is reported to be a major prognostic factor of mortality for patients with MI. One of the conditions capable of producing systemic inflammation is periodontal disease. It has been proposed that the oral microbiota is translocated through the bloodstream to the liver and intestine, generating intestinal dysbiosis. The aim of this protocol is to assess oral microbiota diversity and circulating inflammatory profile in STEMI patients stratified according to an inflammation-based risk scoring system. We found that Bacteriodetes phylum was the most abundant in STEMI patients, and Prevotella was the most abundant genus, with a higher proportion in periodontitis patients. In fact, Prevotella genus was found to correlate positively and significantly with elevated IL-6 concentration. Our study defined a non-causal association inferred between the cardiovascular risk of STEMI patients, determined by changes in the oral microbiota that influence the development of periodontal disease and its relationship with the exacerbation of the systemic inflammatory response.

Use of machine learning models to predict in-hospital mortality in patients with acute coronary syndrome

BACKGROUND

Cardiovascular diseases are a significant health burden with the prevalence increasing worldwide. Thus, a highly accurate assessment and prediction of death risk are crucial to meet the clinical demand. This study sought to develop and validate a model to predict in-hospital mortality among patients with the acute coronary syndrome (ACS) using nonlinear algorithms.

METHODS

A total of 2414 ACS patients were enrolled in this study. All samples were divided into five groups for cross-validation. The logistic regression (LR) model and XGboost model were applied to predict in-hospital mortality. The results of two models were compared between the variable set by the global registry of acute coronary events (GRACE) score and the selected variable set.

RESULTS

The in-hospital mortality rate was 3.5% in the dataset. Model performance on the selected variable set was better than that on GRACE variables: a 3% increase in area under the receiver operating characteristic (ROC) curve (AUC) for LR and 1.3% for XGBoost. The AUC of XGBoost is 0.913 (95% confidence interval [CI]: 0.910-0.916), demonstrating a better discrimination ability than LR (AUC = 0.904, 95% CI: 0.902-0.905) on the selected variable set. Almost perfect calibration was found in XGBoost (slope of predicted to observed events, 1.08; intercept, -0.103; p < .001).

CONCLUSIONS

XGboost modeling, an advanced machine learning algorithm, identifies new variables and provides high accuracy for the prediction of in-hospital mortality in ACS patients.

Lactobacillus levels and prognosis of patients with acute myocardial infarction

OBJECTIVE

To explore the relationship between Lactobacillus and prognosis of acute myocardial infarction (AMI) patients treated by percutaneous coronary intervention (PCI) and its correlation with clinical parameters.

METHODS

Consecutive patients with AMI in the coronary care unit of Tianjin Chest Hospital in China who received emergency PCI between July 2017 and December 2018 were enrolled. Subjects' fecal 16S rDNA gene sequencing data were analyzed and subjects were categorized into low, medium and high level groups according to stool Lactobacillus measurements. The primary endpoints were major adverse cardiac events. Cox regression analysis was used to analyze the relationship between Lactobacillus and prognosis. Spearman correlation analysis and trend tests were used to assess the relationship between Lactobacillus and the clinical indicators.

RESULTS

The data of 254 patients were included in the analysis. Mean age was 65.90 ± 11.56 years, and 152 patients (59.84%) were male. Follow-up time was 652 (548.25-753.00) days. Multivariate Cox regression analysis showed a significantly lower risk of major adverse cardiac events in patients with Lactobacillus > 7.1 copies/g [adjusted hazard ratio (HR) = 0.216, 95% CI: 0.094-0.493,P < 0.001] compared to patients with Lactobacillus ≤ 3.6 copies/g. Statistically significant differences were shown in ST-segment elevation myocardial infarction (STEMI) (HR = 0.217, 95% CI: 0.085-0.551, P = 0.001). Lactobacillus was a protective factor for male smokers aged over 60 years whose brain natriuretic peptide was over 1,000 pg/mL. Spearman correlation analysis showed that Lactobacillus correlated negatively with white blood cells, neutrophils, high-sensitivity C-reactive protein, TroponinT, creatine kinase, creatine kinase-MB and brain natriuretic peptide (downward trend), and correlated positively with left ventricular ejection fraction (upward trend).

CONCLUSIONS

This study is the first to reveal the correlation between Lactobacillus and inflammation and myocardial damage after STEMI. STEMI patients, especially male smokers aged over 60 years with severe impairment of cardiac function, have better outcomes with high levels of Lactobacillus, suggesting new therapeutic strategies for improving the prognosis and quality of life of AMI patients.

Usefulness of Easy-to-Use Risk Scoring Systems Rated in the Emergency Department to Predict Major Adverse Outcomes in Hospitalized COVID-19 Patients

Background: Several easy-to-use risk scoring systems have been built to identify patients at risk of developing complications associated with COVID-19. However, information about the ability of each score to early predict major adverse outcomes during hospitalization of severe COVID-19 patients is still scarce. Methods: Eight risk scoring systems were rated upon arrival at the Emergency Department, and the occurrence of thrombosis, need for mechanical ventilation, death, and a composite that included all major adverse outcomes were assessed during the hospital stay. The clinical performance of each risk scoring system was evaluated to predict each major outcome. Finally, the diagnostic characteristics of the risk scoring system that showed the best performance for each major outcome were obtained. Results: One hundred and fifty-seven adult patients (55 ± 12 years, 66% men) were assessed at admission to the Emergency Department and included in the study. A total of 96 patients (61%) had at least one major outcome during hospitalization; 32 had thrombosis (20%), 80 required mechanical ventilation (50%), and 52 eventually died (33%). Of all the scores, Obesity and Diabetes (based on a history of comorbid conditions) showed the best performance for predicting mechanical ventilation (area under the ROC curve (AUC), 0.96; positive likelihood ratio (LR+), 23.7), death (AUC, 0.86; LR+, 4.6), and the composite outcome (AUC, 0.89; LR+, 15.6). Meanwhile, the inflammation-based risk scoring system (including leukocyte count, albumin, and C-reactive protein levels) was the best at predicting thrombosis (AUC, 0.63; LR+, 2.0). Conclusions: Both the Obesity and Diabetes score and the inflammation-based risk scoring system appeared to be efficient enough to be integrated into the evaluation of COVID-19 patients upon arrival at the Emergency Department.

Trp Fluorescence Redshift during HDL Apolipoprotein Denaturation Is Increased in Patients with Coronary Syndrome in Acute Phase: A New Assay to Evaluate HDL Stability

High-density lipoproteins' (HDL) stability is a determinant of their residence times in plasma and consequently an important parameter that influences the beneficial properties of these lipoproteins. Since there are no accessible procedures for this purpose, here, we describe the methodological conditions to assess the stability of the HDL based on the redshift of the fluorescence spectrum of tryptophans contained in the structure of HDL-apolipoproteins during incubation with urea 8M. Along the HDL denaturation kinetics, the main variations of fluorescence were observed at the wavelengths of 330, 344, and 365 nm at room temperature. Therefore, HDL denaturation was estimated using the tryptophan (Trp)-ratio of fluorescence intensity (rfi) at such wavelengths. By setting 100% of the measurable denaturation at 26 h, HDL reached 50% after 8 h of incubation with urea. Then, for further analyses we determined the percentage of HDL denaturation at 8 h as an estimation of the stability of these lipoproteins. To explore the potential usefulness of this test, we analyzed the stability of HDL isolated from the plasma of 24 patients diagnosed with acute coronary syndrome (ACS). These HDL presented significantly higher percentages of denaturation (64.9% (58.7-78.4)) than HDLs of healthy individuals (23.3% (20.3-27.0)). These results indicate that HDL in ACS are less stable than in control subjects. Moreover, the percentage of denaturation of HDL correlated with body mass index and aspartate transaminase plasma activity. Furthermore, apo-I, HDL-cholesterol, HDL-triglycerides, and apo A-I-to-triglycerides ratio correlated with the percentage of HDL denaturation, suggesting that the lipoprotein composition is a main determinant of HDL stability. Finally, the percentage of HDL denaturation is the parameter that predicted the presence of ACS as determined by a machine learning procedure and logistic regression analysis. In conclusion, we established the methodological conditions to assess the stability of HDL by a fluorescence-based method that merits exploration in prospective studies for evaluating the coronary artery disease risk.

A simple and readily available inflammation-based risk scoring system on admission predicts the need for mechanical ventilation in patients with COVID-19

Objective

To investigate whether a simplified inflammation-based risk scoring system comprising three readily available biomarkers (albumin, C-reactive protein, and leukocytes) may predict major adverse outcomes in patients with COVID-19.

Methods

Upon admission to the emergency room, the inflammation-based risk scoring system was applied and patients were classified as having mild, moderate, or severe inflammation. In-hospital occurrence of thrombosis, need for mechanical ventilation, and death were recorded.

Results

One-hundred patients (55 ± 13 years; 71% men) were included and classified as having mild (29%), moderate (12%), or severe (59%) inflammation. The need for mechanical ventilation differed among patients in each group (16%, 50%, and 71%, respectively; P < 0.0001), yielding a 4.1-fold increased risk of requiring mechanical ventilation in patients with moderate inflammation and 5.4 for those with severe inflammation. On the contrary, there were no differences for the occurrence of thrombosis (10%, 8%, and 22%, respectively; P = 0.142) or death (21%, 42%, and 39%, respectively; P = 0.106). In the multivariate analysis, only severe inflammation (hazard ratio [HR] = 4.1), D-dimer > 574 ng/mL (HR = 3.0), and troponin I ≥ 6.7 ng/mL (HR = 2.4) at hospital admission were independent predictors of the need for mechanical ventilation.

Conclusion

The inflammation-based risk scoring system predicts the need for mechanical ventilation in patients with severe COVID-19.

Immune cell infiltration landscapes in pediatric acute myocarditis analyzed by CIBERSORT

BACKGROUND

Myocarditis is an inflammatory disease of the myocardium, which leads to cardiac dysfunction and heart failure. Previous studies have suggested that complex cross-talk between innate and adaptive immune responses is involved in the pathogenesis of acute myocarditis. Immunohistochemistry is the current standard method for the evaluation of infiltrating immune cells, however, it is difficult to investigate and quantify many immune cell populations using this technique.

METHODS

Endomyocardial biopsy samples of five pediatric patients with myocarditis were analyzed by cell-type identification by estimating relative subsets of RNA transcript (CIBERSORT), a computational method for quantifying cell fractions from tissue gene expression profiles. CIBERSORT results were then compared with immunohistochemistry analyses.

RESULTS

Significant results of immune infiltrate deconvolution were obtained in four patients with fulminant myocarditis by CIBERSORT analysis. Among 22 immune cell types, 19 cell types were detected in one or more patients. Activated NK cells were the most prevalent population in two patients, whereas activated memory CD4⁺ T cells and M2 macrophages were the most prevalent population in one patient each. Overall CIBERSORT results were consistent with those of immunohistochemistry, although some discrepancies were observed.

CONCLUSIONS

Infiltrating immune cell subsets detected by CIBERSORT analysis can reflect the time course of innate and adaptive immune responses in acute myocarditis. CIBERSORT may have the potential to characterize the detail of infiltrating immune cells in myocardial tissues and provide novel insights into the pathogenesis of acute myocarditis.

Prognostic value of the combination of GRACE risk score and mean platelet volume to lymphocyte count ratio in patients with ST-segment elevation myocardial infarction after percutaneous coronary intervention

The Global Registry of Acute Coronary Events (GRACE) risk score and the mean platelet volume to lymphocyte count ratio (MPVLR) can be used independently to predict adverse outcomes in patients with acute coronary syndromes. However, the level of MPVLR in relation to the GRACE score, and whether a combination of these methods can better predict the clinical adverse outcome of patients with ST-segment elevation myocardial infarction (STEMI), have not been previously examined. Therefore, the aim of the present study was to investigate whether the combination of GRACE risk score and MPVLR is a good predictor of a 30-day major adverse cardiovascular events (MACE) in patients with STEMI. A total of 464 patients with STEMI undergoing percutaneous coronary intervention (PCI) were enrolled, and divided into four groups based on the optimal cut-off values for GRACE score and MPVLR. GRACE score and MPVLR levels were separately recorded during admission. Spearman's rank correlation analysis showed a positive correlation between GRACE score and MPVLR (ρ=0.304; P<0.001). Both GRACE score [hazard ratio (HR), 1.706; 95% CI, 1.435-3.058; P<0.001] and MPVLR level (HR, 1.668; 95% CI, 1.202-2.170; P<0.001) were found to be independent predictors of a 30-day MACE. Additionally, the high MPVLR + high GRACE score group of patients had an HR of 2.455 (95% CI, 1.736-3.188) for a 30-day MACE, when using the low MPVLR + low GRACE score group as a reference. Based on the area under the curve, MPVLR combined with GRACE scores achieved an improved performance in differentiating angiographic no-reflow during a 30-day MACE, compared with individual MPVLR and GRACE scores. Therefore, the present results suggested that the GRACE score may be positively correlated with MPVLR and that their combination accurately predicted the occurrence of short-term MACE in patients with STEMI after PCI.