Contemporary Risk Stratification After Myocardial Infarction in the Community: Performance of Scores and Incremental Value of Soluble Suppression of Tumorigenicity-2

Comparative evaluation of machine learning models versus TIMI score in ST-segment-elevation myocardial infarction patients

BACKGROUND

Risk stratification is an integral component of ST-segment-elevation myocardial infarction (STEMI) management practices. This study aimed to derive a machine learning (ML) model for risk stratification and identification of factors associated with in-hospital and 30-day mortality in patients with STEMI and compare it with traditional TIMI score.

METHODS

This was a single center prospective study wherein subjects >18 years with STEMI (n = 1700) were enrolled. Patients were divided into two groups: training (n = 1360) and validation dataset (n = 340). Six ML algorithms (Extra Tree, Random Forest, Multiple Perceptron, CatBoost, Logistic Regression and XGBoost) were used to train and tune the ML model and to determine the predictors of worse outcomes using feature selection. Additionally, the performance of ML models both for in-hospital and 30-day outcomes was compared to that of TIMI score.

RESULTS

Of the 1700 patients, 168 (9.88 %) had in-hospital mortality while 30-day mortality was reported in 210 (12.35 %) subjects. In terms of in-hospital mortality, Random Forest ML model (sensitivity: 80 %; specificity: 74 %; AUC: 80.83 %) outperformed the TIMI score (sensitivity: 70 %; specificity: 64 %; AUC:70.7 %). Similarly, Random Forest ML model (sensitivity: 81.63 %; specificity: 78.35 %; AUC: 78.29 %) had better performance as compared to TIMI score (sensitivity: 63.26 %; specificity: 63.91 %; AUC: 63.59 %) for 30-day mortality. Key predictors for worse outcomes at 30-days included mitral regurgitation on presentation, smoking, cardiogenic shock, diabetes, ventricular septal rupture, Killip class, age, female gender, low blood pressure and low ejection fraction.

CONCLUSIONS

ML model outperformed the traditional regression based TIMI score as a risk stratification tool in patients with STEMI.

Soluble suppression of tumorigenicity 2 associated with microvascular obstruction in patients with ST-segment elevation myocardial infarction

BACKGROUND

Microvascular obstruction (MVO) develops in approximately 50% of patients with ST-segment elevation myocardial infarction (STEMI) after undergoing percutaneous coronary intervention (PCI). MVO is strongly linked to inflammation, myocardial fibrosis, and adverse clinical outcomes. Soluble suppression of tumorigenicity 2 (sST2) serves as a biomarker for inflammation and myocardial fibrosis. Yet, the correlation between sST2 and MVO in STEMI patients has not been fully elucidated. This study attempts to evaluate the association between sST2 levels and MVO in STEMI patients following pPCI.

METHODS

In this retrospective study, 315 STEMI patients who underwent pPCI at the Affiliated Hospital of Xuzhou Medical University between June 2018 and August 2023 were included. Cardiac magnetic resonance imaging (CMR) was used to assess the characteristics of myocardial infarction and microvascular obstruction (MVO), while sST2 levels were measured upon admission.

RESULTS

The median time for completion of CMR after hospitalization was 5 (4, 6) days. Multivariate regression analysis showed that sST2 (OR 1.01, 95% CI 1.01-1.02, p < 0.001), peak high-sensitivity troponin T (OR 2.40, 95% CI 1.66-3.47, p < 0.001), peak high-C-reactive protein (OR 1.01, 95% CI 1.01-1.02, p < 0.001), left ventricular ejection fraction (OR 0.93, 95% CI 0.89- 0.98, p = 0.009) and age (OR 1.03, 95% CI 1.01- 1.05, p = 0.042)were independently associated with MVO.

CONCLUSION

sST2 is associated with MVO after pPCI in STEMI patients. Incorporating soluble ST2 (sST2) into the risk model for MVO leads to significant improvement.

Prediction of in-hospital mortality risk for patients with acute ST-elevation myocardial infarction after primary PCI based on predictors selected by GRACE score and two feature selection methods

Introduction

Accurate in-hospital mortality prediction following percutaneous coronary intervention (PCI) is crucial for clinical decision-making. Machine Learning (ML) and Data Mining methods have shown promise in improving medical prognosis accuracy.

Methods

We analyzed a dataset of 4,677 patients from the Regional Vascular Center of Primorsky Regional Clinical Hospital No. 1 in Vladivostok, collected between 2015 and 2021. We utilized Extreme Gradient Boosting, Histogram Gradient Boosting, Light Gradient Boosting, and Stochastic Gradient Boosting for mortality risk prediction after primary PCI in patients with acute ST-elevation myocardial infarction. Model selection was performed using Monte Carlo Cross-validation. Feature selection was enhanced through Recursive Feature Elimination (RFE) and Shapley Additive Explanations (SHAP). We further developed hybrid models using Augmented Grey Wolf Optimizer (AGWO), Bald Eagle Search Optimization (BES), Golden Jackal Optimizer (GJO), and Puma Optimizer (PO), integrating features selected by these methods with the traditional GRACE score.

Results

The hybrid models demonstrated superior prediction accuracy. In scenario (1), utilizing GRACE scale features, the Light Gradient Boosting Machine (LGBM) and Extreme Gradient Boosting (XGB) models optimized with BES achieved Recall values of 0.944 and 0.954, respectively. In scenarios (2) and (3), employing SHAP and RFE-selected features, the LGB models attained Recall values of 0.963 and 0.977, while the XGB models achieved 0.978 and 0.99.

Discussion

The study indicates that ML models, particularly the XGB optimized with BES, can outperform the conventional GRACE score in predicting in-hospital mortality. The hybrid models' enhanced accuracy presents a significant step forward in risk assessment for patients post-PCI, offering a potential alternative to existing clinical tools. These findings underscore the potential of ML in optimizing patient care and outcomes in cardiovascular medicine.

Relationship between NT-proBNP levels and existing/de novo QRS fragmentation in patients with myocardial infarction

Aim: to assess the evolution of fragmented QRS (fQRS) and NT-proBNP levels during myocardial infarction (MI).Methods: Among 511 patients, 205 (40.1%) had fQRS, with 54 (26.3%) developing de novo fragmentation during hospitalization.Results: NT-proBNP levels were significantly higher in the fQRS+ group compared with the fQRS- group (1555 vs. 796 pg/ml, p < 0.001). NT-proBNP levels were higher in patients with de novo fragmentation than in those without (2852 vs. 1370 pg/ml, p = 0.011). The incidence of major adverse cardiovascular events was notably higher in fQRS+ patients compared with fQRS- patients (p = 0.001).Conclusion: In acute MI, there was a significant association between fQRS and NT-proBNP levels, with higher NT-proBNP levels observed in those with de novo fQRS compared with those without.

Incremental Value of a Metabolic Risk Score for Heart Failure Mortality: A Population-Based Study

BACKGROUND

Heart failure is heterogeneous syndrome with persistently high mortality. Nuclear magnetic resonance spectroscopy enables high-throughput metabolomics, suitable for precision phenotyping. We aimed to use targeted metabolomics to derive a metabolic risk score (MRS) that improved mortality risk stratification in heart failure.

METHODS

Nuclear magnetic resonance was used to measure 21 metabolites (lipoprotein subspecies, branched-chain amino acids, alanine, GlycA (glycoprotein acetylation), ketone bodies, glucose, and citrate) in plasma collected from a heart failure community cohort. The MRS was derived using least absolute shrinkage and selection operator penalized Cox regression and temporal validation. The association between the MRS and mortality and whether risk stratification was improved over the Meta-Analysis Global Group in Chronic Heart Failure clinical risk score and NT-proBNP (N-terminal pro-B-type natriuretic peptide) levels were assessed.

RESULTS

The study included 1382 patients (median age, 78 years, 52% men, 43% reduced ejection fraction) with a 5-year survival rate of 48% (95% CI, 46%-51%). The MRS included 9 metabolites measured. In the validation data set, a 1 standard deviation increase in the MRS was associated with a large increased rate of death (hazard ratio, 2.2 [95% CI, 1.9-2.5]) that remained after adjustment for Meta-Analysis Global Group in Chronic Heart Failure score and NT-proBNP (hazard ratio, 1.6 [95% CI, 1.3-1.9]). These associations did not differ by ejection fraction. The integrated discrimination and net reclassification indices, and Uno's C statistic, indicated that the addition of the MRS improved discrimination over Meta-Analysis Global Group in Chronic Heart Failure and NT-proBNP.

CONCLUSIONS

This MRS developed in a heart failure community cohort was associated with a large excess risk of death and improved risk stratification beyond an established risk score and clinical markers.

Possibilities of predicting adverse cardiovascular events based on the analysis of clinical and instrumental research methods, as well as sST2 in patients after myocardial infarction

OBJECTIVE

Aim: To determine the possibility of predicting adverse cardiovascular events based on the analysis of clinical and instrumental research methods, as well as sST2 in patients after myocardial infarction.

PATIENTS AND METHODS

Materials and Methods: The study included 64 patients who suffered an acute myocardial infarction and underwent PCI with balloon angioplasty and stenting of the infarct-related vessel in the acute period. The predictors of adverse cardiovascular events were assessed events during 1 year of observation. Indicators of echocardiography and coronary angiography were assessed and concentrations sST2.

RESULTS

Results: A worse prognosis was associated with intermediate ejection fraction (EF) (odds ratio (OR)=3.981, p<0.05), left aneurysm ventricle (LV) (OR=29.5, p<0.05), high concentrations of sST2 (OR=1.017, p<0.05) and scores on the Syntax scale (OR=1.001, p<0.05).

CONCLUSION

Conclusions: In patients who underwent percutaneous coronary intervention for myocardial infarction, adverse outcome during the next 2 years is associated with coronary and echocardiographic parameters, as well as biochemical indicators of myocardial stress and fibrosis. HF patients with intermediate EF, LV aneurysm, high sST2 concentrations, and high Syntax scores have the worst prognosis.

Soluble ST2 in Heart Failure: A Clinical Role beyond B-Type Natriuretic Peptide

Soluble (s)ST2 has been proposed as a useful biomarker for heart failure (HF) patient management. Myocardial damage or mechanical stress stimulate sST2 release. ST2 competes with a membrane bound receptor (ST2 ligand, or ST2L) for interleukin-33 (IL-33) binding, inhibiting the effects induced by the ST2L/IL-33 interaction so that excessive sST2 may contribute to myocardial fibrosis and ventricular remodeling. Compared to natriuretic peptides (NPs), sST2 concentration is not substantially affected by age, sex, body mass index, kidney function, atrial fibrillation, anemia, or HF etiology, and has low intra-individual variation. Its prognostic role as an independent marker is well reported in the literature. However, there is a gap on its use in combination with NPs, currently the only biomarkers recommended by European and American guidelines for HF management. Reflecting the activation of two distinct biological systems, a benefit from the use of sST2 and NP in combination is advocated. The aim of this review is to report the current scientific knowledge on sST2 in the acute and chronic HF settings with a particular attention to its additive role to natriuretic peptides (NPs).

Soluble suppression of tumorigenicity 2 associated with atrial fibrillation detected after stroke: A retrospective study

Background

The soluble suppression of tumorigenicity 2 (sST2) is closely associated with stroke and atrial fibrillation (AF). However, no studies on sST2 and AF detected after stroke (AFDAS) have been reported. This study investigated the correlation between sST2 and AFDAS.

Methods

This was a single-center, retrospective, clinical observational study. Patients diagnosed with a transient ischemic attack (TIA) or acute ischemic stroke were enrolled, and all patients underwent sST2 detection and electrocardiogram (ECG) or Holter monitoring for at least 24 h.

Results

In total, 970 patients were enrolled, including 72 (7.4 %) with AFDAS. Multivariate analysis showed that age (OR 1.078; 95 % CI, 1.050-1.107; p < 0.001), heart rate (HR) (OR 1.025; 95 % CI, 1.007-1.044; p = 0.007), national institutes of health stroke scale (NIHSS) score (OR 1.089; 95 % CI, 1.029-1.152; p = 0.003), high sensitivity C-reactive protein (hs-CRP) (OR 1.006; 95 % CI, 1.002-1.009; p = 0.001), and sST2 (OR 1.018; 95 % CI, 1.010-1.026; p < 0.001) were independent risk factors of AFDAS. The areas under the curve (AUCs) for age, HR, sST2, hs-CRP, and NIHSS were 0.731, 0.599, 0.815, 0.664, and 0.700, respectively. The conventional model included age, HR, NIHSS score, and hs-CRP level based on multivariate results. After adding sST2 to the model, the model's performance in predicting AFDAS increased significantly.

Conclusion

Higher sST2 levels were associated with the occurrence of AFDAS. Thus, sST2 can improve the risk model for AFDAS.

Association of soluble suppression of tumorigenicity 2 protein with new-onset atrial fibrillation in patients with acute ST-segment elevation myocardial infarction undergoing primary PCI

Background

Previous studies have indicated that the soluble suppression of tumorigenicity 2 protein (sST2) is associated with new-onset atrial fibrillation (NOAF) in patients diagnosed with coronary artery disease (CAD). However, the predictive value of sST2 in patients with acute ST-segment elevation myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention (PCI) has not been well studied.

Methods

A total of 580 patients with STEMI undergoing primary PCI were consecutively recruited between January 2021 and January 2023. These patients were then categorized into two groups: the NOAF group and the no NOAF groups based on the presence of NOAF during admission. The concentration of sST2 in blood samples was measured in all patients. The clinical data from the two groups were prospectively analyzed to investigate the predictive factors of NOAF in patients with STEMI undergoing primary PCI.

Results

A total of 41 (7.1%) patients developed NOAF. The presence of NOAF has been found to be associated with various factors, including age, diabetes mellitus, hypertension, the left atrial (LA) diameter, N-terminal pro-brain natriuretic peptide, C-reactive protein (CRP), sST2, a Killip class of ≥2, and a final TIMI flow grade of <3. After including multiple factors, it was observed that LA diameter, CRP, sST2, a Killip class of ≥2, and a final TIMI flow grade of <3 remained significant risk factors for developing NOAF. The receiver operating characteristic (ROC) curve showed the following findings: (1) when the LA diameter exceeded 38.5 mm, the sensitivity and specificity values were observed to be 67.2% and 68.2%, respectively, and the area under the ROC curve (AUC) was 0.683 [95% confidence interval (CI): 0.545-0.732; p = 0.003]; (2) when the CRP level exceeded 8.59, the sensitivity and specificity values were observed to be 68.6% and 69.2%, respectively, and the AUC was 0.713 (95% CI: 0.621-0.778; p < 0.001); and (3) when the sST2 value exceeded 53.3, the sensitivity and specificity values were 79.2% and 68.7%, respectively, and the AUC was 0.799 (95% CI: 0.675-0.865; p < 0.001).

Conclusion

sST2 has been identified as an independent predictor of NOAF in patients with STEMI undergoing PCI.

Prognostic Implications of Clinical, Laboratory and Echocardiographic Biomarkers in Patients with Acute Myocardial Infarction-Rationale and Design of the ''CLEAR-AMI Study''

BACKGROUND

Acute myocardial infarction (AMI) remains a major cause of death worldwide. Survivors of AMI are particularly at high risk for additional cardiovascular events. Consequently, a comprehensive approach to secondary prevention is necessary to mitigate the occurrence of downstream complications. This may be achieved through a multiparametric tailored risk stratification by incorporating clinical, laboratory and echocardiographic parameters.

METHODS

The ''CLEAR-AMI Study'' (ClinicalTrials.gov Identifier: NCT05791916) is a non-interventional, prospective study including consecutive patients with AMI without a known history of coronary artery disease. All patients satisfying these inclusion criteria are enrolled in the present study. The rationale of this study is to refine risk stratification by using clinical, laboratory and novel echocardiographic biomarkers. All the patients undergo a comprehensive transthoracic echocardiographic assessment, including strain and myocardial work analysis of the left and right heart chambers, within 48 h of admission after coronary angiography. Their laboratory profile focusing on systemic inflammation is captured during the first 24 h upon admission, and their demographic characteristics, past medical history, and therapeutic management are recorded. The angioplasty details are documented, the non-culprit coronary lesions are archived, and the SYNTAX score is employed to evaluate the complexity of coronary artery disease. A 24-month follow-up period will be recorded for all patients recruited.

CONCLUSION

The ''CLEAR-AMI" study is an ongoing prospective registry endeavoring to refine risk assessment in patients with AMI without a known history of coronary artery disease, by incorporating echocardiographic parameters, biochemical indices, and clinical and coronary characteristics in the acute phase of AMI.

Associations of MAFLD and MAFLD subtypes with the risk of the incident myocardial infarction and stroke

Metabolic dysfunction-associated fatty liver disease (MAFLD) is a condition characterized by liver fat accumulation and metabolic abnormalities. Given the potential impact of MAFLD on patient health, it is important to understand its association with major adverse cardiovascular events (MACE) such as myocardial infarction (MI) and stroke. In the prospective UK Biobank cohort, we sought to elucidate the association of MAFLD and its subtypes with incident MI and stroke. In this study, we analyzed the data of 325,129 participants in the UK Biobank and calculated relative risks for MI and stroke using Cox regression analysis. Among 325,129 participants over a median duration of 12.8 years follow-up, participants with MAFLD were significantly more likely to experience a MI (hazard ratio [HR] = 1.35, 95% confidence interval [CI: 1.29;1.41] P < 0.001) or a stroke (HR = 1.26 [1.18-1.33] P < 0.001) compared to those without MAFLD. In addition, diabetic, overweight with metabolic dysfunction (MD), and lean MAFLD subtypes were significantly associated with an increased risk for MI and stroke, whereas overweight without MD subtype did not appear to be associated with this risk. Our findings also revealed graded associations between liver fibrosis scores and risk of MI and stroke in MAFLD patients. However, only diabetic, and overweight patients with MD subtypes exhibited graded associations between liver fibrosis score and the risk of MI and stroke among the MAFLD subtypes. Furthermore, the risk alleles traits of fatty liver did not enhance the effect of MAFLD on the risk of MI and stroke. In conclusion, a diagnosis of MAFLD is associated with an increased risk of MI or stroke, and the assessment of MAFLD and its subtypes should be a component of the cardiovascular risk assessment.

Comparison of the modified Singapore myocardial infarction registry risk score with GRACE 2.0 in predicting 1-year acute myocardial infarction outcomes

Risk stratification plays a key role in identifying acute myocardial infarction (AMI) patients at higher risk of mortality. However, current AMI risk scores such as the Global Registry of Acute Coronary Events (GRACE) score were derived from predominantly Caucasian populations and may not be applicable to Asian populations. We previously developed an AMI risk score from the national-level Singapore Myocardial Infarction Registry (SMIR) confined to ST-segment elevation myocardial infarction (STEMI) patients and did not include non-STEMI (NSTEMI) patients. Here, we derived a modified SMIR risk score for both STEMI and NSTEMI patients and compared its performance to the GRACE 2.0 score for predicting 1-year all-cause mortality in our multi-ethnic population. The most significant predictor of 1-year all-cause mortality in our population using the GRACE 2.0 score was cardiopulmonary resuscitation on admission (adjusted hazards ratio [HR] 6.50), while the most significant predictor using the SMIR score was age 80–89 years (adjusted HR 7.78). Although the variables used in the GRACE 2.0 score and SMIR score were not exactly the same, the c-statistics for 1-year all-cause mortality were similar between the two scores (GRACE 2.0 0.841 and SMIR 0.865). In conclusion, we have shown that in a multi-ethnic Asian AMI population undergoing PCI, the SMIR score performed as well as the GRACE 2.0 score.

Combined use of the GRACE ACS risk score and comorbidity indices to increase the effectiveness of hospital mortality risk assessment in patients with acute coronary syndrome

Aim. To assess the possibilities of using comorbidity indices together with the GRACE (Global Registry of Acute Coronary Events) scale to assess the risk of hospital mortality in acute coronary syndrome (ACS). Materials and methods. The registry study included 2,305 patients with ACS. The frequency of coronary angiography was 54.0%, percutaneous coronary intervention (PCI) 26.9%. Hospital mortality with ACS was 4.8%, with myocardial infarction 9.4%. All patients underwent a comorbidity assessment according to the CIRS system (Cumulative Illness Rating Scale), according to the CCI (Charlson Comorbidity Index) and the CDS (Chronic Disease Score) scale, according to their own scale, which is based on the summation of 9 diseases (diabetes mellitus, atrial fibrillation, stroke, arterial hypertension, obesity, peripheral atherosclerosis, thrombocytopenia, anemia, chronic kidney disease). All patients underwent a mortality risk assessment using the GRACE ACS Risk scale. Results. It was found that the CDS and CIRS indices are not associated with the risk of hospital mortality. With CCI3, the frequency of death outcomes increased from 4.1 to 6.1% (2=4.12, p=0.042). With an increase in the severity of comorbidity from minimal (no more than 1 disease) to severe (4 or more diseases) according to its own scale, hospital mortality increased from 1.2 to 7.4% (2=23.8, p0.0001). In contrast to other scales of comorbidity, our own model more efficiently estimates the hospital prognosis both in the conservative treatment group (2=8.0, p=0.018) and in the PCI group (2=28.5, p=0.00001). It was in the PCI subgroup that the comorbidity factors included in their own model made it possible to increase the area under the ROC curve of the GRACE scale from 0.80 (0.740.87) to 0.90 (0.850.95). Conclusion. CCI and its own comorbidity model, but not CDS and CIRS, are associated with the risk of hospital mortality. The model for assessing comorbidity on a 9-point scale, but not CCI, CDS and CIRS, can significantly improve the predictive value of the GRACE scale.

Next-Generation Sequencing of CYP2C19 in Stent Thrombosis: Implications for Clopidogrel Pharmacogenomics

PURPOSE

Describe CYP2C19 sequencing results in the largest series of clopidogrel-treated cases with stent thrombosis (ST), the closest clinical phenotype to clopidogrel resistance. Evaluate the impact of CYP2C19 genetic variation detected by next-generation sequencing (NGS) with comprehensive annotation and functional studies.

METHODS

Seventy ST cases on clopidogrel identified from the PLATO trial (n = 58) and Mayo Clinic biorepository (n = 12) were matched 1:1 with controls for age, race, sex, diabetes mellitus, presentation, and stent type. NGS was performed to cover the entire CYP2C19 gene. Assessment of exonic variants involved measuring in vitro protein expression levels. Intronic variants were evaluated for potential splicing motif variations.

RESULTS

Poor metabolizers (n = 4) and rare CYP2C19*8, CYP2C19*15, and CYP2C19*11 alleles were identified only in ST cases. CYP2C19*17 heterozygote carriers were observed more frequently in cases (n = 29) than controls (n = 18). Functional studies of CYP2C19 exonic variants (n = 11) revealed 3 cases and only 1 control carrying a deleterious variant as determined by in vitro protein expression studies. Greater intronic variation unique to ST cases (n = 169) compared with controls (n = 84) was observed with predictions revealing 13 allele candidates that may lead to a potential disruption of splicing and a loss-of-function effect of CYP2C19 in ST cases.

CONCLUSION

NGS detected CYP2C19 poor metabolizers and paradoxically greater number of so-called rapid metabolizers in ST cases. Rare deleterious exonic variation occurs in 4%, and potentially disruptive intronic alleles occur in 16% of ST cases. Additional studies are required to evaluate the role of these variants in platelet aggregation and clopidogrel metabolism.

The association between soluble suppression of tumorigenicity-2 and long-term prognosis in patients with coronary artery disease: A meta-analysis

OBJECTIVE

Findings regarding the prognostic value of soluble suppression of tumorigenecity-2 (sST2) in patients with coronary artery disease (CAD) remain inconsistent. Therefore, we conducted this meta-analysis to investigate the long-term prognostic value of sST2 in patients with CAD.

METHODS

A comprehensive literature search was conducted across the PubMed, Embase, and Cochrane Library databases up to June 3, 2020. The primary outcome was major adverse cardiac events (MACEs). The secondary outcomes were all-cause mortality, cardiovascular (CV) death, heart failure (HF), and myocardial infarction (MI). Pooled estimations and 95% confidence intervals (CIs) were assessed using a random-effects model.

RESULTS

Twenty-two articles that enrolled a total of 17,432 patients with CAD were included in the final analysis. CAD patients in the highest categories of baseline sST2 had a significantly higher risk of MACEs (HR: 1.42, 95% CI: 1.09-1.76), all-cause mortality (HR: 2.00, 95% CI: 1.54-2.46), and CV death (HR: 1.42, 95% CI: 1.15-1.68), HF (HR: 2.41, 95% CI: 1.87-2.94), but not that of MI (HR: 1.15, 95% CI: -0.73-3.04), than those in the lowest categories. These results were consistent when baseline sST2 was presented as continuous values in one unit increments. Moreover, subgroup analysis showed that elevated baseline sST2 levels increased the long-term risk of MACEs in the acute coronary syndrome (ACS) population (HR: 1.74, 95% CI: 1.39-2.09) but only showed a trend toward higher risk of MACEs in the non-ACS population (HR: 1.09, 95% CI: 0.87-1.30).

CONCLUSIONS

The findings suggest that a higher concentration of baseline sST2 is associated with a higher risk of MACEs, all-cause mortality, CV death, and HF in patients with CAD. Elevated sST2 levels could significantly predict future MACEs in the ACS population but not in the non-ACS population.

Artificial Intelligence in Cardiology: Present and Future

Artificial intelligence (AI) is a nontechnical, popular term that refers to machine learning of various types but most often to deep neural networks. Cardiology is at the forefront of AI in medicine. For this review, we searched PubMed and MEDLINE databases with no date restriction using search terms related to AI and cardiology. Articles were selected for inclusion on the basis of relevance. We highlight the major achievements in recent years in nearly all areas of cardiology and underscore the mounting evidence suggesting how AI will take center stage in the field. Artificial intelligence requires a close collaboration among computer scientists, clinical investigators, clinicians, and other users in order to identify the most relevant problems to be solved. Best practices in the generation and implementation of AI include the selection of ideal data sources, taking into account common challenges during the interpretation, validation, and generalizability of findings, and addressing safety and ethical concerns before final implementation. The future of AI in cardiology and in medicine in general is bright as the collaboration between investigators and clinicians continues to excel.

Galectin-3 Levels and Outcomes After Myocardial Infarction: A Population-Based Study

BACKGROUND

Galectin-3 (Gal-3) is implicated in cardiac fibrosis, but its association with adverse outcomes after myocardial infarction (MI) is unknown.

OBJECTIVES

The purpose of this study was to examine the prognostic value of Gal-3 in a community cohort of incident MI.

METHODS

A population-based incidence MI cohort was prospectively assembled in Olmsted County, Minnesota, between 2002 and 2012. Gal-3 levels were measured at the time of MI. Patients were followed for heart failure (HF) and death.

RESULTS

A total of 1,342 patients were enrolled (mean age 67.1 years; 61.3% male; 78.8% non-ST-segment elevation MI). Patients with elevated Gal-3 were older and had more comorbidities. Over a mean follow-up of 5.4 years, 484 patients (36.1%) died and 368 (27.4%) developed HF. After adjustment for age, sex, comorbidities, and troponin, patients with Gal-3 values in tertiles 2 and 3 had a 1.3-fold (95% confidence interval [CI]: 0.9-fold to 1.7-fold) and a 2.4-fold (95% CI: 1.8-fold to 3.2-fold) increased risk of death, respectively (p_trend < 0.001) compared with patients with Gal-3 values in tertile 1. Patients with Gal-3 values in tertiles 2 and 3 had a higher risk of HF with hazard ratios of 1.4 (95% CI: 1.0 to 2.0) and 2.3 (95% CI: 1.6 to 3.2), respectively (p_trend < 0.001). With further adjustment for soluble suppression of tumorigenicity-2, elevated Gal-3 remained associated with increased risk of death and HF. The increased risk of HF did not differ by HF type and was independent of the occurrence of recurrent MI.

CONCLUSIONS

Gal-3 is an independent predictor of mortality and HF post-MI. These findings suggest a role for measuring Gal-3 levels for risk stratification post-MI.

Differential Network Analysis Reveals Metabolic Determinants Associated with Mortality in Acute Myocardial Infarction Patients and Suggests Potential Mechanisms Underlying Different Clinical Scores Used To Predict Death

We present here the differential analysis of metabolite–metabolite association networks constructed from an array of 24 serum metabolites identified and quantified via nuclear magnetic resonance spectroscopy in a cohort of 825 patients of which 123 died within 2 years from acute myocardial infarction (AMI). We investigated differences in metabolite connectivity of patients who survived, at 2 years, the AMI event, and we characterized metabolite–metabolite association networks specific to high and low risks of death according to four different risk parameters, namely, acute coronary syndrome classification, Killip, Global Registry of Acute Coronary Events risk score, and metabolomics NOESY RF risk score. We show significant differences in the connectivity patterns of several low-molecular-weight molecules, implying variations in the regulation of several metabolic pathways regarding branched-chain amino acids, alanine, creatinine, mannose, ketone bodies, and energetic metabolism. Our results demonstrate that the characterization of metabolite–metabolite association networks is a promising and powerful tool to investigate AMI patients according to their outcomes at a molecular level.

Short-term and long-term prognostic value of circulating soluble suppression of tumorigenicity-2 concentration in acute coronary syndrome: a meta-analysis

Background: Higher circulating soluble suppression of tumorigenicity-2 (sST2) concentration is suggested as a marker of prognosis in many cardiovascular diseases. However, the short-term and long-term prognostic value of sST2 concentration in acute coronary syndrome (ACS) remains to be summarized.Methods: A meta-analysis of follow-up studies was performed. Studies were identified via systematic search of databases including PubMed, Cochrane's Library, and Embase. A fixed- or random-effect model was applied according to the heterogeneity. We reported the prognostic value of sST2 concentration for all-cause mortality, heart failure (HF) events, and major adverse cardiovascular events (MACEs) within 1 month after hospitalization and during subsequent follow-up.Results: Twelve studies with 11690 ACS patients were included. Higher baseline sST2 concentration as continuous variables predicte the increased risk of all-cause mortality (risk ratio [RR]: 3.16, P=0.002), HF events (RR: 1.48, P<0.001), and MACEs (RR: 1.47, P<0.001) within 1 month after hospitalization, which is consistent with the results with sST2 concentration as categorized variables (RR = 2.14, 2.89, and 2.89 respectively, P all <0.001). Moreover, higher baseline sST2 concentration as continuous variables predict the increased risk of all-cause mortality (RR: 2.20, P<0.001), HF events (RR: 1.39, P<0.001), and MACEs (RR: 1.53, P=0.02) during subsequent follow-up. Meta-analysis with sST2 concentration as categorized variables retrieved similar results (RR = 2.65, 2.59, and 1.81 respectively, P all <0.001).Conclusions: Higher circulating sST2 concentration at baseline predicts poor clinical outcome in ACS patients.

SALMANTICOR study. Rationale and design of a population-based study to identify structural heart disease abnormalities: a spatial and machine learning analysis

INTRODUCTION

This study aims to obtain data on the prevalence and incidence of structural heart disease in a population setting and, to analyse and present those data on the application of spatial and machine learning methods that, although known to geography and statistics, need to become used for healthcare research and for political commitment to obtain resources and support effective public health programme implementation.

METHODS AND ANALYSIS

We will perform a cross-sectional survey of randomly selected residents of Salamanca (Spain). 2400 individuals stratified by age and sex and by place of residence (rural and urban) will be studied. The variables to analyse will be obtained from the clinical history, different surveys including social status, Mediterranean diet, functional capacity, ECG, echocardiogram, VASERA and biochemical as well as genetic analysis.

ETHICS AND DISSEMINATION

The study has been approved by the ethical committee of the healthcare community. All study participants will sign an informed consent for participation in the study. The results of this study will allow the understanding of the relationship between the different influencing factors and their relative importance weights in the development of structural heart disease. For the first time, a detailed cardiovascular map showing the spatial distribution and a predictive machine learning system of different structural heart diseases and associated risk factors will be created and will be used as a regional policy to establish effective public health programmes to fight heart disease. At least 10 publications in the first-quartile scientific journals are planned.

TRIAL REGISTRATION NUMBER

NCT03429452.