Comparison of six risk scores in patients with triple vessel coronary artery disease undergoing PCI: competing factors influence mortality, myocardial infarction, and target lesion revascularization

Integrating platelet reactivity in the age, creatinine and ejection fraction score to predict clinical outcomes following percutaneous coronary intervention in patients with chronic coronary syndrome: the PR-ACEF score

To evaluate if integrating platelet reactivity (PR) evaluation in the original age, creatinine and ejection fraction (ACEF) score could improve the diagnostic accuracy of the model in patients with stable coronary artery disease (CAD). We enrolled patients treated with percutaneous coronary intervention between 2010 and 2011. High PR was included in the model (PR-ACEF). Co-primary end points were a composite of death/myocardial infarction (MI) and major adverse cardiovascular events (MACE). Overall, 471 patients were enrolled. Compared to the ACEF score, the PR-ACEF showed an improved diagnostic accuracy for death/MI (AUC 0.610 vs 0.670, p < 0.001) and MACE (AUC 0.572 vs 0.634, p < 0.001). These findings were confirmed using internal validation with bootstrap resampling. At 5 years, the PR-ACEF value > 1.75 was independently associated with death/MI [HR 3.51, 95% CI (1.97-6.23)] and MACE [HR 2.77, 95% CI (1.69-4.53)]. The PR-ACEF score was effective in improving the diagnostic performance of the ACEF score at the long-term follow-up.

Single-Cell Gene-Regulatory Networks of Advanced Symptomatic Atherosclerosis

BACKGROUND

While our understanding of the single-cell gene expression patterns underlying the transformation of vascular cell types during the progression of atherosclerosis is rapidly improving, the clinical and pathophysiological relevance of these changes remains poorly understood.

METHODS

Single-cell RNA sequencing data generated with SmartSeq2 (≈8000 genes/cell) in 16 588 single cells isolated during atherosclerosis progression in Ldlr-/-Apob100/100 mice with human-like plasma lipoproteins and from humans with asymptomatic and symptomatic carotid plaques was clustered into multiple subtypes. For clinical and pathophysiological context, the advanced-stage and symptomatic subtype clusters were integrated with 135 tissue-specific (atherosclerotic aortic wall, mammary artery, liver, skeletal muscle, and visceral and subcutaneous, fat) gene-regulatory networks (GRNs) inferred from 600 coronary artery disease patients in the STARNET (Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task) study.

RESULTS

Advanced stages of atherosclerosis progression and symptomatic carotid plaques were largely characterized by 3 smooth muscle cells (SMCs), and 3 macrophage subtype clusters with extracellular matrix organization/osteogenic (SMC), and M1-type proinflammatory/Trem2-high lipid-associated (macrophage) phenotypes. Integrative analysis of these 6 clusters with STARNET revealed significant enrichments of 3 arterial wall GRNs: GRN33 (macrophage), GRN39 (SMC), and GRN122 (macrophage) with major contributions to coronary artery disease heritability and strong associations with clinical scores of coronary atherosclerosis severity. The presence and pathophysiological relevance of GRN39 were verified in 5 independent RNAseq data sets obtained from the human coronary and aortic artery, and primary SMCs and by targeting its top-key drivers, FRZB and ALCAM in cultured human coronary artery SMCs.

CONCLUSIONS

By identifying and integrating the most gene-rich single-cell subclusters of atherosclerosis to date with a coronary artery disease framework of GRNs, GRN39 was identified and independently validated as being critical for the transformation of contractile SMCs into an osteogenic phenotype promoting advanced, symptomatic atherosclerosis.

Predicting target lesion failure following percutaneous coronary intervention through machine learning risk assessment models

Aims

Central to the practice of precision medicine in percutaneous coronary intervention (PCI) is a risk-stratification tool to predict outcomes following the procedure. This study is intended to assess machine learning (ML)-based risk models to predict clinically relevant outcomes in PCI and to support individualized clinical decision-making in this setting.

Methods and results

Five different ML models [gradient boosting classifier (GBC), linear discrimination analysis, Naïve Bayes, logistic regression, and K-nearest neighbours algorithm) for the prediction of 1-year target lesion failure (TLF) were trained on an extensive data set of 35 389 patients undergoing PCI and enrolled in the global, all-comers e-ULTIMASTER registry. The data set was split into a training (80%) and a test set (20%). Twenty-three patient and procedural characteristics were used as predictive variables. The models were compared for discrimination according to the area under the receiver operating characteristic curve (AUC) and for calibration. The GBC model showed the best discriminative ability with an AUC of 0.72 (95% confidence interval 0.69-0.75) for 1-year TLF on the test set. The discriminative ability of the GBC model for the components of TLF was highest for cardiac death with an AUC of 0.82, followed by target vessel myocardial infarction with an AUC of 0.75 and clinically driven target lesion revascularization with an AUC of 0.68. The calibration was fair until the highest risk deciles showed an underestimation of the risk.

Conclusion

Machine learning-derived predictive models provide a reasonably accurate prediction of 1-year TLF in patients undergoing PCI. A prospective evaluation of the predictive score is warranted.

Registration

Clinicaltrial.gov identifier is NCT02188355.

The HDAC9-associated risk locus promotes coronary artery disease by governing TWIST1

Genome wide association studies (GWAS) have identified thousands of single nucleotide polymorphisms (SNPs) associated with the risk of common disorders. However, since the large majority of these risk SNPs reside outside gene-coding regions, GWAS generally provide no information about causal mechanisms regarding the specific gene(s) that are affected or the tissue(s) in which these candidate gene(s) exert their effect. The 'gold standard' method for understanding causal genes and their mechanisms of action are laborious basic science studies often involving sophisticated knockin or knockout mouse lines, however, these types of studies are impractical as a high-throughput means to understand the many risk variants that cause complex diseases like coronary artery disease (CAD). As a solution, we developed a streamlined, data-driven informatics pipeline to gain mechanistic insights on complex genetic loci. The pipeline begins by understanding the SNPs in a given locus in terms of their relative location and linkage disequilibrium relationships, and then identifies nearby expression quantitative trait loci (eQTLs) to determine their relative independence and the likely tissues that mediate their disease-causal effects. The pipeline then seeks to understand associations with other disease-relevant genes, disease sub-phenotypes, potential causality (Mendelian randomization), and the regulatory and functional involvement of these genes in gene regulatory co-expression networks (GRNs). Here, we applied this pipeline to understand a cluster of SNPs associated with CAD within and immediately adjacent to the gene encoding HDAC9. Our pipeline demonstrated, and validated, that this locus is causal for CAD by modulation of TWIST1 expression levels in the arterial wall, and by also governing a GRN related to metabolic function in skeletal muscle. Our results reconciled numerous prior studies, and also provided clear evidence that this locus does not govern HDAC9 expression, structure or function. This pipeline should be considered as a powerful and efficient way to understand GWAS risk loci in a manner that better reflects the highly complex nature of genetic risk associated with common disorders.

Multiple independent mechanisms link gene polymorphisms in the region of ZEB2 with risk of coronary artery disease

BACKGROUND AND AIMS

Coronary artery disease (CAD) arises from the interaction of genetic and environmental factors. Although genome-wide association studies (GWAS) have identified multiple risk loci and single nucleotide polymorphisms (SNPs) associated with risk of CAD, they are predominantly located in non-coding or intergenic regions and their mechanisms of effect are largely unknown. Accordingly, our objective was to develop a data-driven informatics pipeline to understand complex CAD risk loci, and to apply this to a poorly understood cluster of SNPs in the vicinity of ZEB2.

METHODS

We developed a unique informatics pipeline leveraging a multi-tissue CAD genetics-of-gene-expression dataset, GWAS datasets, and other resources. The pipeline first dissected SNP locations and their linkage disequilibrium relationships, and progressed through analyses of tissue-specific expression quantitative trait loci, and then gene-gene, gene-phenotype, SNP-phenotype relationships. The pipeline concluded by exploring CAD-relevant gene regulatory networks (GRNs).

RESULTS

We identified three independent CAD risk SNPs in close proximity to the ZEB2 coding region (rs6740731, rs17678683 and rs2252641/rs1830321). Our pipeline determined that these SNPs likely act in concert via the atherosclerotic arterial wall and adipose tissues, by governing metabolic and lipid functions. In addition, ZEB2 is the top key driver of a liver-specific GRN that is related to lipid levels, metabolic and anthropometric measures, and CAD severity.

CONCLUSIONS

Using a novel informatics pipeline, we disclosed the multi-faceted mechanisms of action of the ZEB2-associated CAD risk SNPs. This pipeline can serve as a roadmap to dissect complex SNP-gene-tissue-phenotype relationships and to reveal targets for tissue- and gene-specific therapeutic interventions.

Additive Prognostic Value of Carotid Plaque Score to Enhance the Age, Creatinine, and Ejection Fraction Score in Patients with Acute Coronary Syndrome

Aim: To assess whether combining measurements obtained from carotid ultrasonography in addition to the age, creatinine, and ejection fraction (ACEF) score would improve the predictive ability of outcome in patients with acute coronary syndrome (ACS).

Methods: We examined 264 patients with ACS (194 men; mean age: 68 ± 11 years) who underwent percutaneous coronary intervention. The carotid plaque score (cPS) and intima–media thickness (cIMT) were determined by carotid ultrasonography. The modified ACEF score was calculated using the following formula: (age/left ventricular ejection fraction) +1 point for every 10 mL/min reduction in creatinine clearance below 60 mL/min per 1.73 m². The endpoint of this study was major adverse cardiovascular and cerebrovascular events (MACEs), defined as all-cause death, myocardial infarction, stoke, and target vessel revascularization.

Results: During the median 4-year follow-up, there were 121 incidents of MACEs. Multivariate Cox proportional hazard regression analysis revealed that cPS ≥ 9.8 (hazard ratio [HR], 1.52; 95% confidence interval [CI], 1.01–2.31) and ACEF score ≥ 1.20 (HR, 1.62; 95% CI, 1.11–2.39) were significantly associated with MACEs, whereas cIMT was not. When the new combined risk score was calculated by multiplying the cPS by the modified ACEF score, the freedom from MACEs at 5 years was 71% and 31% for the lower and higher scores, respectively (p < 0.001). The area under the receiver-operating characteristic curve for MACEs for the ACEF score, cPS, and combined risk score were 0.65, 0.66, and 0.71, respectively (p < 0.05).

Conclusion: The cPS offers an incremental predictive value when combined to the simple ACEF score in ACS.

Precision Medicine at the University of Alabama at Birmingham: Laying the Foundational Processes Through Implementation of Genotype-Guided Antiplatelet Therapy

Precision medicine entails tailoring treatment based on patients' unique characteristics. As drug therapy constitutes the cornerstone of treatment for most chronic diseases, pharmacogenomics (PGx), the study of genetic variation influencing individual response to drugs, is an important component of precision medicine. Over the past decade investigations have identified genes and single-nucleotide polymorphisms (SNPs) and quantified their effect on drug response. Parallel development of point-of-care (POC) genotyping platforms has enabled the interrogation of the genes/SNPs within a timeline conducive to the provision of care. Despite these advances, the pace of integration of genotype-guided drug therapy (GGTx) into practice has faced significant challenges. These include difficulty in identifying SNPs with sufficiently robust evidence to guide clinical decision making, lack of clinician training on how to order and use genotype data, lack of clinical decision support (CDS) to guide treatment, and limited reimbursement. The University of Alabama at Birmingham's (UAB) efforts in precision medicine were initiated to address these challenges and improve the health of the racially diverse patients we treat.

Paclitaxel-eluting stents versus sirolimus-eluting stents in patients with diabetes mellitus undergoing percutaneous coronary intervention: a systematic review and meta-analysis of randomized controlled trials

Uncertainties exist with regard to the efficacy of paclitaxel-eluting stents (PES) versus sirolimus-eluting stents (SES) in diabetes patients undergoing percutaneous coronary intervention (PCI). We performed a meta-analysis of randomized controlled trials (RCTs) to investigate the outcome of PES versus SES in diabetes patients undergoing PCI. A literature search was started, and we found all studies conducted from 2005 to 2016. We systematically searched the literature through the MEDLINE, Cochrane library, and EMBASE. Quality assessments were evaluated with the Jadad scale. Data were extracted considering the characteristics of efficacy and the safety of the designs. 12 RCTs satisfy the inclusion criteria. There is a significant decrease of target lesion revascularization (TLR) (MD = 0.65, 95 % CI = 0.42-1.00, P = 0.05) in a year and more than 1 year (MD = 0.54, 95 % CI = 0.37-0.78, P = 0.00010). A significant decrease of target vessel revascularization (TVR) in more than 1 year is (MD = 0.62, 95 % CI = 0.47-0.81, P = 0.0004). A significant decrease of major adverse cardiac events (MACE) in more than 1 year is (MD = 0.73, 95 % CI = 0.60-0.89, P = 0.002). Nevertheless, there is no significant difference in mortality (MD = 0.85, 95 % CI = 0.66-1.11, P = 0.24), stent thrombosis (ST) (MD = 0.65, 95 % CI = 0.35-1.21, P = 0.18), or myocardial infarction (MD = 1.04, 95 % CI = 0.71-1.51, P = 0.84). SES may be more significant in decreasing TLR, TVR, and MACE than PES without significantly increasing mortality, ST and MI in diabetes patients.

Validation of Scoring Systems That Predict Outcomes in Patients With Coronary Artery Disease Undergoing Coronary Artery Bypass Grafting Surgery

Several risk stratification scores, based on angiographic or clinical parameters, have been developed to evaluate outcomes in patients with left main coronary artery disease (LMCAD) who undergo coronary artery bypass grafting (CABG). This study aims to validate the predictive ability of different risk scoring systems with regard to long-term outcomes after CABG. This single-center study retrospectively re-evaluated the Synergy Between PCI with TAXUS and Cardiac Surgery (SYNTAX) score; EuroSCORE; age, creatinine, and ejection fraction (ACEF) score; modified ACEF score; clinical SYNTAX; logistic clinical SYNTAX score (logistic CSS); and Parsonnet scores for 305 patients with LMCAD who underwent CABG. The endpoints were 5-year rate of all-cause death and major adverse cardio-cerebral events (MACCEs), including cardiovascular (CV) death, myocardial infarction (MI), and stroke and target vessel revascularization (TVR). Compared with the SYNTAX score, other scores were significantly higher in discriminative ability for all-cause death (SYNTAX vs others: P < 0.01). The EuroSCORE ≥6 showed significant outcome difference on all-cause death, CV death, MI, and MACCE (P < .01). Multivariate analysis indicated the SYNTAX score was a non-significant predictor for different outcomes. Adjusted multivariate analysis revealed that the EuroSCORE was the strongest predictor of all-cause death (hazard ratio[HR]: 1.17; P < 0.001), CV death (HR: 1.16; P < 0.001), and MACCE (HR: 1.09; P = 0.01). The ACEF score and logistic CSS were predictive factors for TVR (HR: 0.25, P = 0.03; HR: 0.85, P = 0.01). The EuroSCORE scoring system most accurately predicts all-cause death, CV death, and MACCE over 5 years, whereas low ACEF score and logistic CSS are independently associated with TVR over the 5-year period following CABG in patients with LMCAD undergoing CABG.

New vessel formation in the context of cardiomyocyte regeneration--the role and importance of an adequate perfusing vasculature

The history of revascularization for cardiac ischemia dates back to the early 1960's when the first coronary artery bypass graft procedures were performed in humans. With this 50 year history of providing a new vasculature to ischemic and hibernating myocardium, a profound depth of experience has been amassed in clinical cardiovascular medicine as to what does, and does not work in the context of cardiac revascularization, alleviating ischemia and adequacy of myocardial perfusion. These issues are of central relevance to contemporary cell-based cardiac regenerative approaches. While the cardiovascular cell therapy field is surging forward on many exciting fronts, several well accepted clinical axioms related to the cardiac arterial supply appear to be almost overlooked by some of our current basic conceptual and experimental cell therapy paradigms. We present here information drawn from five decades of the clinical revascularization experience, review relevant new data on vascular formation via cell therapy, and put forward the case that for optimal cell-based cardiac regeneration due attention must be paid to providing an adequate vascular supply.

Risk assessment to predict arterial and venous events in patients undergoing percutaneous coronary intervention

Atherosclerosis and venous thromboembolism (VTE) share common risk factors. We set to assess the strength of the association between atherosclerosis risk factors and disease manifestation, and VTE, in patients with coronary artery disease undergoing percutaneous coronary intervention. We pooled data from 6 global randomized controlled trials assessing coronary stenting (ENDEAVOR and SIRIUS programs), developed separate risk scores to predict major adverse cardiac and cerebrovascular events (MACCEs: cardiac death, myocardial infarction, and stroke) and VTE, and compared their performance. The 5-year rates of MACCE and VTE were 10.8% and 2.04%, respectively. Selected predictors for MACCE performed equally well in predicting VTE (area under the receiver-operating characteristic curve [AUC] 0.651 vs 0.672), and selected predictors for VTE performed equally well in predicting MACCE (AUC 0.699 vs 0.620). Ejection fraction and age were associated with both MACCE and VTE. These findings support the concept of overlapping pathophysiology of VTE and atherothrombosis.