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Background Patients with baseline thrombocytopenia can have increased mortality and morbidity, but are typically excluded from randomized clinical trials studying acute coronary syndromes (ACS). We sought to better define the effect thrombocytopenia on clinical outcomes in ACS patients. Methods Patients identified from the NCDR Chest Pain registry at Mayo Clinic Arizona from Oct 2015 to Sep 2018 were retrospectively classified into two groups: TP (platelet Results Five hundred and thirty-six patients were identified, and 72 patients (13%) had thrombocytopenia. The median follow-up time was 1.1 years. The TP group was older (TP vs. control: mean age 73 ± 13 years vs. 70 ± 13 years; P = 0.026). In patients discharged on dual-antiplatelet therapy, the TP group had higher all-cause mortality (23% vs. 7.3%; P = 0.007) but not major bleeding events (11% vs. 5.0%; P = 0.123). Only all-cause mortality increased with the severity of thrombocytopenia (TPmod vs. TPmild vs. control: 33% vs. 24% vs. 7.3%; P = 0.007). Conclusions In patients with ACS, baseline thrombocytopenia is associated with increased all-cause mortality and all bleeding events without net MACE benefit. Further study is needed to identify the optimal antiplatelet strategy in this higher risk population.

Introduction: Syncope was reported to be associated with sudden cardiac death in heart failure patients regardless of the etiology of syncope. We attempted to use unsupervised machine learning to identify the prognostically distinct phenotypes in a large population of heart failure patients presenting with syncope. Hypothesis: Unsupervised machine learning can identify prognostically distinct phenotypes in a large population of heart failure patients presenting with syncope. Methods: Patients with a diagnosis of systolic or diastolic heart failure and syncope at Mayo Clinic sites were identified for baseline and follow-up data. Variables with ≥ 20% missing data were excluded; a total of 57 variables were used for k-means clustering analysis. Patients were grouped according to 4 distinct k-means determined clusters for statistical analysis. Results: A total of 3,557 patients with proper follow-up and echocardiography data were included in the final analysis. The median follow-up duration was 36.3 months, and 2908 (81.7%) patients died. Cluster 2 (n= 680) was identified as the cluster with the highest mortality (92%), which contained patients with high proportions of cardiovascular comorbidities, especially chronic kidney disease. The Kaplan-Meier survival curves are shown in Figure 1. In multivariate Cox regression analysis, the presence of CKD independently predicts mortality (HR 1.62, 95%CI 1.50-1.76, p Conclusions: Unsupervised machine learning identified patients with CKD, CAD and DM are high-risk phenotypes in heart failure patients with syncope, and can be used to guide future studies in this highly heterogeneous, unexplored population.

Introduction: Peripheral arterial disease (PAD) increases cardiovascular (CV) morbidity and mortality, but remains underdiagnosed and undertreated. Several trials support low-dose rivaroxaban in PAD treatment, although this has yet to be widely adopted in clinical practice. The effect of warfarin and direct oral anticoagulants (DOACs) on comprehensive PAD outcomes warrants further study. Hypothesis: Anticoagulant therapy, particularly DOACs, improves CV outcomes in PAD. Methods: We conducted a retrospective study of patients who underwent ankle-brachial index testing (ABI) from 1996 – 2020 at Mayo Clinic. We included patients with PAD defined by abnormal ABI (/=1.4). Prolonged (>6 months) DOAC or warfarin use was compared to no anticoagulant therapy. Multivariable (MV) regression, controlling for baseline CV risk factors, was used. Results: 21,891 patients had abnormal ABI readings; 3,093 were on warfarin and 267 were on DOAC for any indication. Anticoagulation improved mortality, with DOACs superior to warfarin. DOACs improved critical limb ischemia (CLI)/amputation in univariate analysis, although this was not significant in MV. Patients with poorly compressible vessels (ABI >/=1.4) had increased limb outcomes compared to reduced ABI ( Conclusion: Anticoagulant therapy improved mortality in a large cohort of PAD patients. The risk of limb events appears improved in those on DOACs. DOACs showed improved risk profiles compared to warfarin regarding CV and bleeding outcomes.

Introduction: Catheter ablation of the arrhythmogenic substrate reduces the recurrence of ventricular tachycardia (VT) in patients with ischemic cardiomyopathy and implantable cardioverter-defibrillator (ICD). However, the all-cause mortality benefit in this population is unclear. Hypothesis: Prophylactic ablation of the arrhythmogenic substrate decreases all-cause mortality and prevents the recurrence of VT/ventricular fibrillation (VF) in patients with ischemic cardiomyopathy. Methods: We searched the MEDLINE and EMBASE databases from inception through May 2021. We included randomized controlled trials that compared all-cause mortality and ventricular arrhythmia recurrence in patients with ischemic cardiomyopathy who underwent ICD implantation with and without prophylactic ablation. The data from each study was combined using a fixed effects model to calculate the hazard ratio and 95% confidence interval (CI). Results: Four randomized controlled trials during 2007-2020 involving 505 patients (249 patients with and 256 patients without prophylactic VT ablation) were included in our meta-analysis. Compared with control, prophylactic VT ablation did not decrease the all-cause mortality (pooled hazard ratio = 0.94, 95% CI 0.54-1.63, p = 0.818, I2 = 5.8%). However, prophylactic VT ablation significantly decreased appropriate ICD therapy (pooled hazard ratio = 0.58, 95% CI 0.41-0.82, p = 0.002, I 2 = 26.1%) and VT/VF recurrence (pooled hazard ratio = 0.70, 95% CI 0.52-0.93, p = 0.015, I 2 = 0.0%). Conclusions: Prophylactic VT ablation in patients with ischemic cardiomyopathy did not decrease all-cause mortality; however, it did lead to decreased appropriate ICD therapy and VT/VF recurrence.

Introduction: Multiple supervised machine learning (ML) models have been used to assess the outcomes in patients who underwent transcatheter aortic valve replacement (TAVR) procedure, however, supervised ML requires significant expert time for annotation and labeling. Unsupervised ML can provide insight into the dataset without this process. Hypothesis: We hypothesized that unsupervised k-means ML can identify distinct phenogroups in TAVR patients that can predict prognosis without a priori knowledge. Methods: A retrospective study of patients who underwent TAVR (Jan 2012-Dec 2017) at Mayo Clinic sites was performed. Baseline demographics, EKG, cardiac computed tomography, and echocardiographic data were abstracted. Variables with ≥ 20% missing data were excluded. Prior to the k-means clustering algorithm, principal component analysis was used for dimensionality reduction. Input variables were analyzed according to k-means determined clusters. Kaplan-Meier survival analysis was used to assess the survival of each cluster. Results: A total of 1071 consecutive patients were included in the final analysis. A total of 141 unique variables were used. The mean age was 80.7±8.6 years, 58.2% were male. Patients were divided into 2 distinct phenogroups. In the two phenogroups, cluster 0 (n=639) has significantly better survival than cluster 1 (n= 432) (log-rank p=0.0002) (Figure 1). Cluster 1 has features of low flow-low gradient (LFLG) hemodynamic profile, including lower systolic blood pressure, mean aortic valve gradient, peak aortic valve velocity, and worse LV ejection fraction. Conclusions: Unsupervised ML can identify distinct phenotype features with prognostic significance in TAVR patients. Our results suggest that patients with LFLG hemodynamic features are intrinsically different from other TAVR patients and require more attention to avoid adverse outcomes.

Recent advances in treatment have transformed the management of cancer. Despite these advances, cardiovascular disease remains a leading cause of death in cancer survivors. Cardio-oncology has recently evolved as a subspecialty to prevent, diagnose, and manage cardiovascular side effects of antineoplastic therapy. An emphasis on optimal management of comorbidities and close attention to drug interactions are important in cardio-oncologic care. With interdisciplinary collaboration among oncologists, cardiologists, and pharmacists, there is potential to prevent and reduce drug-related toxicities of treatments. The cytochrome P450 (CYP450) family of enzymes and the P-glycoprotein (P-g) transporter play a crucial role in drug metabolism and drug resistance. Here we discuss the role of CYP450 and P-g in drug interactions in the field of cardio-oncology, provide an overview of the cardiotoxicity of a spectrum of cancer agents, highlight the role of precision medicine, and encourage a multidisciplinary treatment approach for patients with cancer.

As many novel cancer therapies continue to emerge, the field of Cardio-Oncology (or onco-cardiology) has become crucial to prevent, monitor and treat cancer therapy-related cardiovascular toxicity. Furthermore, given the narrow therapeutic window of most cancer therapies, drug-drug interactions are prevalent in the cancer population. Consequently, there is an increased risk of affecting drug efficacy or predisposing individual patients to adverse side effects. Here we review the role of cytochrome P450 (CYP450) enzymes in the field of Cardio-Oncology. We highlight the importance of cardiac medications in preventive Cardio-Oncology for high-risk patients or in the management of cardiotoxicities during or following cancer treatment. Common interactions between Oncology and Cardiology drugs are catalogued, emphasizing the impact of differential metabolism of each substrate drug on unpredictable drug bioavailability and consequent inter-individual variability in treatment response or development of cardiovascular toxicity. This inter-individual variability in bioavailability and subsequent response can be further enhanced by genomic variants in CYP450, or by modifications of CYP450 gene, RNA or protein expression or function in various ‘omics’ related to precision medicine. Thus, we advocate for an individualized approach to each patient by a multidisciplinary team with clinical pharmacists evaluating a treatment plan tailored to a practice of precision Cardio-Oncology. This review may increase awareness of these key concepts in the rapidly evolving field of Cardio-Oncology.