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Patients with cancer are at increased risk of myocardial infarction (MI) and stroke. Guidelines do not address lipid profile targets for these patients. Within the lipid profiles, we hypothesized that patients with cancer develop MI or stroke at lower low density lipoprotein cholesterol (LDL-C) concentrations than patients without cancer and suffer worse outcomes. We linked nationwide longitudinal MI, stroke and cancer registries from years 2007–2017. We identified 42,148 eligible patients with MI (2421 prior cancer; 39,727 no cancer) and 43,888 eligible patients with stroke (3152 prior cancer; 40,738 no cancer). Median LDL-C concentration was lower in the prior cancer group than the no cancer group at incident MI [2.43 versus 3.10 mmol/L, adjusted ratio 0.87 (95% CI 0.85–0.89)] and stroke [2.81 versus 3.22 mmol/L, adjusted ratio 0.93, 95% CI 0.91–0.95)]. Similarly, median triglyceride and total cholesterol concentrations were lower in the prior cancer group, with no difference in high density lipoprotein cholesterol. Prior cancer was associated with higher post-MI mortality [adjusted hazard ratio (HR) 1.48, 95% CI 1.37–1.59] and post-stroke mortality (adjusted HR 1.95, 95% CI 1.52–2.52). Despite lower LDL-C concentrations, patients with prior cancer had worse post-MI and stroke mortality than patients without cancer.

Cardiovascular magnetic resonance (CMR) is an important cardiac imaging tool for assessing the prognostic extent of myocardial injury after myocardial infarction (MI). Within the context of clinical trials, CMR is also useful for assessing the efficacy of potential cardioprotective therapies in reducing MI size and preventing adverse left ventricular (LV) remodelling in reperfused MI. However, manual contouring and analysis can be time-consuming with interobserver and intra-observer variability, which can in turn lead to reduction in accuracy and precision of analysis. There is thus a need to automate CMR scan analysis in MI patients to save time, increase accuracy, increase reproducibility and increase precision. In this regard, automated imaging analysis techniques based on artificial intelligence (AI) that are developed with machine learning (ML), and more specifically deep learning (DL) strategies, can enable efficient, robust, accurate and clinician-friendly tools to be built so as to try and improve both clinician productivity and quality of patient care. In this review, we discuss basic concepts of ML in CMR, important prognostic CMR imaging biomarkers in MI and the utility of current ML applications in their analysis as assessed in research studies. We highlight potential barriers to the mainstream implementation of these automated strategies and discuss related governance and quality control issues. Lastly, we discuss the future role of ML applications in clinical trials and the need for global collaboration in growing this field.

Background Stress-induced hyperglycaemia at time of hospital admission has been linked to worse prognosis following acute myocardial infarction (AMI). The stress-hyperglycaemia ratio (SHR) index normalises the acute increase in blood glucose values to background glycaemic status. However, the optimal cut-off blood glucose and SHR values for predicting adverse outcomes post-AMI are unknown. As such, we determined the optimal blood glucose and SHR cut-offs for predicting 1-year all cause mortality in diabetic and non-diabetic non-ST-segment elevation myocardial infarction (NSTEMI) and ST-segment elevation myocardial infarction (STEMI) patients. Methods We undertook a national, registry-based study of patients with AMI from January 2008 to December 2015. We determined the optimal blood glucose and SHR cut-off values using the Youden’s formula for 1-year all-cause mortality. We subsequently analyzed the sensitivity, specificity, positive and negative predictive values of the cut-offs in the diabetic and non-diabetic subgroups, stratified by the type of AMI. Results There were 5,841 STEMI and 4,105 NSTEMI in the study. In STEMI patients, both glucose and SHR were independent predictors of 1-year all-cause mortality [Glucose: OR 2.19 (95% CI 1.74–2.75); SHR: 2.19 (95% CI 1.73–2.78)]. However, in NSTEMI patients, glucose and SHR were not independently associated with 1-year all-cause mortality [Glucose: OR 1.37 (95% CI 1.00-1.89); SHR: 1.27 (95% CI 0.91–1.78)]. In STEMI patients, ROC analysis showed that SHR performed better than glucose (AUC for glucose 0.633 versus AUC for SHR 0.692, P

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Cardiovascular disease and cancer are leading contributors to the global disease burden. As a result of cancer therapy-related cardiotoxicities, cardiovascular disease results in significant morbidity and mortality in cancer survivors and patients with active cancer. There is an unmet need for management of cardio-oncology conditions, which is predicted to reach epidemic proportions, and better understanding of their pathophysiology and treatment is urgently required. The proposed mechanisms underlying cardiotoxicity induced by 5-fluorouracil (5-FU) are vascular endothelial damage followed by thrombus formation, ischaemia secondary to coronary artery vasospasm, direct toxicity on myocardium and thrombogenicity. In patients with angina and electrocardiographic evidence of myocardial ischaemia due to chemotherapy-related coronary artery vasospasm, termination of chemotherapy and administration of calcium channel blockers or nitrates can improve ischaemic symptoms. However, coronary artery vasospasm can reoccur with 5-FU re-administration with limited effectiveness of vasodilator prophylaxis observed. While pre-existing coronary artery disease may increase the ischaemic potential of 5-FU, cardiovascular risk factors do not appear to completely predict the development of cardiac complications. Pharmacogenomic studies and genetic profiling may help predict the occurrence and streamline the treatment of 5-FU-induced coronary artery vasospasm. Echocardiographic measures such as the Tei index may help detect subclinical 5-FU cardiotoxicity. Further research is required to explore the cardioprotective effect of agents such as coenzyme complex, GLP-1 analogues and degradation inhibitors on 5-FU-induced coronary artery vasospasm.

The COVID-19 pandemic has had an unprecedented impact on cardiology training. Novel opportunities have been identified in several domains: patient exposure, procedural experience, didactic education, research and development, advocacy and well-being, and career advancement. Lessons learned from COVID-19 should be used to further improve fellowship training such as, for example, through the development of a competency-based training and evaluation system. Multimodality teaching that incorporates telelearning provides creative solutions for trainee and continuing medical education. Fellow-initiated research should be supported and nurtured. Enhanced attention to trainee well-being and burnout is particularly important. The emerging cardiologists of the future and the way they are trained will be shaped by the COVID-19 challenge of our generation.

We describe a case of reversible Brugada Syndrome in a patient on Clozapine for schizophrenia, highlighting the risk of antipsychotics causing a variety of phenotypic expressions of predisposing sodium channelopathy.