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While multiple pharmacological drugs have been associated with myocarditis, temporal trends and overall mortality have not been reported. Here we report the spectrum and main features of 5108 reports of drug-induced myocarditis, in a worldwide pharmacovigilance analysis, comprising more than 21 million individual-case-safety reports from 1967 to 2020. Significant association between myocarditis and a suspected drug is assessed using disproportionality analyses, which use Bayesian information component estimates. Overall, we identify 62 drugs associated with myocarditis, 41 of which are categorized into 5 main pharmacological classes: antipsychotics ( n  = 3108 reports), salicylates ( n  = 340), antineoplastic-cytotoxics ( n  = 190), antineoplastic-immunotherapies ( n  = 538), and vaccines ( n  = 790). Thirty-eight (61.3%) drugs were not previously reported associated with myocarditis. Antipsychotic was the first (1979) and most reported class ( n  = 3018). In 2019, the two most reported classes were antipsychotics (54.7%) and immunotherapies (29.5%). Time-to-onset between treatment start and myocarditis is 15 [interquartile range: 10; 23] days. Subsequent mortality is 10.3% and differs between drug classes with immunotherapies the highest, 32.5% and salicylates the lowest, 2.6%. These elements highlight the diversity of presentations of myocarditis depending on drug class, and show the emerging role of antineoplastic drugs in the field of drug-induced myocarditis. Multiple drugs have been in the past associated with myocarditis. Here the authors perform a pharmacovigilance study and analyze 5108 reports of drug-induced myocarditis reporting temporal trends and overall mortality and identifying emerging drug classes among the treatments associated with myocarditis.

ST-segment elevation myocardial infarction (STEMI) is the most acute manifestation of coronary artery disease and is associated with great morbidity and mortality. A complete thrombotic occlusion developing from an atherosclerotic plaque in an epicardial coronary vessel is the cause of STEMI in the majority of cases. Early diagnosis and immediate reperfusion are the most effective ways to limit myocardial ischaemia and infarct size and thereby reduce the risk of post-STEMI complications and heart failure. Primary percutaneous coronary intervention (PCI) has become the preferred reperfusion strategy in patients with STEMI; if PCI cannot be performed within 120 minutes of STEMI diagnosis, fibrinolysis therapy should be administered to dissolve the occluding thrombus. The initiation of networks to provide around-the-clock cardiac catheterization availability and the generation of standard operating procedures within hospital systems have helped to reduce the time to reperfusion therapy. Together with new advances in antithrombotic therapy and preventive measures, these developments have resulted in a decrease in mortality from STEMI. However, a substantial amount of patients still experience recurrent cardiovascular events after STEMI. New insights have been gained regarding the pathophysiology of STEMI and feed into the development of new treatment strategies. ST-segment elevation myocardial infarction (STEMI) is an acute coronary syndrome in which transmural ischaemia (mostly caused by the formation of a thrombus on a ruptured atherosclerotic plaque) leads to cardiomyocyte death. STEMI is associated with considerable morbidity and mortality worldwide.

In this trial, bedside platelet-function monitoring to adjust antiplatelet therapy after coronary stent implantation did not reduce the rate of subsequent cardiovascular events, a finding that calls into question the clinical value of this type of testing. Clopidogrel and aspirin play a central role in the treatment of patients undergoing percutaneous coronary intervention. 1 Up to one third of patients have inadequate platelet inhibition, with an increased risk of events. 2 – 5 Platelet-function testing can determine the degree of platelet reactivity during treatment at the bedside and potentially identify patients in whom adjustment of antiplatelet therapy is warranted to minimize the risks of both ischemic and bleeding complications. 6 Cohort studies and meta-analyses have largely shown the prognostic value of high platelet reactivity during antiplatelet therapy in patients undergoing coronary stenting. 7 , 8 Randomized clinical trials have also shown that stronger . . .

Traditional statistical models allow population based inferences and comparisons. Machine learning (ML) explores datasets to develop algorithms that do not assume linear relationships between variables and outcomes and that may account for higher order interactions to make individualized outcome predictions. To evaluate the performance of machine learning models compared to traditional risk stratification methods for the prediction of major adverse cardiovascular events (MACE) and bleeding in patients with acute coronary syndrome (ACS) that are treated with antithrombotic therapy. Data on 24,178 ACS patients were pooled from four randomized controlled trials. The super learner ensemble algorithm selected weights for 23 machine learning models and was compared to traditional models. The efficacy endpoint was a composite of cardiovascular death, myocardial infarction, or stroke. The safety endpoint was a composite of TIMI major and minor bleeding or bleeding requiring medical attention. For the MACE outcome, the super learner model produced a higher c-statistic (0.734) than logistic regression (0.714), the TIMI risk score (0.489), and a new cardiovascular risk score developed in the dataset (0.644). For the bleeding outcome, the super learner demonstrated a similar c-statistic as the logistic regression model (0.670 vs. 0.671). The machine learning risk estimates were highly calibrated with observed efficacy and bleeding outcomes (Hosmer–Lemeshow p value = 0.692 and 0.970, respectively). The super learner algorithm was highly calibrated on both efficacy and safety outcomes and produced the highest c-statistic for prediction of MACE compared to traditional risk stratification methods. This analysis demonstrates a contemporary application of machine learning to guide patient-level antithrombotic therapy treatment decisions.Clinical Trial Registration ATLAS ACS-2 TIMI 46: https://clinicaltrials.gov/ct2/show/NCT00402597. Unique Identifier: NCT00402597. ATLAS ACS-2 TIMI 51: https://clinicaltrials.gov/ct2/show/NCT00809965. Unique Identifier: NCT00809965. GEMINI ACS-1: https://clinicaltrials.gov/ct2/show/NCT02293395. Unique Identifier: NCT02293395. PIONEER-AF PCI: https://clinicaltrials.gov/ct2/show/NCT01830543. Unique Identifier: NCT01830543.

Background Idiopathic inflammatory myopathy (IIM) is a group of autoimmune diseases with systemic myositis which may involve the myocardium. Cardiac involvement in IIM, although often subclinical, may mimic clinical manifestations of acute viral myocarditis (AVM). Our aim was to investigate the usefulness of the combined analysis of cardiovascular magnetic resonance (CMR) T1 and T2 mapping parameters measured both in the myocardium and in the thoracic skeletal muscles to differentiate AVM from IIM cardiac involvement. Methods Sixty subjects were included in this retrospective study (36 male, age 45 ± 16 years): twenty patients with AVM, twenty patients with IIM and cardiac involvement and twenty healthy controls. Study participants underwent CMR imaging with modified Look-Locker inversion-recovery (MOLLI) T1 mapping and 3-point balanced steady-state-free precession T2 mapping. Relaxation times were quantified after endocardial and epicardial delineation on basal and medial short-axis slices, as well as in different thoracic skeletal muscle groups present in the CMR field-of-view. ROC-Analysis was performed to assess the ability of mapping indices to discriminate the study groups. Results Mapping parameters in the thoracic skeletal muscles were able to discriminate between AVM and IIM patients. Best skeletal muscle parameters to identify IIM from AVM patients were reduced post-contrast T1 and increased extracellular volume (ECV), resulting in an area under the ROC curve (AUC) of 0.95 for post-contrast T1 and 0.96 for ECV. Conversely, myocardial mapping parameters did not discriminate IIM from AVM patients but increased native T1 (AUC 0.89 for AVM; 0.84 for IIM) and increased T2 (AUC 0.82 for AVM; 0.88 for IIM) could differentiate both patient groups from healthy controls. Conclusion CMR myocardial mapping detects cardiac inflammation in AVM and IIM compared to normal myocardium in healthy controls but does not differentiate IIM from AVM. However, thoracic skeletal muscle mapping was able to accurately discern IIM from AVM.

Abstract In 2019, the European Society of Cardiology and European Atherosclerosis Society released a new guideline document with substantial changes regarding the assessment of cardiovascular risk and treatments. The update of high-risk criteria and categories led to a better detection and primary prevention of patients at risk of a first cardiovascular event. Nonetheless, additional efforts are needed for a better inclusion of risk modifiers, especially specific to women, to improve risk stratification and direct primary prevention. Eventually, we discuss how these new guidelines implement PCSK9 inhibitors for very high-risk individuals and the evidence supporting new low-density lipoprotein cholesterol goals below, such as 55 and 40 mg/dL.

Background Cardiogenic shock (CS) is a life-threatening condition characterized by circulatory insufficiency caused by an acute dysfunction of the heart pump. The pathophysiological approach to CS has recently been enriched by the tissue consequences of low flow, including inflammation, endothelial dysfunction, and alteration of the hypothalamic-pituitary-adrenal axis. The aim of the present trial is to evaluate the impact of early low-dose corticosteroid therapy on shock reversal in adults with CS. Method/design This is a multicentered randomized, double-blind, placebo-controlled trial with two parallel arms in adult patients with CS recruited from medical, cardiac, and polyvalent intensive care units (ICU) in France. Patients will be randomly allocated into the treatment or control group (1:1 ratio), and we will recruit 380 patients (190 per group). For the treatment group, hydrocortisone (50 mg intravenous bolus every 6 h) and fludrocortisone (50 μg once a day enterally) will be administered for 7 days or until discharge from the ICU. The primary endpoint is catecholamine-free days at day 7. Secondary endpoints include morbidity and all-cause mortality at 28 and 90 days post-randomization. Pre-defined subgroups analyses are planned, including: postcardiotomy, myocardial infarction, etomidate use, vasopressor use, and adrenal profiles according the short corticotropin stimulation test. Each patient will be followed for 90 days. All analyses will be conducted on an intention-to-treat basis. Discussion This trial will provide valuable evidence about the effectiveness of low dose of corticosteroid therapy for CS. If effective, this therapy might improve outcome and become a therapeutic adjunct for patients with CS. Trial registration ClinicalTrials.gov , NCT03773822 . Registered on 12 December 2018

Autoimmune myocarditis is a rare but often fatal complication of immune checkpoint inhibitor therapy for cancer. A case of glucocorticoid-refractory myocarditis in the course of nivolumab treatment for lung cancer was resolved with the use of the cytotoxic T-lymphocyte–associated antigen 4 (CTLA-4) agonist abatacept.

The identification of acutely ill patients at high risk for venous thromboembolism (VTE) may be determined clinically or by use of integer‐based scoring systems. These scores demonstrated modest performance in external data sets. To evaluate the performance of machine learning models compared to the IMPROVE score. The APEX trial randomized 7513 acutely medically ill patients to extended duration betrixaban vs. enoxaparin. Including 68 variables, a super learner model (ML) was built to predict VTE by combining estimates from 5 families of candidate models. A “reduced” model (rML) was also developed using 16 variables that were thought, a priori, to be associated with VTE. The IMPROVE score was calculated for each patient. Model performance was assessed by discrimination and calibration to predict a composite VTE end point. The frequency of predicted risks of VTE were plotted and divided into tertiles. VTE risks were compared across tertiles. The ML and rML algorithms outperformed the IMPROVE score in predicting VTE (c‐statistic: 0.69, 0.68 and 0.59, respectively). The Hosmer‐Lemeshow goodness‐of‐fit P‐value was 0.06 for ML, 0.44 for rML, and <0.001 for the IMPROVE score. The observed event rate in the lowest tertile was 2.5%, 4.8% in tertile 2, and 11.4% in the highest tertile. Patients in the highest tertile of VTE risk had a 5‐fold increase in odds of VTE compared to the lowest tertile. The super learner algorithms improved discrimination and calibration compared to the IMPROVE score for predicting VTE in acute medically ill patients.