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Background Recent studies suggest that tirzepatide, a dual glucose-dependent insulinotropic-peptide (GIP) and glucagon-like peptide-1 receptor agonist (GLP-1 RA), has significant weight loss effects. This systematic review and meta-analysis aims to assess the efficacy and safety of tirzepatide for weight loss in patients with overweight or obesity. Methods Medline, Embase and Cochrane CENTRAL were searched for randomized controlled trials (RCTs) on tirzepatide’s weight loss efficacy for these patients. A single arm meta-analysis of proportions estimated primary outcomes, ≥5%, ≥10%, and ≥15% weight loss, and adverse events (AEs); while meta-analysis of means estimated secondary outcomes. Comparative meta-analysis was conducted between tirzepatide and control arms where mean differences and odds ratios were estimated for continuous and dichotomous outcomes respectively. Results RCTs included in this study revealed that among 5800 patients, 78.22% (95% CI: 72.15% to 83.73%), 55.60% (95% CI: 46.54% to 64.47%), 32.28% (95% CI: 23.17% to 42.12%) achieved ≥5%, ≥10%, and ≥15% weight loss, respectively. Tirzepatide 5 mg demonstrated weight loss superiority relative to placebo (MD: −12.47 kg, 95% CI: −13.94 kg to −11.00 kg) and semaglutide ( n  = 1409, MD: −1.90 kg, 95% CI: −2.97 kg to −0.83 kg) with dose-dependent increase for 10 mg and 15 mg doses. The comparison between tirzepatide and semaglutide was examined in the SURPASS-2 trial that was included in this systematic review. For AEs, there was increase odds of experiencing gastrointestinal AEs with tirzepatide compared to placebo, but no significant difference with semaglutide. Conclusion Tirzepatide has significant potential as a weight loss drug in patients with overweight and obesity, with little increase in AEs compared to other weight loss drugs. With its ability to concurrently target multiple aspects of metabolic syndrome, it should be considered as the next helm of weight loss therapies.

Coronavirus disease 2019 (COVID-19) predisposes patients to thrombotic and thromboembolic events, owing to excessive inflammation, endothelial cell activation and injury, platelet activation and hypercoagulability. Patients with COVID-19 have a prothrombotic or thrombophilic state, with elevations in the levels of several biomarkers of thrombosis, which are associated with disease severity and prognosis. Although some biomarkers of COVID-19-associated coagulopathy, including high levels of fibrinogen and d-dimer, were recognized early during the pandemic, many new biomarkers of thrombotic risk in COVID-19 have emerged. In this Consensus Statement, we delineate the thrombotic signature of COVID-19 and present the latest biomarkers and platforms to assess the risk of thrombosis in these patients, including markers of platelet activation, platelet aggregation, endothelial cell activation or injury, coagulation and fibrinolysis as well as biomarkers of the newly recognized post-vaccine thrombosis with thrombocytopenia syndrome. We then make consensus recommendations for the clinical use of these biomarkers to inform prognosis, assess disease acuity, and predict thrombotic risk and in-hospital mortality. A thorough understanding of these biomarkers might aid risk stratification and prognostication, guide interventions and provide a platform for future research.In this Consensus Statement, the authors delineate the thrombotic signature of COVID-19 and present the latest biomarkers and platforms to assess thrombotic risk in these patients. Consensus recommendations are made about the clinical use of these biomarkers to inform prognosis, assess disease acuity, and predict thrombosis and in-hospital mortality.

Introduction The incidence of left ventricular (LV) thrombus formation in ST-segment elevation myocardial infarction (STEMI) patients in the current era of primary percutaneous coronary intervention (PCI) is not well established. We performed a meta-analysis to assess the actual incidence and predictors of LV thrombus by cardiovascular magnetic resonance (CMR) in STEMI treated by primary PCI. Methods We searched MEDLINE and EMBASE databases up to February 2018. We included all studies published as a full-text article, reporting the incidence of LV thrombus by CMR within 1 month following acute STEMI in patients treated by primary PCI. A binary random-effects model was used to estimate the pooled incidence of LV thrombus. The diagnostic performance of transthoracic echocardiography (TTE) as compared with CMR was pooled to obtain the sensitivity and specificity of TTE with CMR as the gold standard. Embolic and bleeding complications of LV thrombus were also evaluated. Results Ten studies were included in the meta-analysis. The incidence of LV thrombus by CMR in all-comer STEMI patients ( n  = 2072) was 6.3% with 96% of LV thrombus occurring in those with anterior STEMI (12.2% incidence). When only anterior STEMI with LVEF< 50% were considered ( n  = 447), the incidence of LV thrombus was 19.2%. Compared with CMR, the sensitivity of TTE to detect LV thrombus was 29% with a specificity of 98%. The sensitivity of TTE increased to 70% in those with anterior STEMI and reduced LVEF. LV thrombus resolved in 88% of cases by 3 to 6 months. After 1–2 years follow-up, the embolic complication rate was similar at 1.5% ( P  = 0.25) but the bleeding complication rate was significantly higher (8.8% versus 0.5%, P  < 0.001) in the LV thrombus group on triple therapy when compared to the no LV thrombus group on dual antiplatelet therapy. Conclusion In the primary PCI era, CMR detection of an LV thrombus post-STEMI remains high with incidence of nearly 20% in anterior STEMI with depressed LVEF. Patients with LV thrombus treated by triple therapy had similar embolic complications but higher bleeding complications than those with no LV thrombus treated with dual antiplatelet therapy. A 3 month follow-up CMR scan to guide anticoagulation duration might help mitigate bleeding risk.

Post-acute coronary syndrome (ACS) depression is a common but not well understood complication experienced by ACS patients. Research on the effectiveness of various therapies remains limited. Hence, we sought to conduct a network meta-analysis to assess the efficacy of different interventions for post-ACS depression in improving patient outcomes. Three electronic databases were searched for randomised controlled trials describing different depression treatment modalities in post-ACS patients. Each article was screened based on inclusion criteria and relevant data were extracted. A bivariate analysis and a network meta-analysis was performed using risk ratios (RR) and standardized mean differences (SMD) for binary and continuous outcomes, respectively. A total of 30 articles were included in our analysis. Compared to standard care, psychosocial therapy was associated with the greatest reduction in depression scores (SMD:-1.21, 95% CI: -1.81 to -0.61, p<0.001), followed by cognitive behavioural therapy (CBT) (SMD: -0.75, 95% CI: -0.99 to -0.52, p<0.001), antidepressants (SMD: -0.73, 95% CI: -1.14 to -0.31, p<0.001), and lastly, combination therapy (SMD: -0.15, 95% CI: -0.28 to -0.03, p = 0.016). No treatment modalities was found to be more effective in reducing depression scores when compared to one another. Additional analysis showed that these treatment modalities did not have significant impact on the overall mortality, cardiac mortality and recurrent myocardial infarction. This network meta-analysis found that the treatment effect of the various psychological modalities on depression severity were similar. Future trials on psychological interventions assessing clinical outcomes and improvement in adherence to ACS-specific interventions are needed.

Mass spectrometry (MS)-based targeted lipidomics enables the robust quantification of selected lipids under various biological conditions but comprehensive software tools to support such analyses are lacking. Here we present LipidCreator, a software that fully supports targeted lipidomics assay development. LipidCreator offers a comprehensive framework to compute MS/MS fragment masses for over 60 lipid classes. LipidCreator provides all functionalities needed to define fragments, manage stable isotope labeling, optimize collision energy and generate in silico spectral libraries. We validate LipidCreator assays computationally and analytically and prove that it is capable to generate large targeted experiments to analyze blood and to dissect lipid-signaling pathways such as in human platelets. Targeted mass spectrometry enables reproducible and accurate lipid quantification but dedicated software tools to develop targeted lipidomics assays are lacking. Here, the authors develop a targeted lipidomics workbench and lipid knowledgebase for the streamlined generation of targeted assays.

Background Little is known about the impact of the global coronavirus disease-2019 (COVID-19) pandemic on patients with cardiovascular disease (CVD), the biggest global killer and major risk factor for severe COVID-19 infections. We aim to explore the indirect consequences of COVID-19 on health-related quality of life (HRQoL) of patients with CVD. Methods Eighty-one adult outpatients with CVD were assessed using the EQ-5D, a generic health status instrument with five dimensions (mobility, self-care, usual activities, pain/discomfort, anxiety/depression), before and during the pandemic. Changes in the EQ-5D dimensional responses were compared categorically as well as using the dimension-specific sum-score (range 1–3, with a higher score indicating worse health). The responses and sum-score were compared using the exact test of symmetry and the paired t -test, respectively. Results These patients [mean age (SD) 59.8 (10.5); 92.6% males; 56% New York Heart Association (NYHA) functional class I] had coronary artery disease (69%), heart failure (28%), or arrhythmias (15%). None experienced change in NYHA class between assessments. About 30% and 38% of patients reported problems with at least one of the EQ-5D dimensions pre-pandemic and during the pandemic, respectively. The highest increase in health problems was reported for anxiety/depression (12.5% pre-pandemic vs 23.5% during pandemic; p  = 0.035) with mean domain-specific score from 1.12 (SD 0.33) to 1.25 (SD 0.46) (standardized effect size = 0.373, p  = 0.012). There was no meaningful change in other dimensions as well as overall HRQoL. Conclusion The COVID-19 pandemic is associated with a significant worsening of the mental health of patients with CVD.

Background In randomized clinical trials (RCTs) with non-compliance, evaluating the causal effects of interventions would lead to a more precise estimation of treatment effect when the estimand of interest is the effect of treatment amongst compliers. While there is a large body of literature addressing the issue of non-compliance for continuous, binary, and time-to-event outcomes, this issue is seldom discussed for ordinal outcomes. Methods In this paper, we consider one-sided non-compliance. We introduce an extension of the inverse probability weighting (IPW) method for handling non-compliance involving an ordinal outcome by fully utilizing the information of non-compliance and defining it as a categorical variable to describe the extent of non-compliance. This is in contrast to the usual convention where compliance is regarded as a binary variable. We provide the identification and asymptotic distribution of the proposed method. We compare the proposed method (IPW_Dnew) with intention-to-treat (ITT), per protocol (PP), instrumental variable (IV), and IPW method via a simulation study and real-life data from the JOBS II intervention trial and the IMMACULATE trial. Results Simulation results demonstrate that the proposed method performs better than other methods in terms of bias, coverage, mean squared error, power and Type I error under various scenarios, particularly in situations with selection bias and partial compliance. In the empirical study, a substantial estimate of partial compliance by IPW_Dnew implies that there may be a partial compliance effect. Conclusion For ordinal outcome in the presence of non-compliance, we suggest using the proposed method to estimate the causal effect of treatment amongst compliers and partial compliers, especially when there exists selection bias.

There are conflicting data on the relationship between the time of symptom onset during the 24-hour cycle (circadian dependence) and infarct size in ST-elevation myocardial infarction (STEMI). Moreover, the impact of this circadian pattern of infarct size on clinical outcomes is unknown. We sought to study the circadian dependence of infarct size and its impact on clinical outcomes in STEMI. We studied 6,710 consecutive patients hospitalized for STEMI from 2006 to 2009 in a tropical climate with non-varying day-night cycles. We categorized the time of symptom onset into four 6-hour intervals: midnight-6:00 A.M., 6:00 A.M.-noon, noon-6:00 P.M. and 6:00 P.M.-midnight. We used peak creatine kinase as a surrogate marker of infarct size. Midnight-6:00 A.M patients had the highest prevalence of diabetes mellitus (P = 0.03), more commonly presented with anterior MI (P = 0.03) and received percutaneous coronary intervention less frequently, as compared with other time intervals (P = 0.03). Adjusted mean peak creatine kinase was highest among midnight-6:00 A.M. patients and lowest among 6:00 A.M.-noon patients (2,590.8±2,839.1 IU/L and 2,336.3±2,386.6 IU/L, respectively, P = 0.04). Midnight-6:00 A.M patients were at greatest risk of acute heart failure (P<0.001), 30-day mortality (P = 0.03) and 1-year mortality (P = 0.03), while the converse was observed in 6:00 A.M.-noon patients. After adjusting for diabetes, infarct location and performance of percutaneous coronary intervention, circadian variations in acute heart failure incidence remained strongly significant (P = 0.001). We observed a circadian peak and nadir in infarct size during STEMI onset from midnight-6:00A.M and 6:00A.M.-noon respectively. The peak and nadir incidence of acute heart failure paralleled this circadian pattern. Differences in diabetes prevalence, infarct location and mechanical reperfusion may account partly for the observed circadian pattern of infarct size and acute heart failure.

Cardiac myxomas are the most common primary cardiac tumours. The overall prevalence is very low and up to 25% present asymptomatically. In Asia, the data on myxomas is limited. Therefore, we aimed to outline a multi-ethnic Asian population of cardiac myxoma patients. We included all index echocardiographic diagnoses of myxoma at a tertiary cardiovascular referral centre from May 2004 to April 2021. A retrospective review of all patients’ medical records was conducted. Data including patient characteristics, clinical presentation, imaging, treatment, and outcomes were collected and analysed. A total of 64 patients (mean age 66.4 years old, male 61%) were diagnosed with cardiac myxoma. The incidence of major adverse cardiovascular events (MACE) was 10 (16.1%) and 10-year mortality was 11 (17.2%). The proportion of asymptomatic patients (30.2%) increased from 15.4% before 2012 to 35.3% after 2012. Pre-existing chronic kidney disease and ischaemic heart disease were independently associated with increased risk of mortality and MACE (p-values < 0.05). Most patients (75.0%) underwent surgical resection. This was associated with a decreased risk of mortality and MACE (p-values < 0.05). The increase in the diagnosis of asymptomatic patients over the years could be attributed to increased opportunistic screening and incidental pickups. Surgical resection was associated with better outcomes. However, the poor outcomes of non-operated patients may be due to the concurrent comorbidities that preclude surgery.

Background Cardiovascular magnetic resonance (CMR) sequences are commonly used to obtain a complete description of the function and structure of the heart, provided that accurate measurements are extracted from images. New methods of extraction of information are being developed, among them, deep neural networks are powerful tools that showed the ability to perform fast and accurate segmentation. Iq1n order to reduce the time spent by reading physicians to process data and minimize intra- and inter-observer variability, we propose a fully automatic multi-scan CMR image analysis pipeline. Methods Sequence specific U-Net 2D models were trained to perform the segmentation of the left ventricle (LV), right ventricle (RV) and aorta in cine short-axis, late gadolinium enhancement (LGE), native T1 map, post-contrast T1, native T2 map and aortic flow sequences depending on the need. The models were trained and tested on a set of data manually segmented by experts using semi-automatic and manual tools. A set of parameters were computed from the resulting segmentations such as the left ventricular and right ventricular ejection fraction (EF), LGE scar percentage, the mean T1, T1 post, T2 values within the myocardium, and aortic flow. The Dice similarity coefficient, Hausdorff distance, mean surface distance, and Pearson correlation coefficient R were used to assess and compare the results of the U-Net based pipeline with intra-observer variability. Additionally, the pipeline was validated on two clinical studies. Results The sequence specific U-Net 2D models trained achieved fast (≤ 0.2 s/image on GPU) and precise segmentation over all the targeted region of interest with high Dice scores (= 0.91 for LV, = 0.92 for RV, = 0.93 for Aorta in average) comparable to intra-observer Dice scores (= 0.86 for LV, = 0.87 for RV, = 0.95 for aorta flow in average). The automatically and manually computed parameters were highly correlated (R = 0.91 in average) showing results superior to the intra-observer variability (R = 0.85 in average) for every sequence presented here. Conclusion The proposed pipeline allows for fast and robust analysis of large CMR studies while guaranteeing reproducibility, hence potentially improving patient’s diagnosis as well as clinical studies outcome.