Citations

Abstract Background Pulmonary embolism (PE) is a life-threatening cardiovascular condition. Studies showed that PE patients were associated with disorders of lipid metabolism and had higher triglyceride and lower HDL-C levels compared with healthy. We conducted the genome-wide association study to identify novel loci contributing to PE. Methods We conducted a large-scale GWAS of PE in 5,466 PE cases and 461,219 controls of European ancestry from the UK Biobank (466,685 participants total). We used genome-wide summary statistics to test for enrichment of functional annotations using ENRICHR. Example pathways included in Enrichr for testing include membership of genes in pathway databases such as the Kyoto Encyclopedia of Genes and Genomes (KEGG), Wikipathway, PANTHER, BioCarta or NCI-Nature pathways. We analyzed the pathways using combined score and p-values which were well validated by comparing to several methods. For pathway analyses, we considered a nominal P-value threshold of 0.05. Results We identified genome-wide significant genetic associations in 63 independent genetic loci for PE (P<5.0x10–7). Our findings for top pathways highlight that lipid metabolism (LIPC, LCAT, NPC2), caffeine metabolism (NAT2), and sudden cardiac death (ABCG8) related genetic loci play an important role in PE alongside genes already associated with coagulation-thrombosis pathway (VWF, THPO, PTPN11, INPP5D, UROS, HMBS) (all p-values p-values <0.05). Conclusion Our findings uncovered unexpected novel factors of PE etiology, suggesting novel mechanistic concepts of PE pathophysiology. Funding Acknowledgement Type of funding source: None

Cardiovascular disease (CVD) is the most common cause of death worldwide. Cardiac rehabilitation (CR) is increasingly recognised as an essential part of clinical care for patients with CVD. CR is a complex intervention that encompasses a multidisciplinary approach that focuses on health education, modification of cardiovascular risk factors, exercise training, physical activity and stress management. In the current American Heart Association/American College of Cardiology guidelines, cardiac rehabilitation is a class IA recommendation for secondary prevention of acute myocardial infarction, percutaneous coronary intervention and cardiac surgery and Class II recommendation for chronic stable heart failure. Despite proven effectiveness, however, participation in traditional CR remains low. Although the reasons for participation and non-participation in CR have been well described, the use of Twitter to explore public perceptions of CR has not yet been formally reported. To the best of our knowledge, the analysis of Twitter for insights on CR is a novel investigation that is relevant for addressing significant problems related to CR awareness in the digital age. We performed data mining of Twitter to assess patients’ perceptions regarding CR and the reasons for their participation and non-participation in CR between 23 July 2015 and 22 October 2017. We analysed a total of 5515 Twitter messages. Tweets associated with CR were often used to self-report on health status either before or after participating in CR or contained emotional language with positive or negative comments, advertisements or updated news. Twitter users wrote mainly positive comments about CR, suggesting the platform has the potential to disseminate both the benefits of CR and its research to the public on the worldwide scale.

Hyponatremia is a very common electrolyte abnormality, associated with poor short- and long-term outcomes in patients with heart failure (HF). Two opposite processes can result in hyponatremia in this setting: Volume overload with dilutional hypervolemic hyponatremia from congestion, and hypovolemic hyponatremia from excessive use of natriuretics. These two conditions require different therapeutic approaches. While sodium in the form of normal saline can be lifesaving in the second case, the same treatment would exacerbate hyponatremia in the first case. Hypervolemic hyponatremia in HF patients is multifactorial and occurs mainly due to the persistent release of arginine vasopressin (AVP) in the setting of ineffective renal perfusion secondary to low cardiac output. Fluid restriction and loop diuretics remain mainstay treatments for hypervolemic/ dilutional hyponatremia in patients with HF. In recent years, a few strategies, such as AVP antagonists (Tolvaptan, Conivaptan, and Lixivaptan), and hypertonic saline in addition to loop diuretics, have been proposed as potentially promising treatment options for this condition. This review aimed to summarize the current literature on pathogenesis and management of hyponatremia in patients with HF.

Abstract Deep learning (DL) is a branch of machine learning (ML) showing increasing promise in medicine, to assist in data classification, novel disease phenotyping and complex decision making. Deep learning is a form of ML typically implemented via multi-layered neural networks. Deep learning has accelerated by recent advances in computer hardware and algorithms and is increasingly applied in e-commerce, finance, and voice and image recognition to learn and classify complex datasets. The current medical literature shows both strengths and limitations of DL. Strengths of DL include its ability to automate medical image interpretation, enhance clinical decision-making, identify novel phenotypes, and select better treatment pathways in complex diseases. Deep learning may be well-suited to cardiovascular medicine in which haemodynamic and electrophysiological indices are increasingly captured on a continuous basis by wearable devices as well as image segmentation in cardiac imaging. However, DL also has significant weaknesses including difficulties in interpreting its models (the ‘black-box’ criticism), its need for extensive adjudicated (‘labelled’) data in training, lack of standardization in design, lack of data-efficiency in training, limited applicability to clinical trials, and other factors. Thus, the optimal clinical application of DL requires careful formulation of solvable problems, selection of most appropriate DL algorithms and data, and balanced interpretation of results. This review synthesizes the current state of DL for cardiovascular clinicians and investigators, and provides technical context to appreciate the promise, pitfalls, near-term challenges, and opportunities for this exciting new area.

We examined risks for first hospitalization and the rate, risk factors, costs, and 1‐year outcome of 30‐day readmission among patients admitted for complications of cirrhosis. Data were retrospectively analyzed for adult patients with cirrhosis residing in Minnesota, Iowa, or Wisconsin and admitted from 2010 through 2013 at both campuses of the Mayo Clinic Hospital in Rochester, MN. Readmission was captured at the two hospitals as well as at community hospitals in the tristate area within the Mayo Clinic Health System. The incidence of hospitalization for complications of cirrhosis was 100/100,000 population, with increasing age and male sex being the strongest risks for hospitalization. For the 2,048 hospitalized study patients, the overall 30‐day readmission rate was 32%; 498 (24.3%) patients were readmitted to Mayo Clinic hospitals and 157 (7.7%) to community hospitals, mainly for complications of portal hypertension (52%) and infections (30%). Readmission could not be predicted accurately. There were 146 deaths during readmission and an additional 105 deaths up to 1 year of follow‐up (50.4% total mortality). Annual postindex hospitalization costs for those with a 30‐day readmission were substantially higher ($73,252) than those readmitted beyond 30 days ($62,053) or those not readmitted ($5,719). At 1‐year follow‐up, only 20.4% of patients readmitted within 30 days were at home. In conclusion, patients with cirrhosis have high rates of hospitalization, especially among men over 65 years, and of unscheduled 30‐day readmission. Readmission cannot be accurately predicted. Postindex hospitalization costs are high; nationally, the annual costs are estimated to be more than $4.45 billion. Only 20% of patients readmitted within 30 days are home at 1 year. (Hepatology Communications 2018;2:188–198)

Background: A shorter sleep duration has been identified as a risk factor for cardiovascular diseases and increased mortality. It has been hypothesized that a short sleep duration may be linked to changes in ghrelin and leptin production, leading to an alteration of stress hormone production. Here, we conducted a systematic review and meta-analysis to investigate the potential relationship between a sleep duration and cardiovascular disease mortality. Methods: We conducted a comprehensive search of Ovid Medline In-Process and other non-indexed citations, Ovid MEDLINE, Ovid Embase, Ovid Cochrane Central Register of Controlled Trials, and Scopus from database inception to March 2017. Observational studies were included if the studies reported hazard ratios or odds ratios of the associations between sleep durations (short and long) and cardiovascular disease mortality. Data were extracted by a reviewer and then reviewed by two separate reviewers. Conflicts were resolved through consensus. Using the DerSimonian and Laird random effects models, we calculated pooled hazard ratios and pooled odds ratios with 95% confidence intervals (CI). Subgroup analyses were performed to explore potential sources of heterogeneity. The quality of the included studies and publication bias were assessed. Results: In total, our meta-analysis included 19 studies (31 cohorts) with a total of 816,995 individuals with 42,870 cardiovascular disease mortality cases. In pooled analyses, both short (risk ratio 1.19; 95% CI 1.13 to 1.26, P<0.001, I2=30.7, Pheterogeneity=0.034), and long (risk ratio 1.37; 95% CI 1.23 to 1.52, P<0.001, I2=79.75, Pheterogeneity<0.001) sleep durations were associated with a greater risk of cardiovascular disease mortality. Conclusions: Both short (<7 hours) and long sleep durations (>9 hours) can increase the risk of overall cardiovascular disease mortality, particularly in Asian populations and elderly individuals. Future epidemiological studies would ideally include objective sleep measurements, rather than self-report measures, and all potential confounders, such as genetic variants.