Krittanawong, Chayakrit

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.

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.

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.

Globally, among infectious diseases, influenza is one of the leading causes of morbidity and mortality. Individuals with chronic conditions, including cardiovascular disease and diabetes, are particularly vulnerable to complications of an influenza infection. The European Society of Cardiology recommended annual influenza vaccinations for patients with cardiovascular disease. Numerous studies have suggested a link between influenza and increased risk of cardiovascular events. Despite its proven benefits, little is known about the reason for the underutilization of influenza vaccination. We present the findings of an assessment of patients’ perception of the influenza vaccine and the reason for its underutilization, by data mining from Twitter.