Pericarditis and myocarditis after COVID-19 mRNA vaccination in a nationwide setting

INTRODUCTION@#Despite reports suggesting an association between COVID-19 mRNA vaccination and pericarditis and myocarditis, detailed nationwide population-based data are sparsely available. We describe the incidence of pericarditis and myocarditis by age categories and sex after COVID-19 mRNA vaccination from a nationwide mass vaccination programme in Singapore.@*METHODS@#The incidence of adjudicated cases of pericarditis and myocarditis following COVID-19 mRNA vaccination that were reported to the vaccine safety committee between January to July 2021 was compared with the background incidence of myocarditis in Singapore.@*RESULTS@#As of end July 2021, a total of 34 cases were reported (9 pericarditis only, 14 myocarditis only, and 11 concomitant pericarditis and myocarditis) with 7,183,889 doses of COVID-19 mRNA vaccine administered. Of the 9 cases of pericarditis only, all were male except one. The highest incidence of pericarditis was in males aged 12-19 years with an incidence of 1.11 cases per 100,000 doses. Of the 25 cases of myocarditis, 80% (20 cases) were male and the median age was 23 years (range 12-55 years) with 16 cases after the second dose. A higher-than-expected number of cases were seen in males aged 12-19 and 20-29 years, with incidence rates of 3.72 and 0.98 case per 100,000 doses, respectively.@*CONCLUSION@#Data from the national registry in Singapore indicate an increased incidence of pericarditis and myocarditis in younger men after COVID-19 mRNA vaccination.

Artificial Intelligence Applications for COVID-19 in Intensive Care and Emergency Settings: A Systematic Review

BackgroundLittle is known about the role of artificial intelligence (AI) as a decisive technology in the clinical management of COVID-19 patients. We aimed to systematically review and critically appraise the current evidence on AI applications for COVID-19 in intensive care and emergency settings, focusing on methods, reporting standards, and clinical utility. MethodsWe systematically searched PubMed, Embase, Scopus, CINAHL, IEEE Xplore, and ACM Digital Library databases from inception to 1 October 2020, without language restrictions. We included peer-reviewed original studies that applied AI for COVID-19 patients, healthcare workers, or health systems in intensive care, emergency or prehospital settings. We assessed predictive modelling studies using PROBAST (prediction model risk of bias assessment tool) and a modified TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) statement for AI. We critically appraised the methodology and key findings of all other studies. ResultsOf fourteen eligible studies, eleven developed prognostic or diagnostic AI predictive models, all of which were assessed to be at high risk of bias. Common pitfalls included inadequate sample sizes, poor handling of missing data, failure to account for censored participants, and weak validation of models. Studies had low adherence to reporting guidelines, with particularly poor reporting on model calibration and blinding of outcome and predictor assessment. Of the remaining three studies, two evaluated the prognostic utility of deep learning-based lung segmentation software and one studied an AI-based system for resource optimisation in the ICU. These studies had similar issues in methodology, validation, and reporting. ConclusionsCurrent AI applications for COVID-19 are not ready for deployment in acute care settings, given their limited scope and poor quality. Our findings underscore the need for improvements to facilitate safe and effective clinical adoption of AI applications, for and beyond the COVID-19 pandemic.

Therapeutic temperature management (TTM): post-resuscitation care for adult cardiac arrest, with recommendations from the National TTM Workgroup

Therapeutic temperature management (TTM) was strongly recommended by the 2015 International Liaison Committee on Resuscitation as a component of post-resuscitation care. It has been known to be effective in improving the survival rate and neurologic functional outcome of patients after cardiac arrest. In an effort to increase local adoption of TTM as a standard of post-resuscitation care, this paper discusses and makes recommendations on the treatment for local providers.

Breakthrough in heart failure with preserved ejection fraction: are we there yet?

Heart failure with preserved ejection fraction (HFPEF) is a global health problem of considerable socioeconomic burden. It is projected to worsen with the aging population worldwide. The lack of effective therapies underscores our incomplete understanding of this complex heterogeneous syndrome. A novel paradigm has recently emerged, in which central roles are ascribed to systemic inflammation and generalized endothelial dysfunction in the pathophysiology of HFPEF. In this review, we discuss the role of the endothelium in cardiovascular homeostasis and how deranged endothelial-related signaling pathways contribute to the development of HFPEF. We also review the novel therapies in various stages of research and development that target different components of this signaling pathway.

Framingham risk score inadequately predicts cardiac risk in young patients presenting with a first myocardial infarction

<p><b>INTRODUCTION</b>The Framingham Risk Score (FRS) is a well-validated epidemiologic tool used to assess the risk for a fi rst cardiac event. Because young patients presenting with a fi rst myocardial infarction (MI) tend to have less significant risk profiles compared with older patients, we hypothesized that FRS may underestimate cardiac risk in these patients.</p><p><b>MATERIALS AND METHODS</b>We studied 1267 patients between January 2002 and November 2007 presenting with a fi rst MI. Patients with pre-existing diabetes mellitus and vascular disease were excluded. FRS was calculated for each patient. Patients were divided based on their age: group A (<40 years), group B (40 to 64 years) and group C (> or =65 years).</p><p><b>RESULTS</b>The mean age was 54.7 +/- 11 years, 88.4% of the patients were males. Younger patients were more likely to be assigned with lower scores. Based on FRS, 63.0%, 29.3% and 14.2% of group A, B and C patients were classified as low risk (10-year risk for cardiac events<10%) respectively, P <0.001. The sensitivity of FRS in identifying at least intermediate risk subjects (10-year risk for cardiac events >10%) was 37.0% in group A vs 85.8% in group C (P <0.001). The incidence of newly diagnosed diabetes mellitus was higher in younger patients (12.0% vs 13.2% vs 7.1 % in groups A, B and C respectively, P = 0.027).</p><p><b>CONCLUSIONS</b>FRS inadequately predicts cardiac risk in young patients presenting with a fi rst MI. This could be because a significant proportion of these young patients have undiagnosed diabetes mellitus, a coronary artery disease risk equivalent.</p>