Prediction of Sudden Cardiac Arrest in the General Population: Review of Traditional and Emerging Risk Factors.

Sudden cardiac death (SCD) is the most common and devastating outcome of sudden cardiac arrest (SCA), defined as an abrupt and unexpected cessation of cardiovascular function leading to circulatory collapse. The incidence of SCD is relatively infrequent for individuals in the general population, in the range of 0.03%-0.10% per year. Yet, the absolute number of cases around the world is high because of the sheer size of the population at risk, making SCA/SCD a major global health issue. On the basis of conservative estimates, there are at least 2 million cases of SCA occurring worldwide on a yearly basis. As such, identification of risk factors associated with SCA in the general population is an important objective from a clinical and public health standpoint. This review will provide an in-depth discussion of established and emerging factors predictive of SCA/SCD in the general population beyond coronary artery disease and impaired left ventricular ejection fraction. Contemporary studies on the association of age, sex, race, socioeconomic status, and the emerging contribution of diabetes and obesity to SCD risk beyond their role as atherosclerotic risk factors are reviewed. In addition, the role of biomarkers, particularly electrocardiographic ones, on SCA/SCD risk prediction in the general population are discussed. Finally, the use of machine learning as a tool to facilitate SCA/SCD risk prediction is examined.

A Tale of 2 Aneurysms: Cardiogenic Shock Secondary to Vascular Behçet's Syndrome.

A patient with vascular Behçet's syndrome (BS), a subtype of BS with mainly venous/arterial manifestations, presented with a left main aneurysm/thrombus and cardiogenic shock. The clinical diagnosis of BS includes mucocutaneous, vascular, and neurologic criteria. It is important to consider vascular BS as a nonatherosclerotic cause of coronary aneurysms. (Level of Difficulty: Intermediate.).

Sex-Specific Differences in Heart Failure: Pathophysiology, Risk Factors, Management, and Outcomes.

Heart failure (HF) is a leading cause of hospitalisation, morbidity, and mortality in Canada. There are sex-specific differences in the etiology, epidemiology, comorbidities, treatment response, and treatment adverse effects that have implications on outcomes in HF. Sex-specific analyses of some HF trials indicate that optimal doses of drug therapies and benefit of device therapies may differ between male and female patients, but the trials were not designed to test sex differences. The under-representation of female participants in HF randomised controlled trials (RCTs) is a major limitation in assessing the sex-specific efficacy and safety of treatments. To ensure that female patients receive safe and effective HF therapies, RCTs should include participants proportionate to the sex-specific distribution of disease. This review outlines the sex-specific differences in HF phenotype and treatment response, and highlights disparities in services and gaps in knowledge that merit further investigation.

The effect of pre-heart transplant body mass index on posttransplant outcomes: An analysis of the ISHLT Registry Data.

We evaluated the effect of pre-heart transplant body mass index (BMI) on posttransplant outcomes using the International Society for Heart and Lung Transplantation Registry. Kaplan-Meier analysis and a multivariable Cox proportional hazard regression model were used for all-cause mortality, and cause-specific hazard regression for cause-specific mortality and morbidity. We assessed 38 498 recipients from 2000 to 2014 stratified by pretransplant BMI. Ten-year survival was 56% in underweight, 59% in normal weight, 57% in overweight, 52% in obese class I, 54% in class II, and 47% in class III patients (P < 0.001). Mortality was increased in underweight (HR 1.29, 95% CI 1.24-1.35), obese class I (HR 1.19, 95% CI 1.13-1.26), class II (HR 1.20, 95% CI 1.08-1.32), and class III patients (HR 1.45, 95% CI 1.15-1.83). Obesity was independently associated with increased death from myocardial infarction, chronic rejection, infection, and renal dysfunction. An underweight BMI lead to increased death from infection, acute and chronic rejection, malignancy, and bleeding. Obese patients had a higher incidence of renal dysfunction, diabetes, stroke, acute rejection, cardiac allograft vasculopathy, and malignancy, and underweight recipients had increased acute rejection. We have shown that pretransplant obese and underweight patients have increased post-heart transplant mortality and morbidity. This has implications for candidate selection and posttransplant management.

Impact of pretransplant recipient body mass index on post heart transplant mortality: A systematic review and meta-analysis.

The ISHLT's 2016 Guidelines on the selection of heart transplant (HT) candidates recommends weight loss prior to listing for persons with body mass (BMI) index greater than 35 kg/m2 . We conducted a systematic review to assess the impact of BMI on all-cause mortality. We searched to identify eligible observational studies that followed HT recipients. We used the GRADE system to quantify absolute effects and quality of evidence, and meta-analyzed survival curves to assess post-transplant mortality across BMI categories. We found a significantly increased risk of mortality in patients with BMI > 30 kg/m2 across all age categories, independently of transplant era and study source (BMI 30-34.9: HR 1.10, 95% CI 1.04-1.17; BMI ≥ 35: HR 1.24, 95% CI 1.12-1.38). We also found an increased risk of death in underweight (BMI < 18.5 kg/m2 ) candidates over 39 years of age (Age 40-65: HR 1.24, 95% CI 1.02-1.53; Age > 65: HR 1.70, 95% 1.13-2.57). We found obesity and underweight BMI to be associated with mortality post-HT. The similar and overlapping increased risk of mortality in patients with BMI 30-34.9 and BMI ≥ 35 does not support the recently updated ISHLT guidelines. Future evidence in the form of randomized controlled trials is required to assess effectiveness of interventions targeting obesity-related comorbidities and weight management.

An Appraisal of Biomarker-Based Risk-Scoring Models in Chronic Heart Failure: Which One Is Best?

PURPOSE OF REVIEW: While prediction models incorporating biomarkers are used in heart failure, these have shown wide-ranging discrimination and calibration. This review will discuss externally validated biomarker-based risk models in chronic heart failure patients assessing their quality and relevance to clinical practice. RECENT FINDINGS: Biomarkers may help in determining prognosis in chronic heart failure patients as they reflect early pathologic processes, even before symptoms or worsening disease. We present the characteristics and describe the performance of 10 externally validated prediction models including at least one biomarker among their predictive factors. Very few models report adequate discrimination and calibration. Some studies evaluated the additional predictive value of adding a biomarker to a model. However, these have not been routinely assessed in subsequent validation studies. New and existing prediction models should include biomarkers, which improve model performance. Ongoing research is needed to assess the performance of models in contemporary patients.

Surfactant protein D interacts with alpha2-macroglobulin and increases its innate immune potential.

Surfactant protein D (SP-D) is an innate immune collectin that recognizes microbes via its carbohydrate recognition domains, agglutinates bacteria, and forms immune complexes. During microbial infections, proteases, such as elastases, cleave the carbohydrate recognition domains and can inactivate the innate immune functions of SP-D. Host responses to counterbalance the reduction of SP-D-mediated innate immune response under these conditions are not clearly understood. We have unexpectedly identified that SP-D could interact with protein fractions containing ovomucin and ovomacroglobulin. Here, we show that SP-D interacts with human alpha(2)-macroglobulin (A2M), a protease inhibitor present in the lungs and serum. Using enzyme-linked immunosorbent assays, surface plasmon resonance, and carbohydrate competition assays, we show that SP-D interacts with A2M both in solid phase (K(D) of 7.33 nM) and in solution via lectin-carbohydrate interactions under physiological calcium conditions. Bacterial agglutination assays further show that SP-D x A2M complexes increase the ability of SP-D to agglutinate bacteria. Western blot analyses show that SP-D, but not A2M, avidly binds bacteria. Interestingly, intact and activated A2M also protect SP-D against elastase-mediated degradation, and the cleaved A2M still interacts with SP-D and is able to enhance its agglutination abilities. We also found that SP-D and A2M can interact with each other in the airway-lining fluid. Therefore, we propose that SP-D utilizes a novel mechanism in which the collectin interacts with protease inhibitor A2M to decrease its degradation and to concurrently increase its innate immune function. These interactions particularly enhance bacterial agglutination and immune complex formation.