Krittanawong, Chayakrit

Introduction: Current proposed Dietary Guidelines for Americans, 2015 suggested replacing refined grains (e.g., white rice) with whole grains (e.g., brown rice) for improved health outcomes; however, the association between white rice consumption and risk of cardiovascular disease (CVD) was not fully investigated. The purpose of this study was to explore the associations between rice consumption, metabolic syndrome and cardiovascular disease. Methods: We conducted a comprehensive search of MEDLINE, MEDLINE In-Process & Other Non-Indexed Citations, EMBASE, Scopus, and the Cochrane Central Register of Controlled Trials from database inception through May 2015. Observational studies were included if the studies reported hazard ratios (HRs) or odd ratios (ORs) of the associations between rice consumption, metabolic syndrome and cardiovascular disease. Data were extracted by one reviewer and then reviewed by two independent reviewers. Conflicts were resolved through consensus. Using the DerSimonian and Laird random effects models, we calculated pooled HRs and pooled ORs with 95% confidence intervals (95% CIs). Subgroup analyses were performed to explore potential sources of heterogeneity. The quality of the included studies and publication bias were assessed. Results: Out of 343 retrieved articles, we identified 6 cross-sectional and 9 prospective studies with a total of 575,852 individuals with 24,649 total CVD events and 4,409 metabolic syndrome cases. The highest category of white rice consumption (≥5 times/week) was associated with a 37% higher risk of metabolic syndrome (pooled OR, 1.37; 95% CI: 1.03-1.84; p<0.001; I 2 = 78.7%) but not a higher risk of cardiovascular disease (pooled HR, 1.01; 95% CI: 0.95-1.08; p=0.65; I 2 = 0%), compared to the lowest category (≤1 time/month). Conclusions: Higher white rice consumption has not been shown to increase the risk of cardiovascular diseases but may be associated with increased risk of metabolic syndrome.

Introduction: Obesity is a growing global health problem associated with substantial morbidity and mortality. The risk of obesity on incident new-onset heart failure is controversial. The aim of this systematic review and meta-analysis was to explore the association between overweight, obesity and incident new-onset heart failure. Methods: We systematically searched MEDLINE, MEDLINE In-Process & Other Non-Indexed Citations, EMBASE, Scopus, and the Cochrane Central Register of Controlled Trials from database inception through April 2015. Prospective cohort studies were included if the studies reported hazard ratios (HRs) of the association between overweight or obesity and new-onset heart failure (HF) relative to individuals with normal weight. Data were extracted by one reviewer followed by independent screening and extraction of study characteristics and outcome data by two other reviewers. Conflicts were resolved through consensus. Random-effects meta-analyses were used to pool the hazard ratio from the included studies. Subgroup analyses were performed to explore the potential sources of heterogeneity. Results: Out of 1,865 retrieved articles, we identified 10 relevant studies. Median follow-up was 12 years with a total of 82,167 individuals and 4,570 total HF events. Compared to normal body weight (body mass index (BMI) <25 kg/m2), overweight (BMI 25-29.9 kg/m2) was associated with 27% higher risk of new-onset heart failure events (pooled HR, 1.27; 95% confidence intervals (95% CI): 1.15-1.41; p<0.001; I 2 = 0%) and obesity (BMI ≥30 kg/m2) was associated with a two-fold increased risk for new-onset heart failure events (pooled HR, 2.09; 95% CI, 1.68-2.60; p<0.001; I 2 = 67.8%). Conclusions: Compared with normal-weight individuals, both overweight and obesity were independently associated with significantly higher incidence of heart failure. These results highlight the need for better understanding of mechanisms of obesity and heart failure.

Polygenic diseases, which are genetic disorders caused by the combined action of multiple genes, pose unique and significant challenges for the diagnosis and management of affected patients. A major goal of cardiovascular medicine has been to understand how genetic variation leads to the clinical heterogeneity seen in polygenic cardiovascular diseases (CVDs). Recent advances and emerging technologies in artificial intelligence (AI), coupled with the ever-increasing availability of next generation sequencing (NGS) technologies, now provide researchers with unprecedented possibilities for dynamic and complex biological genomic analyses. Combining these technologies may lead to a deeper understanding of heterogeneous polygenic CVDs, better prognostic guidance, and, ultimately, greater personalized medicine. Advances will likely be achieved through increasingly frequent and robust genomic characterization of patients, as well the integration of genomic data with other clinical data, such as cardiac imaging, coronary angiography, and clinical biomarkers. This review discusses the current opportunities and limitations of genomics; provides a brief overview of AI; and identifies the current applications, limitations, and future directions of AI in genomics.

Abstract Background Transcatheter mitral valve repair (TMVR) has become an important minimally invasive alternative to open surgical repair in patients with severe mitral regurgitation. As TMVR develops as an established practice, real-world outcome data is needed to guide clinical decision making as the body of literature on this topic is limited. In our study, we investigate TMVR outcomes in a large urban health system. Methods We retrospectively analyzed clinical data from the electronic health records of the extensive urban health system Mount Sinai Data Warehouse and analyzed all individuals who underwent TMVR. Baseline demographics were obtained as well as significant morbidity and mortality outcomes. Finally, multivariable analysis was used to determine associated characteristics with mortality. Results We identified 3533 patients who underwent TMVR from 2008 to 2018. The overall mortality was 3.7%. The most common comorbidities observed were heart failure (56%), pulmonary hypertension (26%), atrial fibrillation (26%) and CKD or ESRD (16%). After multivariable analyses, ischemic stroke (adjusted OR 3.46; 95% CI 1.93–5.97, p = &lt;0.001), CKD or ESRD (3.95; 2.72–5.75, p 0.01), aortic stenosis (2.23; 1.12–4.15, p&lt;0.001), and heart failure (8.18; 4.16–18.5, p&lt;0.001) were independently associated with mortality. Conclusion In all patients who underwent TMVR at a large urban health system, ischemic stroke, aortic stenosis, heart failure, CKD and ESRD were independently associated with mortality. Future prospective studies will be needed to further define these observed associations. Funding Acknowledgement Type of funding sources: None.

Background: Randomized controlled trials and meta-analyses have demonstrated the benefits of cardiac rehabilitation (CR) in patients with acute myocardial infarction (AMI). Spontaneous coronary artery dissection (SCAD) is an increasingly recognized cause of AMI. However, the benefit of CR has not been well studied in patients with SCAD. Purpose: Our objective was to compare the psychosocial and physiological characteristics and outcomes of CR in SCAD patients who participated in CR compared to those who did not. Methods: We performed a retrospective descriptive analysis of patients with angiographically confirmed SCAD enrolled in the Mayo Clinic SCAD Registry from January 2010 through December 2014 who had completed questionnaires (n=354). Demographics, clinical characteristics, Generalized Anxiety Disorder 7-Item Scale (GAD-7), Patient Health Questionnaire-9 (depression measure; PHQ-9), and details about participants’ CR participation and experience were collected via medical record review and questionnaires. Univariate analyses were performed to determine the differences between baseline characteristics and outcomes according to CR participation status. Results: Mean age of participants at time of SCAD was 46 ± 10 years; 95% were female. Seventy-six percent of participants participated in at least one CR session, and those users averaged 18 ± 12 sessions. A majority of CR participants reported physical and emotional benefits (82% and 75%, respectively). Of those who did not participate in CR, the primary reason was because their healthcare provider did not recommend CR (67%). Other reasons included inadequate transportation (12%), no insurance coverage (8%), cost (2%), no energy (2%), and being too ill (2%). On comparative analyses, CR participants had higher mean GAD-7 scores (4.9 vs. 3.9; p=0.03) and were more likely to increase their amount of exercise (p<0.01). Conclusions: Despite the well-known benefits of CR, in this international registry, only 76% of SCAD patients participated. Lack of recommendation for CR by a health care provider was the primary reason patients did not participate. The majority of patients reported benefits from CR. Higher GAD-7 scores were associated with CR participation, an interesting result requiring further evaluation in a larger or prospective cohort.

Background: The recent increase in the public’s interest in health matters has been accompanied by greater attention to the development of Web-based programs by many health-related professional and educational services. Wikiheart is a Web-based program that has been designed to narrow the gaps between care and secondary prevention by directly delivering knowledge to patients in their homes and communities. Purpose: To develop a Web-based program aimed at educating patients and targeted to the specific learning needs of patients with heart disease and associated risk factors. Methods and results: This pilot program is relatively easy to use, web-based educational model. It consists of a collaborative feature, the content of which can be easily edited by users, thus enabling powerful information sharing, interactive chat and article discussions. Participants are categorized into two principal groups: (1) invited or volunteer medical professionals; and (2) patients with their own experiences. This program can be used as an information resource for patients and method of real-time communication between healthcare professionals and patients. This virtual program allows patients to engage with each other, share experiences and become part of an online community. As an online interactive medical reference, this program is also intended to help patients with heart disease learn more about their specific conditions and be educated about preventive measures. For patient confidentiality, password protection would be employed and all clinical images and videos would be required to be de-identified. Material that violates patient privacy will be easily removed. Conclusions: Wikiheart aims to enable the sharing of knowledge relevant to secondary prevention and thus improve the quality of care for patients with heart disease or cardiovascular risk factors. If successfully implemented, the program is likely to enhance researchers’, healthcare providers’ and patients’ learning experiences, and communication within digital world, leading to the improvement of clinic outcome and adherence for patients. Further research is needed in order to identify the best approaches to apply these tools and ultimately boost research productivity, learning experience and patient education.

Introduction: At-work stress is rapidly becoming a significant contributory cause of occupational disease, while sedentary work and dietary indiscretion, taken together, have contributed to cardiovascular disease, known to be associated with substantial morbidity and mortality. The aim of the CV-Ecosystem Model is to provide an individual approach by systematic environmental modification of the work site. Methods and Results: Our model which we term the CV-Ecosystem Model includes two basic tenets: 1) Workstation technologic design 2) Flexible physiologic activity. Workstation technologic design utilizes the services of a certified Ergonomist to engineer a comfortable, safe, and effective work environment. It is tailored to the unique physiologic needs of the worker while enhancing productivity within different environments. Computer aided design software would evaluate ergonomics to maximally mitigate the risk and incidence of eye strain, musculoskeletal disorders, repetitive movement disorders such as Carpal Tunnel Syndrome, etc unique to that work activity. Flexible physiologic activity would permit a worker to actively focus on basic needs during work time, on site; this would include exercise activity and food intake, channeled into healthy options. Exercise time would provide a sense of calm during stressful projects and enhance emotional well-being. In this regard, a three hour period of time, weekly, could be allocated to fitness or meditation. Conveniently located food stations could provide a variety of healthy, plant-based, fresh food options. Both would come with flexible, relative control and scheduling of these activities, within a social environment and context. Conclusions: The CV Ecosystem Model, due to its simple technology and relative low cost, could be successfully implemented in a number of enterprises, including all healthcare facilities, with benefit to employee health, wellbeing, and favorable to the business bottom line.

Introduction: Heart failure (HF) is a leading cause of hospitalization, morbidity and mortality. Deep learning (DL) techniques appear to show promising results in risk stratification and prognosis in several conditions in medicine. However, few methods using DL exist to help quantitatively estimate prognosis of HF. We hypothesized that deep learning (DL) techniques could prognosis of HF using simple variables. We propose application of a custom-built deep-neural-network model to identify mortality in HF patients. Methods: Custom-built deep-neural-networks were assessed using survey data from 42,147 participants from the National Health and Nutrition Examination Survey 1999-2016 (NHANES). Variables were selected using clinical judgment and stepwise backward regressions to develop prediction models. We partitioned the data into training and testing sets and repetitive experiments. We then evaluated model performance based on discrimination and calibration including the area under the receiver-operator characteristics curve (C-statistics), balanced accuracy, probability calibration with sigmoid, and the Brier score, respectively. As sensitivity analyses, we examined results limited to cases with complete clinical information available. We validated models’ performance using Mount Sinai database. Results: Of 42,147 participants with 4,060 variables, 1,491 (3.5%) had HF and HF mortality was 51.8%. In validation cohort, of 26,333 HF patients, the mortality in HF patients was 405 (1.5%). Final model using only 20 variables (age, race, gender, BMI, smoking, alcohol consumption, HTN, COPD, SBP, DBP, HR, HDL, LDL, CRP, A1C, BUN, creatinine, hemoglobin, sodium level, on statin) was tested. A state-of-the-art deep learning models achieved high accuracy for predicting mortality in HF patients with an AUC of 0.96 (95% CI: 0.95-0.99) in the first cohort and AUC of 0.93 (95% CI: 0.91-0.96) in validation cohort. Conclusions: A deep neural network model has shown to have high predictive accuracy and discriminative and calibrative power for prediction of HF mortality. Further research can delineate the clinical implications of DL in predicting HF mortality.