Cardiovascular disease in space: A systematic review.

BACKGROUND: With expanding commercial space programs, uncertainty remains about the cardiovascular effects of space environmental exposures including microgravity, confinement, isolation, space radiation, and altered bacterial virulence. Current limited data suggests additional health threats compared to Earth. METHODS: We systematically reviewed PubMed, CENTRAL, Web of Science, EMBASE and Cochrane databases for prospective studies on spaceflight and cardiovascular outcomes. Search terms combined cardiovascular disease topics with spaceflight concepts. No date or language restrictions were imposed. RESULTS: 35 studies representing 2696 space travelers met inclusion criteria. Studies were grouped into spaceflight associations with: atherosclerosis, mortality, cardiac function, orthostatic intolerance, and arrhythmias. Atherosclerosis evidence was limited, with animal studies linking space radiation to endothelial damage, oxidative stress, and inflammation. However, human data showed no significantly increased atherosclerotic disease in astronauts. Mortality studies demonstrated lower cardiovascular mortality in astronauts compared to the general population however there was conflicting data. Cardiac function studies revealed physiologic ventricular atrophy, increased arterial stiffness, and altered blood flow distribution attributed to microgravity exposure. Effects appeared transient and reversible post-flight. Orthostatic intolerance studies found astronauts experienced altered heart rate variability, baroreflex response, and blood pressure changes post-flight. Arrhythmia studies showed increased ventricular ectopy during spaceflight, but limited data on long term flights. CONCLUSIONS: Environmental space hazards impact the cardiovascular system through multiple mechanisms. Microgravity causes cardiac atrophy and orthostatic intolerance while space radiation may potentially accelerate atherosclerosis. Further research is needed, especially regarding long-term spaceflights.

Artificial Intelligence and Cardiovascular Genetics.

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.

Association of Optimism with Cardiovascular Events and All-Cause Mortality: Systematic Review and Meta-Analysis.

BACKGROUND: The effect of psychological health on cardiovascular disease is an underappreciated yet important area of study. Understanding the relationship between these two entities may allow for more comprehensive care of those with cardiovascular disease. The primary objective of this meta-analysis is to evaluate the relationship between optimism and risk of developing adverse events such as all-cause mortality or fatal and non-fatal cardiovascular disease in community-based populations. METHOD: A systematic search of electronic databases was conducted from inception through November 2021 for prospective studies evaluating optimism and adverse outcomes. Two reviewers independently selected prospective cohort studies that evaluated optimism and either all-cause mortality or cardiovascular disease and reported hazard ratios of these outcomes between optimistic and non-optimistic groups. Studies that reported odds ratio or other risk assessments were excluded. Pooled hazard ratios were calculated in random-effects meta-analyses. RESULTS: Pooled analysis of six studies (n = 181,709) showed a pooled hazard ratio of 0.87 (95% confidence interval [CI], 0.82-0.92) for all-cause mortality among those with more optimistic mindset. Analysis of seven studies (n = 201,210) showed a pooled hazard ratio of 0.59 (95% CI, 0.37-0.93) for cardiovascular disease and pooled hazard ratio of 0.57 (95% CI, 0.07-4.56) for stroke. CONCLUSIONS: In this pooled meta-analysis, optimism was associated with a reduced risk of all-cause mortality and of cardiovascular disease. These results suggest an important relationship between psychological health and cardiovascular disease that may serve as an area for intervention by clinicians.

Artificial Intelligence-Powered Blockchains for Cardiovascular Medicine.

Clinical databases, particularly those composed of big data, face growing security challenges. Blockchain, the open, decentralized, distributed public ledger technology powering cryptocurrency, records transactions securely without the need for third-party verification. In the health care setting, decentralized blockchain networks offer a secure interoperable gateway for clinical research and practice data. Here, we discuss recent advances and potential future directions for the application of blockchain and its integration with artificial intelligence (AI) in cardiovascular medicine. We first review the basic underlying concepts of this technology and contextualise it within the spectrum of current, well known applications. We then consider specific applications for cardiovascular medicine and research in areas such as high-throughput gene sequencing, wearable technologies, and clinical trials. We then evaluate current challenges to effective implementation and future directions. We also summarise the health care applications that can be realised by combining decentralized blockchain computing platforms (for data security) and AI computing (for data analytics). By leveraging high-performance computing and AI capable of securely managing large and rapidly expanding medical databases, blockchain incorporation can provide clinically meaningful predictions, help advance research methodology (eg, via robust AI-blockchain decentralized clinical trials), and provide virtual tools in clinical practice (eg, telehealth, sensory-based technologies, wearable medical devices). Integrating AI and blockchain approaches synergistically amplifies the strengths of both technologies to create novel solutions to serve the objective of providing precision cardiovascular medicine.

Association Between Cinnamon Consumption and Risk of Cardiovascular Health: A Systematic Review and Meta-Analysis.

BACKGROUND: Cinnamon has been used as a traditional herbal medication for decades. Several studies have investigated cinnamon consumption and cardiovascular risk. So far, the evidence remains inconclusive. Thus, we aim to systematically review the currently available literature and quantify the evidence, if possible. METHODS: We systematically searched Ovid MEDLINE, Ovid Embase, Ovid Cochrane Database of Systematic Reviews, Scopus, and Web of Science from database inception in 1966 through December 2020. The exposure of interest was cinnamon consumption, the outcome was cardiovascular risk defined as hemoglobin A1C, low-density lipoprotein cholesterol (LDL-c), and high-density lipoprotein cholesterol (HDL-c). Two investigators independently reviewed the data. Conflicts were resolved through consensus. Random-effects meta-analyses were used. RESULTS: Of 23 studies (1070 subjects), the included studies were heterogeneous, generally of very poor quality. We found no difference in LDL-c levels in patients who consumed cinnamon vs those who did not, with a weighted mean difference (WMD) of 0.38 (confidence interval [CI], -6.07-6.83). We also found no difference in HDL-c between the 2 groups with WMD 0.40 (CI, -1.14-1.94). In addition, we found no statistical differences in hemoglobin A1C between the 2 groups with WMD of 0.0 (CI, -0.44-0.45). CONCLUSIONS: Our meta-analysis suggests that there is no association between cinnamon consumption and differences in LDL-c, HDL-c, and hemoglobin A1C levels. Further randomized control trials studies using a robust design with long-term cinnamon consumption are needed to further investigate any potential effect.

Association of Social Gaming with Well-Being (Escape COVID-19): A Sentiment Analysis.

BACKGROUND: During the 2020-2021 coronavirus disease 2019 (COVID-19) lockdown, social activities were limited by the government-recommended social distancing guidelines, leading to an abundance of mental health issues. METHODS: We hypothesized that Twitter sentiment analysis may shed some light on Animal Crossing: New Horizons and its impact on mental health during the COVID-19 pandemic. RESULTS: We found that social gaming and social media may be used as tools to cope with stress during the COVID-19 pandemic. CONCLUSIONS: Further research, including randomized study designs and prospective measurements of mental health outcomes related to social gaming behavior are required.

Human Health during Space Travel: State-of-the-Art Review.

The field of human space travel is in the midst of a dramatic revolution. Upcoming missions are looking to push the boundaries of space travel, with plans to travel for longer distances and durations than ever before. Both the National Aeronautics and Space Administration (NASA) and several commercial space companies (e.g., Blue Origin, SpaceX, Virgin Galactic) have already started the process of preparing for long-distance, long-duration space exploration and currently plan to explore inner solar planets (e.g., Mars) by the 2030s. With the emergence of space tourism, space travel has materialized as a potential new, exciting frontier of business, hospitality, medicine, and technology in the coming years. However, current evidence regarding human health in space is very limited, particularly pertaining to short-term and long-term space travel. This review synthesizes developments across the continuum of space health including prior studies and unpublished data from NASA related to each individual organ system, and medical screening prior to space travel. We categorized the extraterrestrial environment into exogenous (e.g., space radiation and microgravity) and endogenous processes (e.g., alteration of humans' natural circadian rhythm and mental health due to confinement, isolation, immobilization, and lack of social interaction) and their various effects on human health. The aim of this review is to explore the potential health challenges associated with space travel and how they may be overcome in order to enable new paradigms for space health, as well as the use of emerging Artificial Intelligence based (AI) technology to propel future space health research.

Hospital Readmission in Patients With Spontaneous Coronary Artery Dissection.

Spontaneous coronary artery dissection (SCAD) can present with various clinical symptoms, including chest pain, syncope, and sudden cardiac death, particularly in those without atherosclerotic risk factors. In this contemporary analysis, we aimed to identify the causes and predictors of 30-day hospital readmission in SCAD patients. We utilized the latest Nationwide Readmissions Database from 2016 - 2017 to identify patients with a primary discharge diagnosis of SCAD. The primary outcome was 30-day readmission. Among 795 patients admitted with a principal discharge diagnosis of SCAD, 85 (11.3%) were readmitted within 30 days of discharge from index admission (69.8% women, mean age of 54.3 ± 0.8). More than half of the readmissions (57%) were cardiac-related readmissions. Common cardiac causes for 30-day hospital readmission were acute coronary syndrome (27.3%), chest pain/unspecified angina (24.6%), heart failure (17.5%), and recurrent SCAD (8.3%). In conclusion, we found that following hospitalization for SCAD, almost one-tenth of patients were readmitted within 30 days, largely due to cardiac cause . Risk stratifying patients with SCAD, identifying high-risk features or atypical phenotypes of SCAD, and using appropriate management strategies may prevent hospital readmissions and reduce healthcare-related costs. Further studies are warranted to confirm these causes of readmission in SCAD patients.

Fish Consumption and Cardiovascular Health: A Systematic Review.

Studies evaluating fish consumption and cardiovascular disease events have shown inconsistent results. We performed a systematic review of peer-reviewed publications from an extensive query of Ovid MEDLINE, Ovid Embase, Ovid Cochrane Database of Systematic Reviews, Scopus, and Web of Science from database inception to September 2020 for observational studies that reported the association between fish consumption and cardiovascular disease events. We identified and reviewed 24 studies related to fish consumption and the effect on cardiovascular outcomes. The study population included a total of 714,526 individuals and multiple cohorts from several countries. We found that nonfried fish consumption is probably associated with a reduced risk of overall cardiovascular disease events and myocardial infarction risk. In contrast, fried fish consumption is probably associated with an increased risk of overall cardiovascular disease events and myocardial infarction risk. No studies to date have shown any significant association between fish consumption and stroke. Our analysis suggests that fish consumption may reduce cardiovascular disease events, but fried fish consumption was associated with an increased risk of cardiovascular events.

Machine learning prediction in cardiovascular diseases: a meta-analysis.

Several machine learning (ML) algorithms have been increasingly utilized for cardiovascular disease prediction. We aim to assess and summarize the overall predictive ability of ML algorithms in cardiovascular diseases. A comprehensive search strategy was designed and executed within the MEDLINE, Embase, and Scopus databases from database inception through March 15, 2019. The primary outcome was a composite of the predictive ability of ML algorithms of coronary artery disease, heart failure, stroke, and cardiac arrhythmias. Of 344 total studies identified, 103 cohorts, with a total of 3,377,318 individuals, met our inclusion criteria. For the prediction of coronary artery disease, boosting algorithms had a pooled area under the curve (AUC) of 0.88 (95% CI 0.84-0.91), and custom-built algorithms had a pooled AUC of 0.93 (95% CI 0.85-0.97). For the prediction of stroke, support vector machine (SVM) algorithms had a pooled AUC of 0.92 (95% CI 0.81-0.97), boosting algorithms had a pooled AUC of 0.91 (95% CI 0.81-0.96), and convolutional neural network (CNN) algorithms had a pooled AUC of 0.90 (95% CI 0.83-0.95). Although inadequate studies for each algorithm for meta-analytic methodology for both heart failure and cardiac arrhythmias because the confidence intervals overlap between different methods, showing no difference, SVM may outperform other algorithms in these areas. The predictive ability of ML algorithms in cardiovascular diseases is promising, particularly SVM and boosting algorithms. However, there is heterogeneity among ML algorithms in terms of multiple parameters. This information may assist clinicians in how to interpret data and implement optimal algorithms for their dataset.

Predictors of In-Hospital Mortality after Transcatheter Aortic Valve Implantation.

The development of aortic valve stenosis is strongly associated with older adults. Patients who undergo transcatheter aortic valve implantation (TAVI) for severe aortic stenosis frequently have heart failure (HF). We investigated the predictors of mortality after TAVI according to the presence of HF, and specifically HF with preserved ejection fraction (HFpEF) versus HF with reduced ejection fraction (HFrEF). Patients were identified from the Nationwide Inpatient Sample registry from January 2011 to September 2015 using the ICD-9 codes. Patients with HF who underwent TAVI were classified according to whether they were diagnosed with HFrEF or HFpEF. The principal outcome of interest was in-hospital mortality. Multivariable analysis was used to adjust for potential baseline confounders. Among 11,609 patients undergoing TAVI, 6,368 (54.9%) had baseline HF, including 4,290 (67.4%) with HFpEF and 2,078 (32.6%) with HFrEF. In TAVI patients with HF, in-hospital mortality was also not significantly different in those with HFrEF compared with HFpEF (3.66% vs 3.17%, respectively; adjusted odds ratio 1.14, 95% confidence interval 0.84 to 1.53; p = 0.38). Polyvalvular heart disease was an additional independent predictor of in-hospital mortality in HFrEF, whereas age, liver disease, and the absence of depression and anemia were additional independent predictors of mortality in HFpEF. In conclusion, baseline HF in patients undergoing TAVI is prevalent and is more commonly due to HFpEF than HFrEF. Mortality is similar in those with HFrEF and HFpEF. Knowledge of the specific predictors of mortality after TAVI in HF patients may be useful for patient selection and prognostic guidance.