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The Framingham Study: Across 6 Decades, Cardiovascular Disease Among Middle-Aged Adults – mean life expectancy increased and the RLR of ASCVD decreased. Effective primary prevention efforts and better screening increased.
Reporter: Aviva Lev-Ari, PhD, RN
Temporal Trends in the Remaining Lifetime Risk of Cardiovascular Disease Among Middle-Aged Adults Across 6 Decades: The Framingham Study
Background: The remaining lifetime risk (RLR) is the probability of developing an outcome over the remainder of one’s lifespan at any given age. The RLR for atherosclerotic cardiovascular disease (ASCVD) in three 20-year periods were assessed using data from a single community-based cohort study of predominantly White participants
Methods: Longitudinal data from the Framingham study in 3 epochs (epoch 1, 1960-1979; epoch 2, 1980-1999; epoch 3, 2000-2018) were evaluated. The RLR of a first ASCVD event (myocardial infarction, coronary heart disease death, or stroke) from 45 years of age (adjusting for competing risk of death) in the 3 epochs were compared overall, and according to the following strata: sex, body mass index, blood pressure and cholesterol categories, diabetes, smoking, and Framingham risk score groups.
Results: There were 317 849 person-years of observations during the 3 epochs (56% women; 94% White) and 4855 deaths occurred. Life expectancy rose by 10.1 years (men) to 11.9 years (women) across the 3 epochs. There were 1085 ASCVD events over the course of 91 330 person-years in epoch 1, 1330 ASCVD events over the course of 107 450 person years in epoch 2, and 775 ASCVD events over the course of 119 069 person-years in epoch 3. The mean age at onset of first ASCVD event was greater in the third epoch by 8.1 years (men) to 10.3 years (women) compared with the first epoch. The RLR of ASCVD from 45 years of age declined from 43.7% in epoch 1 to 28.1% in epoch 3 (P<0.0001), a finding that was consistent in both sexes (RLR [epoch 1 versus epoch 3], 36.3% versus 26.5% [women]; 52.5% versus 30.1% [men]; P<0.001 for both). The lower RLR of ASCVD in the last 2 epochs was observed consistently across body mass index, blood pressure, cholesterol, diabetes, smoking, and Framingham risk score strata (P<0.001 for all). The RLR of coronary heart disease events and stroke declined in both sexes (P<0.001).
Conclusions: Over the past 6 decades, mean life expectancy increased and the RLR of ASCVD decreased in the community based, predominantly White Framingham study. The residual burden of ASCVD underscores the importance of continued and effective primary prevention efforts with better screening for risk factors and their effective treatment.
Parasym™ neuromodulation device reveals promising developments in the treatment of heart failure patients with preserved ejection fraction: Clinical Trial Results
Reporter and Curator: Aviva Lev-Ari, PhD, RN
Neuromodulation of Inflammation to Treat Heart Failure With Preserved Ejection Fraction: A Pilot Randomized Clinical Trial
A systemic proinflammatory state plays a central role in the development of heart failure with preserved ejection fraction. Low‐level transcutaneous vagus nerve stimulation suppresses inflammation in humans. We conducted a sham‐controlled, double‐blind, randomized clinical trial to examine the effect of chronic low‐level transcutaneous vagus nerve stimulation on cardiac function, exercise capacity, and inflammation in patients with heart failure with preserved ejection fraction.
Methods and Results
Patients with heart failure with preserved ejection fraction and at least 2 additional comorbidities (obesity, diabetes, hypertension, or age ≥65 years) were randomized to either active (tragus) or sham (earlobe) low‐level transcutaneous vagus nerve stimulation (20 Hz, 1 mA below discomfort threshold), for 1 hour daily for 3 months. Echocardiography, 6‐minute walk test, quality of life, and serum cytokines were assessed at baseline and 3 months. Fifty‐two patients (mean age 70.4±9.2 years; 70% female) were included (active, n=26; sham, n=26). Baseline characteristics were balanced between the 2 arms. Adherence to the protocol of daily stimulation was >90% in both arms (P>0.05). While the early mitral inflow Doppler velocity to the early diastolic mitral annulus velocity ratio did not differ between groups, global longitudinal strain and tumor necrosis factor‐α levels at 3 months were significantly improved in the active compared with the sham arm (−18.6%±2.5% versus −16.0%±2.4%, P=0.002; 8.9±2.8 pg/mL versus 11.3±2.9 pg/mL, P=0.007, respectively). The reduction in tumor necrosis factor‐α levels correlated with global longitudinal strain improvement (r=−0.73, P=0.001). Quality of life was better in the active arm. No device‐related side effects were observed.
Conclusions
Neuromodulation with low‐level transcutaneous vagus nerve stimulation over 3 months resulted in a significant improvement in global longitudinal strain, inflammatory cytokines, and quality of life in patients with heart failure with preserved ejection fraction.
Press Release Announcement by Parasym™ is a neurotechnology company dedicated to shaping the future of bioelectric medicine. Founded in 2015 by Sophie and Nathan Dundovic, is focused on providing innovative neuromodulation products that restore health. The company has over 60 clinical partnerships across 4 continents, and over 1,000,000 treatment sessions completed. For more information about Parasym™’s latest products, visit nurosym.com
Parasym™ is the only company to have developed a device that utilises advances in electroceutical technology to provide ground-breaking non-invasive treatment for numerous health and wellness conditions ranging from mental to physical health including heart failure, without the need for heart failure medication. For further information about Parasym™ visit parasym.co.
The neuromodulation device is non-invasive, patients are able to use it in addition to medication should they want to. Electroceuticals are set to revolutionise the treatment paradigm in heart failure, especially neuromodulation with its capacity to provide highly targeted treatment without drug interaction or side effects.
Clinical trial results
The study revealed significant improvements in levels of proinflammatory cytokines Interleukin-8 and Tumour Necrosis Factor alpha, indicating that the treatment had a significant anti-inflammatory effect, as well as in global longitudinal strain, a core indicator of cardiac mechanics.
Dr Stavros Stavrakis MD, PhD, Associate Professor at University of Oklahoma College of Medicine commented: “We conducted a sham-controlled, double-blind, randomized clinical trial to examine the effect of chronic low-level transcutaneous vagus nerve stimulation on cardiac function, exercise capacity, and inflammation in a subgroup of patients with heart failure with preserved ejection fraction with a predominantly inflammatory-metabolic phenotype. In this patient population, neuromodulation with low-level transcutaneous vagus nerve stimulation over three months resulted in a significant improvement in global longitudinal strain, inflammatory cytokines, and quality of life. Our results support the emerging paradigm of noninvasive neuromodulation to treat selected patients with heart failure with preserved ejection fraction and provide the basis for further randomized trials.”
Parasym™️ is committed to supporting groundbreaking cardiac research and we are working to bring non-invasive electroceutical treatments to patients suffering from heart failure.
“The results published in the Journal of the American Heart Association highlight the brilliant work done by researchers at the University of Oklahoma and show the incredible potential that Parasym’s neuromodulatory technology can have in a condition where there is an urgent unmet clinical need for new treatment options. We are incredibly proud of the trial results and hope to continue to demonstrate the positive impact of neuromodulation in healthcare.”
SOURCE
From: Sofia Leadbetter <sofia@lem-uhn.com> Date: Tuesday, February 22, 2022 at 9:56 AM To: Aviva Lev-Ari <avivalev-ari@alum.berkeley.edu> Subject: Re: A groundbreaking clinical trial using Parasym™ neuromodulation device reveals promising developments in the treatment of heart failure
Other related articles published in this Open Access Online Scientific Journal includes the following:
I. A related topic is Renal denervation for Hypertension control by a medical device
Single-Author Reporting on MedTech and Cardiac Medical Devices by
Experimental Therapy (Left inter-atrial shunt implant device) for Heart Failure: Expert Opinion on a Preliminary Study on Heart Failure with preserved Ejection Fraction
This book is a comprehensive review of Nitric Oxide, its discovery, function, and related opportunities for Targeted Therapy written by Experts, Authors, Writers: PhDs, MDs, MD/PhDs, PharmDs. Nitric oxide plays a wide variety of roles in cardiovascular system and acts as a central point for signal transduction pathway in endothelium. NITRIC OXIDE modulates vascular tone, fibrinolysis, blood pressure and proliferation of vascular smooth muscle cells. In the cardiovascular system disruption of NITRIC OXIDE pathways or alterations in NITRIC OXIDE production can result in predisposition to hypertension, hypercholesterolemia, diabetes mellitus, atherosclerosis and thrombosis. The essential role of NITRIC OXIDE is seen widely in organ function and in disease development. The role of NITRIC OXIDE covers the cardiovascular system, the acuity of sepsis and septic shock, gastrointestinal disease, renal disease, and neurological disorders. The final chapter is the essential role of NITRIC OXIDE in carcinogenesis. Therapeutic Targets to Clinical Applications: Pharmaco-therapy was developed and it represents methods to induce the production of Nitric Oxide and its enzymes for novel combination drug therapies.
This e-Book is a comprehensive review of recent Original Research on Cardiovascular Diseases: Causes, Risks and Management and related opportunities for Targeted Therapy written by Experts, Authors and Writers. The results of Original Research are gaining value added for the e-Reader by the Methodology of Curation. The e-Book’s articles have been published on the Open Access Online Scientific Journal, since April 2012. Topics covered in greater details include: •Alternative solutions in Treatment of Heart Failure (HF), medical devices, biomarkers and agent efficacy are handled all in one chapter. •PCI for valves vs Open heart Valve replacement •PDA and Complications of Surgery — only curation could create the picture of this unique combination of debate, as exemplified of Endarterectomy (CEA) vs Stenting the Carotid Artery (CAS), ischemic leg, renal artery stenosis.
This e-Book is a comprehensive review of recent Original Research on Cardiovascular Diseases: Causes, Risks and Management and related opportunities for Targeted Therapy written by Experts, Authors and Writers. The results of Original Research are gaining value added for the e-Reader by the Methodology of Curation. The e-Book’s articles have been published on the Open Access Online Scientific Journal, since April 2012. This e-Book includes a thorough evaluation of a rich source of research literature on the genomic influences, which may have variable strength in the biological causation of atherosclerosis, microvascular disease, plaque formation, not necessarily having expressing, except in a multivariable context that includes the environment, dietary factors, level of emotional stress, sleep habits, and the daily activities of living for affected individuals. The potential of genomics is carried in the DNA, copied to RNA, and this is most well studied in the micro RNAs (miRNA). The miRNA has been explored for the appearance in the circulation of specific miRNAs that might be associated with myocyte or endothelial cell injury, and they are also being used as targets for therapeutics by the creation of silencing RNAs (siRNA).
This e-Book is a comprehensive review of recent Original Research on Cardiovascular Diseases: Causes, Risks and Management and related opportunities for Targeted Therapy written by Experts, Authors and Writers. The results of Original Research are gaining value added for the e-Reader by the Methodology of Curation. The e-Book’s articles have been published on the Open Access Online Scientific Journal, since April 2012. Part 1 is concerned with Posttranslational Modification of Proteins, vital for understanding cellular regulation and dysregulation. Part 2 is concerned with Translational Medical Therapeutics, the efficacy of medical and surgical decisions based on bringing the knowledge gained from the laboratory, and from clinical trials into the realm opf best practice. The time for this to occur in practice in the past has been through roughly a generation of physicians. That was in part related to the busy workload of physicians, and inability to easily access specialty literature as the volume and complexity increased. This had an effect of making access of a family to a primary care provider through a lifetime less likely than the period post WWII into the 1980s.
Pharmacologic therapy represents the dominant strategy for management of cardiovascular disease and consequences, deferring, complementing and often supplanting structural and functional interventions. The general strategy of medical management is to identify the biochemicals that control cardiovascular functions and responses, identify the consequences of push and pull (stimulation, potentiation, inhibition, blockade, counteractivity), check benefits and harm, systematically document the impact, both in population studies and in individuals, make wise choices, and optimize dosing. Medications mimic or modify natural biologic activities. Therefore genomics (the study of gene products, especially, messengers and receptors) and the cascade of signaling pathways that modulate responses identifies the myriad but theoretically finite possibilities for chemical intervention. Often there are many pathways that affect or are affected by cardiovascular disease, and multiple ways to promote desirable changes. Elucidation of the biochemical signal changes that correspond to or respond to cardiovascular disease conditions and treatments provides both biomarkers of patient health status and targets for therapy. The process of homeostasis resists change, including resisting desirable changes that aim to correct maladaptive biology. Thus medication to block an excess in heart rate and blood pressure, for example, leads to upregulation in the number and sensitivity of blocked receptors as well changes in activity of sibling pathways, which mitigate the impact of the blocking medication and promote rebound worsening of the primary concern if the medication gets interrupted. These issues influence combination therapy choices as well as concern about compliance with prescriptions. Therefore this guided tour of curated data relating to medical management of cardiovascular diseases draws from the human genome project to identify treatment opportunities, pathophysiology to understand the impact of disease and maladaptive responses, clinical disease and pharmaceutical classifications, and clinical trial results to clarify expected outcomes. Curation also addresses context, insight and opportunity. Review of all of the above by teams of experts leads to formulation of guidelines, but each patient is a unique individual for whom customized optimization offers further benefits. Optimal care requires understanding of all of the above to guide and optimize the offering and patient education for wise choices promoting optimal quality and quantity of life despite the presence of cardiovascular disease. Current health care priorities, current cardiovascular medication classification and offerings, and in depth review of the achievements and limitations of current and anticipated future pharmaceutical therapies for cardiovascular disease are. The current priorities adapt to cost benefit analysis of prevalent cardiovascular disorders, as limited resources are arguably best directed to where they will do the most good. The scope of that concern includes prevention as well as curtailment of severity of impairment, by improving out patient management, aiming at alleviated suffering and achieve sufficient quality of life to avoid expensive hospitalizations, interference with productivity, and shortened lifespan. Major categories of cardiovascular disease are reviewed in separate chapters, based on distinct pathways and therapeutic considerations. The closing chapter addresses adverse effects of therapy. In Part Two we focus on biomarkers – indicators of disease status. Chapter 15 presented recent new examples, such as BNP and high-sensitivity Troponin. Ch.16 addressed how the completion of the mapping of the human genome paves the way for identifying many more biomarkers. Ch.17 reviewed biomarker utility in various disease conditions. Ch.18 reviewed biomarker utility in acute disorders. Ch.19 on cholesterol, lipids, diet and Ch.20 on Inflammation.
In Cardiology, “Interventional” is reserved for procedures that directly produce physical changes. Surgical interventions for cardiovascular diseases include heart or heart and lung transplant, implantation of cardiac assist devices, shock devices and pacemakers, bypass grafts for coronary or other arteries, valve repairs or replacement, removal of plaque (endarterectomy), removal of tumors, and repair or palliation of injuries or of congenital anomalies. All of these interventions are continually studied and improved, with a major effort at minimizing the risk, reducing recovery time and reducing the size of entry scar, for example by use of video scopes instead of direct visualization, and mechanical devices and robotics instead of direct manual access. Interventional Cardiology refers to an often competing non-surgical approach in which access is limited to entry by vein or artery (catheterization). The two teams have joined forces to achieve a major success in replacing aortic valves by femoral artery access without opening the chest at all (TAVR), with on-going progress towards a similar approach to mitral valve replacement. This book addresses disease prevalence, personalized patient and doctor experiences with Cardiac Surgery, the role of transfusion, status of the MedTech market, and a review of major accomplishments from pathology, anesthesiology, radiology, cardiology and surgery. The contributions of specific groups, such as the Texas Heart Institute, the Dalio Institute at New York Presbyterian/Weill Cornell, the Cleveland Clinic, and the Scripps Institute are reviewed. Individual contributions from Eric Topol, Arthur Moss, Paul Zoll, Tim Wu, and Earl E. Bakken (Medtronic co-founder) are included. Discoveries in relevant biology, including ATP (the metabolic paycheck) and plasma metabolomics, and novel technologies such as tethered-liquid perfluorocabon surface biocoating to prevent clotting. Additional curations present views of cardiothoracic surgeons, vascular surgeons and of Catheterization lab interventionists. Business aspects are addressed by review of costs, prevalence, payment methods, prevention impact and business models. Decision support tools are also reviewed, and changes in guidelines. Voices of three Open Heart Surgery Survivors are included. Chapters 4-6 addressed clinical trial data in coronary disease, biomarkers of cardiovascular disorders, coagulation including top roles of nitric oxide, C-reative protein, protein C, aprotinin and thrombin. Chapters 7-8 covered amyloidosis, atherosclerosis, valve disease, flow reserve, atrial fibrillation and roles for advanced imaging. Chapters 9-10 covered unstable angina, transplants, and ventricular assist devices. Chapters 11-14 span interventions on the aorta, peripheral arteries, and coronary arteries, valve surgery and percutaneous valve repair or replacement, plus the growing role of prosthetics and repair by stem cells and tissue engineering. As catheter techniques evolved to compete with bypass surgery they progressed from balloon cracking of obstructive lesions (POBA=plain old balloon angioplasty) to placement of stents (wire fences). Surgeons sometimes use in-stent valves, and now devices analogous to in-stent valves can be placed by catheter for valve replacement in patients with too much co-morbidity to go through heart surgery. Aortic valve replacement by stent (TAVR) has had sufficient success to be considered for all patients who have sufficient impairment to merit intervention. The diameter is large, so a vascular surgeon participates in the arterial access and repair of the access site. Minimally invasive repair of abdominal aorta aneurysm: atherosclerosis offers potentially somewhat protective stiffening of the arterial wall, it can promote clots, athero-emboli, and failure of the remodeling can lead to an outward ballooning, or aneurysm, that promotes both clot formation and wall or lining tears or rupture, cause of sudden death.
Retrospective Study of National Danish Registry Sheds Light on Cardiotoxic Risks of Immune Checkpoint Inhibitors
Reporter: Adina Hazan, PhD
Immune checkpoint inhibitors are used to treat over 12 different types of cancers, as a way of encouraging T cells of the immune system to target and kill cancer cells. While this method has proved to be an incredibly important mechanism in fighting deadly cancers, the enhanced immune response has been documented to produce a wide array of side effects, including an increased incidence of cardiovascular events such as arrhythmias and sudden cardiac arrest. Due to the nature of the disease and treatment, most evidence of cardiotoxicity from the treatment comes from small cohorts of patients or individual case studies.
D’Souza and colleagues used four Danish healthcare registries to collect data from over 13,000 patients with malignant melanoma, and over 25,000 patients with lung cancer, starting from 2011 with the introduction of checkpoint inhibitors up until 2017. Each group included patients who received checkpoint inhibitors and those that did not, providing the basis of their retrospective study to determine the risk of developing cardiac events with treatment over a long period of time. The study was published in the European Heart Journal in December 2020.
The authors confirmed the increased risk of cardiac events with the treatment of immune checkpoint inhibitors PD1i and CTLA-4i.
The relative rate of cardiovascular deaths were increased in patients with lung cancer treated with PD1i by three-fold, and
eight times higher in patients with malignant melanoma treated with CTLA-4i.
Moreover, they show increased relative rates of arrhythmias in both diseases treated with PD1i, and malignant melanoma treated with CTLA-4i.
This was also true of the timeframe of treatment, where it was previously believed that most cardiac events would develop within the first six months of checkpoint inhibitor use.
By using data from patients over years, this study shows that these adverse events develop even after the first six months.
The myocardium, or heart muscle tissue, has previously been reported to be damaged with the use of checkpoint inhibitors, suspected to be due to
increasing inflammatory cytokines and enhanced inflammatory pathways. Significantly, previous pharmacovigilance studies have suggested a rate of developing myocarditis at less than 1%, but
data from this study shows that the 1 year risk was 1.8%, “suggesting that the risk may be higher than previously estimated”.
Even though Immune checkpoint inhibitors have been shown to be invaluable tools in the treatment of certain cancers, “the findings [in this study] urge increased awareness of cardiac events in patients receiving ICI”.
Original Research
The risk of cardiac events in patients receiving immune checkpoint inhibitors: a nationwide Danish study
The study aimed to estimate the risk of cardiac events in immune checkpoint inhibitor (ICI)-treated patients with lung cancer or malignant melanoma.
Methods and results
The study included consecutive patients with lung cancer or malignant melanoma in 2011–17 nationwide in Denmark. The main composite outcome was cardiac events (arrhythmia, peri- or myocarditis, heart failure) or cardiovascular death. Absolute risks were estimated and the association of ICI and cardiac events was analysed in multivariable Cox models. We included 25 573 patients with lung cancer. Of these, 743 were treated with programmed cell death-1 inhibitor (PD1i) and their 1-year absolute risk of cardiac events was 9.7% [95% confidence interval (CI) 6.8–12.5]. Of the 13 568 patients with malignant melanoma, 145 had PD1i and 212 had cytotoxic T-lymphocyte-associated protein-4 inhibitor (CTLA-4i) treatment. Their 1-year risks were 6.6% (1.8–11.3) and 7.5% (3.7–11.3). The hazard rates of cardiac events were higher in patients with vs. without ICI treatment. Within 6 months from 1st ICI administration, the hazard ratios were 2.14 (95% CI 1.50–3.05) in patients with lung cancer and 4.30 (1.38–13.42) and 4.93 (2.45–9.94) in patients with malignant melanoma with PD1i and CTLA-4i, respectively. After 6 months, HRs were 2.26 (1.27–4.02) for patients with lung cancer and 3.48 (1.91–6.35) for patients with malignant melanoma and CTLA-4i.
Conclusions
Among patients with lung cancer and malignant melanoma, ICI treated had increased rates of cardiac events. The absolute risks were higher in these data compared with previous pharmacovigilance studies (e.g. 1.8% peri-/myocarditis 1-year risk).
Post TAVR: Management of conduction disturbances and number of valve recapture and/or repositioning attempts – Optimize self-expanding transcatheter aortic valve replacement (TAVR) positioning reduced the need for permanent pacemaker (PPM) implants down the road
Reporter: Aviva Lev-Ari, PhD, RN
UPDATED on 4/14/2021
Specialists complete first procedure in historic head-to-head TAVR study
A study with significant implications for the future of patient care is officially underway. Specialists at Penn Medicine in Philadelphia have reported completing the first procedure in a global head-to-head study comparing the safety and effectiveness of two leading transcatheter aortic valve replacement (TAVR) systems.
The Small Annuli Randomized to Evolut or Sapien (SMART) post-market trial is designed to compare the safety and effectiveness of self-expanding TAVR systems manufactured by Medtronic and balloon-expandable systems manufactured by Edwards Lifesciences when treating patients with small annuli. More than 90 different facilities are taking part in the trial, and approximately 700 patients with severe symptomatic aortic stenosis are expected to participate. A majority of study participants are expected to be women due to the trial’s focus on individuals with small annuli.
Howard C. Herrmann, MD, a professor of cardiovascular diseases at the University of Pennsylvania’s Perelman School of Medicine, is leading the trial.
“Penn Medicine has been in on the ground floor of many of the early and landmark trials in TAVR research since performing one of the first investigational procedures in 2007, and, since then, we have completed thousands of these procedures,” he said in a prepared statement. “Subsequently, multiple devices have been FDA approved, and the procedure has grown to become the predominant one for all patients with aortic stenosis. The outcome of this study will help cardiac teams to make more tailored decisions about which kind of valve to use on which patients.”
The SMART trial’s estimated primary completion date is May 2023. The estimated study completion date, meanwhile, is May 2028.
The PPM rate dropped from 9.7% to 3.0% (P=0.035), according to a team led by Hasan Jilaihawi, MD, of NYU Langone Health in New York City.
The rate of new left bundle branch block (LBBB) went down from 25.8% to 9.0% (P<0.001),
the PARTNER 3 and CoreValve Low Risk trials in patients at low surgical risk showed PPM implant rates of 17.4% with the Evolut line, 6.6% with the balloon-expandable Sapien 3, and 4.1%-6.1% with surgery.
“The His bundle passes through the membranous septum, a few millimeters beneath the non-coronary/right coronary cusps. It is therefore not surprising that a deeper valve implantation increases the likelihood of mechanical damage of the His bundle leading to a transient or persistent conduction disturbance,” according to Rodés-Cabau.
To capture factors that contributed to need for PPM implantation, Jilaihawi and colleagues performed a detailed restrospective analysis on 248 consecutive Evolut recipients at Langone treated with the standard TAVR approach — aiming for 3-4 mm implant depth (in relation to the non-coronary cusp) and recapturing and repositioning when the device landed considerably lower. Patients with prior PPM implantation were excluded. Devices used were Medtronic’s Evolut R, Evolut Pro, and Evolut 34XL.
This analysis revealed that use of the large Evolut 34XL (OR 4.96, 95% CI 1.68-14.63) and implant depth exceeding membranous septum length (OR 8.04, 95% CI 2.58-25.04) were independent predictors of later PPM implantation.
From there, operators came up with the MIDAS technique and applied it prospectively to another 100 consecutive patients.
Besides bringing down the PPM implant rate to 3.0%, there were no more cases of valve embolization, dislocation, or need for a second valve.
The standard and MIDAS groups shared similar membranous septum lengths but diverged in average actual device depth, such that the standard group tended to have Evolut devices positioned deeper (3.3 mm vs 2.3 mm, P<0.001).
Artificial Intelligence and Cardiovascular Disease
Reporter and Curator: Dr. Sudipta Saha, Ph.D.
3.3.18 Artificial Intelligence and Cardiovascular Disease, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 2: CRISPR for Gene Editing and DNA Repair
Cardiology is a vast field that focuses on a large number of diseases specifically dealing with the heart, the circulatory system, and its functions. As such, similar symptomatologies and diagnostic features may be present in an individual, making it difficult for a doctor to easily isolate the actual heart-related problem. Consequently, the use of artificial intelligence aims to relieve doctors from this hurdle and extend better quality to patients. Results of screening tests such as echocardiograms, MRIs, or CT scans have long been proposed to be analyzed using more advanced techniques in the field of technology. As such, while artificial intelligence is not yet widely-used in clinical practice, it is seen as the future of healthcare.
The continuous development of the technological sector has enabled the industry to merge with medicine in order to create new integrated, reliable, and efficient methods of providing quality health care. One of the ongoing trends in cardiology at present is the proposed utilization of artificial intelligence (AI) in augmenting and extending the effectiveness of the cardiologist. This is because AI or machine-learning would allow for an accurate measure of patient functioning and diagnosis from the beginning up to the end of the therapeutic process. In particular, the use of artificial intelligence in cardiology aims to focus on research and development, clinical practice, and population health. Created to be an all-in-one mechanism in cardiac healthcare, AI technologies incorporate complex algorithms in determining relevant steps needed for a successful diagnosis and treatment. The role of artificial intelligence specifically extends to the identification of novel drug therapies, disease stratification or statistics, continuous remote monitoring and diagnostics, integration of multi-omic data, and extension of physician effectivity and efficiency.
Artificial intelligence – specifically a branch of it called machine learning – is being used in medicine to help with diagnosis. Computers might, for example, be better at interpreting heart scans. Computers can be ‘trained’ to make these predictions. This is done by feeding the computer information from hundreds or thousands of patients, plus instructions (an algorithm) on how to use that information. This information is heart scans, genetic and other test results, and how long each patient survived. These scans are in exquisite detail and the computer may be able to spot differences that are beyond human perception. It can also combine information from many different tests to give as accurate a picture as possible. The computer starts to work out which factors affected the patients’ outlook, so it can make predictions about other patients.
In current medical practice, doctors will use risk scores to make treatment decisions for their cardiac patients. These are based on a series of variables like weight, age and lifestyle. However, they do not always have the desired levels of accuracy. A particular example of the use of artificial examination in cardiology is the experimental study on heart disease patients, published in 2017. The researchers utilized cardiac MRI-based algorithms coupled with a 3D systolic cardiac motion pattern to accurately predict the health outcomes of patients with pulmonary hypertension. The experiment proved to be successful, with the technology being able to pick-up 30,000 points within the heart activity of 250 patients. With the success of the aforementioned study, as well as the promise of other researches on artificial intelligence, cardiology is seemingly moving towards a more technological practice.
One study was conducted in Finland where researchers enrolled 950 patients complaining of chest pain, who underwent the centre’s usual scanning protocol to check for coronary artery disease. Their outcomes were tracked for six years following their initial scans, over the course of which 24 of the patients had heart attacks and 49 died from all causes. The patients first underwent a coronary computed tomography angiography (CCTA) scan, which yielded 58 pieces of data on the presence of coronary plaque, vessel narrowing and calcification. Patients whose scans were suggestive of disease underwent a positron emission tomography (PET) scan which produced 17 variables on blood flow. Ten clinical variables were also obtained from medical records including sex, age, smoking status and diabetes. These 85 variables were then entered into an artificial intelligence (AI) programme called LogitBoost. The AI repeatedly analysed the imaging variables, and was able to learn how the imaging data interacted and identify the patterns which preceded death and heart attack with over 90% accuracy. The predictive performance using the ten clinical variables alone was modest, with an accuracy of 90%. When PET scan data was added, accuracy increased to 92.5%. The predictive performance increased significantly when CCTA scan data was added to clinical and PET data, with accuracy of 95.4%.
Another study findings showed that applying artificial intelligence (AI) to the electrocardiogram (ECG) enables early detection of left ventricular dysfunction and can identify individuals at increased risk for its development in the future. Asymptomatic left ventricular dysfunction (ALVD) is characterised by the presence of a weak heart pump with a risk of overt heart failure. It is present in three to six percent of the general population and is associated with reduced quality of life and longevity. However, it is treatable when found. Currently, there is no inexpensive, noninvasive, painless screening tool for ALVD available for diagnostic use. When tested on an independent set of 52,870 patients, the network model yielded values for the area under the curve, sensitivity, specificity, and accuracy of 0.93, 86.3 percent, 85.7 percent, and 85.7 percent, respectively. Furthermore, in patients without ventricular dysfunction, those with a positive AI screen were at four times the risk of developing future ventricular dysfunction compared with those with a negative screen.
In recent years, the analysis of big data database combined with computer deep learning has gradually played an important role in biomedical technology. For a large number of medical record data analysis, image analysis, single nucleotide polymorphism difference analysis, etc., all relevant research on the development and application of artificial intelligence can be observed extensively. For clinical indication, patients may receive a variety of cardiovascular routine examination and treatments, such as: cardiac ultrasound, multi-path ECG, cardiovascular and peripheral angiography, intravascular ultrasound and optical coherence tomography, electrical physiology, etc. By using artificial intelligence deep learning system, the investigators hope to not only improve the diagnostic rate and also gain more accurately predict the patient’s recovery, improve medical quality in the near future.
The primary issue about using artificial intelligence in cardiology, or in any field of medicine for that matter, is the ethical issues that it brings about. Physicians and healthcare professionals prior to their practice swear to the Hippocratic Oath—a promise to do their best for the welfare and betterment of their patients. Many physicians have argued that the use of artificial intelligence in medicine breaks the Hippocratic Oath since patients are technically left under the care of machines than of doctors. Furthermore, as machines may also malfunction, the safety of patients is also on the line at all times. As such, while medical practitioners see the promise of artificial technology, they are also heavily constricted about its use, safety, and appropriateness in medical practice.
Issues and challenges faced by technological innovations in cardiology are overpowered by current researches aiming to make artificial intelligence easily accessible and available for all. With that in mind, various projects are currently under study. For example, the use of wearable AI technology aims to develop a mechanism by which patients and doctors could easily access and monitor cardiac activity remotely. An ideal instrument for monitoring, wearable AI technology ensures real-time updates, monitoring, and evaluation. Another direction of cardiology in AI technology is the use of technology to record and validate empirical data to further analyze symptomatology, biomarkers, and treatment effectiveness. With AI technology, researchers in cardiology are aiming to simplify and expand the scope of knowledge on the field for better patient care and treatment outcomes.
3.3.21 Multiple Barriers Identified Which May Hamper Use of Artificial Intelligence in the Clinical Setting, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 2: CRISPR for Gene Editing and DNA Repair
In a commentary article from Jennifer Couzin-Frankel entitled “Medicine contends with how to use artificial intelligence” the barriers to the efficient and reliable adoption of artificial intelligence and machine learning in the hospital setting are discussed. In summary these barriers result from lack of reproducibility across hospitals. For instance, a major concern among radiologists is the AI software being developed to read images in order to magnify small changes, such as with cardiac images, is developed within one hospital and may not reflect the equipment or standard practices used in other hospital systems. To address this issue, lust recently, US scientists and government regulators issued guidance describing how to convert research-based AI into improved medical images and published these guidance in the Journal of the American College of Radiology. The group suggested greater collaboration among relevant parties in developing of AI practices, including software engineers, scientists, clinicians, radiologists etc.
As thousands of images are fed into AI algorithms, according to neurosurgeon Eric Oermann at Mount Sinai Hospital, the signals they recognize can have less to do with disease than with other patient characteristics, the brand of MRI machine, or even how a scanner is angled. For example Oermann and Mount Sinai developed an AI algorithm to detect spots on a lung scan indicative of pneumonia and when tested in a group of new patients the algorithm could detect pneumonia with 93% accuracy.
However when the group from Sinai tested their algorithm from tens of thousands of scans from other hospitals including NIH success rate fell to 73-80%, indicative of bias within the training set: in other words there was something unique about the way Mt. Sinai does their scans relative to other hospitals. Indeed, many of the patients Mt. Sinai sees are too sick to get out of bed and radiologists would use portable scanners, which generate different images than stand alone scanners.
The results were published in Plos Medicine as seen below:
PLoS Med. 2018 Nov 6;15(11):e1002683. doi: 10.1371/journal.pmed.1002683. eCollection 2018 Nov.
Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study.
There is interest in using convolutional neural networks (CNNs) to analyze medical imaging to provide computer-aided diagnosis (CAD). Recent work has suggested that image classification CNNs may not generalize to new data as well as previously believed. We assessed how well CNNs generalized across three hospital systems for a simulated pneumonia screening task.
METHODS AND FINDINGS:
A cross-sectional design with multiple model training cohorts was used to evaluate model generalizability to external sites using split-sample validation. A total of 158,323 chest radiographs were drawn from three institutions: National Institutes of Health Clinical Center (NIH; 112,120 from 30,805 patients), Mount Sinai Hospital (MSH; 42,396 from 12,904 patients), and Indiana University Network for Patient Care (IU; 3,807 from 3,683 patients). These patient populations had an age mean (SD) of 46.9 years (16.6), 63.2 years (16.5), and 49.6 years (17) with a female percentage of 43.5%, 44.8%, and 57.3%, respectively. We assessed individual models using the area under the receiver operating characteristic curve (AUC) for radiographic findings consistent with pneumonia and compared performance on different test sets with DeLong’s test. The prevalence of pneumonia was high enough at MSH (34.2%) relative to NIH and IU (1.2% and 1.0%) that merely sorting by hospital system achieved an AUC of 0.861 (95% CI 0.855-0.866) on the joint MSH-NIH dataset. Models trained on data from either NIH or MSH had equivalent performance on IU (P values 0.580 and 0.273, respectively) and inferior performance on data from each other relative to an internal test set (i.e., new data from within the hospital system used for training data; P values both <0.001). The highest internal performance was achieved by combining training and test data from MSH and NIH (AUC 0.931, 95% CI 0.927-0.936), but this model demonstrated significantly lower external performance at IU (AUC 0.815, 95% CI 0.745-0.885, P = 0.001). To test the effect of pooling data from sites with disparate pneumonia prevalence, we used stratified subsampling to generate MSH-NIH cohorts that only differed in disease prevalence between training data sites. When both training data sites had the same pneumonia prevalence, the model performed consistently on external IU data (P = 0.88). When a 10-fold difference in pneumonia rate was introduced between sites, internal test performance improved compared to the balanced model (10× MSH risk P < 0.001; 10× NIH P = 0.002), but this outperformance failed to generalize to IU (MSH 10× P < 0.001; NIH 10× P = 0.027). CNNs were able to directly detect hospital system of a radiograph for 99.95% NIH (22,050/22,062) and 99.98% MSH (8,386/8,388) radiographs. The primary limitation of our approach and the available public data is that we cannot fully assess what other factors might be contributing to hospital system-specific biases.
CONCLUSION:
Pneumonia-screening CNNs achieved better internal than external performance in 3 out of 5 natural comparisons. When models were trained on pooled data from sites with different pneumonia prevalence, they performed better on new pooled data from these sites but not on external data. CNNs robustly identified hospital system and department within a hospital, which can have large differences in disease burden and may confound predictions.
Surprisingly, not many researchers have begun to use data obtained from different hospitals. The FDA has issued some guidance in the matter but considers “locked” AI software or unchanging software as a medical device. However they just announced development of a framework for regulating more cutting edge software that continues to learn over time.
Still the key point is that collaboration over multiple health systems in various countries may be necessary for development of AI software which is used in multiple clinical settings. Otherwise each hospital will need to develop their own software only used on their own system and would provide a regulatory headache for the FDA.
Other articles on Artificial Intelligence in Clinical Medicine on this Open Access Journal include:
scPopCorn: A New Computational Method for Subpopulation Detection and their Comparative Analysis Across Single-Cell Experiments
Reporter and Curator: Dr. Sudipta Saha, Ph.D.
4.2.5 scPopCorn: A New Computational Method for Subpopulation Detection and their Comparative Analysis Across Single-Cell Experiments, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 4: Single Cell Genomics
Present day technological advances have facilitated unprecedented opportunities for studying biological systems at single-cell level resolution. For example, single-cell RNA sequencing (scRNA-seq) enables the measurement of transcriptomic information of thousands of individual cells in one experiment. Analyses of such data provide information that was not accessible using bulk sequencing, which can only assess average properties of cell populations. Single-cell measurements, however, can capture the heterogeneity of a population of cells. In particular, single-cell studies allow for the identification of novel cell types, states, and dynamics.
One of the most prominent uses of the scRNA-seq technology is the identification of subpopulations of cells present in a sample and comparing such subpopulations across samples. Such information is crucial for understanding the heterogeneity of cells in a sample and for comparative analysis of samples from different conditions, tissues, and species. A frequently used approach is to cluster every dataset separately, inspect marker genes for each cluster, and compare these clusters in an attempt to determine which cell types were shared between samples. This approach, however, relies on the existence of predefined or clearly identifiable marker genes and their consistent measurement across subpopulations.
Although the aligned data can then be clustered to reveal subpopulations and their correspondence, solving the subpopulation-mapping problem by performing global alignment first and clustering second overlooks the original information about subpopulations existing in each experiment. In contrast, an approach addressing this problem directly might represent a more suitable solution. So, keeping this in mind the researchers developed a computational method, single-cell subpopulations comparison (scPopCorn), that allows for comparative analysis of two or more single-cell populations.
The performance of scPopCorn was tested in three distinct settings. First, its potential was demonstrated in identifying and aligning subpopulations from single-cell data from human and mouse pancreatic single-cell data. Next, scPopCorn was applied to the task of aligning biological replicates of mouse kidney single-cell data. scPopCorn achieved the best performance over the previously published tools. Finally, it was applied to compare populations of cells from cancer and healthy brain tissues, revealing the relation of neoplastic cells to neural cells and astrocytes. Consequently, as a result of this integrative approach, scPopCorn provides a powerful tool for comparative analysis of single-cell populations.
This scPopCorn is basically a computational method for the identification of subpopulations of cells present within individual single-cell experiments and mapping of these subpopulations across these experiments. Different from other approaches, scPopCorn performs the tasks of population identification and mapping simultaneously by optimizing a function that combines both objectives. When applied to complex biological data, scPopCorn outperforms previous methods. However, it should be kept in mind that scPopCorn assumes the input single-cell data to consist of separable subpopulations and it is not designed to perform a comparative analysis of single cell trajectories datasets that do not fulfill this constraint.
Several innovations developed in this work contributed to the performance of scPopCorn. First, unifying the above-mentioned tasks into a single problem statement allowed for integrating the signal from different experiments while identifying subpopulations within each experiment. Such an incorporation aids the reduction of biological and experimental noise. The researchers believe that the ideas introduced in scPopCorn not only enabled the design of a highly accurate identification of subpopulations and mapping approach, but can also provide a stepping stone for other tools to interrogate the relationships between single cell experiments.
The international CABANA trial (Catheter Ablation versus Arrhythmia Drug Therapy for Atrial Fibrillation) was the biggest buzz at the Heart Rhythm Society Scientific Sessions earlier this year, and it’s still making waves several months later.
Cleveland Clinic is among the 120 centers participating in the trial, and electrophysiologist Bruce Lindsay, MD, is the site’s principal investigator for the study. He recently sat down with Oussama Wazni, MD, Cleveland Clinic’s Section Head of Cardiac Electrophysiology and Pacing, to discuss the CABANA trial’s findings and implications. Below is an edited transcript of their conversation.
The problem was this: About 9 percent of the patients who were supposed to get ablations never did, and it’s not clear why. The reasons could have been financial issues or patients merely changing their mind or perhaps being too sick. If it was the latter reason, that would of course bias the results. But the problem is we don’t know.
On the other side, a substantial number of patients assigned to drug therapy — 27.5 percent — crossed over and received ablation. That rate of crossover was a bit higher than anticipated.
It’s difficult to use an intention-to-treat analysis when there’s a large crossover and a lot of people don’t get the treatment they were supposed to get. Nonetheless, the study design specified an intention-to-treat analysis, which found no significant differences between the groups in the composite primary end point or any of its components. There were, however, significant reductions in hospitalization for cardiovascular problems and in time to atrial fibrillation recurrence in the ablation group, and the latter finding is consistent with results from past studies.
Because of the large number of crossovers, there was much interest in the as-treated analysis, which was prespecified as a sensitivity analysis of the primary results.
This analysis showed a 3.9 percent absolute risk reduction — and
a 27 percent relative reduction — in the primary end point with ablation versus drug therapy.
That was a statistically significant effect, as was the 3.1 percent absolute reduction in all-cause death with ablation versus drug therapy.
Since its original description in 1893 by Willem van Einthoven, the electrocardiogram (ECG) has been instrumental in the recognition of a wide array of cardiac disorders1,2. Although many electrocardiographic patterns have been well described, the underlying biology is incompletely understood. Genetic associations of particular features of the ECG have been identified by genome wide studies. This snapshot approach only provides fragmented information of the underlying genetic makeup of the ECG. Here, we follow the effects of individual genetic variants through the complete cardiac cycle the ECG represents. We found that genetic variants have unique morphological signatures not identified by previous analyses. By exploiting identified abberations of these morphological signatures, we show that novel genetic loci can be identified for cardiac disorders. Our results demonstrate how an integrated approach to analyse high-dimensional data can further our understanding of the ECG, adding to the earlier undertaken snapshot analyses of individual ECG components. We anticipate that our comprehensive resource will fuel in silico explorations of the biological mechanisms underlying cardiac traits and disorders represented on the ECG. For example, known disease causing variants can be used to identify novel morphological ECG signatures, which in turn can be utilized to prioritize genetic variants or genes for functional validation. Furthermore, the ECG plays a major role in the development of drugs, a genetic assessment of the entire ECG can drive such developments.
Lesson 8 Cell Signaling and Motility: Lesson and Supplemental Information on Cell Junctions and ECM: #TUBiol3373
Curator: Stephen J. Williams, Ph.D.
Please click on the following link for the PowerPoint Presentation for Lecture 8 on Cell Junctions and the Extracellular Matrix: (this is same lesson from 2018 so don’t worry that file says 2018)
(for this lesson pay attention to the part that shows how Receptor Tyrosine Kinase activation (RTK) can lead to signaling to an integrin and also how the thrombin receptor leads to cellular signals both to GPCR (G-protein coupled receptors like the thrombin receptor, the ADP receptor; but also the signaling cascades that lead to integrin activation of integrins leading to adhesion to insoluble fibrin mesh of the newly formed clot and subsequent adhesion of platelets, forming the platelet plug during thrombosis.)
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Aashir Awan, Phd
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Alan F. Kaul, PharmD., MS, MBA, FCCP
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David Orchard-Webb, PhD
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