On the use of machine learning techniques for the mechanical characterization of soft biological tissues

Motivated by the search for new strategies for fitting a material model, a new approach is explored in the present work. The use of numerical and complex algorithms based on machine learning techniques such as support vector machines for regression, bagged decision trees, and artificial neural networks is proposed for solving the parameter identification of constitutive laws for soft biological tissues. First, the mathematical tools were trained with analytical uniaxial data (circumferential and longitudinal directions) as inputs, and their corresponding material parameters of the Gasser, Ogden, and Holzapfel strain energy function as outputs. The train and test errors show great efficiency during the training process in finding correlations between inputs and outputs; besides, the correlation coefficients were very close to 1. Second, the tool was validated with unseen observations of analytical circumferential and longitudinal uniaxial data. The results show an excellent agreement between the prediction of the material parameters of the strain energy function and the analytical curves. Finally, data from real circumferential and longitudinal uniaxial tests on different cardiovascular tissues were fitted; thus, the material model of these tissues was predicted. We found that the method was able to consistently identify model parameters, and we believe that the use of these numerical tools could lead to an improvement in the characterization of soft biological tissues

COVID-19 Clinical Guidance For the Cardiovascular Care Team

COVID-19 is a quickly evolving public health emergency. The guidance provided in this document is based on the best available published information and expert evaluation. This document is intended to supplement, not supersede, relevant guidance from the Centers for Disease Control and Prevention, state and local health authorities, and your institution’s infectious disease containment, mitigation, and response plan

Predicting technique survival in peritoneal dialysis patients: comparing artificial neural networks and logistic regression

Background. Early technique failure has been a major limitation on the wider adoption of peritoneal dialysis (PD). The objectives of this study were to use data from a large, multi-centre, prospective database, the United Kingdom Renal Registry (UKRR), in order to determine the ability of an artificial neural network (ANN) model to predict early PD technique failure and to compare its performance with a logistic regression (LR)-based approach

World Heart Federation Roadmap for Digital Health in Cardiology.

More than 500 million people worldwide live with cardiovascular disease (CVD). Health systems today face fundamental challenges in delivering optimal care due to ageing populations, healthcare workforce constraints, financing, availability and affordability of CVD medicine, and service delivery. Digital health technologies can help address these challenges. They may be a tool to reach Sustainable Development Goal 3.4 and reduce premature mortality from non-communicable diseases (NCDs) by a third by 2030. Yet, a range of fundamental barriers prevents implementation and access to such technologies. Health system governance, health provider, patient and technological factors can prevent or distort their implementation. World Heart Federation (WHF) roadmaps aim to identify essential roadblocks on the pathway to effective prevention, detection, and treatment of CVD. Further, they aim to provide actionable solutions and implementation frameworks for local adaptation. This WHF Roadmap for digital health in cardiology identifies barriers to implementing digital health technologies for CVD and provides recommendations for overcoming them

Long-term Safety and Efficacy of New-Generation Drug-Eluting Stents inWomenWith AcuteMyocardial Infarction From theWomen in Innovation and Drug-Eluting Stents (WIN-DES) Collaboration

Importance Women with acute myocardial infarction (MI) undergoing mechanical reperfusion remain at increased risk of adverse cardiac events and mortality compared with their male counterparts. Whether the benefits of new-generation drug-eluting stents (DES) are preserved in women with acute MI remains unclear. Objective To investigate the long-term safety and efficacy of new-generation DES vs early-generation DES in women with acute MI. Design, Setting, and Participants Collaborative, international, individual patient-level data of women enrolled in 26 randomized clinical trials of DES were analyzed between July and December 2016. Only women presenting with an acute coronary syndrome were included. Study population was categorized according to presentation with unstable angina (UA) vs acute MI. Acute MI included non–ST-segment elevation MI (NSTEMI) or ST-segment elevation MI (STEMI). Interventions Randomization to early- (sirolimus- or paclitaxel-eluting stents) vs new-generation (everolimus-, zotarolimus-, or biolimus-eluting stents) DES. Main Outcomes and Measures Composite of death, MI or target lesion revascularization, and definite or probable stent thrombosis at 3-year follow-up. Results Overall, the mean age of participants was 66.8 years. Of 11 577 women included in the pooled data set, 4373 (37.8%) had an acute coronary syndrome as clinical presentation. Of these 4373 women, 2176 (49.8%) presented with an acute MI. In women with acute MI, new-generation DES were associated with lower risk of death, MI or target lesion revascularization (14.9% vs 18.4%; absolute risk difference, −3.5%; number needed to treat [NNT], 29; adjusted hazard ratio, 0.78; 95% CI, 0.61-0.99), and definite or probable stent thrombosis (1.4% vs 4.0%; absolute risk difference, −2.6%; NNT, 46; adjusted hazard ratio, 0.36; 95% CI, 0.19-0.69) without evidence of interaction for both end points compared with women without acute MI (P for interaction = .59 and P for interaction = .31, respectively). A graded absolute benefit with use of new-generation DES was observed in the transition from UA, to NSTEMI, and to STEMI (for death, MI, or target lesion revascularization: UA, −0.5% [NNT, 222]; NSTEMI, −3.1% [NNT, 33]; STEMI, −4.0% [NNT, 25] and for definite or probable ST: UA, −0.4% [NNT, 278]; NSTEMI, −2.2% [NNT, 46]; STEMI, −4.0% [NNT, 25]). Conclusions and Relevance New-generation DES are associated with consistent and durable benefits over 3 years in women presenting with acute MI. The magnitude of these benefits appeared to be greater per increase in severity of acute coronary syndrome

Omecamtiv mecarbil in chronic heart failure with reduced ejection fraction, GALACTIC‐HF: baseline characteristics and comparison with contemporary clinical trials

Aims: The safety and efficacy of the novel selective cardiac myosin activator, omecamtiv mecarbil, in patients with heart failure with reduced ejection fraction (HFrEF) is tested in the Global Approach to Lowering Adverse Cardiac outcomes Through Improving Contractility in Heart Failure (GALACTIC‐HF) trial. Here we describe the baseline characteristics of participants in GALACTIC‐HF and how these compare with other contemporary trials. Methods and Results: Adults with established HFrEF, New York Heart Association functional class (NYHA) ≥ II, EF ≤35%, elevated natriuretic peptides and either current hospitalization for HF or history of hospitalization/ emergency department visit for HF within a year were randomized to either placebo or omecamtiv mecarbil (pharmacokinetic‐guided dosing: 25, 37.5 or 50 mg bid). 8256 patients [male (79%), non‐white (22%), mean age 65 years] were enrolled with a mean EF 27%, ischemic etiology in 54%, NYHA II 53% and III/IV 47%, and median NT‐proBNP 1971 pg/mL. HF therapies at baseline were among the most effectively employed in contemporary HF trials. GALACTIC‐HF randomized patients representative of recent HF registries and trials with substantial numbers of patients also having characteristics understudied in previous trials including more from North America (n = 1386), enrolled as inpatients (n = 2084), systolic blood pressure < 100 mmHg (n = 1127), estimated glomerular filtration rate < 30 mL/min/1.73 m2 (n = 528), and treated with sacubitril‐valsartan at baseline (n = 1594). Conclusions: GALACTIC‐HF enrolled a well‐treated, high‐risk population from both inpatient and outpatient settings, which will provide a definitive evaluation of the efficacy and safety of this novel therapy, as well as informing its potential future implementation

Artificial Intelligence in Cardiology.

This review examines the current state and application of artificial intelligence (AI) and machine learning (ML) in cardiovascular medicine. AI is changing the clinical practice of medicine in other specialties. With progress continuing in this emerging technology, the impact for cardiovascular medicine is highlighted to provide insight for the practicing clinician and to identify potential patient benefits

Heart Failure in Women Due to Hypertensive Heart Disease.

Heart failure (HF) is a significant cause of cardiovascular morbidity and mortality for women in the United States. There are clear sex-specific differences between men and women in etiology, disease progression, and outcomes. HF with preserved ejection fraction is the most common type of HF in women, with hypertensive heart disease playing a pivotal role in its etiology. The Practice Guidelines do not endorse sex-specific recommendations for standard medical therapy of HF management. Women are underrepresented in HF clinical trials, leading to a lacking evidence base supporting sex-specific therapy. Further studies are needed to evaluate targeted HF therapies in women