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Purpose of review Refinement in machine learning (ML) techniques and approaches has rapidly expanded artificial intelligence applications for the diagnosis and classification of heart failure (HF). This review is designed to provide the clinician with the basics of ML, as well as this technologies future utility in HF diagnosis and the potential impact on patient outcomes. Recent findings Recent studies applying ML methods to unique data sets available from electrocardiography, vectorcardiography, echocardiography, and electronic health records show significant promise for improving diagnosis, enhancing detection, and advancing treatment of HF. Innovations in both supervised and unsupervised methods have heightened the diagnostic accuracy of models developed to identify the presence of HF and further augmentation of model capabilities are likely utilizing ensembles of ML algorithms derived from different techniques. Summary This article is an overview of recent applications of ML to achieve improved diagnosis of HF and the resultant implications for patient management.

Background and Objective Coronary artery disease (CAD) and heart failure are the most common cardiovascular diseases. Non-invasive diagnostic testing for CAD requires radiation, heart rate acceleration, and imaging infrastructure. Early detection of left ventricular dysfunction is critical in heart failure management, the best measure of which is an elevated left ventricular end-diastolic pressure (LVEDP) that can only be measured using invasive cardiac catheterization. There exists a need for non-invasive, safe, and fast diagnostic testing for CAD and elevated LVEDP. This research employs nonlinear dynamics to assess for significant CAD and elevated LVEDP using non-invasively acquired photoplethysmographic (PPG) and three-dimensional orthogonal voltage gradient (OVG) signals. PPG (variations of the blood volume perfusing the tissue) and OVG (mechano-electrical activity of the heart) signals represent the dynamics of the cardiovascular system. Methods PPG and OVG were simultaneously acquired from two cohorts, (i) symptomatic subjects that underwent invasive cardiac catheterization, the gold standard test (408 CAD positive with stenosis≥ 70% and 186 with LVEDP≥ 20 mmHg) and (ii) asymptomatic healthy controls (676). A set of Poincare-based synchrony features were developed to characterize the interactions between the OVG and PPG signals. The extracted features were employed to train machine learning models for CAD and LVEDP. Five-fold cross-validation was used and the best model was selected based on the average area under the receiver operating characteristic curve (AUC) across 100 runs, then assessed using a hold-out test set. Results The Elastic Net model developed on the synchrony features can effectively classify CAD positive subjects from healthy controls with an average validation AUC=0.90±0.03 and an AUC= 0.89 on the test set. The developed model for LVEDP can discriminate subjects with elevated LVEDP from healthy controls with an average validation AUC=0.89±0.03 and an AUC=0.89 on the test set. The feature contributions results showed that the selection of a proper registration point for Poincare analysis is essential for the development of predictive models for different disease targets. Conclusions Nonlinear features from simultaneously-acquired signals used as inputs to machine learning can assess CAD and LVEDP safely and accurately with an easy-to-use, portable device, utilized at the point-of-care without radiation, contrast, or patient preparation.

Background Artificial intelligence (AI) techniques are increasingly applied to cardiovascular (CV) medicine in arenas ranging from genomics to cardiac imaging analysis. Cardiac Phase Space Tomography Analysis (cPSTA), employing machine-learned linear models from an elastic net method optimized by a genetic algorithm, analyzes thoracic phase signals to identify unique mathematical and tomographic features associated with the presence of flow-limiting coronary artery disease (CAD). This novel approach does not require radiation, contrast media, exercise, or pharmacological stress. The objective of this trial was to determine the diagnostic performance of cPSTA in assessing CAD in patients presenting with chest pain who had been referred by their physician for coronary angiography. Methods This prospective, multicenter, non-significant risk study was designed to: 1) develop machine-learned algorithms to assess the presence of CAD (defined as one or more ≥ 70% stenosis, or fractional flow reserve ≤ 0.80) and 2) test the accuracy of these algorithms prospectively in a naive verification cohort. This report is an analysis of phase signals acquired from 606 subjects at rest just prior to angiography. From the collective phase signal data, features were extracted and paired with the known angiographic results. A development set, consisting of signals from 512 subjects, was used for machine learning to determine an algorithm that correlated with significant CAD. Verification testing of the algorithm was performed utilizing previously untested phase signals from 94 subjects. Results The machine-learned algorithm had a sensitivity of 92% (95% CI: 74%-100%) and specificity of 62% (95% CI: 51%-74%) on blind testing in the verification cohort. The negative predictive value (NPV) was 96% (95% CI: 85%-100%). Conclusions These initial multicenter results suggest that resting cPSTA may have comparable diagnostic utility to functional tests currently used to assess CAD without requiring cardiac stress (exercise or pharmacological) or exposure of the patient to radioactivity.

Machine learning utilizing convolutional neural networks and elastic net combined with a novel cardiac phase space tomography (cPST) now permits rapid analysis of thoracic phase signals for the assessment of coronary artery disease. This new approach does not require radiation, exercise, or

All NHS hospital trusts are required to operate morbidity and mortality (M&M) meetings as a part of their clinical governance. Typically, these review deaths occurring among inpatients and are regarded as an important patient safety measure despite heterogeneous approaches and a lack of proven

This study demonstrated that, irrespective of hormone type or dose, administering cocoa butter implants during egg development affected the growth of female brown trout Salmo trutta and reduced the size of their offspring. Cortisol treatment also increased adult mortality. Caution is urged in the use of implants for studies of maternal hormonal influences on adult fishes and their offspring. (C) 2011 Crown Copyright Journal of Fish Biology (C) 2011 The Fisheries Society of the British Isles

Summary 1. Social status can vary considerably among individuals and has significant implications for performance. In addition to a genetic component, social status may be influenced by environmental factors including maternal effects such as prenatal hormone exposure. Maternal effects on traits determining social status have previously been examined in species where mothers provide parental care for relatively few offspring and therefore directly influence postnatal development. However, the generality of conclusions arising from these investigations is unclear because species that employ different reproductive strategies have not been studied. 2. We investigated the hypothesis that egg steroid hormone levels influence the social status of juvenile brown trout (Salmo trutta). We manipulated intra-clutch levels of cortisol and testosterone in eggs from 15 mothers using dilute hormone baths at the time of fertilization and examined their effects on traits that correlate with social status in juveniles [including standard body size, aggression, competitive ability and standard metabolic rate (SMR)]. 3. Hormone treatment did not affect whole-animal or mass-corrected SMR at the critical developmental stage when juveniles switch from reliance on a maternally provisioned yolk sac to independent feeding. However, juveniles from cortisol-treated eggs were smaller at this developmental stage. They were also less aggressive than, and subordinate to, fish from untreated eggs in socially competitive conditions, even after correcting for the observed effect of cortisol on body size. Egg testosterone treatment resulted in a likely pharmacological or toxicological dose with subsequent effects on both body size and behaviour in independently feeding juveniles. 4. Results from this study demonstrate that variation in the amount of cortisol deposited in eggs by spawning females influences juvenile social status and performance. The effects of elevated egg cortisol in fish are similar to the actions of embryonic glucocorticoids reported in other vertebrate taxa with very different reproductive strategies, suggesting a widespread mechanism for the effects of maternal stress on offspring. Possible adaptive aspects of this relationship are discussed.

Machine learning through convolutional neural networks and elastic net combined with a novel form of cardiac phase space tomography (cPST) now permits rapid analysis of thoracic phase signals for the assessment of coronary artery disease (CAD). Single photon emission computed tomography (SPECT) is

Some beta-blockers are less effective in reducing central blood pressure than other antihypertensive drugs, which may explain the higher rate of events in subjects randomized to atenolol in recent trials. We hypothesized that nebivolol, a mixed beta-blocker/nitro-vasodilator, would be more effective than atenolol in reducing central blood pressure and augmentation index (AIx). The aim of the present study was to test this in a double-blind, randomized, cross-over study, in a cohort of subjects with isolated systolic hypertension.Following a 2-week placebo run-in, 16 never-treated hypertensive subjects received atenolol (50 mg), nebivolol (5 mg) and placebo, each for 5 weeks, in a random order. Seated brachial blood pressure and heart rate were measured. Aortic blood pressure, AIx and pulse wave velocity (PWV) were assessed non-invasively.The placebo-corrected fall in brachial pressure was similar between nebivolol and atenolol, as was the reduction in PWV (mean change +/- SEM: -1.0 +/- 0.3 and -1.2 +/- 0.2 m/s; P = 0.2). However, there was less reduction in heart rate (-19 +/- 2 versus -23 +/- 2 beats/min; P0.01) and increase in AIx (+6 +/- 1 versus +10 +/- 1%; P = 0.04), following nebivolol. Aortic pulse pressure was significantly lower (50 +/- 2 versus 54 +/- 2 mmHg; P = 0.02) after nebivolol. N-terminal pro-B-type natriuretic peptide (proBNP) rose on both drugs (100 +/- 33 versus 75 +/- 80 pg/ml; P0.01 for both, NS for comparison).Nebivolol and atenolol have similar effects on brachial blood pressure and aortic stiffness. However, nebivolol reduces aortic pulse pressure more than atenolol, which may be related to a less pronounced rise in AIx and bradycardia. Whether this will translate into differences in clinical outcome requires further investigation.

The author describes methods of handling medication in resistant patients. He shows us how such methods can aid psychiatrists and nonmedical staff in collaborating against barriers to effective treatment.