Redefining β-blocker response in heart failure patients with sinus rhythm and atrial fibrillation: a machine learning cluster analysis

Epigallocatechin-3-Gallate Inhibits Atrial Fibrosis and Reduces the Occurrence and Maintenance of Atrial Fibrillation and its Possible Mechanisms

BACKGROUND

Atrial fibrosis is one of the main causes of the onset and recurrence of atrial fibrillation (AF), for which there is no effective treatment. The aim of this study was to investigate the effect and mechanism of epigallocatechin-3-gallate (EGCG) on AF in rats.

METHODS

The rat model of AF was established by rapid pacing induction after angiotensin-II (Ang-II) induced atrial fibrosis to verify the relationship between atrial fibrosis and the AF. The expression levels of TGF-β/Smad3 pathway molecules and lysyl oxidase (LOX) in AF were detected. Subsequently, EGCG was used to intervene Ang-II-induced atrial fibrosis to explore the role of EGCG in the treatment of AF and its inhibitory mechanism on fibrosis. It was further verified that EGCG inhibited the production of collagen and the expression of LOX through the TGF-β/Smad3 pathway at the cellular level.

RESULTS

The results showed that the induction rate and maintenance time of AF in rats increased with the increase of the degree of atrial fibrosis. Meanwhile, the expressions of Col I, Col III, molecules related to TGF-β/Smad3 pathway, and LOX increased significantly in the atrial tissues of rats in the Ang-II induced group. EGCG could reduce the occurrence and maintenance time of AF by inhibiting the degree of Ang-induced rat atrial fibrosis. Cell experiments confirmed that EGCG could reduce the synthesis of collagen and the expression of LOX in cardiac fibroblast induced by Ang-II. The possible mechanism is to down-regulate the expression of genes and proteins related to the TGF-β/Smad3 pathway.

CONCLUSION

EGCG could downregulate the expression levels of collagen and LOX by inhibiting the TGF-β/Smad3 signaling pathway, alleviating Ang-II-induced atrial fibrosis, which in turn inhibited the occurrence and curtailed the duration of AF.

Role of Artificial Intelligence and Machine Learning to Create Predictors, Enhance Molecular Understanding, and Implement Purposeful Programs for Myocardial Recovery

Heart failure (HF) affects millions of individuals and causes hundreds of thousands of deaths each year in the United States. Despite the public health burden, medical and device therapies for HF significantly improve clinical outcomes and, in a subset of patients, can cause reversal of abnormalities in cardiac structure and function, termed "myocardial recovery." By identifying novel patterns in high-dimensional data, artificial intelligence (AI) and machine learning (ML) algorithms can enhance the identification of key predictors and molecular drivers of myocardial recovery. Emerging research in the area has begun to demonstrate exciting results that could advance the standard of care. Although major obstacles remain to translate this technology to clinical practice, AI and ML hold the potential to usher in a new era of purposeful myocardial recovery programs based on precision medicine. In this review, we discuss applications of ML to the prediction of myocardial recovery, potential roles of ML in elucidating the mechanistic basis underlying recovery, barriers to the implementation of ML in clinical practice, and areas for future research.

Predicting angiographic coronary artery disease using machine learning and high-frequency QRS

AIM

Exercise stress ECG is a common diagnostic test for stable coronary artery disease, but its sensitivity and specificity need to be further improved. In this paper, we construct a machine learning model for the prediction of angiographic coronary artery disease by HFQRS analysis of cycling exercise ECG.

METHODS AND RESULTS

This study prospectively included 140 inpatients and 59 healthy volunteers undergoing cycling exercise ECG. The CHD group (N=104) and non-CHD group (N=95) were determined by coronary angiography gold standard. Automated HF QRS analysis was performed by the blinded method. The coronary group was predominantly male, with a higher prevalence of age, BMI, hypertension, and diabetes than the non-coronary group ( P < 0.001 ), higher lipid levels in the coronary group ( P < 0.005 ), significantly longer QRS duration during exercise testing ( P < 0.005 ), more positive leads ( P < 0.001 ), and a greater proportion of significant changes in HFQRS ( P < 0.001 ). Age, Gender, Hypertension, Diabetes, and HF QRS Conclusions were screened by correlation analysis and multifactorial retrospective analysis to construct the machine learning models of the XGBoost Classifier, Logistic Regression, LightGBM Classifier, RandomForest Classifier, Artificial Neural Network and Support Vector Machine, respectively.

CONCLUSION

Male, elderly, with hypertension, diabetes mellitus, and positive exercise stress test HFQRS conclusions suggested a high risk of CHD. The best performance of the Logistic Regression model was compared, and a column line graph for assessing the risk of CHD was further developed and validated.

Consumer wearable devices for evaluation of heart rate control using digoxin versus beta-blockers: the RATE-AF randomized trial

Consumer-grade wearable technology has the potential to support clinical research and patient management. Here, we report results from the RATE-AF trial wearables study, which was designed to compare heart rate in older, multimorbid patients with permanent atrial fibrillation and heart failure who were randomized to treatment with either digoxin or beta-blockers. Heart rate (n = 143,379,796) and physical activity (n = 23,704,307) intervals were obtained from 53 participants (mean age 75.6 years (s.d. 8.4), 40% women) using a wrist-worn wearable linked to a smartphone for 20 weeks. Heart rates in participants treated with digoxin versus beta-blockers were not significantly different (regression coefficient 1.22 (95% confidence interval (CI) -2.82 to 5.27; P = 0.55); adjusted 0.66 (95% CI -3.45 to 4.77; P = 0.75)). No difference in heart rate was observed between the two groups of patients after accounting for physical activity (P = 0.74) or patients with high activity levels (≥30,000 steps per week; P = 0.97). Using a convolutional neural network designed to account for missing data, we found that wearable device data could predict New York Heart Association functional class 5 months after baseline assessment similarly to standard clinical measures of electrocardiographic heart rate and 6-minute walk test (F1 score 0.56 (95% CI 0.41 to 0.70) versus 0.55 (95% CI 0.41 to 0.68); P = 0.88 for comparison). The results of this study indicate that digoxin and beta-blockers have equivalent effects on heart rate in atrial fibrillation at rest and on exertion, and suggest that dynamic monitoring of individuals with arrhythmia using wearable technology could be an alternative to in-person assessment. ClinicalTrials.gov identifier: NCT02391337 .

Can we effectively manage chronic kidney disease with a precision-based pharmacotherapy plan? Where are we?

INTRODUCTION

In recent years, thanks to significant advances in basic science and biotechnologies, nephrology has witnessed a deeper understanding of the mechanisms leading to various conditions associated with or causing kidney disease, opening new perspectives for developing specific treatments. These new possibilities have brought increased challenges to physicians, who face with a new complexity in disease characterization and selection the right treatment for individual patients.

AREAS COVERED

We chose four therapeutic situations: anaemia in chronic kidney disease (CKD), heart failure in CKD, IgA nephropathy (IgAN) and membranous nephropathy (MN). The literature search was made through PubMed.

EXPERT OPINION

Anaemia management remains challenging in CKD; a personalized therapeutic approach is often needed. Identifying patients who could benefit from a specific therapy is also an important goal for patients with CKD and heart failure with reduced ejection fraction. Several new treatments are under clinical development for IgAN; interestingly, they target specifically the pathogenetic mechanisms of the disease. The understanding of MN pathogenesis as an autoimmune disease and the discovery of several autoantibodies allows a better characterization of patients. High-sensible techniques for lymphocyte counting open the possibility of more personalized use of anti CD20 therapies.

Efficacy and safety of intravenous beta-blockers in acute atrial fibrillation and flutter is dependent on beta-1 selectivity: a systematic review and meta-analysis of randomised trials

BACKGROUND

Intravenous beta-blockers are commonly used to manage patients with acute atrial fibrillation (AF) and atrial flutter (AFl), but the choice of specific agent is often not evidence-based.

METHODS

A prospectively-registered systematic review and meta-analysis of randomised trials (PROSPERO: CRD42020204772) to compare the safety and efficacy of intravenous beta-blockers against alternative pharmacological agents.

RESULTS

Twelve trials comparing beta-blockers with diltiazem, digoxin, verapamil, anti-arrhythmic drugs and placebo were included, with variable risk of bias and 1152 participants. With high heterogeneity (I2 = 87%; p < 0.001), there was no difference in the primary outcomes of heart rate reduction (standardised mean difference - 0.65 beats/minute compared to control, 95% CI - 1.63 to 0.32; p = 0.19) or the proportion that achieved target heart rate (risk ratio [RR] 0.85, 95% CI 0.36-1.97; p = 0.70). Conventional selective beta-1 blockers were inferior for target heart rate reduction versus control (RR 0.33, 0.17-0.64; p < 0.001), whereas super-selective beta-1 blockers were superior (RR 1.98, 1.54-2.54; p < 0.001). There was no significant difference between beta-blockers and comparators for secondary outcomes of conversion to sinus rhythm (RR 1.15, 0.90-1.46; p = 0.28), hypotension (RR 1.85, 0.87-3.93; p = 0.11), bradycardia (RR 1.29, 0.25-6.82; p = 0.76) or adverse events leading to drug discontinuation (RR 1.03, 0.49-2.17; p = 0.93). The incidence of hypotension and bradycardia were greater with non-selective beta-blockers (p = 0.031 and p < 0.001).

CONCLUSIONS

Across all intravenous beta-blockers, there was no difference with other medications for acute heart rate control in atrial fibrillation and flutter. Efficacy and safety may be improved by choosing beta-blockers with higher beta-1 selectivity.

Heart failure management with β-blockers: can we do better?

Heart failure (HF) is associated with disabling symptoms, poor quality of life, and a poor prognosis with substantial excess mortality in the years following diagnosis. Overactivation of the sympathetic nervous system is a key feature of the pathophysiology of HF and is an important driver of the process of adverse remodelling of the left ventricular wall that contributes to cardiac failure. Drugs which suppress the activity of the renin-angiotensin-aldosterone system, including β-blockers, are foundation therapies for the management of heart failure with reduced ejection fraction (HFrEF) and despite a lack of specific outcomes trials, are also widely used by cardiologist in patients with HF with preserved ejection fraction (HFpEF). Today, expert opinion has moved away from recommending that treatment for HF should be guided solely by the LVEF and interventions should rather address signs and symptoms of HF (e.g. oedema and tachycardia), the severity of HF, and concomitant conditions. β-blockers improve HF symptoms and functional status in HF and these agents have demonstrated improved survival, as well as a reduced risk of other important clinical outcomes such as hospitalisation for heart failure, in randomised, placebo-controlled outcomes trials. In HFpEF, β-blockers are anti-ischemic and lower blood pressure and heart rate. Moreover, β-blockers also reduce mortality in the setting of HF occurring alongside common comorbid conditions, such as diabetes, CKD (of any severity), and COPD. Higher doses of β-blockers are associated with better clinical outcomes in populations with HF, so that ensuring adequate titration of therapy to their maximal (or maximally tolerated) doses is important for ensuring optimal outcomes for people with HF. In principle, a patient with HF could have combined treatment with a β-blocker, renin-angiotensin-aldosterone system inhibitor/neprilysin inhibitor, mineralocorticoid receptor antagonist, and a SGLT2 inhibitor, according to tolerability.

Does Heterogeneity Exist in Treatment Associations With Renin-Angiotensin-System Inhibitors or Beta-blockers According to Phenotype Clusters in Heart Failure with Preserved Ejection Fraction?

BACKGROUND

We explored the association between use of renin-angiotensin system inhibitors and beta-blockers, with mortality/morbidity in 5 previously identified clusters of patients with heart failure with preserved ejection fraction (HFpEF).

METHODS AND RESULTS

We analyzed 20,980 patients with HFpEF from the Swedish HF registry, phenotyped into young-low comorbidity burden (12%), atrial fibrillation-hypertensive (32%), older-atrial fibrillation (24%), obese-diabetic (15%), and a cardiorenal cluster (17%). In Cox proportional hazard models with inverse probability weighting, there was no heterogeneity in the association between renin-angiotensin system inhibitor use and cluster membership for any of the outcomes: cardiovascular (CV) mortality, all-cause mortality, HF hospitalisation, CV hospitalisation, or non-CV hospitalisation. In contrast, we found a statistical interaction between beta-blocker use and cluster membership for all-cause mortality (P = .03) and non-CV hospitalisation (P = .001). In the young-low comorbidity burden and atrial fibrillation-hypertensive cluster, beta-blocker use was associated with statistically significant lower all-cause mortality and non-CV hospitalisation and in the obese-diabetic cluster beta-blocker use was only associated with a statistically significant lower non-CV hospitalisation. The interaction between beta-blocker use and cluster membership for all-cause mortality could potentially be driven by patients with improved EF. However, patient numbers were diminished when excluding those with improved EF and the direction of the associations remained similar.

CONCLUSIONS

In patients with HFpEF, the association with all-cause mortality and non-CV hospitalisation was heterogeneous across clusters for beta-blockers. It remains to be elucidated how heterogeneity in HFpEF could influence personalized medicine and future clinical trial design.

Perceptions on artificial intelligence-based decision-making for coexisting multiple long-term health conditions: protocol for a qualitative study with patients and healthcare professionals

INTRODUCTION

Coexisting multiple health conditions is common among older people, a population that is increasing globally. The potential for polypharmacy, adverse events, drug interactions and development of additional health conditions complicates prescribing decisions for these patients. Artificial intelligence (AI)-generated decision-making tools may help guide clinical decisions in the context of multiple health conditions, by determining which of the multiple medication options is best. This study aims to explore the perceptions of healthcare professionals (HCPs) and patients on the use of AI in the management of multiple health conditions.

METHODS AND ANALYSIS

A qualitative study will be conducted using semistructured interviews. Adults (≥18 years) with multiple health conditions living in the West Midlands of England and HCPs with experience in caring for patients with multiple health conditions will be eligible and purposively sampled. Patients will be identified from Clinical Practice Research Datalink (CPRD) Aurum; CPRD will contact general practitioners who will in turn, send a letter to patients inviting them to take part. Eligible HCPs will be recruited through British HCP bodies and known contacts. Up to 30 patients and 30 HCPs will be recruited, until data saturation is achieved. Interviews will be in-person or virtual, audio recorded and transcribed verbatim. The topic guide is designed to explore participants' attitudes towards AI-informed clinical decision-making to augment clinician-directed decision-making, the perceived advantages and disadvantages of both methods and attitudes towards risk management. Case vignettes comprising a common decision pathway for patients with multiple health conditions will be presented during each interview to invite participants' opinions on how their experiences compare. Data will be analysed thematically using the Framework Method.

ETHICS AND DISSEMINATION

This study has been approved by the National Health Service Research Ethics Committee (Reference: 22/SC/0210). Written informed consent or verbal consent will be obtained prior to each interview. The findings from this study will be disseminated through peer-reviewed publications, conferences and lay summaries.

Artificial Intelligence-Based Clinical Decision Support Systems in Cardiovascular Diseases

Despite all the advancements in science, medical knowledge, healthcare, and the healthcare industry, cardiovascular disease (CVD) remains the leading cause of morbidity and mortality worldwide. The main reasons are the inadequacy of preventive health services and delays in diagnosis due to the increasing population, the failure of physicians to apply guide-based treatments, the lack of continuous patient follow-up, and the low compliance of patients with doctors' recommendations. Artificial intelligence (AI)-based clinical decision support systems (CDSSs) are systems that support complex decision-making processes by using AI techniques such as data analysis, foresight, and optimization. Artificial intelligence-based CDSSs play an important role in patient care by providing more accurate and personalized information to healthcare professionals in risk assessment, diagnosis, treatment optimization, and monitoring and early warning of CVD. These are just some examples, and the use of AI for CVD decision support systems is rapidly evolving. However, for these systems to be fully reliable and effective, they need to be trained with accurate data and carefully evaluated by medical professionals.

Systematic reviews of machine learning in healthcare: a literature review

INTRODUCTION

The increasing availability of data and computing power has made machine learning (ML) a viable approach to faster, more efficient healthcare delivery.

METHODS

A systematic literature review (SLR) of published SLRs evaluating ML applications in healthcare settings published between1 January 2010 and 27 March 2023 was conducted.

RESULTS

In total 220 SLRs covering 10,462 ML algorithms were reviewed. The main application of AI in medicine related to the clinical prediction and disease prognosis in oncology and neurology with the use of imaging data. Accuracy, specificity, and sensitivity were provided in 56%, 28%, and 25% SLRs respectively. Internal and external validation was reported in 53% and less than 1% of the cases respectively. The most common modeling approach was neural networks (2,454 ML algorithms), followed by support vector machine and random forest/decision trees (1,578 and 1,522 ML algorithms, respectively).

EXPERT OPINION

The review indicated considerable reporting gaps in terms of the ML's performance, both internal and external validation. Greater accessibility to healthcare data for developers can ensure the faster adoption of ML algorithms into clinical practice.

Genetic Atrial Cardiomyopathies: Common Features, Specific Differences, and Broader Relevance to Understanding Atrial Cardiomyopathy

Atrial cardiomyopathy is a condition that causes electrical and contractile dysfunction of the atria, often along with structural and functional changes. Atrial cardiomyopathy most commonly occurs in conjunction with ventricular dysfunction, in which case it is difficult to discern the atrial features that are secondary to ventricular dysfunction from those that arise as a result of primary atrial abnormalities. Isolated atrial cardiomyopathy (atrial-selective cardiomyopathy [ASCM], with minimal or no ventricular function disturbance) is relatively uncommon and has most frequently been reported in association with deleterious rare genetic variants. The genes involved can affect proteins responsible for various biological functions, not necessarily limited to the heart but also involving extracardiac tissues. Atrial enlargement and atrial fibrillation are common complications of ASCM and are often the predominant clinical features. Despite progress in identifying disease-causing rare variants, an overarching understanding and approach to the molecular pathogenesis, phenotypic spectrum, and treatment of genetic ASCM is still lacking. In this review, we aim to analyze the literature relevant to genetic ASCM to understand the key features of this rather rare condition, as well as to identify distinct characteristics of ASCM and its arrhythmic complications that are related to specific genotypes. We outline the insights that have been gained using basic research models of genetic ASCM in vitro and in vivo and correlate these with patient outcomes. Finally, we provide suggestions for the future investigation of patients with genetic ASCM and improvements to basic scientific models and systems. Overall, a better understanding of the genetic underpinnings of ASCM will not only provide a better understanding of this condition but also promises to clarify our appreciation of the more commonly occurring forms of atrial cardiomyopathy associated with ventricular dysfunction.

Treatment response to spironolactone in patients with heart failure with preserved ejection fraction: a machine learning-based analysis of two randomized controlled trials

BACKGROUND

Whether there is a subset of patients with heart failure with preserved ejection fraction (HFpEF) that benefit from spironolactone therapy is unclear. We applied a machine learning approach to identify responders and non-responders to spironolactone among patients with HFpEF in two large randomized clinical trials.

METHODS

Using a reiterative cluster allocating permutation approach, patients from the derivation cohort (Aldo-DHF) were identified according to their treatment response to spironolactone with respect to improvement in E/e'. Heterogenous features of response ('responders' and 'non-responders') were characterized by an extreme gradient boosting (XGBoost) algorithm. XGBoost was used to predict treatment response in the validation cohort (TOPCAT). The primary endpoint of the validation cohort was a combined endpoint of cardiovascular mortality, aborted cardiac arrest, or heart failure hospitalization. Patients with missing variables for the XGboost model were excluded from the validation analysis.

FINDINGS

Out of 422 patients from the derivation cohort, reiterative cluster allocating permutation identified 159 patients (38%) as spironolactone responders, in whom E/e' significantly improved (p = 0.005). Within the validation cohort (n = 525) spironolactone treatment significantly reduced the occurrence of the primary outcome among responders (n = 185, p log rank = 0.008), but not among patients in the non-responder group (n = 340, p log rank = 0.52).

INTERPRETATION

Machine learning approaches might aid in identifying HFpEF patients who are likely to show a favorable therapeutic response to spironolactone.

FUNDING

See Acknowledgements section at the end of the manuscript.

Discovering Distinct Phenotypical Clusters in Heart Failure Across the Ejection Fraction Spectrum: a Systematic Review

REVIEW PURPOSE

This systematic review aims to summarise clustering studies in heart failure (HF) and guide future clinical trial design and implementation in routine clinical practice.

FINDINGS

34 studies were identified (n = 19 in HF with preserved ejection fraction (HFpEF)). There was significant heterogeneity invariables and techniques used. However, 149/165 described clusters could be assigned to one of nine phenotypes: 1) young, low comorbidity burden; 2) metabolic; 3) cardio-renal; 4) atrial fibrillation (AF); 5) elderly female AF; 6) hypertensive-comorbidity; 7) ischaemic-male; 8) valvular disease; and 9) devices. There was room for improvement on important methodological topics for all clustering studies such as external validation and transparency of the modelling process. The large overlap between the phenotypes of the clustering studies shows that clustering is a robust approach for discovering clinically distinct phenotypes. However, future studies should invest in a phenotype model that can be implemented in routine clinical practice and future clinical trial design. HF = heart failure, EF = ejection fraction, HFpEF = heart failure with preserved ejection fraction, HFrEF = heart failure with reduced ejection fraction, CKD = chronic kidney disease, AF = atrial fibrillation, IHD = ischaemic heart disease, CAD = coronary artery disease, ICD = implantable cardioverter-defibrillator, CRT = cardiac resynchronization therapy, NT-proBNP = N-terminal pro b-type natriuretic peptide, BMI = Body Mass Index, COPD = Chronic obstructive pulmonary disease.

Multimorbidity in Heart Failure: Leveraging Cluster Analysis to Guide Tailored Treatment Strategies

REVIEW PURPOSE

This review summarises key findings on treatment effects within phenotypical clusters of patients with heart failure (HF), making a distinction between patients with preserved ejection fraction (HFpEF) and reduced ejection fraction (HFrEF).

FINDINGS

Treatment response differed among clusters; ACE inhibitors were beneficial in all HFrEF phenotypes, while only some studies show similar beneficial prognostic effects in HFpEF patients. Beta-blockers had favourable effects in all HFrEF patients but not in HFpEF phenotypes and tended to worsen prognosis in older, cardiorenal patients. Mineralocorticoid receptor antagonists had more favourable prognostic effects in young, obese males and metabolic HFpEF patients. While a phenotype-guided approach is a promising solution for individualised treatment strategies, there are several aspects that still require improvements before such an approach could be implemented in clinical practice. Stronger evidence from clinical trials and real-world data may assist in establishing a phenotype-guided treatment approach for patient with HF in the future.

Artificial intelligence in cardiovascular diseases: diagnostic and therapeutic perspectives

Artificial intelligence (AI), the technique of extracting information from complex database using sophisticated computer algorithms, has incorporated itself in medical field. AI techniques have shown the potential to accelerate the progression of diagnosis and treatment of cardiovascular diseases (CVDs), including heart failure, atrial fibrillation, valvular heart disease, hypertrophic cardiomyopathy, congenital heart disease and so on. In clinical scenario, AI have been proved to apply well in CVD diagnosis, enhance effectiveness of auxiliary tools, disease stratification and typing, and outcome prediction. Deeply developed to capture subtle connections from massive amounts of healthcare data, recent AI algorithms are expected to handle even more complex tasks than traditional methods. The aim of this review is to introduce current applications of AI in CVDs, which may allow clinicians who have limited expertise of computer science to better understand the frontier of the subject and put AI algorithms into clinical practice.

Beta-blocker use and mortality among patients with systolic heart failure and pacemaker rhythm

AIMS

Beta-blockers are proven to improve survival among patients with heart failure with reduced ejection fraction. Their efficacy in patients with heart failure with reduced ejection fraction and pacemaker devices has not been demonstrated. Our aim was to test the hypothesis that beta-blocker therapy is associated with improved survival in patients with chronic heart failure and a pacemaker rhythm on electrocardiogram (ECG).

METHODS AND RESULTS

This is a post hoc analysis from the GISSI-HF randomized clinical trial. We evaluated efficacy of beta-blockers by creating Cox proportional hazards models adjusting for pacemaker rhythm and heart rate, among other variables. Interactions between pacemaker rhythm, heart rate, and beta-blocker were also examined. Of the 6975 patients enrolled in the GISSI-HF trial, 813 (11.7%) had a pacemaker rhythm on baseline ECG. Of these 813 patients, 511 (62.9%) were receiving beta-blocker therapy. The effect of beta-blocker therapy on mortality was assessed using multivariable Cox proportional hazards adjusted for 27 co-variates. In the whole cohort, beta-blocker therapy was significantly associated with reduced mortality (hazard ratio 0.79 [0.72-0.87], P < 0.001), without interaction between beta-blockers, pacemaker rhythm and heart rate. Beta-blocker therapy was beneficial in the sub-group restricted to baseline pacemaker rhythm (hazard ratio 0.62 [0.49-0.79], P < 0.001).

CONCLUSIONS

Beta-blocker therapy is associated with improved survival among patients with heart failure and a pacemaker rhythm on ECG. Further studies are necessary to analyse differences between atrial and ventricular pacemakers.

Identifying distinct clinical clusters in heart failure with mildly reduced ejection fraction

INTRODUCTION

Heart failure (HF) is a heterogeneous syndrome, and the specific sub-category HF with mildly reduced ejection fraction (EF) range (HFmrEF; 41-49% EF) is only recently recognized as a distinct entity. Cluster analysis can characterize heterogeneous patient populations and could serve as a stratification tool in clinical trials and for prognostication. The aim of this study was to identify clusters in HFmrEF and compare cluster prognosis.

METHODS AND RESULTS

Latent class analysis to cluster HFmrEF patients based on their characteristics was performed in the Swedish HF registry (n = 7316). Identified clusters were validated in a Dutch cross-sectional HF registry-based dataset CHECK-HF (n = 1536). In Sweden, mortality and hospitalisation across the clusters were compared using a Cox proportional hazard model, with a Fine-Gray sub-distribution for competing risks and adjustment for age and sex. Six clusters were discovered with the following prevalence and hazard ratio with 95% confidence intervals (HR [95%CI]) vs. cluster 1: 1) low-comorbidity (17%, reference), 2) ischaemic-male (13%, HR 0.9 [95% CI 0.7-1.1]), 3) atrial fibrillation (20%, HR 1.5 [95% CI 1.2-1.9]), 4) device/wide QRS (9%, HR 2.7 [95% CI 2.2-3.4]), 5) metabolic (19%, HR 3.1 [95% CI 2.5-3.7]) and 6) cardio-renal phenotype (22%, HR 2.8 [95% CI 2.2-3.6]). The cluster model was robust between both datasets.

CONCLUSION

We found robust clusters with potential clinical meaning and differences in mortality and hospitalisation. Our clustering model could be valuable as a clinical differentiation support and prognostic tool in clinical trial design.

Artificial Intelligence Technologies in Cardiology

As the world produces exabytes of data, there is a growing need to find new methods that are more suitable for dealing with complex datasets. Artificial intelligence (AI) has significant potential to impact the healthcare industry, which is already on the road to change with the digital transformation of vast quantities of information. The implementation of AI has already achieved success in the domains of molecular chemistry and drug discoveries. The reduction in costs and in the time needed for experiments to predict the pharmacological activities of new molecules is a milestone in science. These successful applications of AI algorithms provide hope for a revolution in healthcare systems. A significant part of artificial intelligence is machine learning (ML), of which there are three main types-supervised learning, unsupervised learning, and reinforcement learning. In this review, the full scope of the AI workflow is presented, with explanations of the most-often-used ML algorithms and descriptions of performance metrics for both regression and classification. A brief introduction to explainable artificial intelligence (XAI) is provided, with examples of technologies that have developed for XAI. We review important AI implementations in cardiology for supervised, unsupervised, and reinforcement learning and natural language processing, emphasizing the used algorithm. Finally, we discuss the need to establish legal, ethical, and methodical requirements for the deployment of AI models in medicine.

Personalized Management for Heart Failure with Preserved Ejection Fraction

Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous clinical syndrome with multiple underlying mechanisms and comorbidities that leads to a variety of clinical phenotypes. The identification and characterization of these phenotypes are essential for better understanding the precise pathophysiology of HFpEF, identifying appropriate treatment strategies, and improving patient outcomes. Despite accumulating data showing the potentiality of artificial intelligence (AI)-based phenotyping using clinical, biomarker, and imaging information from multiple dimensions in HFpEF management, contemporary guidelines and consensus do not incorporate these in daily practice. In the future, further studies are required to authenticate and substantiate these findings in order to establish a more standardized approach for clinical implementation.

Which congestion presentation pattern on the physical findings is associated with future adverse events? A cluster analysis in the multicenter acute heart failure registry

BACKGROUND

Clinical congestion is the most frequent reason for hospital admission in patients with acute heart failure (AHF). However, few studies have investigated the patterns and prognostic implication of the physical congestion using unbiased and robust statistical methods.

METHODS

A hierarchical agglomerative clustering analysis was performed in the multicenter Japanese AHF registry (N = 3151) with the distance calculated by Jaccard's distance for jugular vein distention (JVD), leg edema, S3, crackles, and orthopnea. The primary outcome was a composite of cardiac death and heart failure readmission within 1-year.

RESULTS

At the time of admission, the median number of prevalent congestive signs was 2. We identified three phenogroups: 'no physical congestions' (N = 251); 'congestion without JVD' (N = 1415); and 'congestion with JVD' (N = 1495). Patients in 'no physical congestion' were the youngest (median 75 [62, 83] years) with the lowest systolic blood pressure (122 [106, 142] mmHg). Patients in 'congestion without JVD', and 'congestion with JVD' were similar in terms of age (77 [67, 84] vs. 78 [69, 84] years) and systolic blood pressure (138 [118, 160] vs. 137 [118, 158] mmHg). While 30-day mortality was similar (4.0%, 3.7%, and 4.3% in 'no physical congestion,' 'congestion without JVD,' and 'congestion with JVD', respectively), the patients in 'congestion with JVD' were at the highest risk for the primary outcome (adjusted hazard ratio 1.79, 95% CI 1.26-2.55 when 'no physical congestion' was a reference).

CONCLUSIONS

Our clustering analysis demonstrated that congestion signs, particularly JVD, allowed identification of AHF phenogroups with distinct clinical characteristics and long-term outcomes.

A cardiologist's guide to machine learning in cardiovascular disease prognosis prediction

A modern-day physician is faced with a vast abundance of clinical and scientific data, by far surpassing the capabilities of the human mind. Until the last decade, advances in data availability have not been accompanied by analytical approaches. The advent of machine learning (ML) algorithms might improve the interpretation of complex data and should help to translate the near endless amount of data into clinical decision-making. ML has become part of our everyday practice and might even further change modern-day medicine. It is important to acknowledge the role of ML in prognosis prediction of cardiovascular disease. The present review aims on preparing the modern physician and researcher for the challenges that ML might bring, explaining basic concepts but also caveats that might arise when using these methods. Further, a brief overview of current established classical and emerging concepts of ML disease prediction in the fields of omics, imaging and basic science is presented.

Artificial intelligence to enhance clinical value across the spectrum of cardiovascular healthcare

Artificial intelligence (AI) is increasingly being utilized in healthcare. This article provides clinicians and researchers with a step-wise foundation for high-value AI that can be applied to a variety of different data modalities. The aim is to improve the transparency and application of AI methods, with the potential to benefit patients in routine cardiovascular care. Following a clear research hypothesis, an AI-based workflow begins with data selection and pre-processing prior to analysis, with the type of data (structured, semi-structured, or unstructured) determining what type of pre-processing steps and machine-learning algorithms are required. Algorithmic and data validation should be performed to ensure the robustness of the chosen methodology, followed by an objective evaluation of performance. Seven case studies are provided to highlight the wide variety of data modalities and clinical questions that can benefit from modern AI techniques, with a focus on applying them to cardiovascular disease management. Despite the growing use of AI, further education for healthcare workers, researchers, and the public are needed to aid understanding of how AI works and to close the existing gap in knowledge. In addition, issues regarding data access, sharing, and security must be addressed to ensure full engagement by patients and the public. The application of AI within healthcare provides an opportunity for clinicians to deliver a more personalized approach to medical care by accounting for confounders, interactions, and the rising prevalence of multi-morbidity.

The Neurohormonal Overactivity Syndrome in Heart Failure

Heart failure (HF) is categorized arbitrarily based on the left ventricular ejection fraction (LVEF) in HF with reduced (HFrEF; LVEF < 40%), mildly reduced (HFmrEF; LVEF 40−49%), or preserved ejection fraction (HFpEF; LVEF ≥ 50%). In this opinion paper, based on (patho)physiological considerations, we contend that the neurohormonal overactivity syndrome (NOHS), which is present in all symptomatic HF patients irrespective of their LVEF, not only contributes to the development of signs and symptoms but it is also a major determinant of patients’ outcomes. In this regard, NHOS is the only currently available treatment target in HF and should be combatted in most patients with the combined use of diuretics and neurohormonal inhibitors (β-blockers, angiotensin receptor-neprilysin inhibitor/angiotensin-converting enzyme inhibitors/angiotensin receptor blockers, mineralocorticoid antagonists, and sodium-glucose co-transporter 2 inhibitors). Unfortunately, despite the advances in therapeutics, HF mortality remains high. Probably machine learning approaches could better assess the multiple and higher-dimension interactions leading to the HF syndrome and define clusters of HF treatment efficacy.

Amiodarone vs. metoprolol succinate in HFrEF complicated with persistent atrial fibrillation with rapid ventricular response: A prospective observational study

Background

β-blockers have been recommended for patients with heart failure (HF) and atrial fibrillation (AF), but studies have shown that β-blockers do not reduce all-cause mortality or cardiovascular mortality in patients with HF and AF.

Objective

To investigate the difference in efficacy between oral amiodarone and metoprolol succinate for patients with HF with reduced ejection fraction (HFrEF) and persistent atrial fibrillation (pAF) with rapid ventricular response (RVR).

Methods

Patients with HFrEF complicated with pAF with RVR treated in the People's Hospital of Chongqing Hechuan between March 2018 and March 2019 were enrolled in this prospective observational study. The primary outcomes were cardiovascular mortality and the first hospitalization for HF rate. The secondary outcomes were type B pro-brain natriuretic peptide (NT-proBNP) before/after treatment, left ventricular ejection fraction (LVEF) before/after treatment, average heart rate (AhR), and the rate of sinus rhythm after 1 year of follow-up.

Results

A total of 242 patients with HFrEF complicated with pAF with RVR were enrolled and divided into amiodarone + perindopril + spironolactone+ routine drug (amiodarone group, n = 121) and metoprolol succinate + perindopril + spironolactone +routine drug (metoprolol succinate group, n = 121) according to their treatment strategy. Cardiovascular mortality (4.9 vs. 12.4%, HR: 2.500, 95%CI: 1.002-6.237, P = 0.040) and first hospitalization for HF (52.9 vs. 67.8%, HR: 1.281, 95%CI: 1.033-1.589, P = 0.024) were significantly lower in the amiodarone group than in the metoprolol group. The mean ventricular rate in the amiodarone group was significantly lower than in the metoprolol group (64.5 ± 3.2 vs. 72.4 ± 4.2, P < 0.001). After 1 year of follow-up, the sinus rhythm rate was significantly higher in the amiodarone group than in the metoprolol group (38.8 vs. 7.4%, HR: 0.191, 95%CI: 0.098-0.374, P < 0.001). The difference in proBNP (3,914.88 vs. 2,558.07, P < 0.001) and LVEF (-6.89 vs. -0.98, P < 0.001) before and after treatment was significantly higher in the amiodarone group than in the metoprolol group.

Conclusion

In conclusion, in this prospective observational study, the amiodarone group had lower risk of cardiovascular death and the first hospitalization for HF than metoprolol in HFrEF and persistent atrial fibrillation (pAF) with RVR. The mechanism may be related to improved cardiac function, rhythm control and ventricular rate control.

Registration number

ChiCTR2200057816; Registered 7 March 2022-Retrospectively registered: http://www.medresman.org.cn/pub/cn/proj/projectshshow.aspx?proj=4222.

Patient clusters and cost trajectories in the Swiss Atrial Fibrillation cohort

Objective

Evidence on long-term costs of atrial fibrillation (AF) and associated factors is scarce. As part of the Swiss-AF prospective cohort study, we aimed to characterise AF costs and their development over time, and to assess specific patient clusters and their cost trajectories.

Methods

Swiss-AF enrolled 2415 patients with variable duration of AF between 2014 and 2017. Patient clusters were identified using hierarchical cluster analysis of baseline characteristics. Ongoing yearly follow-ups include health insurance clinical and claims data. An algorithm was developed to adjudicate costs to AF and related complications.

Results

A subpopulation of 1024 Swiss-AF patients with available claims data was followed up for a median (IQR) of 3.24 (1.09) years. Average yearly AF-adjudicated costs amounted to SFr5679 (€5163), remaining stable across the observation period. AF-adjudicated costs consisted mainly of inpatient and outpatient AF treatment costs (SFr4078; €3707), followed by costs of bleeding (SFr696; €633) and heart failure (SFr494; €449). Hierarchical analysis identified three patient clusters: cardiovascular (CV; N=253 with claims), isolated-symptomatic (IS; N=586) and severely morbid without cardiovascular disease (SM; N=185). The CV cluster and SM cluster depicted similarly high costs across all cost outcomes; IS patients accrued the lowest costs.

Conclusion

Our results highlight three well-defined patient clusters with specific costs that could be used for stratification in both clinical and economic studies. Patient characteristics associated with adjudicated costs as well as cost trajectories may enable an early understanding of the magnitude of upcoming AF-related healthcare costs.

Applying Artificial Intelligence to Wearable Sensor Data to Diagnose and Predict Cardiovascular Disease: A Review

Cardiovascular disease (CVD) is the world's leading cause of mortality. There is significant interest in using Artificial Intelligence (AI) to analyse data from novel sensors such as wearables to provide an earlier and more accurate prediction and diagnosis of heart disease. Digital health technologies that fuse AI and sensing devices may help disease prevention and reduce the substantial morbidity and mortality caused by CVD worldwide. In this review, we identify and describe recent developments in the application of digital health for CVD, focusing on AI approaches for CVD detection, diagnosis, and prediction through AI models driven by data collected from wearables. We summarise the literature on the use of wearables and AI in cardiovascular disease diagnosis, followed by a detailed description of the dominant AI approaches applied for modelling and prediction using data acquired from sensors such as wearables. We discuss the AI algorithms and models and clinical applications and find that AI and machine-learning-based approaches are superior to traditional or conventional statistical methods for predicting cardiovascular events. However, further studies evaluating the applicability of such algorithms in the real world are needed. In addition, improvements in wearable device data accuracy and better management of their application are required. Lastly, we discuss the challenges that the introduction of such technologies into routine healthcare may face.

Advanced nanoparticles that can target therapy and reverse drug resistance may be the dawn of leukemia treatment: A bibliometrics study

With the development of nanomedicine, more and more nanoparticles are used in the diagnosis and treatment of leukemia. This study aimed to identify author, country, institutional, and journal collaborations and their impacts, assess the knowledge base, identify existing trends, and uncover emerging topics related to leukemia research. 1825 Articles and reviews were obtained from the WoSCC and analyzed by Citespace and Vosviewer. INTERNATIONAL JOURNAL OF NANOMEDICINE is the journal with the highest output. The contribution of FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY is also noteworthy. The three main aspects of research in Nanoparticles-leukemia-related fields included nanoparticles for the diagnosis and treatment of leukemia, related to the type and treatment of leukemia, the specific molecular mechanism, and existing problems of the application of nanoparticles in leukemia. In the future, synthesize nano-drugs that have targeted therapy and chemotherapy resistance according to the mechanism, which may be the dawn of the solution to leukemia. This study offers a comprehensive overview of the Nanoparticles-leukemia-related field using bibliometrics and visual methods for the first time, providing a valuable reference for researchers interested in Nanoparticles-leukemia.

CODE-EHR best practice framework for the use of structured electronic healthcare records in clinical research

Big data is central to new developments in global clinical science aiming to improve the lives of patients. Technological advances have led to the routine use of structured electronic healthcare records with the potential to address key gaps in clinical evidence. The covid-19 pandemic has demonstrated the potential of big data and related analytics, but also important pitfalls. Verification, validation, and data privacy, as well as the social mandate to undertake research are key challenges. The European Society of Cardiology and the BigData@Heart consortium have brought together a range of international stakeholders, including patient representatives, clinicians, scientists, regulators, journal editors and industry. We propose the CODE-EHR Minimum Standards Framework as a means to improve the design of studies, enhance transparency and develop a roadmap towards more robust and effective utilisation of healthcare data for research purposes.

Post-mortem examination of high mortality in patients with heart failure and atrial fibrillation

BACKGROUND

The prevalence of combined heart failure (HF) and atrial fibrillation (AF) is rising, and these patients suffer from high rates of mortality. This study aims to provide robust data on factors associated with death, uniquely supported by post-mortem examination.

METHODS

A retrospective cohort study of hospitalized adults with a clinical diagnosis of HF and AF at a tertiary centre in Romania between 2014 and 2017. A standardized post-mortem examination was performed where death occurred within 24 h of admission, when the cause of death was not clear or by physician request. National records were used to collect mortality data, subsequently categorized and analysed as HF-related death, vascular death and non-cardiovascular death using Cox proportional hazards regression.

RESULTS

A total of 1009 consecutive patients with a mean age of 73 ± 11 years, 47% women, NYHA class 3.0 ± 0.9, left ventricular ejection fraction (LVEF) 40.1 ± 11.0% and 100% anticoagulated were followed up for 1.5 ± 0.9 years. A total of 291 (29%) died, with post-mortems performed on 186 (64%). Baseline factors associated with mortality were dependent on the cause of death. HF-related death in 136 (47%) was associated with higher NYHA class (hazard ratio [HR] 2.45 per one class increase, 95% CI 1.73-3.46; p < 0.001) and lower LVEF (0.95 per 1% increase, 0.93-0.97; p < 0.001). Vascular death occurred in 75 (26%) and was associated with hypertension (HR 2.83, 1.36-5.90; p = 0.005) and higher LVEF (1.08 per 1% increase, 1.05-1.11; p < 0.001). Non-cardiovascular death in 80 (28%) was associated with clinical obesity (HR 2.20, 1.21-4.00; p = 0.010) and higher LVEF (1.10 per 1% increase, 1.06-1.13; p < 0.001). Across all causes, there was no relationship between mortality and AF type (p = 0.77), HF type (p = 0.85) or LVEF (p = 0.58).

CONCLUSIONS

Supported by post-mortem data, the cause of death in HF and AF patients is heterogeneous, and the relationships with typical markers of mortality are critically dependent on the mode of death. The poor prognosis in this group demands further attention to improve management beyond anticoagulation.

CODE-EHR best-practice framework for the use of structured electronic health-care records in clinical research

Big data is important to new developments in global clinical science that aim to improve the lives of patients. Technological advances have led to the regular use of structured electronic health-care records with the potential to address key deficits in clinical evidence that could improve patient care. The COVID-19 pandemic has shown this potential in big data and related analytics but has also revealed important limitations. Data verification, data validation, data privacy, and a mandate from the public to conduct research are important challenges to effective use of routine health-care data. The European Society of Cardiology and the BigData@Heart consortium have brought together a range of international stakeholders, including representation from patients, clinicians, scientists, regulators, journal editors, and industry members. In this Review, we propose the CODE-EHR minimum standards framework to be used by researchers and clinicians to improve the design of studies and enhance transparency of study methods. The CODE-EHR framework aims to develop robust and effective utilisation of health-care data for research purposes.

Early diagnosis and better rhythm management to improve outcomes in patients with atrial fibrillation: the 8th AFNET/EHRA consensus conference

Despite marked progress in the management of atrial fibrillation (AF), detecting AF remains difficult and AF-related complications cause unacceptable morbidity and mortality even on optimal current therapy. This document summarizes the key outcomes of the 8th AFNET/EHRA Consensus Conference of the Atrial Fibrillation NETwork (AFNET) and the European Heart Rhythm Association (EHRA). Eighty-three international experts met in Hamburg for 2 days in October 2021. Results of the interdisciplinary, hybrid discussions in breakout groups and the plenary based on recently published and unpublished observations are summarized in this consensus paper to support improved care for patients with AF by guiding prevention, individualized management, and research strategies. The main outcomes are (i) new evidence supports a simple, scalable, and pragmatic population-based AF screening pathway; (ii) rhythm management is evolving from therapy aimed at improving symptoms to an integrated domain in the prevention of AF-related outcomes, especially in patients with recently diagnosed AF; (iii) improved characterization of atrial cardiomyopathy may help to identify patients in need for therapy; (iv) standardized assessment of cognitive function in patients with AF could lead to improvement in patient outcomes; and (v) artificial intelligence (AI) can support all of the above aims, but requires advanced interdisciplinary knowledge and collaboration as well as a better medico-legal framework. Implementation of new evidence-based approaches to AF screening and rhythm management can improve outcomes in patients with AF. Additional benefits are possible with further efforts to identify and target atrial cardiomyopathy and cognitive impairment, which can be facilitated by AI.

Management of Atrial Fibrillation Across the Spectrum of Heart Failure With Preserved and Reduced Ejection Fraction

Atrial fibrillation (AF) is the most common arrhythmia among patients with heart failure (HF), and HF is the most common cause of death for patients presenting with clinical AF. AF is frequently associated with pathological atrial myocardial dysfunction and remodeling, a triad that has been called atrial myopathy. AF can be the cause or consequence of clinical HF, and the directionality varies between individual patients and across the spectrum of HF. Although initial trials suggested no advantage for a systematic rhythm control strategy in HF with reduced ejection fraction, recent data suggest that select patients may benefit from attempts to maintain sinus rhythm with catheter ablation. Preliminary data also show a close relationship among AF, left atrial myopathy, mitral regurgitation, and HF with preserved ejection, with potential clinical benefits to catheter ablation therapy. The modern management of AF in HF also requires consideration of the degree of atrial myopathy and chronicity of AF, in addition to the pathogenesis and phenotype of the underlying left ventricular HF. In this review, we summarize the contemporary management of AF and provide practical guidance and areas in need of future investigation.

Echocardiographic phenotypes of Chinese patients with type 2 diabetes may indicate early diabetic myocardial disease

AIM

Type 2 diabetes may impair cardiac structure and function at very early stage, other factors, for example, obesity and hypertension, can induce aforementioned abnormalities individually. This study aimed to explore precise prevention and treatment of diabetic cardiomyopathy (DCM) by using cluster analysis of echocardiographic variables.

METHODS AND RESULTS

A total of 66 536 inpatients with diabetes from 2013 to 2018 were investigated, and 7112 patients were available for analysis after nadir. The cluster analysis was performed on echocardiographic variables to assess the clinical profiles and risk factors of clusters. Two clusters were identified. Cluster 1 with 3576 patients (50.3%, including 62.5% female) had hypertension in 62.4%, while the lower rate of obesity (13.7%). Ultrasound findings showed that 79.9% of them had left ventricular diastolic dysfunction (LVDD), the most characteristic change in the early stages of DCM. Systolic blood pressure (SBP), uric acid and antithrombin III were independent risk factors for LVDD (P < 0.0001); 64.0% of the 3536 patients in the second group were male, with a high prevalence of obesity (30.1%) and a higher prevalence of hypertension (79.5%), In particular, decreased systolic function and a high rate of LV hypertrophy (46.8%) represented the progressive phase of DCM (P < 0.0001). SBP, diastolic blood pressure, BMI and creatinine were independent correlates of LV mass index (P < 0.05).

CONCLUSION

The cluster analysis of echocardiographic variables may improve the identification of groups of patients with similar risks and different disease courses and will facilitate the achievement of targeted early prevention and treatment of DCM.

Novel Artificial Intelligence Applications in Cardiology: Current Landscape, Limitations, and the Road to Real-World Applications

Cardiovascular diseases are the leading cause of death globally and contribute significantly to the cost of healthcare. Artificial intelligence (AI) is poised to reshape cardiology. Using supervised and unsupervised learning, the two main branches of AI, several applications have been developed in recent years to improve risk prediction, allow large-scale analysis of medical data, and phenotype patients for personalized medicine. In this review, we examine the key advances in AI in cardiology and its limitations regarding bias in the data, standardization in reporting, data access, and model trust and accountability in cases of error. Finally, we discuss implementation methods to unleash AI's potential in making healthcare more accurate and efficient. Several steps need to be followed and challenges overcome in order to successfully integrate AI in clinical practice and ensure its longevity.

Machine learning in the detection and management of atrial fibrillation

Machine learning has immense novel but also disruptive potential for medicine. Numerous applications have already been suggested and evaluated concerning cardiovascular diseases. One important aspect is the detection and management of potentially thrombogenic arrhythmias such as atrial fibrillation. While atrial fibrillation is the most common arrhythmia with a lifetime risk of one in three persons and an increased risk of thromboembolic complications such as stroke, many atrial fibrillation episodes are asymptomatic and a first diagnosis is oftentimes only reached after an embolic event. Therefore, screening for atrial fibrillation represents an important part of clinical practice. Novel technologies such as machine learning have the potential to substantially improve patient care and clinical outcomes. Additionally, machine learning applications may aid cardiologists in the management of patients with already diagnosed atrial fibrillation, for example, by identifying patients at a high risk of recurrence after catheter ablation. We summarize the current state of evidence concerning machine learning and, in particular, artificial neural networks in the detection and management of atrial fibrillation and describe possible future areas of development as well as pitfalls.

Catheter ablation for atrial fibrillation: impact on mortality, morbidity, quality of life, and implications for the future

Despite the advances in technologies and techniques in the field of catheter ablation for cardiac arrhythmias, it is estimated that the prevalence of atrial fibrillation (AF) will further increase in the nearest future. The latest trials have proven the beneficial effect on mortality after pulmonary vein isolation in patients with impaired left ventricular function, while no such effect has been seen in patients without left ventricular dysfunction. This raises the question of whether catheter ablation for AF is still suited for the latter patient cohort or whether the endpoint of mortality is not adequate enough. Not only does pulmonary vein isolation reduce the burden of atrial fibrillation, but it also somehow alters the patients’ perception of it in the case of recurrence. Independent of the presence of ventricular dysfunction, patients experience a relief of AF-related symptoms, which is accompanied by an increase in quality of life based on the available patient-reported outcome measures, despite AF recurrence. Trials that are currently recruiting patients seek to unveil the accountable circumstances for these remaining uncertainties and help expand our understanding of a procedure that has been routinely performed for two decades.

Using machine learning to uncover heterogeneity of beta blocker response in heart failure

A recent study by Karwath et al.1 in The Lancet applied machine learning-based cluster analysis to pooled data from nine double-blind, randomized controlled trials of beta blockers, identifying subgroups of efficacy in patients with sinus rhythm and atrial fibrillation.

Identifying Atrial Fibrillation Mechanisms for Personalized Medicine

Atrial fibrillation (AF) is a major cause of heart failure and stroke. The early maintenance of sinus rhythm has been shown to reduce major cardiovascular endpoints, yet is difficult to achieve. For instance, it is unclear how discoveries at the genetic and cellular level can be used to tailor pharmacotherapy. For non-pharmacologic therapy, pulmonary vein isolation (PVI) remains the cornerstone of rhythm control, yet has suboptimal success. Improving these therapies will likely require a multifaceted approach that personalizes therapy based on mechanisms measured in individuals across biological scales. We review AF mechanisms from cell-to-organ-to-patient from this perspective of personalized medicine, linking them to potential clinical indices and biomarkers, and discuss how these data could influence therapy. We conclude by describing approaches to improve ablation, including the emergence of several mapping systems that are in use today.

Artificial intelligence in cardiology: the debate continues

In 1955, when John McCarthy and his colleagues proposed their first study of artificial intelligence, they suggested that 'every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it'. Whether that might ever be possible would depend on how we define intelligence, but what is indisputable is that new methods are needed to analyse and interpret the copious information provided by digital medical images, genomic databases, and biobanks. Technological advances have enabled applications of artificial intelligence (AI) including machine learning (ML) to be implemented into clinical practice, and their related scientific literature is exploding. Advocates argue enthusiastically that AI will transform many aspects of clinical cardiovascular medicine, while sceptics stress the importance of caution and the need for more evidence. This report summarizes the main opposing arguments that were presented in a debate at the 2021 Congress of the European Society of Cardiology. Artificial intelligence is an advanced analytical technique that should be considered when conventional statistical methods are insufficient, but testing a hypothesis or solving a clinical problem-not finding another application for AI-remains the most important objective. Artificial intelligence and ML methods should be transparent and interpretable, if they are to be approved by regulators and trusted to provide support for clinical decisions. Physicians need to understand AI methods and collaborate with engineers. Few applications have yet been shown to have a positive impact on clinical outcomes, so investment in research is essential.