Automated Echocardiographic Detection of Heart Failure With Preserved Ejection Fraction Using Artificial Intelligence

The role of echocardiography in the diagnosis of heart failure with preserved ejection fraction

Heart failure (HF) with preserved ejection fraction (HFpEF) is the most common form of HF in older adults. While manifest as distinct clinical phenotypes, almost all patients with HFpEF will present with exercise intolerance or exertional dyspnea. Distinguishing HFpEF from other clinical conditions remains challenging, as the accurate diagnosis of HFpEF involves integrating a diverse array of cardiovascular (CV) structural and physiologic inputs. Owing to its intrinsic ability to characterize the structure and function of the myocardium, cardiac valves, pericardium, and vasculature, echocardiography (TTE) has emerged as an essential modality for diagnosing HFpEF. In contrast to HF with reduced EF, however, no single TTE variable defines HFpEF. Abnormal diastolic function is typically associated with HFpEF, but "diastolic dysfunction" per se is not synonymous with "HFpEF": the pathophysiology of HFpEF is more complex than diastolic dysfunction alone. HFpEF may involve abnormalities at multiple loci within the CV system, including (1) dysfunction of the left ventricle, left atrium, or right ventricle; (2) pulmonary hypertension or pulmonary vascular disease; (3) pericardial restraint; (4) abnormal systemic vascular impedance; (5) coronary or peripheral microcirculatory dysfunction; and (6) defects of tissue oxygen uptake within the periphery. Thus, the accurate diagnosis of HFpEF - and its specific clinical phenotypes - requires diagnostic algorithms that comprise multiple clinical variables, many of which may be derived from TTE data. Refining such algorithms to better discriminate among specific HFpEF phenotypes is the subject of continued investigation.

Clinical applications of artificial intelligence and machine learning in neurocardiology: a comprehensive review

Neurocardiology is an evolving field focusing on the interplay between the nervous system and cardiovascular system that can be used to describe and understand many pathologies. Acute ischemic stroke can be understood through this framework of an interconnected, reciprocal relationship such that ischemic stroke occurs secondary to cardiac pathology (the Heart-Brain axis), and cardiac injury secondary to various neurological disease processes (the Brain-Heart axis). The timely assessment, diagnosis, and subsequent management of cerebrovascular and cardiac diseases is an essential part of bettering patient outcomes and the progression of medicine. Artificial intelligence (AI) and machine learning (ML) are robust areas of research that can aid diagnostic accuracy and clinical decision making to better understand and manage the disease of neurocardiology. In this review, we identify some of the widely utilized and upcoming AI/ML algorithms for some of the most common cardiac sources of stroke, strokes of undetermined etiology, and cardiac disease secondary to stroke. We found numerous highly accurate and efficient AI/ML products that, when integrated, provided improved efficacy for disease prediction, identification, prognosis, and management within the sphere of stroke and neurocardiology. In the focus of cryptogenic strokes, there is promising research elucidating likely underlying cardiac causes and thus, improved treatment options and secondary stroke prevention. While many algorithms still require a larger knowledge base or manual algorithmic training, AI/ML in neurocardiology has the potential to provide more comprehensive healthcare treatment, increase access to equitable healthcare, and improve patient outcomes. Our review shows an evident interest and exciting new frontier for neurocardiology with artificial intelligence and machine learning.

The Growing Role of Intracardiac Echo in Congenital Heart Disease Interventions

Advancements in congenital heart disease (CHD) care have significantly improved survival, leading to a growing population of adults with congenital heart disease (ACHDs). Many of these patients require ongoing interventions for residual defects, conduit or valve dysfunction, and arrhythmia management, often performed using transcatheter techniques. Imaging plays a critical role in ensuring procedural success and safety. Intracardiac echocardiography (ICE) has emerged as an essential imaging modality in ACHD interventions. With continuous technological advancements, ICE offers several advantages over transesophageal echocardiography (TEE) and transthoracic echocardiography (TTE), including superior visualization, real-time guidance, and the ability to avoid general anesthesia. These benefits have made ICE the preferred imaging tool for many transcatheter procedures. This review explores the expanding role of ICE in ACHD interventions, highlighting its applications in structural and electrophysiological procedures. By enhancing procedural precision and reducing complications, ICE is transforming the management of ACHD patients, optimizing outcomes, and improving long-term care for this complex and growing population.

External validation of artificial intelligence for detection of heart failure with preserved ejection fraction

Artificial intelligence (AI) models to identify heart failure (HF) with preserved ejection fraction (HFpEF) based on deep-learning of echocardiograms could help address under-recognition in clinical practice, but they require extensive validation, particularly in representative and complex clinical cohorts for which they could provide most value. In this study enrolling patients with HFpEF (cases; n = 240), and age, sex, and year of echocardiogram matched controls (n = 256), we compare the diagnostic performance (discrimination, calibration, classification, and clinical utility) and prognostic associations (mortality and HF hospitalization) between an updated AI HFpEF model (EchoGo Heart Failure v2) and existing clinical scores (H2FPEF and HFA-PEFF). The AI HFpEF model and H2FPEF score demonstrate similar discrimination and calibration, but classification is higher with AI than H2FPEF and HFA-PEFF, attributable to fewer intermediate scores, due to discordant multivariable inputs. The continuous AI HFpEF model output adds information beyond the H2FPEF, and integration with existing scores increases correct management decisions. Those with a diagnostic positive result from AI have a two-fold increased risk of the composite outcome. We conclude that integrating an AI HFpEF model into the existing clinical diagnostic pathway would improve identification of HFpEF in complex clinical cohorts, and patients at risk of adverse outcomes.

A bibliometric analysis of perioperative medicine and artificial intelligence

BACKGROUND

Artificial intelligence holds the potential to transform perioperative medicine by leveraging complex datasets to predict risks and optimise patient management in response to rising surgical volumes and patient complexity.

AIM

This bibliometric analysis aims to analyse trends, contributions, collaborations and research hotspots in artificial intelligence and perioperative medicine.

METHODS

A Scopus search on 11 October 2024 identified articles on artificial intelligence in perioperative medicine. Relevant peer-reviewed studies were screened by two reviewers, with a third resolving discrepancies. Data were analysed using VOSviewer, Biblioshiny and Microsoft Excel.

RESULTS

A total of 240 articles were included; 84% of articles were published after 2018, indicating rapid recent growth. The United States, China and Italy led contributions. Single-country publications comprised 76.6% of the dataset, reflecting limited international collaboration. Key research areas included perioperative risk prediction, intraoperative monitoring, blood management and echocardiography.

CONCLUSION

Artificial intelligence in perioperative medicine is rapidly advancing but requires increased international collaboration to fully realise its potential.

Artificial Intelligence in Cardiovascular Imaging and Interventional Cardiology: Emerging Trends and Clinical Implications

Artificial intelligence (AI) has revolutionized the field of cardiovascular imaging, serving as a unifying force that brings together multiple modalities under a single platform. The utility of noninvasive imaging ranges from diagnostic assessment and guiding interventions to prognostic stratification. Multimodality imaging has demonstrated important potential, particularly in patients with heterogeneous diseases, such as heart failure and atrial fibrillation. Facilitating complex interventional procedures requires accurate image acquisition and interpretation along with precise decision-making. The unique nature of interventional cardiology procedures benefiting from different imaging modalities presents an ideal target for the development of AI-assisted decision-making tools to improve workflow in the catheterization laboratory and personalize the need for transcatheter interventions. This review explores the advancements of AI in noninvasive cardiovascular imaging and interventional cardiology, addressing the clinical use and challenges of current imaging modalities, emerging trends, and promising applications as well as considerations for safe implementation of AI tools in clinical practice. Current practice has moved well beyond the question of whether we should or should not use AI in clinical health care settings. AI, in all its forms, has become deeply embedded in clinical workflows, particularly in cardiovascular imaging and interventional cardiology. It can, in the future, not only add precision and quantification but also serve as a means by which to fuse and link multimodalities together. It is only by understanding how AI techniques work, that the field can be harnessed for the greater good and avoid uninformed bias or misleading diagnoses.

Non-Invasive Hemodynamic Assessment of Heart Failure With Preserved Ejection Fraction

Heart failure with preserved ejection fraction (HFpEF) is a major healthcare problem with increasing prevalence. There has been a shift in HFpEF management towards early diagnosis and phenotype-specific targeted treatment. However, diagnosing HFpEF remains challenging due to a lack of universal criteria and patient heterogeneity. This review aims to provide a comprehensive assessment of the diagnostic workup of HFpEF, highlighting the role of echocardiography in HFpEF phenotyping.

What is the Future of Diagnostics in Heart Failure?

Heart failure (HF) is a common and malignant condition. Disease-modifying therapies are available, with early diagnosis being crucial as these therapies modify risk within weeks of commencement. A higher proportion of patients are now being diagnosed with HF during a hospital admission, rather than in the community, with an associated poorer prognosis. There is a need to reduce the time spent to diagnosis and treatment in the community. Advances in the diagnostic tools deployed in HF diagnostics, in particular the use of artificial intelligence, hold promise to deliver this.

Underpinnings of Heart Failure With Preserved Ejection Fraction in Women - From Prevention to Improving Function. A Co-publication With the American Journal of Preventive Cardiology and the Journal of Cardiac Failure

Heart failure with preserved ejection fraction (HFpEF) represents a major clinical challenge with rising global prevalence. Women have a nearly double lifetime risk of developing HFpEF compared to heart failure with reduced ejection fraction (HFrEF). In HFpEF, sex differences emerge both in how traditional cardiovascular risk factors (such as hypertension, obesity, and diabetes) affect cardiac function and through distinct pathophysiological mechanisms triggered by sex-specific events like menopause and adverse pregnancy outcomes. These patterns influence not only disease development, but also therapeutic responses, necessitating sex-specific approaches to treatment. This review aims to synthesize existing knowledge regarding HFpEF in women including traditional and sex-specific risk factors, pathophysiology, presentation, and therapies, while outlining important knowledge gaps that warrant further investigation. The impact of HFpEF spans a woman's entire lifespan, requiring prevention and management strategies tailored to different life stages. While understanding of sex-based differences in HFpEF has improved, significant knowledge gaps persist. Through examination of current evidence and challenges, this review highlights promising opportunities for innovative research, therapeutic development, and clinical care approaches that could transform the management of HFpEF in women.

Noninvasive Oral Hyperspectral Imaging-Driven Digital Diagnosis of Heart Failure With Preserved Ejection Fraction: Model Development and Validation Study

BACKGROUND

Oral microenvironmental disorders are associated with an increased risk of heart failure with preserved ejection fraction (HFpEF). Hyperspectral imaging (HSI) technology enables the detection of substances that are visually indistinguishable to the human eye, providing a noninvasive approach with extensive applications in medical diagnostics.

OBJECTIVE

The objective of this study is to develop and validate a digital, noninvasive oral diagnostic model for patients with HFpEF using HSI combined with various machine learning algorithms.

METHODS

Between April 2023 and August 2023, a total of 140 patients were recruited from Renmin Hospital of Wuhan University to serve as the training and internal testing groups for this study. Subsequently, from August 2024 to September 2024, an additional 35 patients were enrolled from Three Gorges University and Yichang Central People's Hospital to constitute the external testing group. After preprocessing to ensure image quality, spectral and textural features were extracted from the images. We extracted 25 spectral bands from each patient image and obtained 8 corresponding texture features to evaluate the performance of 28 machine learning algorithms for their ability to distinguish control participants from participants with HFpEF. The model demonstrating the optimal performance in both internal and external testing groups was selected to construct the HFpEF diagnostic model. Hyperspectral bands significant for identifying participants with HFpEF were identified for further interpretative analysis. The Shapley Additive Explanations (SHAP) model was used to provide analytical insights into feature importance.

RESULTS

Participants were divided into a training group (n=105), internal testing group (n=35), and external testing group (n=35), with consistent baseline characteristics across groups. Among the 28 algorithms tested, the random forest algorithm demonstrated superior performance with an area under the receiver operating characteristic curve (AUC) of 0.884 and an accuracy of 82.9% in the internal testing group, as well as an AUC of 0.812 and an accuracy of 85.7% in the external testing group. For model interpretation, we used the top 25 features identified by the random forest algorithm. The SHAP analysis revealed discernible distinctions between control participants and participants with HFpEF, thereby validating the diagnostic model's capacity to accurately identify participants with HFpEF.

CONCLUSIONS

This noninvasive and efficient model facilitates the identification of individuals with HFpEF, thereby promoting early detection, diagnosis, and treatment. Our research presents a clinically advanced diagnostic framework for HFpEF, validated using independent data sets and demonstrating significant potential to enhance patient care.

TRIAL REGISTRATION

China Clinical Trial Registry ChiCTR2300078855; https://www.chictr.org.cn/showproj.html?proj=207133.

Machine learning in the prevention of heart failure

Heart failure (HF) is a global pandemic with a growing prevalence and is a growing burden on the healthcare system. Machine learning (ML) has the potential to revolutionize medicine and can be applied in many different forms to aid in the prevention of symptomatic HF (stage C). HF prevention currently has several challenges, specifically in the detection of pre-HF (stage B). HF events are missed in contemporary models, limited therapeutic options are proven to prevent HF, and the prevention of HF with preserved ejection is particularly lacking. ML has the potential to overcome these challenges through existing and future models. ML has limitations, but the many benefits of ML outweigh these limitations and risks in most scenarios. ML can be applied in HF prevention through various strategies such as refinement of incident HF risk prediction models, capturing diagnostic signs from available tests such as electrocardiograms, chest x-rays, or echocardiograms to identify structural/functional cardiac abnormalities suggestive of pre-HF (stage B HF), and interpretation of biomarkers and epigenetic data. Altogether, ML is able to expand the screening of individuals at risk for HF (stage A HF), identify populations with pre-HF (stage B HF), predict the risk of incident stage C HF events, and offer the ability to intervene early to prevent progression to or decline in stage C HF. In this narrative review, we discuss the methods by which ML is utilized in HF prevention, the benefits and pitfalls of ML in HF risk prediction, and the future directions.

How to diagnose heart failure with preserved ejection fraction

Heart failure with preserved ejection fraction (HFpEF) is a major healthcare problem that is raising in prevalence. There has been a shift in HpEF management towards early diagnosis and phenotype-specific targeted treatment. However, the diagnosis of HFpEF remains a challenge due to the lack of universal criteria and patient heterogeneity. This review aims to provide a comprehensive assessment of the diagnostic workup of HFpEF, highlighting the role of echocardiography in HFpEF phenotyping.

Advancements and applications of artificial intelligence in cardiovascular imaging: a comprehensive review

Artificial intelligence (AI) is transforming cardiovascular imaging by offering advancements across multiple modalities, including echocardiography, cardiac computed tomography (CCT), cardiovascular magnetic resonance (CMR), interventional cardiology, nuclear medicine, and electrophysiology. This review explores the clinical applications of AI within each of these areas, highlighting its ability to improve patient selection, reduce image acquisition time, enhance image optimization, facilitate the integration of data from different imaging modality and clinical sources, improve diagnosis and risk stratification. Moreover, we illustrate both the advantages and the limitations of AI across these modalities, acknowledging that while AI can significantly aid in diagnosis, risk stratification, and workflow efficiency, it cannot replace the expertise of cardiologists. Instead, AI serves as a powerful tool to streamline routine tasks, allowing clinicians to focus on complex cases where human judgement remains essential. By accelerating image interpretation and improving diagnostic accuracy, AI holds great potential to improve patient care and clinical decision-making in cardiovascular imaging.

Current Challenges in Diagnosis and Treatment of Cardiovascular Disease

Cardiovascular disease is a leading the cause of death worldwide among the various cardiac pathologies that directly or indirectly affect the quality of life of patients [...].

Artificial intelligence-guided detection of under-recognized cardiomyopathies on point-of-care cardiac ultrasound: a multi-center study

Background

Point-of-care ultrasonography (POCUS) enables cardiac imaging at the bedside and in communities but is limited by abbreviated protocols and variation in quality. We developed and tested artificial intelligence (AI) models to automate the detection of underdiagnosed cardiomyopathies from cardiac POCUS.

Methods

In a development set of 290,245 transthoracic echocardiographic videos across the Yale-New Haven Health System (YNHHS), we used augmentation approaches and a customized loss function weighted for view quality to derive a POCUS-adapted, multi-label, video-based convolutional neural network (CNN) that discriminates HCM (hypertrophic cardiomyopathy) and ATTR-CM (transthyretin amyloid cardiomyopathy) from controls without known disease. We evaluated the final model across independent, internal and external, retrospective cohorts of individuals who underwent cardiac POCUS across YNHHS and Mount Sinai Health System (MSHS) emergency departments (EDs) (2011-2024) to prioritize key views and validate the diagnostic and prognostic performance of single-view screening protocols.

Findings

We identified 33,127 patients (median age 61 [IQR: 45-75] years, n=17,276 [52·2%] female) at YNHHS and 5,624 (57 [IQR: 39-71] years, n=1,953 [34·7%] female) at MSHS with 78,054 and 13,796 eligible cardiac POCUS videos, respectively. An AI-enabled single-view screening approach successfully discriminated HCM (AUROC of 0·90 [YNHHS] & 0·89 [MSHS]) and ATTR-CM (YNHHS: AUROC of 0·92 [YNHHS] & 0·99 [MSHS]). In YNHHS, 40 (58·0%) HCM and 23 (47·9%) ATTR-CM cases had a positive screen at median of 2·1 [IQR: 0·9-4·5] and 1·9 [IQR: 1·0-3·4] years before clinical diagnosis. Moreover, among 24,448 participants without known cardiomyopathy followed over 2·2 [IQR: 1·1-5·8] years, AI-POCUS probabilities in the highest (vs lowest) quintile for HCM and ATTR-CM conferred a 15% (adj.HR 1·15 [95%CI: 1·02-1·29]) and 39% (adj.HR 1·39 [95%CI: 1·22-1·59]) higher age- and sex-adjusted mortality risk, respectively.

Interpretation

We developed and validated an AI framework that enables scalable, opportunistic screening of treatable cardiomyopathies wherever POCUS is used.

Funding

National Heart, Lung and Blood Institute, Doris Duke Charitable Foundation, BridgeBio.

Revolutionizing Cardiology through Artificial Intelligence-Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment-A Comprehensive Review of the Past 5 Years

BACKGROUND

Artificial intelligence (AI) can radically change almost every aspect of the human experience. In the medical field, there are numerous applications of AI and subsequently, in a relatively short time, significant progress has been made. Cardiology is not immune to this trend, this fact being supported by the exponential increase in the number of publications in which the algorithms play an important role in data analysis, pattern discovery, identification of anomalies, and therapeutic decision making. Furthermore, with technological development, there have appeared new models of machine learning (ML) and deep learning (DP) that are capable of exploring various applications of AI in cardiology, including areas such as prevention, cardiovascular imaging, electrophysiology, interventional cardiology, and many others. In this sense, the present article aims to provide a general vision of the current state of AI use in cardiology.

RESULTS

We identified and included a subset of 200 papers directly relevant to the current research covering a wide range of applications. Thus, this paper presents AI applications in cardiovascular imaging, arithmology, clinical or emergency cardiology, cardiovascular prevention, and interventional procedures in a summarized manner. Recent studies from the highly scientific literature demonstrate the feasibility and advantages of using AI in different branches of cardiology.

CONCLUSIONS

The integration of AI in cardiology offers promising perspectives for increasing accuracy by decreasing the error rate and increasing efficiency in cardiovascular practice. From predicting the risk of sudden death or the ability to respond to cardiac resynchronization therapy to the diagnosis of pulmonary embolism or the early detection of valvular diseases, AI algorithms have shown their potential to mitigate human error and provide feasible solutions. At the same time, limits imposed by the small samples studied are highlighted alongside the challenges presented by ethical implementation; these relate to legal implications regarding responsibility and decision making processes, ensuring patient confidentiality and data security. All these constitute future research directions that will allow the integration of AI in the progress of cardiology.

Vision-language foundation model for echocardiogram interpretation

The development of robust artificial intelligence models for echocardiography has been limited by the availability of annotated clinical data. Here, to address this challenge and improve the performance of cardiac imaging models, we developed EchoCLIP, a vision-language foundation model for echocardiography, that learns the relationship between cardiac ultrasound images and the interpretations of expert cardiologists across a wide range of patients and indications for imaging. After training on 1,032,975 cardiac ultrasound videos and corresponding expert text, EchoCLIP performs well on a diverse range of benchmarks for cardiac image interpretation, despite not having been explicitly trained for individual interpretation tasks. EchoCLIP can assess cardiac function (mean absolute error of 7.1% when predicting left ventricular ejection fraction in an external validation dataset) and identify implanted intracardiac devices (area under the curve (AUC) of 0.84, 0.92 and 0.97 for pacemakers, percutaneous mitral valve repair and artificial aortic valves, respectively). We also developed a long-context variant (EchoCLIP-R) using a custom tokenizer based on common echocardiography concepts. EchoCLIP-R accurately identified unique patients across multiple videos (AUC of 0.86), identified clinical transitions such as heart transplants (AUC of 0.79) and cardiac surgery (AUC 0.77) and enabled robust image-to-text search (mean cross-modal retrieval rank in the top 1% of candidate text reports). These capabilities represent a substantial step toward understanding and applying foundation models in cardiovascular imaging for preliminary interpretation of echocardiographic findings.

Beyond standard echocardiography: Strain imaging as the AI-powered key to comprehensive cardiac function evaluation

Application of left ventricular and left atrial strain to distinguish cardiac from non-cardiac dyspnea.

Left ventricular to left arial volume ratio in the assessment of filling pressure in patients with dyspnoea and preserved ejection fraction

Introduction

Assessing filling pressure (FP) remains a clinical challenge despite advancements in non-invasive imaging techniques. This study investigates the utility of echocardiographic left ventricular (LV) to left atrial (LA) volume ratio in estimating the resting FP in patients with dyspnoea and preserved ejection fraction (EF).

Methods

This study is a prospective, single-centre analysis of 53 consecutive patients with dyspnoea (New York Heart Association grade 2 or 3) and LVEF of ≥50% (mean age 71 ± 10 years) who underwent cardiac catheterisation, including direct measurement of LA pressure at rest using retrograde technique. Echocardiographic data were obtained 1.5 ± 1.0 h after cardiac catheterisation. The patients were divided into two groups: Group 1 consisted of individuals with elevated FP, indicated by a mean LA pressure or mean pulmonary capillary wedge pressure of >12 mmHg, and Group 2 comprised of patients with normal FP. The LV and LA volumes were measured at three specific points: the minimum volume (LVES, LAmin), the volume during diastasis (LVdias, LAdias), and the maximum volume (LVED, LAmax). The corresponding LV/LA volume ratios were analysed: end-systole (LVES/LAmax), diastasis (LVdias/LAdias), and end-diastole (LVED/LAmin).

Results

The patients in Group 1 exhibited lower LV/LA volume ratios compared with those in Group 2 (LVES/LAmax 0.44 ± 0.12 vs. 0.60 ± 0.23, P = 0.0032; LVdias/LAdias 1.13 ± 0.30 vs. 1.56 ± 0.49, P = 0.0007; LVED/LAmin 2.71 ± 1.57 vs. 4.44 ± 1.70, P = 0.0004). The LV/LA volume ratios correlated inversely with an increased FP (LVES/LAmax, r = -0.40, P = 0.0033; LVdias/LAdias, r = -0.45, P = 0.0007; LVED/LAmin, r = -0.55, P < 0.0001). Among all the measurements, the LVdias/LAdias ratio demonstrated the highest discriminatory power to distinguish patients with elevated FP from normal FP, with a cut-off value of ≤1.24 [area under the curve (AUC) = 0.822] for the entire group, encompassing both sinus rhythm and atrial fibrillation. For patients in sinus rhythm specifically, the cut-off value was ≤1.28 (AUC = 0.799), with P < 0.0001 for both. The LVdias/LAdias index demonstrated non-inferiority to the E/e' ratio [ΔAUC = 0.159, confidence interval (CI) = -0.020-0.338; P = 0.0809], while surpassing the indices of LA reservoir function (ΔAUC = 0.249, CI = 0.044-0.454; P = 0.0176), LA reservoir strain (ΔAUC = 0.333, CI = 0.149-0.517; P = 0.0004), and LAmax index (ΔAUC = 0.224, CI = 0.043-0.406; P = 0.0152) in diagnosing patients with elevated FP.

Conclusion

The study presents a straightforward and reproducible method for non-invasive estimation of FP using routine TTE in patients with dyspnoea and preserved EF. The LVdias/LAdias index emerges as a promising indicator for identifying elevated FP, demonstrating comparable or even superior performance to established parameters.

Closing the Last Mile Gap in Access to Multimodality Imaging in Rural Settings: Design of the Imaging Core of the Risk Underlying Rural Areas Longitudinal Study

Achieving optimal cardiovascular health in rural populations can be challenging for several reasons including decreased access to care with limited availability of imaging modalities, specialist physicians, and other important health care team members. Therefore, innovative solutions are needed to optimize health care and address cardiovascular health disparities in rural areas. Mobile examination units can bring imaging technology to underserved or remote communities with limited access to health care services. Mobile examination units can be equipped with a wide array of assessment tools and multiple imaging modalities such as computed tomography scanning and echocardiography. The detailed structural assessment of cardiovascular and lung pathology, as well as the detection of extracardiac pathology afforded by computed tomography imaging combined with the functional and hemodynamic assessments acquired by echocardiography, yield deep phenotyping of heart and lung disease for populations historically underrepresented in epidemiological studies. Moreover, by bringing the mobile examination unit to local communities, innovative approaches are now possible including engagement with local professionals to perform these imaging assessments, thereby augmenting local expertise and experience. However, several challenges exist before mobile examination unit-based examinations can be effectively integrated into the rural health care setting including standardizing acquisition protocols, maintaining consistent image quality, and addressing ethical and privacy considerations. Herein, we discuss the potential importance of cardiac multimodality imaging to improve cardiovascular health in rural regions, outline the emerging experience in this field, highlight important current challenges, and offer solutions based on our experience in the RURAL (Risk Underlying Rural Areas Longitudinal) cohort study.

Artificial intelligence methods for improved detection of undiagnosed heart failure with preserved ejection fraction

AIM

Heart failure with preserved ejection fraction (HFpEF) remains under-diagnosed in clinical practice despite accounting for nearly half of all heart failure (HF) cases. Accurate and timely diagnosis of HFpEF is crucial for proper patient management and treatment. In this study, we explored the potential of natural language processing (NLP) to improve the detection and diagnosis of HFpEF according to the European Society of Cardiology (ESC) diagnostic criteria.

METHODS AND RESULTS

In a retrospective cohort study, we used an NLP pipeline applied to the electronic health record (EHR) to identify patients with a clinical diagnosis of HF between 2010 and 2022. We collected demographic, clinical, echocardiographic and outcome data from the EHR. Patients were categorized according to the left ventricular ejection fraction (LVEF). Those with LVEF ≥50% were further categorized based on whether they had a clinician-assigned diagnosis of HFpEF and if not, whether they met the ESC diagnostic criteria. Results were validated in a second, independent centre. We identified 8606 patients with HF. Of 3727 consecutive patients with HF and LVEF ≥50% on echocardiogram, only 8.3% had a clinician-assigned diagnosis of HFpEF, while 75.4% met ESC criteria but did not have a formal diagnosis of HFpEF. Patients with confirmed HFpEF were hospitalized more frequently; however the ESC criteria group had a higher 5-year mortality, despite being less comorbid and experiencing fewer acute cardiovascular events.

CONCLUSIONS

This study demonstrates that patients with undiagnosed HFpEF are an at-risk group with high mortality. It is possible to use NLP methods to identify likely HFpEF patients from EHR data who would likely then benefit from expert clinical review and complement the use of diagnostic algorithms.

Heart Failure with Preserved Ejection Fraction: The Pathophysiological Mechanisms behind the Clinical Phenotypes and the Therapeutic Approach

Heart failure (HF) with preserved ejection fraction (HFpEF) is an increasingly frequent form and is estimated to be the dominant form of HF. On the other hand, HFpEF is a syndrome with systemic involvement, and it is characterized by multiple cardiac and extracardiac pathophysiological alterations. The increasing prevalence is currently reaching epidemic levels, thereby making HFpEF one of the greatest challenges facing cardiovascular medicine today. Compared to HF with reduced ejection fraction (HFrEF), the medical attitude in the case of HFpEF was a relaxed one towards the disease, despite the fact that it is much more complex, with many problems related to the identification of physiopathogenetic mechanisms and optimal methods of treatment. The current medical challenge is to develop effective therapeutic strategies, because patients suffering from HFpEF have symptoms and quality of life comparable to those with reduced ejection fraction, but the specific medication for HFrEF is ineffective in this situation; for this, we must first understand the pathological mechanisms in detail and correlate them with the clinical presentation. Another important aspect of HFpEF is the diversity of patients that can be identified under the umbrella of this syndrome. Thus, before being able to test and develop effective therapies, we must succeed in grouping patients into several categories, called phenotypes, depending on the pathological pathways and clinical features. This narrative review critiques issues related to the definition, etiology, clinical features, and pathophysiology of HFpEF. We tried to describe in as much detail as possible the clinical and biological phenotypes recognized in the literature in order to better understand the current therapeutic approach and the reason for the limited effectiveness. We have also highlighted possible pathological pathways that can be targeted by the latest research in this field.

Comprehensive diagnostic workup in patients with suspected heart failure and preserved ejection fraction

Diagnosis of heart failure with preserved ejection fraction (HFpEF) can be challenging and it could require different tests, some of which are affected by limited availability. Nowadays, considering that new therapies are available for HFpEF and related conditions, a prompt and correct diagnosis is relevant. However, the diagnostic role of biomarker level, imaging tools, score-based algorithms and invasive evaluation, should be based on the strengths and weaknesses of each test. The aim of this review is to help the clinician in diagnosing HFpEF, overcoming the diagnostic uncertainty and disentangling among the different underlying causes, in order to properly treat this kind of patient.