Different Pathophysiology and Outcomes of Heart Failure With Preserved Ejection Fraction Stratified by K-Means Clustering

Mineralocorticoid receptor antagonists and heart failure with preserved ejection fraction: current understanding and future prospects

The mineralocorticoid receptor (MR), part of the steroid hormone receptor subfamily within nuclear hormone receptors, is found in the kidney and various non-epithelial tissues, including the heart and blood vessels. When improperly activated, it can contribute to heart failure processes such as cardiac hypertrophy, fibrosis, stiffening of arteries, inflammation, and oxidative stress. MR antagonists (MRAs) have shown clear clinical benefits in patients with heart failure with reduced ejection fraction (HFrEF). However, in cases of heart failure with preserved ejection fraction (HFpEF), there is considerable diversity due to its complex underlying mechanisms, resulting in conflicting findings regarding the effectiveness of MRAs in relevant studies. The concept of phenomapping presents an encouraging avenue for investigating different intervention targets and novel therapies for HFpEF. Post hoc analysis of the TOPCAT trial identified certain HFpEF phenotypes that responded favorably to spironolactone. Growing clinical and preclinical evidence suggests that non-steroidal MRAs, which exhibit greater receptor selectivity, stronger anti-fibrotic and anti-inflammatory properties, and fewer hormone-related side effects, may emerge as another promising treatment option for HFpEF alongside sodium-glucose co-transporter 2 (SGLT2) inhibitors. This review aims to outline the structural and functional characteristics of MR, discuss the physiological effects of its activation and inhibition, and delve into the potential for personalized MRA therapy based on the concept of HFpEF phenotype.

Development of interpretable machine learning models to predict in-hospital prognosis of acute heart failure patients

AIMS

In recent years, there has been remarkable development in machine learning (ML) models, showing a trend towards high prediction performance. ML models with high prediction performance often become structurally complex and are frequently perceived as black boxes, hindering intuitive interpretation of the prediction results. We aimed to develop ML models with high prediction performance, interpretability, and superior risk stratification to predict in-hospital mortality and worsening heart failure (WHF) in patients with acute heart failure (AHF).

METHODS AND RESULTS

Based on the Kyoto Congestive Heart Failure registry, which enrolled 4056 patients with AHF, we developed prediction models for in-hospital mortality and WHF using information obtained on the first day of admission (demographics, physical examination, blood test results, etc.). After excluding 16 patients who died on the first or second day of admission, the original dataset (n = 4040) was split 4:1 into training (n = 3232) and test datasets (n = 808). Based on the training dataset, we developed three types of prediction models: (i) the classification and regression trees (CART) model; (ii) the random forest (RF) model; and (iii) the extreme gradient boosting (XGBoost) model. The performance of each model was evaluated using the test dataset, based on metrics including sensitivity, specificity, area under the receiver operating characteristic curve (AUC), Brier score, and calibration slope. For the complex structure of the XGBoost model, we performed SHapley Additive exPlanations (SHAP) analysis, classifying patients into interpretable clusters. In the original dataset, the proportion of females was 44.8% (1809/4040), and the average age was 77.9 ± 12.0. The in-hospital mortality rate was 6.3% (255/4040) and the WHF rate was 22.3% (900/4040) in the total study population. In the in-hospital mortality prediction, the AUC for the XGBoost model was 0.816 [95% confidence interval (CI): 0.815-0.818], surpassing the AUC values for the CART model (0.683, 95% CI: 0.680-0.685) and the RF model (0.755, 95% CI: 0.753-0.757). Similarly, in the WHF prediction, the AUC for the XGBoost model was 0.766 (95% CI: 0.765-0.768), outperforming the AUC values for the CART model (0.688, 95% CI: 0.686-0.689) and the RF model (0.713, 95% CI: 0.711-0.714). In the XGBoost model, interpretable clusters were formed, and the rates of in-hospital mortality and WHF were similar among each cluster in both the training and test datasets.

CONCLUSIONS

The XGBoost models with SHAP analysis provide high prediction performance, interpretability, and reproducible risk stratification for in-hospital mortality and WHF for patients with AHF.

Multifaced risk factors and clinical impact of a deep Y descent in patients with heart failure irrespective of RV-PA coupling

Background

A deep Y descent in the jugular venous pulse (JVP) is associated with diseases such as a decrease in right ventricular (RV) preload reserve. The present study investigated the relationship between RV-pulmonary arterial (PA) coupling and a deep Y descent, examined risk factors for a deep Y descent and clarified whether a deep Y descent was an independent risk factor for cardiac events irrespective of RV-PA coupling in patients with heart failure (HF).

Methods

We enrolled 350 patients with HF who underwent echocardiography and JVP examination. A deep Y descent was identified by a deeper 'Y' descent than 'X' descent in the JVP waveform. We defined cardiac events of HF as follows: sudden death, death from HF, the emergent infusion of loop diuretics, or hospitalization for decompensated HF.

Results and Conclusions

A deep Y descent and cardiac events were observed in 129 and 83 patients, respectively. The prevalence of a deep Y descent increased with decreases in the tricuspid annular plane systolic excursion (TAPSE)/systolic pulmonary arterial pressure (SPAP) ratio. Not only the TAPSE/SPAP ratio (odds ratio,0.756 per0.1 mm/mmHg, 95 %confidence interval [CI], 0.660-0.866, p < 0.001), but also age, atrial fibrillation, and the use of beta-blockers were independent factors for a deep Y descent in multivariate logistic model. Multivariate Cox hazard model demonstrated that a deep Y descent was for cardiac events in patients with HF (Hazard ratio,2.682, 95 %CI, 1.599-4.497, p < 0.001) irrespective of the TAPSE/SPAP ratio. The development of therapeutic strategies based on central venous waveform may be needed for patients with HF.

Device therapy for patients with atrial fibrillation and heart failure with preserved ejection fraction

Device therapy is a nonpharmacological approach that presents a crucial advancement for managing patients with atrial fibrillation (AF) and heart failure with preserved ejection fraction (HFpEF). This review investigated the impact of device-based interventions and emphasized their potential for optimizing treatment for this complex patient demographic. Cardiac resynchronization therapy, augmented by atrioventricular node ablation with His-bundle pacing or left bundle-branch pacing, is effective for enhancing cardiac function and establishing atrioventricular synchrony. Cardiac contractility modulation and vagus nerve stimulation represent novel strategies for increasing myocardial contractility and adjusting the autonomic balance. Left ventricular expanders have demonstrated short-term benefits in HFpEF patients but require more investigation for long-term effectiveness and safety, especially in patients with AF. Research gaps regarding complications arising from left ventricular expander implantation need to be addressed. Device-based therapies for heart valve diseases, such as transcatheter aortic valve replacement and transcatheter edge-to-edge repair, show promise for patients with AF and HFpEF, particularly those with mitral or tricuspid regurgitation. Clinical evaluations show that these device therapies lessen AF occurrence, improve exercise tolerance, and boost left ventricular diastolic function. However, additional studies are required to perfect patient selection criteria and ascertain the long-term effectiveness and safety of these interventions. Our review underscores the significant potential of device therapy for improving the outcomes and quality of life for patients with AF and HFpEF.

Right ventricular diastolic dysfunction worsens clinical outcomes in Japanese patients with heart failure

Background

Heart failure (HF) is a rapidly growing public health issue in super aging societies, such as Japan. Right HF is common in older patients. Therefore, the present study investigated the relationship between right ventricular diastolic function and poor clinical outcomes in patients with HF.

Methods

We retrospectively enrolled 387 Japanese HF patients. All data were obtained from our echocardiographic and jugular venous pulse (JVP) databases and medical records. A less-distensible right ventricle (RV) was identified by a deeper 'Y' descent than 'X' descent in the JVP waveform. We defined cardiac events of HF as follows: sudden death, death from HF, emergent infusion of loop diuretics, or hospitalization for deterioration of HF. Comparisons between patients with and without cardiac events and a multivariate analysis of cardiac events were performed.

Results

Eighty-five patients had cardiac events. Left ventricular ejection fraction (LVEF) was lower, average mitral E/e' and the prevalence of a less-distensible RV were higher, and tricuspid annular plane systolic excursion was shorter in patients with than in those without cardiac events (median55vs65, p < 0.001; median15vs11, p < 0.001; 64 %vs27%, p < 0.001; median17vs20, p < 0.001, respectively). In a multivariate Cox proportional hazard model, LVEF and a less-distensible RV were independent risk factors for cardiac events (hazard ratio [HR]:0.983 per 1 % increase, p = 0.048; HR:3.150, p < 0.001, respectively). The event-free rate was the lowest for patients with LVEF < 50 % and a less-distensible RV (p for trend < 0.001).

Conclusions

When right ventricular diastolic function is impaired and irreversible, Japanese patients with HF may become intractable regardless of LVEF.

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.

Phenomapping in heart failure with preserved ejection fraction: insights, limitations, and future directions

Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous entity with complex pathophysiology and manifestations. Phenomapping is the process of applying statistical learning techniques to patient data to identify distinct subgroups based on patterns in the data. Phenomapping has emerged as a technique with potential to improve the understanding of different HFpEF phenotypes. Phenomapping efforts have been increasing in HFpEF over the past several years using a variety of data sources, clinical variables, and statistical techniques. This review summarizes methodologies and key takeaways from these studies, including consistent discriminating factors and conserved HFpEF phenotypes. We argue that phenomapping results to date have had limited implications for clinical care and clinical trials, given that the phenotypes, as currently described, are not reliably identified in each study population and may have significant overlap. We review the inherent limitations of aggregating and utilizing phenomapping results. Lastly, we discuss potential future directions, including using phenomapping to optimize the likelihood of clinical trial success or to drive discovery in mechanisms of the disease process of HFpEF.

Cardiac Structure and Function Phenogroups and Risk of Incident Heart Failure (from the Multi-ethnic Study of Atherosclerosis)

Indices of cardiac structure and function, such as left ventricular (LV) mass and ejection fraction, have been associated with risk of incident heart failure (HF), but the clinical relevance of data-driven grouping of a comprehensive set of cardiac parameters is unclear. In Multi-Ethnic Study of Atherosclerosis participants, latent class analysis was applied in the sample stratified by gender to define phenogroups on the basis of cardiovascular magnetic resonance imaging parameters of right ventricular and LV structure and function at baseline. Cox proportional hazard models in gender-stratified analyses were used to assess the association between phenogroup membership and risk of HF subtypes adjusting for potential confounders. In the 4,204 participants (mean age 61 ± 10 years, 53% women), the mean follow-up time was 14 ± 4 years for men and 15 ± 4 years for women. For both genders, 4 distinct phenogroups were identified: (1) ideal cardiac mechanics; (2) higher output/hypertrophied LV; (3) impaired ejection fraction/dilated LV; and (4) higher output/hyperdynamic (LV). Men in phenogroups 4 (hazard ratio [HR] 2.91, 95% confidence interval [CI] 1.60 to 5.31, p = 0.0005), 3 (HR 3.52, 95% CI 1.90 to 6.53, p <0.0001), and 2 (HR 3.49, 95% CI 1.94 to 6.28, p <0.0001) had higher rates of incident HF than did men in phenogroup 1, in fully adjusted models. No significant associations were found between phenogroup membership and incident HF in women. In conclusion, phenogroup membership based on cardiac structure and function in men was significantly associated with incident HF. Integration of cardiac magnetic resonance imaging variables may help identify differential risk for HF in men.

The future of heart failure with preserved ejection fraction : Deep phenotyping for targeted therapeutics

Heart failure (HF) with preserved ejection fraction (HFpEF) is a multi-organ, systemic syndrome that involves multiple cardiac and extracardiac pathophysiologic abnormalities. Because HFpEF is a heterogeneous syndrome and resistant to a "one-size-fits-all" approach it has proven to be very difficult to treat. For this reason, several research groups have been working on methods for classifying HFpEF and testing targeted therapeutics for the HFpEF subtypes identified. Apart from conventional classification strategies based on comorbidity, etiology, left ventricular remodeling, and hemodynamic subtypes, researchers have been combining deep phenotyping with innovative analytical strategies (e.g., machine learning) to classify HFpEF into therapeutically homogeneous subtypes over the past few years. Despite the growing excitement for such approaches, there are several potential pitfalls to their use, and there is a pressing need to follow up on data-driven HFpEF subtypes in order to determine their underlying mechanisms and molecular basis. Here we provide a framework for understanding the phenotype-based approach to HFpEF by reviewing (1) the historical context of HFpEF; (2) the current HFpEF paradigm of comorbidity-induced inflammation and endothelial dysfunction; (3) various methods of sub-phenotyping HFpEF; (4) comorbidity-based classification and treatment of HFpEF; (5) machine learning approaches to classifying HFpEF; (6) examples from HFpEF clinical trials; and (7) the future of phenomapping (machine learning and other advanced analytics) for the classification of HFpEF.

Identifying novel subgroups in heart failure patients with unsupervised machine learning: A scoping review

Background

Heart failure is currently divided into three main forms, HFrEF, HFpEF, and HFmrEF, but its etiology is diverse and highly heterogeneous. Many studies reported a variety of novel subgroups in heart failure patients, with unsupervised machine learning methods. The aim of this scoping review is to provide insights into how these techniques can diagnose and manage HF faster and better, thus providing direction for future research and facilitating its routine use in clinical practice.

Methods

The review was performed following PRISMA-SCR guideline. We searched the PubMed database for eligible publications. Studies were included if they defined new subgroups in HF patients using clustering analysis methods, and excluded if they are (1) Reviews, commentary, or editorials, (2) Studies not about defining new sub-types, or (3) Studies not using unsupervised algorithms. All study screening and data extraction were conducted independently by two investigators and narrative integration of data extracted from included studies was performed.

Results

Of the 498 studies identified, 47 were included in the analysis. Most studies (61.7%) were published in 2020 and later. The largest number of studies (46.8%) coming from the United States, and most of the studies were authored and included in the same country. The most commonly used machine learning method was hierarchical cluster analysis (46.8%), the most commonly used cluster variable type was comorbidity (61.7%), and the least used cluster variable type was genomics (12.8%). Most of the studies used data sets of less than 500 patients (48.9%), and the sample size had negative correlation with the number of clustering variables. The majority of studies (85.1%) assessed the association between cluster grouping and at least one outcomes, with death and hospitalization being the most commonly used outcome measures.

Conclusion

This scoping review provides an overview of recent studies proposing novel HF subgroups based on clustering analysis. Differences were found in study design, study population, clustering methods and variables, and outcomes of interests, and we provided insights into how these studies were conducted and identify the knowledge gaps to guide future research.

The Effect of Renal Denervation on Cardiac Diastolic Function in Patients with Hypertension and Paroxysmal Atrial Fibrillation

Objective

Renal artery denervation (RDN) can treat hypertension and paroxysmal atrial fibrillation (PAF). Hypertension and PAF can affect cardiac diastolic function. The study aimed to evaluate the effect of RDN on cardiac diastolic function in patients with refractory hypertension and PAF.

Methods

190 consecutive patients with hypertension and PAF were recruited. The levels of NT-proBNP and metrics of echocardiography were measured before and after RDN in patients with refractory hypertension and PAF. The 190 patients were divided into the decreasing HR and nondecreasing HR group, the decreasing MAP and nondecreasing MAP group, the HFPEF group, and the normal diastolic function group, respectively.

Results

Before RDN, the indices about cardiac diastolic function were out of the normal range. After RDN, the diastolic function improved in the indices of NT-proBNP, E/e′, e′. The diastolic function about the indices of NT-proBNP, E/e′, e′ was improved in the decreasing HR group, the decreasing mean arterial pressure (MAP) group, and the HFPEF group, correspondingly compared to the nondecreasing HR group, the non-decreasing MAP group, and the preoperative normal diastolic function group. In the multivariate analysis, the MAP and HR were the only two indicators significantly associated with the improvement of diastolic function.

Conclusion

RDN could improve the diastolic function in patients with refractory hypertension and PAF. Patients with HFPEF could receive benefits through RDN. It was speculated that RDN improved the diastolic function mainly through decreasing HR and MAP.

Comparison of Unsupervised Machine Learning Approaches for Cluster Analysis to Define Subgroups of Heart Failure with Preserved Ejection Fraction with Different Outcomes

Heart failure with preserved ejection (HFpEF) is a heterogenous condition affecting nearly half of all patients with heart failure (HF). Artificial intelligence methodologies can be useful to identify patient subclassifications with important clinical implications. We sought a comparison of different machine learning (ML) techniques and clustering capabilities in defining meaningful subsets of patients with HFpEF. Three unsupervised clustering strategies, hierarchical clustering, K-prototype, and partitioning around medoids (PAM), were used to identify distinct clusters in patients with HFpEF, based on a wide range of demographic, laboratory, and clinical parameters. The study population had a median age of 77 years, with a female majority, and moderate diastolic dysfunction. Hierarchical clustering produced six groups but two were too small (two and seven cases) to be clinically meaningful. The K-prototype methods produced clusters in which several clinical and biochemical features did not show statistically significant differences and there was significant overlap between the clusters. The PAM methodology provided the best group separations and identified six mutually exclusive groups (HFpEF1-6) with statistically significant differences in patient characteristics and outcomes. Comparison of three different unsupervised ML clustering strategies, hierarchical clustering, K-prototype, and partitioning around medoids (PAM), was performed on a mixed dataset of patients with HFpEF containing clinical and numerical data. The PAM method identified six distinct subsets of patients with HFpEF with different long-term outcomes or mortality. By comparison, the two other clustering algorithms, the hierarchical clustering and K-prototype, were less optimal.

Naive Bayes Prediction of the Development of Cardiac Events in Heart Failure With Preserved Ejection Fraction in an Outpatient Clinic - Beyond B-Type Natriuretic Peptide

BACKGROUND

The heterogeneity of B-type natriuretic peptide (BNP) levels among individuals with heart failure and preserved ejection fraction (HFpEF) makes predicting the development of cardiac events difficult. This study aimed at creating high-performance Naive Bayes (NB) classifiers, beyond BNP, to predict the development of cardiac events over a 3-year period in individual outpatients with HFpEF.

METHODS AND RESULTS

We retrospectively enrolled 234 outpatients with HFpEF who were followed up for 3 years. Parameters with a coefficient of association ≥0.1 for cardiac events were applied as features of classifiers. We used the step forward method to find a high-performance model with the maximum area under the receiver operating characteristics curve (AUC). A 10-fold cross-validation method was used to validate the generalization performance of the classifiers. The mean kappa statistics, AUC, sensitivity, specificity, and accuracy were evaluated and compared between classifiers learning multiple factors and only the BNP. Kappa statistics, AUC, and sensitivity were significantly higher for NB classifiers learning 13 features than for those learning only BNP (0.69±0.14 vs. 0.54±0.12 P=0.024, 0.94±0.03 vs. 0.84±0.05 P<0.001, 85±8% vs. 64±20% P=0.006, respectively). The specificity and accuracy were similar.

CONCLUSIONS

We created high-performance NB classifiers for predicting the development of cardiac events in individual outpatients with HFpEF. Our NB classifiers may be useful for providing precision medicine for these patients.

The Impact of Deep Y Descent on Hemodynamics in Patients With Heart Failure and Preserved Left Ventricular Systolic Function

Background: Influence of right ventricular diastolic function on the hemodynamics of heart failure (HF). We aimed to clarify the hemodynamic features of deep Y descent in the right atrial pressure waveform in patients with HF and preserved left ventricular systolic function.

Methods: In total, 114 consecutive inpatients with HF who had preserved left ventricular systolic function (left ventricular ejection fraction ≥ 50%) and right heart catheterization were retrospectively enrolled in this study. The patients were divided into two groups according to right atrial pressure waveform, and those with Y descent deeper than X descent in the right atrial pressure waveform were assigned to the deep Y descent group. We enrolled another seven patients (two men, five women; mean age, 87 ± 6) with HF and preserved ejection fraction, and implanted a pacemaker to validate the results of this study.

Results: The patients with deep Y descent had a higher rate of atrial fibrillation, higher right atrial pressure and mean pulmonary arterial pressure, and lower stroke volume and cardiac index than those with normal Y descent (76 vs. 7% p < 0.001, median 8 vs. 5 mmHg p = 0.001, median 24 vs. 21 mmHg p = 0.036, median 33 vs. 43 ml/m²p < 0.001, median 2.2 vs. 2.7 L/m², p < 0.001). Multiple linear regression revealed a negative correlation between stroke volume index and pulmonary vascular resistance index (wood unit*m²) only in the patients with deep Y descent (estimated regression coefficient: −1.281, p = 0.022). A positive correlation was also observed between cardiac index and heart rate in this group (r = 0.321, p = 0.038). In the other seven patients, increasing the heart rate (from median 60 to 80/min, p = 0.001) significantly reduced the level of BNP (from median 419 to 335 pg/ml, p = 0.005).

Conclusions: The hemodynamics of patients with HF with deep Y descent and preserved left ventricular systolic function resembled right ventricular restrictive physiology. Optimizing the heart rate may improve hemodynamics in these patients.

Disease evolution and risk-based disease trajectories in congestive heart failure patients

Congestive Heart Failure (CHF) is among the most prevalent chronic diseases worldwide, and is commonly associated with comorbidities and complex health conditions. Consequently, CHF patients are typically hospitalized frequently, and are at a high risk of premature death. Early detection of an envisaged patient disease trajectory is crucial for precision medicine. However, despite the abundance of patient-level data, cardiologists currently struggle to identify disease trajectories and track the evolution patterns of the disease over time, especially in small groups of patients with specific disease subtypes. The present study proposed a five-step method that allows clustering CHF patients, detecting cluster similarity, and identifying disease trajectories, and promises to overcome the existing difficulties. This work is based on a rich dataset of patients' records spanning ten years of hospital visits. The dataset contains all the health information documented in the hospital during each visit, including diagnoses, lab results, clinical data, and demographics. It utilizes an innovative Cluster Evolution Analysis (CEA) method to analyze the complex CHF population where each subject is potentially associated with numerous variables. We have defined sub-groups for mortality risk levels, which we used to characterize patients' disease evolution by refined data clustering in three points in time over ten years, and generating patients' migration patterns across periods. The results elicited 18, 23, and 25 clusters respective to the first, second, and third visits, uncovering clinically interesting small sub-groups of patients. In the following post-processing stage, we identified meaningful patterns. The analysis yielded fine-grained patient clusters divided into several finite risk levels, including several small-sized groups of high-risk patients. Significantly, the analysis also yielded longitudinal patterns where patients' risk levels changed over time. Four types of disease trajectories were identified: decline, preserved state, improvement, and mixed-progress. This stage is a unique contribution of the work. The resulting fine partitioning and longitudinal insights promise to significantly assist cardiologists in tailoring personalized interventions to improve care quality. Cardiologists could utilize these results to glean previously undetected relationships between symptoms and disease evolution that would allow a more informed clinical decision-making and effective interventions.

Machine Learning Augmented Echocardiography for Diastolic Function Assessment

Cardiac diastolic dysfunction is prevalent and is a diagnostic criterion for heart failure with preserved ejection fraction—a burgeoning global health issue. As gold-standard invasive haemodynamic assessment of diastolic function is not routinely performed, clinical guidelines advise using echocardiography measures to determine the grade of diastolic function. However, the current process has suboptimal accuracy, regular indeterminate classifications and is susceptible to confounding from comorbidities. Advances in artificial intelligence in recent years have created revolutionary ways to evaluate and integrate large quantities of cardiology data. Imaging is an area of particular strength for the sub-field of machine-learning, with evidence that trained algorithms can accurately discern cardiac structures, reliably estimate chamber volumes, and output systolic function metrics from echocardiographic images. In this review, we present the emerging field of machine-learning based echocardiographic diastolic function assessment. We summarise how machine-learning has made use of diastolic parameters to accurately differentiate pathology, to identify novel phenotypes within diastolic disease, and to grade diastolic function. Perspectives are given about how these innovations could be used to augment clinical practice, whilst areas for future investigation are identified.