Non-invasive technologies for heart failure, systolic and diastolic dysfunction modeling: a scoping review

Mitral Valve Transcatheter Edge-to-Edge Repair (MV-TEER) in Patients with Secondary Mitral Regurgitation Improves Hemodynamics, Enhances Renal Function, and Optimizes Quality of Life in Patients with Advanced Renal Insufficiency

BACKGROUND/OBJECTIVES

Secondary mitral regurgitation (MR) is a common valvular heart disease burdening the prognosis of patients with co-existing chronic heart failure. Transcatheter edge-to-edge mitral valve repair (MV-TEER) is a minimally invasive treatment option for high-risk patients. However, the effects of MV-TEER on expanded hemodynamics, tissue perfusion, and quality of life, particularly in patients with advanced renal failure, remain underexplored.

METHODS

This prospective, single-center study evaluated the impact of MV-TEER on hemodynamics, renal function, and quality of life in 45 patients with severe MR. Non-invasive bioimpedance monitoring with NICaS® was used to assess hemodynamics pre- and 3-5 days post-procedure. Quality of life was assessed using the EQ-5D-3L questionnaire before and 3 months post-procedure. For further analysis, patients were divided into subgroups based on the estimated baseline glomerular filtration rate (eGFR < 35 mL/min vs. eGFR ≥ 35 mL/min).

RESULTS

A significant reduction in systemic vascular resistance (SVR; p = 0.003) and an increase in eGFR (p = 0.03) were observed in the entire cohort after MV-TEER, indicating improved tissue perfusion. Notably, particularly patients with eGFR < 35 mL/min showed a significant increase in cardiac output (CO; p = 0.035), cardiac index (CI; p = 0.031), and eGFR (p = 0.018), as well as a reduction in SVR (p = 0.007). Consistent with these findings, quality of life significantly improved, with the EQ-5D-3L index and EQ-VAS score increasing from 0.44 to 0.66 (p < 0.001) and from 51.7% to 62.9% (p < 0.001).

Prediction of heart failure patients with distinct left ventricular ejection fraction levels using circadian ECG features and machine learning

Heart failure (HF) encompasses a diverse clinical spectrum, including instances of transient HF or HF with recovered ejection fraction, alongside persistent cases. This dynamic condition exhibits a growing prevalence and entails substantial healthcare expenditures, with anticipated escalation in the future. It is essential to classify HF patients into three groups based on their ejection fraction: reduced (HFrEF), mid-range (HFmEF), and preserved (HFpEF), such as for diagnosis, risk assessment, treatment choice, and the ongoing monitoring of heart failure. Nevertheless, obtaining a definitive prediction poses challenges, requiring the reliance on echocardiography. On the contrary, an electrocardiogram (ECG) provides a straightforward, quick, continuous assessment of the patient's cardiac rhythm, serving as a cost-effective adjunct to echocardiography. In this research, we evaluate several machine learning (ML)-based classification models, such as K-nearest neighbors (KNN), neural networks (NN), support vector machines (SVM), and decision trees (TREE), to classify left ventricular ejection fraction (LVEF) for three categories of HF patients at hourly intervals, using 24-hour ECG recordings. Information from heterogeneous group of 303 heart failure patients, encompassing HFpEF, HFmEF, or HFrEF classes, was acquired from a multicenter dataset involving both American and Greek populations. Features extracted from ECG data were employed to train the aforementioned ML classification models, with the training occurring in one-hour intervals. To optimize the classification of LVEF levels in coronary artery disease (CAD) patients, a nested cross-validation approach was employed for hyperparameter tuning. HF patients were best classified using TREE and KNN models, with an overall accuracy of 91.2% and 90.9%, and average area under the curve of the receiver operating characteristics (AUROC) of 0.98, and 0.99, respectively. Furthermore, according to the experimental findings, the time periods of midnight-1 am, 8-9 am, and 10-11 pm were the ones that contributed to the highest classification accuracy. The results pave the way for creating an automated screening system tailored for patients with CAD, utilizing optimal measurement timings aligned with their circadian cycles.

The Impact of Positive Inotropic Therapy on Hemodynamics and Organ Function in Acute Heart Failure: A Differentiated View

BACKGROUND

Little is known about the impact of treatment with inotropic drugs on the interaction of hemodynamics, biomarkers, and end-organ function in patients with acute decompensated heart failure (HF) of different origins and heart rhythms.

METHODS

Fifty patients with different causes of acute decompensated HF (dilated cardiomyopathy DCM, ischemic cardiomyopathy ICM, atrial fibrillation AF, sinus rhythm/pacemaker lead rhythm SR/PM) were treated with dobutamine or levosimendan. Non-invasive hemodynamics, biomarkers, and parameters of renal organ function were evaluated at hospital admission and after myocardial recompensation (day 5 to 7).

RESULTS

Twenty-seven patients with ICM and twenty-three patients with DCM were included. Thirty-nine patients were treated with dobutamine and eleven with levosimendan. Sixteen were accompanied by persistent AF and thirty-four presented either with SR or PM. In the overall cohort, body weight and biomarkers (NT-proBNP/ST2) significantly decreased. GFR significantly increased during therapy with either dobutamine or levosimendan. However, hemodynamic parameters seem to be only improved in patients with DCM, in the levosimendan sub-group, and in patients with SR/PM.

CONCLUSION

Patients with acute decompensated HF benefit from positive inotropic therapy during short-term follow-ups. In particular, patients with DCM, those after levosimendan therapy and those with SR/PM, seem to benefit most from inotropic therapy.

Investigating automated regression models for estimating left ventricular ejection fraction levels in heart failure patients using circadian ECG features

Heart Failure (HF) significantly impacts approximately 26 million people worldwide, causing disruptions in the normal functioning of their hearts. The estimation of left ventricular ejection fraction (LVEF) plays a crucial role in the diagnosis, risk stratification, treatment selection, and monitoring of heart failure. However, achieving a definitive assessment is challenging, necessitating the use of echocardiography. Electrocardiogram (ECG) is a relatively simple, quick to obtain, provides continuous monitoring of patient's cardiac rhythm, and cost-effective procedure compared to echocardiography. In this study, we compare several regression models (support vector machine (SVM), extreme gradient boosting (XGBOOST), gaussian process regression (GPR) and decision tree) for the estimation of LVEF for three groups of HF patients at hourly intervals using 24-hour ECG recordings. Data from 303 HF patients with preserved, mid-range, or reduced LVEF were obtained from a multicentre cohort (American and Greek). ECG extracted features were used to train the different regression models in one-hour intervals. To enhance the best possible LVEF level estimations, hyperparameters tuning in nested loop approach was implemented (the outer loop divides the data into training and testing sets, while the inner loop further divides the training set into smaller sets for cross-validation). LVEF levels were best estimated using rational quadratic GPR and fine decision tree regression models with an average root mean square error (RMSE) of 3.83% and 3.42%, and correlation coefficients of 0.92 (p<0.01) and 0.91 (p<0.01), respectively. Furthermore, according to the experimental findings, the time periods of midnight-1 am, 8-9 am, and 10-11 pm demonstrated to be the lowest RMSE values between the actual and predicted LVEF levels. The findings could potentially lead to the development of an automated screening system for patients with coronary artery disease (CAD) by using the best measurement timings during their circadian cycles.