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

Al Younis, Sona M.; Hadjileontiadis, Leontios J.; Stefanini, Cesare; Khandoker, Ahsan H.

doi:10.3389/fbioe.2023.1261022

Cited by 5 publications

(4 citation statements)

References 70 publications

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“…NICaS ® can assess cardiac function and provide information on several parameters, like cardiac output (CO), cardiac index (CI), and systemic vascular resistance (SVR). NICaS ® -measurement procedures and validation studies compared to Swan-Ganz-and PICCO ® -catheterization techniques were reported recently [14][15][16][17][18].…”

Section: Nicas ® Device and Proceduresmentioning

confidence: 99%

“…Intensified clinical and biomarkerbased patient monitoring and early re-evaluation of therapeutic strategies are crucial for therapy management and can favorably influence outcomes. Therefore, supporting straightforward diagnostic tools, such as non-invasive bedside bio-impedance monitoring (NICaS ® NIMedical, Israel Advanced Technology Industries, Hertzliya Pituach 4676672, Israel), provides an accurate and approved method to obtaining hemodynamic parameters in acute and chronic HF [14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

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

Cheko,

Patsalis,

Kreutz

et al. 2023

JPM

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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.

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Section: Nicas ® Device and Proceduresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Cheko,

Patsalis,

Kreutz

et al. 2023

JPM

View full text Add to dashboard Cite

show abstract

“…Numerous cardiovascular conditions, such as atrial fibrillation, myocardial infarction, congestive heart failure, and premature contractions in the atria or ventricles can be identified through the analysis of ECG data [20][21][22][23][24][25]. As a result, the automatic and precise analysis of ECG data has emerged as a prominent area of study, particularly with the utilization of artificial intelligence (AI) tools.…”

Section: Introductionmentioning

confidence: 99%

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

Al Younis,

Hadjileontiadis,

Khandoker

et al. 2024

PLoS ONE

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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.

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“…ECG is one of the most widely used non-invasive diagnostic tools for tracking the physiological activities of the heart throughout time. Many cardiovascular disorders, including atrial fibrillation, myocardial infarction, premature contractions of the ventricles or atria, and congestive heart failure, can be diagnosed with the help of ECG data [20][21][22][23][24][25]. Consequently, automatic and accurate ECG data analysis has become a popular study area, especially using artificial intelligence (AI) tools.…”

Section: Introductionmentioning

confidence: 99%

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

Al Younis,

Hadjileontiadis,

Al Shehhi

et al. 2023

PLoS ONE

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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.

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

Cited by 5 publications

References 70 publications

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

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

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

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

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