Cardiac Magnetic Resonance Quantification of Structure-Function Relationships in Heart Failure

Accurate Modeling of Ejection Fraction and Stroke Volume With Mobile Phone Auscultation: Prospective Case-Control Study

BACKGROUND

Heart failure (HF) contributes greatly to morbidity, mortality, and health care costs worldwide. Hospital readmission rates are tracked closely and determine federal reimbursement dollars. No current modality or technology allows for accurate measurement of relevant HF parameters in ambulatory, rural, or underserved settings. This limits the use of telehealth to diagnose or monitor HF in ambulatory patients.

OBJECTIVE

This study describes a novel HF diagnostic technology using audio recordings from a standard mobile phone.

METHODS

This prospective study of acoustic microphone recordings enrolled convenience samples of patients from 2 different clinical sites in 2 separate areas of the United States. Recordings were obtained at the aortic (second intercostal) site with the patient sitting upright. The team used recordings to create predictive algorithms using physics-based (not neural networks) models. The analysis matched mobile phone acoustic data to ejection fraction (EF) and stroke volume (SV) as evaluated by echocardiograms. Using the physics-based approach to determine features eliminates the need for neural networks and overfitting strategies entirely, potentially offering advantages in data efficiency, model stability, regulatory visibility, and physical insightfulness.

RESULTS

Recordings were obtained from 113 participants. No recordings were excluded due to background noise or for any other reason. Participants had diverse racial backgrounds and body surface areas. Reliable echocardiogram data were available for EF from 113 patients and for SV from 65 patients. The mean age of the EF cohort was 66.3 (SD 13.3) years, with female patients comprising 38.3% (43/113) of the group. Using an EF cutoff of ≤40% versus >40%, the model (using 4 features) had an area under the receiver operating curve (AUROC) of 0.955, sensitivity of 0.952, specificity of 0.958, and accuracy of 0.956. The mean age of the SV cohort was 65.5 (SD 12.7) years, with female patients comprising 34% (38/65) of the group. Using a clinically relevant SV cutoff of <50 mL versus >50 mL, the model (using 3 features) had an AUROC of 0.922, sensitivity of 1.000, specificity of 0.844, and accuracy of 0.923. Acoustics frequencies associated with SV were observed to be higher than those associated with EF and, therefore, were less likely to pass through the tissue without distortion.

CONCLUSIONS

This work describes the use of mobile phone auscultation recordings obtained with unaltered cellular microphones. The analysis reproduced the estimates of EF and SV with impressive accuracy. This technology will be further developed into a mobile app that could bring screening and monitoring of HF to several clinical settings, such as home or telehealth, rural, remote, and underserved areas across the globe. This would bring high-quality diagnostic methods to patients with HF using equipment they already own and in situations where no other diagnostic and monitoring options exist.

Evolving Applications of Echocardiography in the Evaluation of Left Atrial and Right Ventricular Strain

PURPOSE OF REVIEW

Speckle-tracking echocardiography (STE) can assess myocardial motion in non-LV chambers-including assessment of left atrial (LA) and right ventricular (RV) strain. This review seeks to highlight the diagnostic, prognostic, and clinical significance of these parameters in heart failure, atrial fibrillation (AF), diastolic dysfunction, pulmonary hypertension (PH), tricuspid regurgitation, and heart transplant recipients.

RECENT FINDINGS

Impaired LA strain reflects worse LV diastolic function in individuals with and without HF, and this is associated with decreased exercise capacity. Initiating treatments targeting these functional aspects may enhance exercise capacity and potentially prevent heart failure (HF). Impaired LA strain also identifies patients with a high risk of AF, and this recognition may lead to preventive strategies. Impaired RV strain has significant clinical and prognostic implications across various clinical scenarios, including HF, PH, tricuspid regurgitation, or in heart transplant recipients. STE should not be limited to the assessment of deformation of the LV myocardium. The use of LA and RV strain is supported by a substantial evidence base, and these parameters should be used more widely.

Prognostic value of ventricle epicardial fat volume by cardiovascular magnetic resonance in chronic heart failure

The purpose of this study is to explore the prognostic values of ventricle epicardial fat volume (EFV) calculated by cardiac magnetic resonance in patients with chronic heart failure (CHF). A total of 516 patients with CHF (left ventricular ejection fraction ≤ 50%) were recruited, and 136 (26.4%) of whom experienced major adverse cardiovascular events (MACE) within median follow-up of 24 months. The target marker-EFV was found to be associated with MACE in both univariate and multivariable analysis adjusted for various clinical variables (p < 0.01), regardless as a continuous variable and categorized by X-tile program. EFV also showed promising predictive ability, with an area under the curve of 0.612, 0.618, and 0.687 for the prediction of 1-year, 2-year, and 3-year MACE, respectively. In conclusion, EFV could be a useful prognostic marker for CHF patients, helping to identify individuals at greater risk of MACE.

The Role of MR Assessments of Cardiac Morphology, Function, and Tissue Characteristics on Exercise Capacity in Well-Functioning Older Adults

BACKGROUND

The relationship between resting cardiac indices and exercise capacity in older adults was still not well understood. New developments in cardiac magnetic resonance imaging (MRI) enable a much fuller assessment of cardiac characteristics.

PURPOSE/HYPOTHESIS

To assess the association between exercise capacity and specific aspects of resting cardiac structure, function, and tissue.

STUDY TYPE

Cross-sectional study.

POPULATION

A total of 112 well-functioning older adults (mean age 69 years, 52 men).

FIELD STRENGTH/SEQUENCE

All participants underwent 3.0 T MRI, using scan protocols including balanced steady-state free precession cine sequence, modified look-locker inversion recovery, and T2-prepared single-shot balanced steady-state free precession.

ASSESSMENT

Demographic and geriatric characteristics were collected. Blood samples were assayed for lipid and glucose related biomarkers. All participants performed a symptom-limited cardiopulmonary exercise test to achieve peakVO₂ . Cardiac MRI parameters were measured with semi-automatic software by S.Y., an 18-year experienced radiologist.

STATISTICAL TESTS

Demographic, geriatric characteristics and MR measurements were compared among quartiles of peakVO2, with different methods according to the data type. Spearman's partial correlation and least absolute shrinkage selection operator regression were performed to select significant MR features associated with peakVO₂ . Mediation effect analysis was conducted to test any indirect connection between age and peakVO₂ . A two-sided P value of <0.05 was defined statistical significance.

RESULTS

Epicardial fat volume, left atrial volume indexed to height, right ventricular end-systolic volume indexed to body surface area and global circumferential strain (GCS) were correlated with peakVO₂ (regression coefficients were -0.040, -0.093, 0.127, and 0.408, respectively). Mediation analysis showed that the total effect of peakVO₂ change was 43.6% from the change of age. The proportion of indirect effect from epicardial fat volume and GCS were 11.8% and 15.1% in total effect, respectively.

DATA CONCLUSION

PeakVO₂ was associated with epicardial fat volume, left atrial volume, right ventricular volume and GCS of left ventricle.

EVIDENCE LEVEL

4 TECHNICAL EFFICACY: Stage 5.

A Systematic Quality Scoring Analysis to Assess Automated Cardiovascular Magnetic Resonance Segmentation Algorithms

Background

The quantitative measures used to assess the performance of automated methods often do not reflect the clinical acceptability of contouring. A quality-based assessment of automated cardiac magnetic resonance (CMR) segmentation more relevant to clinical practice is therefore needed.

Objective

We propose a new method for assessing the quality of machine learning (ML) outputs. We evaluate the clinical utility of the proposed method as it is employed to systematically analyse the quality of an automated contouring algorithm.

Methods

A dataset of short-axis (SAX) cine CMR images from a clinically heterogeneous population (n = 217) were manually contoured by a team of experienced investigators. On the same images we derived automated contours using a ML algorithm. A contour quality scoring application randomly presented manual and automated contours to four blinded clinicians, who were asked to assign a quality score from a predefined rubric. Firstly, we analyzed the distribution of quality scores between the two contouring methods across all clinicians. Secondly, we analyzed the interobserver reliability between the raters. Finally, we examined whether there was a variation in scores based on the type of contour, SAX slice level, and underlying disease.

Results

The overall distribution of scores between the two methods was significantly different, with automated contours scoring better than the manual (OR (95% CI) = 1.17 (1.07–1.28), p = 0.001; n = 9401). There was substantial scoring agreement between raters for each contouring method independently, albeit it was significantly better for automated segmentation (automated: AC2 = 0.940, 95% CI, 0.937–0.943 vs manual: AC2 = 0.934, 95% CI, 0.931–0.937; p = 0.006). Next, the analysis of quality scores based on different factors was performed. Our approach helped identify trends patterns of lower segmentation quality as observed for left ventricle epicardial and basal contours with both methods. Similarly, significant differences in quality between the two methods were also found in dilated cardiomyopathy and hypertension.

Conclusions

Our results confirm the ability of our systematic scoring analysis to determine the clinical acceptability of automated contours. This approach focused on the contours' clinical utility could ultimately improve clinicians' confidence in artificial intelligence and its acceptability in the clinical workflow.

Right Ventricular Longitudinal Strain in Patients with Heart Failure

Patients with heart failure (HF) have high morbidity and mortality. Accurate assessment of right ventricular (RV) function has important prognostic significance in patients with HF. However, conventional echocardiographic parameters of RV function have limitations in RV assessments due to the complex geometry of right ventricle. In recent years, speckle tracking echocardiography (STE) has been developed as promising imaging technique to accurately evaluate RV function. RV longitudinal strain (RVLS) using STE, as a sensitive index for RV function evaluation, displays the powerfully prognostic value in patients with HF. Therefore, the aim of the present review was to summarize the utility of RVLS in patients with HF.

Artificial Intelligence-Based Echocardiographic Left Atrial Volume Measurement with Pulmonary Vein Comparison

This paper combines echocardiographic signal processing and artificial intelligence technology to propose a deep neural network model adapted to echocardiographic signals to achieve left atrial volume measurement and automatic assessment of pulmonary veins efficiently and quickly. Based on the echocardiographic signal generation mechanism and detection method, an experimental scheme for the echocardiographic signal acquisition was designed. The echocardiographic signal data of healthy subjects were measured in four different experimental states, and a database of left atrial volume measurements and pulmonary veins was constructed. Combining the correspondence between ECG signals and echocardiographic signals in the time domain, a series of preprocessing such as denoising, feature point localization, and segmentation of the cardiac cycle was realized by wavelet transform and threshold method to complete the data collection. This paper proposes a comparative model based on artificial intelligence, adapts to the characteristics of one-dimensional time-series echocardiographic signals, automatically extracts the deep features of echocardiographic signals, effectively reduces the subjective influence of manual feature selection, and realizes the automatic classification and evaluation of human left atrial volume measurement and pulmonary veins under different states. The experimental results show that the proposed BP neural network model has good adaptability and classification performance in the tasks of LV volume measurement and pulmonary vein automatic classification evaluation and achieves an average test accuracy of over 96.58%. The average root-mean-square error percentage of signal compression is only 0.65% by extracting the coding features of the original echocardiographic signal through the convolutional autoencoder, which completes the signal compression with low loss. Comparing the training time and classification accuracy of the LSTM network with the original signal and encoded features, the experimental results show that the AI model can greatly reduce the model training time cost and achieve an average accuracy of 97.97% in the test set and increase the real-time performance of the left atrial volume measurement and pulmonary vein evaluation as well as the security of the data transmission process, which is very important for the comparison of left atrial volume measurement and pulmonary vein. It is of great practical importance to compare left atrial volume measurements with pulmonary veins.