Unsupervised Machine Learning for Assessment of Left Ventricular Diastolic Function and Risk Stratification

Exercise Stress Echocardiography-Based Phenotyping of Heart Failure With Preserved Ejection Fraction

BACKGROUND

Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous syndrome requiring improved phenotypic classification. Previous studies have identified subphenotypes of HFpEF, but the lack of exercise assessment is a major limitation. The aim of this study was to identify distinct pathophysiologic clusters of HFpEF based on clinical characteristics, and resting and exercise assessments.

METHODS

A total of 265 patients with HFpEF underwent ergometry exercise stress echocardiography with simultaneous expired gas analysis. Cluster analysis was performed by the K-prototype method with 21 variables (10 clinical and resting echocardiographic variables and 11 exercise echocardiographic parameters). Pathophysiologic features, exercise tolerance, and prognosis were compared among phenogroups.

RESULTS

Three distinct phenogroups were identified. Phenogroup 1 (n = 112 [42%]) was characterized by preserved biventricular systolic reserve and cardiac output augmentation. Phenogroup 2 (n = 58 [22%]) was characterized by a high prevalence of atrial fibrillation, increased pulmonary arterial and right atrial pressures, depressed right ventricular systolic functional reserve, and impaired right ventricular-pulmonary artery coupling during exercise. Phenogroup 3 (n = 95 [36%]) was characterized by the smallest body mass index, ventricular and vascular stiffening, impaired left ventricular diastolic reserve, and worse exercise capacity. Phenogroups 2 and 3 had higher rates of composite outcomes of all-cause mortality or heart failure events than phenogroup 1 (log-rank P = .02).

CONCLUSION

Exercise echocardiography-based cluster analysis identified three distinct phenogroups of HFpEF, with unique exercise pathophysiologic features, exercise capacity, and clinical outcomes.

Clinical validation of commercial deep-learning based auto-segmentation models for organs at risk in the head and neck region: a single institution study

Purpose

To evaluate organ at risk (OAR) auto-segmentation in the head and neck region of computed tomography images using two different commercially available deep-learning-based auto-segmentation (DLAS) tools in a single institutional clinical applications.

Methods

Twenty-two OARs were manually contoured by clinicians according to published guidelines on planning computed tomography (pCT) images for 40 clinical head and neck cancer (HNC) cases. Automatic contours were generated for each patient using two deep-learning-based auto-segmentation models-Manteia AccuContour and MIM ProtégéAI. The accuracy and integrity of autocontours (ACs) were then compared to expert contours (ECs) using the Sørensen-Dice similarity coefficient (DSC) and Mean Distance (MD) metrics.

Results

ACs were generated for 22 OARs using AccuContour and 17 OARs using ProtégéAI with average contour generation time of 1 min/patient and 5 min/patient respectively. EC and AC agreement was highest for the mandible (DSC 0.90 ± 0.16) and (DSC 0.91 ± 0.03), and lowest for the chiasm (DSC 0.28 ± 0.14) and (DSC 0.30 ± 0.14) for AccuContour and ProtégéAI respectively. Using AccuContour, the average MD was<1mm for 10 of the 22 OARs contoured, 1-2mm for 6 OARs, and 2-3mm for 6 OARs. For ProtégéAI, the average mean distance was<1mm for 8 out of 17 OARs, 1-2mm for 6 OARs, and 2-3mm for 3 OARs.

Conclusions

Both DLAS programs were proven to be valuable tools to significantly reduce the time required to generate large amounts of OAR contours in the head and neck region, even though manual editing of ACs is likely needed prior to implementation into treatment planning. The DSCs and MDs achieved were similar to those reported in other studies that evaluated various other DLAS solutions. Still, small volume structures with nonideal contrast in CT images, such as nerves, are very challenging and will require additional solutions to achieve sufficient results.

A deep patient-similarity learning framework for the assessment of diastolic dysfunction in elderly patients

AIMS

Age-related changes in cardiac structure and function are well recognized and make the clinical determination of abnormal left ventricular (LV) diastolic dysfunction (LVDD) particularly challenging in the elderly. We investigated whether a deep neural network (DeepNN) model of LVDD, previously validated in a younger cohort, can be implemented in an older population to predict incident heart failure (HF).

METHODS AND RESULTS

A previously developed DeepNN was tested on 5596 older participants (66-90 years; 57% female; 20% Black) from the Atherosclerosis Risk in Communities Study. The association of DeepNN predictions with HF or all-cause death for the American College of Cardiology Foundation/American Heart Association Stage A/B (n = 4054) and Stage C/D (n = 1542) subgroups was assessed. The DeepNN-predicted high-risk compared with the low-risk phenogroup demonstrated an increased incidence of HF and death for both Stage A/B and Stage C/D (log-rank P < 0.0001 for all). In multi-variable analyses, the high-risk phenogroup remained an independent predictor of HF and death in both Stages A/B {adjusted hazard ratio [95% confidence interval (CI)] 6.52 [4.20-10.13] and 2.21 [1.68-2.91], both P < 0.0001} and Stage C/D [6.51 (4.06-10.44) and 1.03 (1.00-1.06), both P < 0.0001], respectively. In addition, DeepNN showed incremental value over the 2016 American Society of Echocardiography/European Association of Cardiovascular Imaging (ASE/EACVI) guidelines [net re-classification index, 0.5 (CI 0.4-0.6), P < 0.001; C-statistic improvement, DeepNN (0.76) vs. ASE/EACVI (0.70), P < 0.001] overall and maintained across stage groups.

CONCLUSION

Despite training with a younger cohort, a deep patient-similarity-based learning framework for assessing LVDD provides a robust prediction of all-cause death and incident HF for older patients.

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.

Artificial intelligence-enabled ECG for left ventricular diastolic function and filling pressure

Assessment of left ventricular diastolic function plays a major role in the diagnosis and prognosis of cardiac diseases, including heart failure with preserved ejection fraction. We aimed to develop an artificial intelligence (AI)-enabled electrocardiogram (ECG) model to identify echocardiographically determined diastolic dysfunction and increased filling pressure. We trained, validated, and tested an AI-enabled ECG in 98,736, 21,963, and 98,763 patients, respectively, who had an ECG and echocardiographic diastolic function assessment within 14 days with no exclusion criteria. It was also tested in 55,248 patients with indeterminate diastolic function by echocardiography. The model was evaluated using the area under the curve (AUC) of the receiver operating characteristic curve, and its prognostic performance was compared to echocardiography. The AUC for detecting increased filling pressure was 0.911. The AUCs to identify diastolic dysfunction grades ≥1, ≥2, and 3 were 0.847, 0.911, and 0.943, respectively. During a median follow-up of 5.9 years, 20,223 (20.5%) died. Patients with increased filling pressure predicted by AI-ECG had higher mortality than those with normal filling pressure, after adjusting for age, sex, and comorbidities in the test group (hazard ratio (HR) 1.7, 95% CI 1.645-1.757) similar to echocardiography and in the indeterminate group (HR 1.34, 95% CI 1.298-1.383). An AI-enabled ECG identifies increased filling pressure and diastolic function grades with a good prognostic value similar to echocardiography. AI-ECG is a simple and promising tool to enhance the detection of diseases associated with diastolic dysfunction and increased diastolic filling pressure.

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

Background

Detection of heart failure with preserved ejection fraction (HFpEF) involves integration of multiple imaging and clinical features which are often discordant or indeterminate.

Objectives

The authors applied artificial intelligence (AI) to analyze a single apical 4-chamber transthoracic echocardiogram video clip to detect HFpEF.

Methods

A 3-dimensional convolutional neural network was developed and trained on apical 4-chamber video clips to classify patients with HFpEF (diagnosis of heart failure, ejection fraction ≥50%, and echocardiographic evidence of increased filling pressure; cases) vs without HFpEF (ejection fraction ≥50%, no diagnosis of heart failure, normal filling pressure; controls). Model outputs were classified as HFpEF, no HFpEF, or nondiagnostic (high uncertainty). Performance was assessed in an independent multisite data set and compared to previously validated clinical scores.

Results

Training and validation included 2,971 cases and 3,785 controls (validation holdout, 16.8% patients), and demonstrated excellent discrimination (area under receiver-operating characteristic curve: 0.97 [95% CI: 0.96-0.97] and 0.95 [95% CI: 0.93-0.96] in training and validation, respectively). In independent testing (646 cases, 638 controls), 94 (7.3%) were nondiagnostic; sensitivity (87.8%; 95% CI: 84.5%-90.9%) and specificity (81.9%; 95% CI: 78.2%-85.6%) were maintained in clinically relevant subgroups, with high repeatability and reproducibility. Of 701 and 776 indeterminate outputs from the Heart Failure Association-Pretest Assessment, Echocardiographic and Natriuretic Peptide Score, Functional Testing (HFA-PEFF), and Final Etiology and Heavy, Hypertensive, Atrial Fibrillation, Pulmonary Hypertension, Elder, and Filling Pressure (H2FPEF) scores, the AI HFpEF model correctly reclassified 73.5% and 73.6%, respectively. During follow-up (median: 2.3 [IQR: 0.5-5.6] years), 444 (34.6%) patients died; mortality was higher in patients classified as HFpEF by AI (HR: 1.9 [95% CI: 1.5-2.4]).

Conclusions

An AI HFpEF model based on a single, routinely acquired echocardiographic video demonstrated excellent discrimination of patients with vs without HFpEF, more often than clinical scores, and identified patients with higher mortality.

The Role of Artificial Intelligence in Echocardiography

Echocardiography is an integral part of the diagnosis and management of cardiovascular disease. The use and application of artificial intelligence (AI) is a rapidly expanding field in medicine to improve consistency and reduce interobserver variability. AI can be successfully applied to echocardiography in addressing variance during image acquisition and interpretation. Furthermore, AI and machine learning can aid in the diagnosis and management of cardiovascular disease. In the realm of echocardiography, accurate interpretation is largely dependent on the subjective knowledge of the operator. Echocardiography is burdened by the high dependence on the level of experience of the operator, to a greater extent than other imaging modalities like computed tomography, nuclear imaging, and magnetic resonance imaging. AI technologies offer new opportunities for echocardiography to produce accurate, automated, and more consistent interpretations. This review discusses machine learning as a subfield within AI in relation to image interpretation and how machine learning can improve the diagnostic performance of echocardiography. This review also explores the published literature outlining the value of AI and its potential to improve patient care.