What is ‘normal’ left ventricular ejection fraction?

Hudson, Sarah; Pettit, Stephen

doi:10.1136/heartjnl-2020-317604

Cited by 33 publications

(24 citation statements)

References 7 publications

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“…Most of the treatments mentioned in their letter require an ejection fraction to be measured to ensure prudent, safe, and evidence-based care. While categorisation of systolic dysfunction has useful but limited benefits (mainly to non-specialists), we agree that the numerical reporting of ejection fraction is important for all prescribing clinicians and especially heart failure specialists (3). It is recommended as standard practice by the BSE.…”

Section: Correspondence Should Be Addressed To a Harkness: Allanharkmentioning

confidence: 68%

Response to Latest British Society of Echocardiography recommendations for left ventricular ejection fraction categorisation: potential implications and relevance to contemporary heart failure management

et al. 2020

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We thank Dr Kanagala and Professor Squire for their keen interest in our paper and their insight into the challenge of grading left ventricular ejection fraction (LVEF). We must emphasise that our paper’s remit was not to be a clinical guide on heart failure nor on its treatment. We have briefly outlined the rationale and context for the BSE cutoff for severe LVEF and why we continue to differ from our European and American colleagues. We then discuss the need for stating the actual LVEF% in reports as opposed to a category alone for guiding appropriate therapeutic choices. Finally we address the concerns raised over clinical harm they perceive may occur from adopting our guideline.

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Section: Correspondence Should Be Addressed To a Harkness: Allanharkmentioning

confidence: 68%

Response to Latest British Society of Echocardiography recommendations for left ventricular ejection fraction categorisation: potential implications and relevance to contemporary heart failure management

et al. 2020

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“…To further analyze the texture features that may help differentiate the control group from PH patients with preserved LVEF, a subgroup analysis was also performed. This subgroup included patients from the PH group who had preserved left ventricle ejection fraction (LVEF) ≥ 50% [20] and were compared against the controls. The subgroup of PH patients (20) with preserved LVEF included patients with an echocardiographic diagnosis of diastolic dysfunction (patients with heart failure with preserved ejection fraction (HFpEF)), and patients without evidence of systolic or diastolic dysfunction (Figure 1).…”

Section: Methodsmentioning

confidence: 99%

“…The subgroup of PH patients (20) with preserved LVEF included patients with an echocardiographic diagnosis of diastolic dysfunction (patients with heart failure with preserved ejection fraction (HFpEF)), and patients without evidence of systolic or diastolic dysfunction (Figure 1). [20] and were compared against the controls. The subgroup of PH patients (20) with preserved LVEF included patients with an echocardiographic diagnosis of diastolic dysfunction (patients with heart failure with preserved ejection fraction (HFpEF)), and patients without evidence of systolic or diastolic dysfunction (Figure 1).…”

Section: Methodsmentioning

confidence: 99%

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Radiomics Detection of Pulmonary Hypertension via Texture-Based Assessments of Cardiac MRI: A Machine-Learning Model Comparison—Cardiac MRI Radiomics in Pulmonary Hypertension

Priya

Aggarwal

Ward

et al. 2021

JCM

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The role of reliable, non-invasive imaging-based recognition of pulmonary hypertension (PH) remains a diagnostic challenge. The aim of the current pilot radiomics study was to assess the diagnostic performance of cardiac MRI (cMRI)-based texture features to accurately predict PH. The study involved IRB-approved retrospective analysis of cMRIs from 72 patients (42 PH and 30 healthy controls) for the primary analysis. A subgroup analysis was performed including patients from the PH group with left ventricle ejection fraction ≥ 50%. Texture features were generated from mid-left ventricle myocardium using balanced steady-state free precession (bSSFP) cine short-axis imaging. Forty-five different combinations of classifier models and feature selection techniques were evaluated. Model performance was assessed using receiver operating characteristic curves. A multilayer perceptron model fitting using full feature sets was the best classifier model for both the primary analysis (AUC 0.862, accuracy 78%) and the subgroup analysis (AUC 0.918, accuracy 80%). Model performance demonstrated considerable variation between the models (AUC 0.523–0.918) based on the chosen model–feature selection combination. Cardiac MRI-based radiomics recognition of PH using texture features is feasible, even with preserved left ventricular ejection fractions.

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“…The primary analysis included all patients in PH and control groups. A subgroup analysis was also performed that included patients from the PH group who had preserved left ventricle ejection fraction ≥ 50% [32] and were compared against all controls.…”

Section: Methodsmentioning

confidence: 99%

Title: Cardiac MRI derived radiomics based machine learning (ML) models in identifying pulmonary hypertension (PH)- Impact of segmentation, radiomics side experiments and DAFIT approach on model performance

Priya

Aggarwal

Ward

et al. 2021

Preprint

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Side experiments are performed on radiomics models to improve their reproducibility. We measure the impact of myocardial masks, radiomic side experiments and data augmentation for information transfer (DAFIT) approach to differentiate patients with and without pulmonary hypertension (PH) using cardiac MRI (CMRI) derived radiomics. Feature extraction was performed from the left ventricle (LV) and right ventricle (RV) myocardial masks using CMRI in 82 patients (42 PH and 40 controls). Various side study experiments were evaluated: Original data without and with intraclass correlation (ICC) feature-filtering and DAFIT approach (without and with ICC feature-filtering). Multiple machine learning and feature selection strategies were evaluated. Primary analysis included all PH patients with subgroup analysis including PH patients with preserved LVEF (≥ 50%). For both primary and subgroup analysis, DAFIT approach without feature-filtering was the highest performer (AUC 0.957–0.958). ICC approaches showed poor performance compared to DAFIT approach. The performance of combined LV and RV masks was superior to individual masks alone. There was variation in top performing models across all approaches (AUC 0.862–0.958). DAFIT approach with features from combined LV and RV masks provide superior performance with poor performance of feature filtering approaches. Model performance varies based upon the feature selection and model combination.

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What is ‘normal’ left ventricular ejection fraction?

Cited by 33 publications

References 7 publications

Response to Latest British Society of Echocardiography recommendations for left ventricular ejection fraction categorisation: potential implications and relevance to contemporary heart failure management

Response to Latest British Society of Echocardiography recommendations for left ventricular ejection fraction categorisation: potential implications and relevance to contemporary heart failure management

Radiomics Detection of Pulmonary Hypertension via Texture-Based Assessments of Cardiac MRI: A Machine-Learning Model Comparison—Cardiac MRI Radiomics in Pulmonary Hypertension

Title: Cardiac MRI derived radiomics based machine learning (ML) models in identifying pulmonary hypertension (PH)- Impact of segmentation, radiomics side experiments and DAFIT approach on model performance

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