Are HFpEF and HFmrEF So Different? The Need to Understand Distinct Phenotypes

Palazzuoli, Alberto; Beltrami, Matteo

doi:10.3389/fcvm.2021.676658

Cited by 11 publications

(12 citation statements)

References 51 publications

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“…According to recently published studies, clinical profiles of patients allow for the discrimination between the three HF categories, especially the presence of comorbidities and quality of life based on the ESC guidelines (16)(17)(18)(19)(20)(21). Based on these clinical results, HFmEF patients were found to fall between HFpEF and HFrEF while more closely resembling HFpEF (22,23). Additionally, they were more likely to be younger than HFpEF and more prone to diabetes and hypertension than HFrEF (20,24).…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Predicts Heart Failure With Preserved, Mid-Range, and Reduced Left Ventricular Ejection Fraction From Patient Clinical Profiles

Alkhodari

Jelinek

Karlas

et al. 2021

Front. Cardiovasc. Med.

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Background: Left ventricular ejection fraction (LVEF) is the gold standard for evaluating heart failure (HF) in coronary artery disease (CAD) patients. It is an essential metric in categorizing HF patients as preserved (HFpEF), mid-range (HFmEF), and reduced (HFrEF) ejection fraction but differs, depending on whether the ASE/EACVI or ESC guidelines are used to classify HF.Objectives: We sought to investigate the effectiveness of using deep learning as an automated tool to predict LVEF from patient clinical profiles using regression and classification trained models. We further investigate the effect of utilizing other LVEF-based thresholds to examine the discrimination ability of deep learning between HF categories grouped with narrower ranges.Methods: Data from 303 CAD patients were obtained from American and Greek patient databases and categorized based on the American Society of Echocardiography and the European Association of Cardiovascular Imaging (ASE/EACVI) guidelines into HFpEF (EF > 55%), HFmEF (50% ≤ EF ≤ 55%), and HFrEF (EF < 50%). Clinical profiles included 13 demographical and clinical markers grouped as cardiovascular risk factors, medication, and history. The most significant and important markers were determined using linear regression fitting and Chi-squared test combined with a novel dimensionality reduction algorithm based on arc radial visualization (ArcViz). Two deep learning-based models were then developed and trained using convolutional neural networks (CNN) to estimate LVEF levels from the clinical information and for classification into one of three LVEF-based HF categories.Results: A total of seven clinical markers were found important for discriminating between the three HF categories. Using statistical analysis, diabetes, diuretics medication, and prior myocardial infarction were found statistically significant (p < 0.001). Furthermore, age, body mass index (BMI), anti-arrhythmics medication, and previous ventricular tachycardia were found important after projections on the ArcViz convex hull with an average nearest centroid (NC) accuracy of 94%. The regression model estimated LVEF levels successfully with an overall accuracy of 90%, average root mean square error (RMSE) of 4.13, and correlation coefficient of 0.85. A significant improvement was then obtained with the classification model, which predicted HF categories with an accuracy ≥93%, sensitivity ≥89%, 1-specificity <5%, and average area under the receiver operating characteristics curve (AUROC) of 0.98.Conclusions: Our study suggests the potential of implementing deep learning-based models clinically to ensure faster, yet accurate, automatic prediction of HF based on the ASE/EACVI LVEF guidelines with only clinical profiles and corresponding information as input to the models. Invasive, expensive, and time-consuming clinical testing could thus be avoided, enabling reduced stress in patients and simpler triage for further intervention.

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Section: Introductionmentioning

confidence: 99%

Deep Learning Predicts Heart Failure With Preserved, Mid-Range, and Reduced Left Ventricular Ejection Fraction From Patient Clinical Profiles

Alkhodari

Jelinek

Karlas

et al. 2021

Front. Cardiovasc. Med.

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“…Clinical-epidemiological studies have shown that patients with HFmrEF had different clinical characteristics and may be intermediate between the those with HFrEF or HFpEF. 22 In the meta-analysis of 12 observational studies with 109 257 patients, all-cause mortality and hospitalisation for HF were lower in patients with HFmrEF than in those with HFrEF and HFpEF. 23 However, the study population was heterogeneous, and only five studies provided outcomes of cardiovascular death or hospitalisation for HF.…”

Section: Discussionmentioning

confidence: 99%

Echocardiographic features of left ventricular dysfunction and outcomes in chronic kidney disease

Chao

Tsai

et al. 2022

Heart

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ObjectiveHeart failure (HF) imposes a substantial burden and the prevalence of HF is high in patients with chronic kidney disease (CKD). HF results in multiple hospital admissions, but whether HF subtypes worsen long-term outcomes and renal function in patients with CKD remains inconclusive.MethodsThe study comprised 10 904 patients with CKD aged ≥20 years who underwent echocardiography between 1 January 2011 and 31 December 2018. The patients were stratiﬁed into four groups: non-HF, HF with reduced ejection fraction (HFrEF), HF with mildly reduced ejection fraction (HFmrEF) and HF with preserved ejection fraction (HFpEF). The primary end points were all-cause mortality, major adverse cardiovascular events (MACEs) and adverse renal outcomes.ResultsIn inverse probability of treatment weighting-adjusted method, the risk of all-cause mortality and MACEs relative to the non-HF group was greatest in the HFrEF group (HR 3.18 (95% CI 2.57 to 3.93) and HR 3.83 (95% CI 3.20 to 4.59)), followed by the HFmrEF (HR 2.75 (95% CI 2.22 to 3.42) and HR 3.08 (95% CI 2.57 to 3.69)) and HFpEF (HR 1.85 (95% CI 1.59 to 2.15) and HR 2.43 (95% CI 2.16 to 2.73) groups. In addition, the HFrEF group had the greatest risks of end-stage renal disease (HR 2.58 (95% CI 1.94 to 3.44)) compared with other groups.ConclusionsHF is associated with subsequent worse clinical outcomes, which may be more pronounced in patients with HFrEF, followed by those with HFmrEF and those with HFpEF relative to non-HF group.

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“…In 2016, heart failure guidelines changed introducing a third category (heart failure with mid-range ejection fraction) with LVEF 40 to <50%( 27 , 49 ). This class shares features from HFrEF and HFpEF and is underrepresented in current studies ( 50 ).…”

Section: Discussionmentioning

confidence: 99%

“…In conclusion, our study shows that remote patient management may reduce all-cause mortality in the highly vulnerable population of patients with heart failure and CKD. In Germany telemedicine for patients with advanced heart failure has been implemented recently to further reduce mortality ( 50 – 53 , 55 , 56 ).…”

Section: Discussionmentioning

confidence: 99%

Remote Patient Management May Reduce All-Cause Mortality in Patients With Heart-Failure and Renal Impairment

et al. 2022

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BackgroundRemote patient management (RPM) in heart failure (HF) patients has been investigated in several prospective randomized trials. The Telemedical Interventional Management in Heart Failure II (TIM-HF2)-trial showed reduced all-cause mortality and hospitalizations in heart failure (HF) patients using remote patient management (RPM) vs. usual care (UC). We report the trial's results for prespecified eGFR-subgroups.MethodsTIM-HF2 was a prospective, randomized, controlled, parallel-group, unmasked (with randomization concealment), multicenter trial. A total of 1,538 patients with stable HF were enrolled in Germany from 2013 to 2017 and randomized to RPM (+UC) or UC. Using CKD-EPI-formula at baseline, prespecified subgroups were defined. In RPM, patients transmitted their vital parameters daily. The telemedical center reviewed and co-operated with the patient's General Practitioner (GP) and cardiologist. In UC, patients were treated by their GPs or cardiologist applying the current guidelines for HF management and treatment. The primary endpoint was the percentage of days lost due to unplanned cardiovascular hospitalizations or death, secondary outcomes included hospitalizations, all-cause, and cardiovascular mortality.ResultsOur sub analysis showed no difference between RPM and UC in both eGFR-subgroups for the primary endpoint (<60 ml/min/1.73 m2: 40.9% vs. 43.6%, p = 0.1, ≥60 ml/min/1.73 m2 26.5 vs. 29.3%, p = 0.36). In patients with eGFR < 60 ml/min/1.73 m2, 1-year-survival was higher in RPM than UC (89.4 vs. 84.6%, p = 0.02) with an incident rate ratio (IRR) 0.67 (p = 0.03). In the recurrent event analysis, HF hospitalizations and all-cause death were lower in RPM than UC in both eGFR-subgroups (<60 ml/min/1.73 m2: IRR 0.70, p = 0.02; ≥60 ml/min/1.73 m2: IRR 0.64, p = 0.04). In a cox regression analysis, age, NT-pro BNP, eGFR, and BMI were associated with all-cause mortality.ConclusionRPM may reduce all-cause mortality and HF hospitalizations in patients with HF and eGFR < 60 ml/min/1.73 m2. HF hospitalizations and all-cause death were lower in RPM in both eGFR-subgroups in the recurrent event analysis. Further studies are needed to investigate and confirm this finding.

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Are HFpEF and HFmrEF So Different? The Need to Understand Distinct Phenotypes

Cited by 11 publications

References 51 publications

Deep Learning Predicts Heart Failure With Preserved, Mid-Range, and Reduced Left Ventricular Ejection Fraction From Patient Clinical Profiles

Deep Learning Predicts Heart Failure With Preserved, Mid-Range, and Reduced Left Ventricular Ejection Fraction From Patient Clinical Profiles

Echocardiographic features of left ventricular dysfunction and outcomes in chronic kidney disease

Remote Patient Management May Reduce All-Cause Mortality in Patients With Heart-Failure and Renal Impairment

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