Development and prospective validation of a clinical index to predict survival in ambulatory patients referred for cardiac transplant evaluation

When all Else Fails, Try This: The HeartMate III Left Ventricle Assist Device

Heart failure (HF) is a progressive disease. It is estimated that more than 250,000 patients suffer from advanced HF with reduced ejection fraction refractory to medical therapy. With limited donor pool for heart transplant, continue flow left ventricle assist device (LVAD) is a lifesaving treatment option for patients with advanced HF. This review will provide an update on indications, contraindications, and associated adverse events for LVAD support with a summary of the current outcomes data.

International Society for Heart and Lung Transplantation Guidelines for the Evaluation and Care of Cardiac Transplant Candidates-2024

The "International Society for Heart and Lung Transplantation Guidelines for the Evaluation and Care of Cardiac Transplant Candidates-2024" updates and replaces the "Listing Criteria for Heart Transplantation: International Society for Heart and Lung Transplantation Guidelines for the Care of Cardiac Transplant Candidates-2006" and the "2016 International Society for Heart Lung Transplantation Listing Criteria for Heart Transplantation: A 10-year Update." The document aims to provide tools to help integrate the numerous variables involved in evaluating patients for transplantation, emphasizing updating the collaborative treatment while waiting for a transplant. There have been significant practice-changing developments in the care of heart transplant recipients since the publication of the International Society for Heart and Lung Transplantation (ISHLT) guidelines in 2006 and the 10-year update in 2016. The changes pertain to 3 aspects of heart transplantation: (1) patient selection criteria, (2) care of selected patient populations, and (3) durable mechanical support. To address these issues, 3 task forces were assembled. Each task force was cochaired by a pediatric heart transplant physician with the specific mandate to highlight issues unique to the pediatric heart transplant population and ensure their adequate representation. This guideline was harmonized with other ISHLT guidelines published through November 2023. The 2024 ISHLT guidelines for the evaluation and care of cardiac transplant candidates provide recommendations based on contemporary scientific evidence and patient management flow diagrams. The American College of Cardiology and American Heart Association modular knowledge chunk format has been implemented, allowing guideline information to be grouped into discrete packages (or modules) of information on a disease-specific topic or management issue. Aiming to improve the quality of care for heart transplant candidates, the recommendations present an evidence-based approach.

The chronic heart failure evolutions: Different fates and routes

AIMS

Individual prognostic assessment and disease evolution pathways are undefined in chronic heart failure (HF). The application of unsupervised learning methodologies could help to identify patient phenotypes and the progression in each phenotype as well as to assess adverse event risk.

METHODS AND RESULTS

From a bulk of 7948 HF patients included in the MECKI registry, we selected patients with a minimum 2-year follow-up. We implemented a topological data analysis (TDA), based on 43 variables derived from clinical, biochemical, cardiac ultrasound, and exercise evaluations, to identify several patients' clusters. Thereafter, we used the trajectory analysis to describe the evolution of HF states, which is able to identify bifurcation points, characterized by different follow-up paths, as well as specific end-stages conditions of the disease. Finally, we conducted a 5-year survival analysis (composite of cardiovascular death, left ventricular assist device, or urgent heart transplant). Findings were validated on internal (n = 527) and external (n = 777) populations. We analyzed 4876 patients (age = 63 [53-71], male gender n = 3973 (81.5%), NYHA class I-II n = 3576 (73.3%), III-IV n = 1300 (26.7%), LVEF = 33 [25.5-39.9], atrial fibrillation n = 791 (16.2%), peak VO2% pred = 54.8 [43.8-67.2]), with a minimum 2-year follow-up. Nineteen patient clusters were identified by TDA. Trajectory analysis revealed a path characterized by 3 bifurcation and 4 end-stage points. Clusters survival rate varied from 44% to 100% at 2 years and from 20% to 100% at 5 years, respectively. The event frequency at 5-year follow-up for each study cohort cluster was successfully compared with those in the validation cohorts (R = 0.94 and R = 0.84, P < 0.001, for internal and external cohort, respectively). Finally, we conducted a 5-year survival analysis (composite of cardiovascular death, left ventricular assist device, or urgent heart transplant observed in 22% of cases).

CONCLUSIONS

Each HF phenotype has a specific disease progression and prognosis. These findings allow to individualize HF patient evolutions and to tailor assessment.

Risk Factors for All-Cause Mortality in Patients Diagnosed with Advanced Heart Failure: A Scoping Review

Introduction: Identifying the evolving needs of patients with advanced heart failure (AdHF) and triaging those at high risk of death can facilitate timely referrals to palliative care and advance patient-centered individualized care. There are limited models specific for patients with end-stage HF. We aim to identify risk factors associated with up to three-year all-cause mortality (ACM) and describe prognostic models developed or validated in AdHF populations. Methods: Frameworks proposed by Arksey, O'Malley, and Levac were adopted for this scoping review. We searched the Medline, EMBASE, PubMed, CINAHL, Cochrane library, Web of Science and gray literature databases for articles published between January 2010 and September 2020. Primary studies that included adults aged ≥ 18 years, diagnosed with AdHF defined as New York Heart Association class III/IV, American Heart Association/American College of Cardiology Stage D, end-stage HF, and assessed for risk factors associated with up to three-year ACM using multivariate analysis were included. Studies were appraised using the Quality of Prognostic Studies tool. Data were analyzed using a narrative synthesis approach. Results: We reviewed 167 risk factors that were associated with up to three-year ACM and prognostic models specific to AdHF patients across 65 articles with low-to-moderate bias. Studies were mostly based in Western and/or European cohorts (n = 60), in the acute care setting (n = 56), and derived from clinical trials (n = 40). Risk factors were grouped into six domains. Variables related to cardiovascular and overall health were frequently assessed. Ten prognostic models developed/validated on AdHF patients displayed acceptable model performance [area under the curve (AUC) range: 0.71-0.81]. Among the ten models, the model for end-stage-liver disease (MELD-XI) and acute decompensated HF with N-terminal pro b-type natriuretic peptide (ADHF/proBNP) model attained the highest discriminatory performance against short-term ACM (AUC: 0.81). Conclusions: To enable timely referrals to palliative care interventions, further research is required to develop or validate prognostic models that consider the evolving landscape of AdHF management.

Predicting heart failure outcomes by integrating breath-by-breath measurements from cardiopulmonary exercise testing and clinical data through a deep learning survival neural network

Aims

Mathematical models previously developed to predict outcomes in patients with heart failure (HF) generally have limited performance and have yet to integrate complex data derived from cardiopulmonary exercise testing (CPET), including breath-by-breath data. We aimed to develop and validate a time-to-event prediction model using a deep learning framework using the DeepSurv algorithm to predict outcomes of HF.

Methods and results

Inception cohort of 2490 adult patients with high-risk cardiac conditions or HF underwent CPET with breath-by-breath measurements. Potential predictive features included known clinical indicators, standard summary statistics from CPETs, and mathematical features extracted from the breath-by-breath time series of 13 measurements. The primary outcome was a composite of death, heart transplant, or mechanical circulatory support treated as a time-to-event outcomes. Predictive features ranked as most important included many of the features engineered from the breath-by-breath data in addition to traditional clinical risk factors. The prediction model showed excellent performance in predicting the composite outcome with an area under the curve of 0.93 in the training and 0.87 in the validation data sets. Both the predicted vs. actual freedom from the composite outcome and the calibration of the prediction model were excellent. Model performance remained stable in multiple subgroups of patients.

Conclusion

Using a combined deep learning and survival algorithm, integrating breath-by-breath data from CPETs resulted in improved predictive accuracy for long-term (up to 10 years) outcomes in HF. DeepSurv opens the door for future prediction models that are both highly performing and can more fully use the large and complex quantity of data generated during the care of patients with HF.

Artificial Intelligence Approaches for Predicting the Risks of Durable Mechanical Circulatory Support Therapy and Cardiac Transplantation

Background: The use of AI-driven technologies in probing big data to generate better risk prediction models has been an ongoing and expanding area of investigation. The AI-driven models may perform better as compared to linear models; however, more investigations are needed in this area to refine their predictability and applicability to the field of durable MCS and cardiac transplantation. Methods: A literature review was carried out using Google Scholar/PubMed from 2000 to 2023. Results: This review defines the knowledge gaps and describes different AI-driven approaches that may be used to further our understanding. Conclusions: The limitations of current models are due to missing data, data imbalances, and the uneven distribution of variables in the datasets from which the models are derived. There is an urgent need for predictive models that can integrate a large number of clinical variables from multicenter data to account for the variability in patient characteristics that influence patient selection, outcomes, and survival for both durable MCS and HT; this may be fulfilled by AI-driven risk prediction models.

The modified Rajan's heart failure risk score predicts all-cause mortality in patients hospitalized for heart failure with reduced ejection fraction: a retrospective cohort study

Background

The dimensionless Rajan's heart failure (R-hf) risk score was proposed to predict all-cause mortality in patients hospitalized with chronic heart failure (HF) and reduced ejection fraction (EF) (HFrEF).

Purpose

To examine the association between the modified R-hf risk score and all-cause mortality in patients with HFrEF.

Methods

Retrospective cohort study included adults hospitalized with HFrEF, as defined by clinical symptoms of HF with biplane EF less than 40% on transthoracic echocardiography, at a tertiary centre in Dalian, China, between 1 November 2015, and 31 October 2019. All patients were followed up until 31 October 2020. A modified R-hf risk score was calculated by substituting brain natriuretic peptide (BNP) for N-terminal prohormone of BNP (NT-proBNP) using EF× estimated glomerular filtration rate (eGFR)× haemoglobin (Hb))/BNP. The patients were stratified into tertiles according to the R-hf risk score. The measured outcome was all-cause mortality. The score performance was assessed using C-statistics.

Results

A total of 840 patients were analyzed (70.2% males; mean age, 64±14 years; median (interquartile range) follow-up 37.0 (27.8) months). A lower modified R-hf risk score predicted a higher risk of all-cause mortality, independent of sex and age [1st tertile vs. 3rd tertile: adjusted hazard ratio (aHR), 3.46; 95% CI: 2.11-5.67; P<0.001]. Multivariate Cox regression analysis indicated that a lower modified R-hf risk score was associated with increased cumulative all-cause mortality [univariate: (1st tertile vs. 3rd tertile: aHR, 3.45; 95% CI: 2.11-5.65; P<0.001) and multivariate: (1st tertile vs. 3rd tertile: aHR 2.21, 95% CI: 1.29-3.79; P=0.004)]. The performance of the model, as reported by C-statistic was 0.67 (95% CI: 0.62-0.72).

Conclusion

The modified R-hf risk score predicted all-cause mortality in patients hospitalized with HFrEF. Further validation of the modified R-hf risk score in other cohorts of patients with HFrEF is needed before clinical application.

Pressure overload is associated with right ventricular dyssynchrony in heart failure with reduced ejection fraction

AIMS

The determinants and relevance of right ventricular (RV) mechanical dyssynchrony in heart failure with reduced ejection fraction (HFrEF) are poorly understood. We hypothesized that increased afterload may adversely affect the synchrony of RV contraction.

METHODS AND RESULTS

A total of 148 patients with HFrEF and 36 controls underwent echocardiography, right heart catheterization, and gated single-photon emission computed tomography to measure RV chamber volumes and mechanical dyssynchrony (phase standard deviation of systolic displacement timing). Exams were repeated after preload (N = 135) and afterload (N = 15) modulation. Patients with HFrEF showed higher RV dyssynchrony compared with controls (40.6 ± 17.5° vs. 27.8 ± 9.1°, P < 0.001). The magnitude of RV dyssynchrony in HFrEF correlated with larger RV and left ventricular (LV) volumes, lower RV ejection fraction (RVEF) and LV ejection fraction, reduced intrinsic contractility, increased heart rate, higher pulmonary artery (PA) load, and impaired RV-PA coupling (all P ≤ 0.01). Low RVEF was the strongest predictor of RV dyssynchrony. Left bundle branch block (BBB) was associated with greater RV dyssynchrony than right BBB, regardless of QRS duration. RV afterload reduction by sildenafil improved RV dyssynchrony (P = 0.004), whereas preload change with passive leg raise had modest effect. Patients in the highest tertiles of RV dyssynchrony had an increased risk of adverse clinical events compared with those in the lower tertile [T2/T3 vs. T1: hazard ratio 1.98 (95% confidence interval 1.20-3.24), P = 0.007].

CONCLUSIONS

RV dyssynchrony is associated with RV remodelling, dysfunction, adverse haemodynamics, and greater risk for adverse clinical events. RV dyssynchrony is mitigated by acute RV afterload reduction and could be a potential therapeutic target to improve RV performance in HFrEF.

Enhancing heart failure treatment decisions: interpretable machine learning models for advanced therapy eligibility prediction using EHR data

Timely and accurate referral of end-stage heart failure patients for advanced therapies, including heart transplants and mechanical circulatory support, plays an important role in improving patient outcomes and saving costs. However, the decision-making process is complex, nuanced, and time-consuming, requiring cardiologists with specialized expertise and training in heart failure and transplantation. In this study, we propose two logistic tensor regression-based models to predict patients with heart failure warranting evaluation for advanced heart failure therapies using irregularly spaced sequential electronic health records at the population and individual levels. The clinical features were collected at the previous visit and the predictions were made at the very beginning of the subsequent visit. Patient-wise ten-fold cross-validation experiments were performed. Standard LTR achieved an average F1 score of 0.708, AUC of 0.903, and AUPRC of 0.836. Personalized LTR obtained an F1 score of 0.670, an AUC of 0.869 and an AUPRC of 0.839. The two models not only outperformed all other machine learning models to which they were compared but also improved the performance and robustness of the other models via weight transfer. The AUPRC scores of support vector machine, random forest, and Naive Bayes are improved by 8.87%, 7.24%, and 11.38%, respectively. The two models can evaluate the importance of clinical features associated with advanced therapy referral. The five most important medical codes, including chronic kidney disease, hypotension, pulmonary heart disease, mitral regurgitation, and atherosclerotic heart disease, were reviewed and validated with literature and by heart failure cardiologists. Our proposed models effectively utilize EHRs for potential advanced therapies necessity in heart failure patients while explaining the importance of comorbidities and other clinical events. The information learned from trained model training could offer further insight into risk factors contributing to the progression of heart failure at both the population and individual levels.

Chronic heart failure in patients undergoing major head and neck surgery: A hospital-based study

OBJECTIVE

To investigate the effects of chronic heart failure on various post-operative outcomes in head and neck cancer patients undergoing major cancer surgery.

STUDY DESIGN

For this retrospective cohort study of patients undergoing major head and neck cancer surgery, a sample of 10,002 patients between 2017 and 2019 were identified through the Nationwide Inpatient Sample.

SETTING

Patients were selected as undergoing major head and neck cancer surgery, defined as laryngectomy, pharyngectomy, glossectomy, neck dissection, mandibulectomy, and maxillectomy, then separated based on pre-surgical diagnosis of chronic heart failure.

METHODS

The effects of pre-operative chronic heart failure on post-surgical outcomes in these patients were investigated by univariable and multivariable logistic regression using ICD-10 codes and SPSS.

RESULTS

A diagnosis of chronic heart failure was observed in 265 patients (2.6 %). Patients with chronic heart failure had more preexisting comorbidities when compared to patients without chronic heart failure (mean ± SD; 4 ± 1 vs. 2 ± 1). Multivariable logistic regression showed that chronic heart failure patients had significantly greater odds of dying during hospitalization (OR 2.86, 95 % CI 1.38-5.91) and experiencing non-routine discharge from admission (OR 1.89, 95 % CI 1.41-2.54) after undergoing major head and neck cancer surgery.

CONCLUSION

Chronic heart failure is associated with greater length of stay and hospital charges among head and neck cancer patients undergoing major head and neck cancer surgeries. Chronic heart failure patients have significantly greater rates of unfavorable post-operative outcomes, including death during hospitalization and non-routine discharge from admission.

Usefulness of Age, Creatinine and Ejection Fraction - Modification of Diet in Renal Disease Score for Predicting Survival in Patients with Heart Failure

BACKGROUND

Central Illustration: Usefulness of Age, Creatinine and Ejection Fraction - Modification of Diet in Renal Disease Score for Predicting Survival in Patients with Heart Failure Summary of the study design and key findings. ACEF: Age, creatinine and ejection fraction, MDRD: Modified Diet in Renal Disease. While many risk models have been developed to predict prognosis in heart failure (HF), these models are rarely useful for the clinical practitioner as they include multiple variables that might be time-consuming to obtain, they are usually difficult to calculate, and they may suffer from statistical overfitting.

OBJECTIVES

To investigate whether a simpler model, namely the ACEF-MDRD score, could be used for predicting one-year mortality in HF patients.

METHODS

748 cases within the SELFIE-HF registry had complete data to calculate the ACEF-MDRD score. Patients were grouped into tertiles for analyses. For all tests, a p-value <0.05 was accepted as significant.

RESULTS

Significantly more patients within the ACEF-MDRD high tertile (30.0%) died within one year, as compared to other tertiles (10.8% and 16.1%, respectively, for ACEF-MDRD low and ACEF-MDRD med , p<0.001 for both comparisons). There was a stepwise decrease in one-year survival as the ACEF-MDRD score increased (log-rank p<0.001). ACEF-MDRD was an independent predictor of survival after adjusting for other variables (OR: 1.14, 95%CI:1.04 - 1.24, p=0.006). ACEF-MDRD score offered similar accuracy to the GWTG-HF score for predicting one-year mortality (p=0.14).

CONCLUSIONS

ACEF-MDRD is a predictor of mortality in patients with HF, and its usefulness is comparable to similar yet more complicated models.

Predicting need for heart failure advanced therapies using an interpretable tropical geometry-based fuzzy neural network

BACKGROUND

Timely referral for advanced therapies (i.e., heart transplantation, left ventricular assist device) is critical for ensuring optimal outcomes for heart failure patients. Using electronic health records, our goal was to use data from a single hospitalization to develop an interpretable clinical decision-making system for predicting the need for advanced therapies at the subsequent hospitalization.

METHODS

Michigan Medicine heart failure patients from 2013-2021 with a left ventricular ejection fraction ≤ 35% and at least two heart failure hospitalizations within one year were used to train an interpretable machine learning model constructed using fuzzy logic and tropical geometry. Clinical knowledge was used to initialize the model. The performance and robustness of the model were evaluated with the mean and standard deviation of the area under the receiver operating curve (AUC), the area under the precision-recall curve (AUPRC), and the F1 score of the ensemble. We inferred membership functions from the model for continuous clinical variables, extracted decision rules, and then evaluated their relative importance.

RESULTS

The model was trained and validated using data from 557 heart failure hospitalizations from 300 patients, of whom 193 received advanced therapies. The mean (standard deviation) of AUC, AUPRC, and F1 scores of the proposed model initialized with clinical knowledge was 0.747 (0.080), 0.642 (0.080), and 0.569 (0.067), respectively, showing superior predictive performance or increased interpretability over other machine learning methods. The model learned critical risk factors predicting the need for advanced therapies in the subsequent hospitalization. Furthermore, our model displayed transparent rule sets composed of these critical concepts to justify the prediction.

CONCLUSION

These results demonstrate the ability to successfully predict the need for advanced heart failure therapies by generating transparent and accessible clinical rules although further research is needed to prospectively validate the risk factors identified by the model.

Impact of Cardiac Power Output on Exercise Capacity and Clinical Outcome in Patients With Chronic Heart Failure

Less data are available regarding the impact of cardiac power output on exercise capacity or clinical outcome in patients with chronic heart failure (CHF). The study enrolled 280 consecutive patients with CHF referred for cardiopulmonary exercise testing and right-sided heart catheterization between 2013 and 2018. The primary outcome was composite of heart failure hospitalization or death. Cardiac power output was calculated as (mean arterial pressure × CO) ÷ 451. Patients with low cardiac power output (<0.53 W, n = 99) were older and had a higher brain natriuretic peptide level than patients with high cardiac power output (≥0.53W, n = 181). Cardiac power output was correlated with peak oxygen consumption (peak V̇O2), peak workload achievement, and ventilatory efficiency (V̇E/V̇CO2 slope) in cardiopulmonary exercise testing, whereas each of cardiac output or mean arterial pressure was not. There were 48 patients with events over a median follow-up period of 3.5 (interquartile range 1.0 to 6.0) years. Patients with low cardiac power output had about a 2-fold higher risk of events than those with a high cardiac power output (hazard ratio 1.97, 95% confidence interval 1.12 to 3.48). In the multivariable Cox regression, a 0.1-W decrease in cardiac power output was associated with 19% increased adverse events (hazard ratio 0.81, 95% confidence interval 0.67 to 0.99). In conclusion, cardiac power output was associated with reduced exercise capacity and poor clinical outcome, suggesting that cardiac power output is useful for risk stratification in patients with CHF. Further study is required to identify therapies targeting cardiac power output to improve the exercise capacity or clinical outcome in patients with CHF.

When all Else Fails, Try This: The HeartMate III Left Ventricle Assist Device

Heart failure (HF) is a progressive disease. It is estimated that more than 250,000 patients suffer from advanced HF with reduced ejection fraction refractory to medical therapy. With limited donor pool for heart transplant, continue flow left ventricle assist device (LVAD) is a lifesaving treatment option for patients with advanced HF. This review will provide an update on indications, contraindications, and associated adverse events for LVAD support with a summary of the current outcomes data.

Relationship between baseline electrocardiographic measurements and outcomes in patients with high-risk heart failure: Insights from the VerICiguaT Global Study in Subjects with Heart Failure with Reduced Ejection Fraction (VICTORIA) trial

AIMS

Whether electrocardiographic (ECG) measurements predict mortality in chronic heart failure with reduced ejection fraction (HFrEF) is unknown.

METHODS AND RESULTS

We studied 4880 patients from the Vericiguat Global Study in Subjects with Heart Failure with Reduced Ejection Fraction (VICTORIA) trial with a baseline 12-lead ECG. Associations between ECG measurements and mortality were estimated as hazard ratios (HR) and adjusted for the Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC) risk score, N-terminal pro-B-type natriuretic peptide, and index event. Select interactions between ECG measurements, patient characteristics and mortality were examined. Over a median of 10.8 months, there were 824 cardiovascular (CV) deaths (214 sudden) and 1005 all-cause deaths. Median age was 68 years (interquartile range [IQR] 60-76), 24% were women, median ejection fraction was 30% (IQR 23-35), 41% had New York Heart Association class III/IV, and median MAGGIC score was 24 (IQR 19-28). After multivariable adjustment, significant associations existed between heart rate (per 5 bpm: HR 1.02), QRS duration (per 10 ms: HR 1.02), absence of left ventricular hypertrophy (HR 0.64) and CV death, and similarly so with all-cause death (HR 1.02; HR 1.02; HR 0.61, respectively). Contiguous pathologic Q waves were significantly associated with sudden death (HR 1.46), and right ventricular hypertrophy with all-cause death (HR 1.44). The only sex-based interaction observed was for pathologic Q waves on CV (men: HR 1.05; women: HR 1.64, pinteraction  = 0.024) and all-cause death (men: HR 0.99; women: HR 1.57; pinteraction  = 0.010). Whereas sudden death doubled in females, it did not differ among males (male: HR 1.25, 95% confidence interval [CI] 0.87-1.79; female: HR 2.50, 95% CI 1.23-5.06; pinteraction  = 0.141).

CONCLUSION

Routine ECG measurements provide additional prognostication of mortality in high-risk HFrEF patients, particularly in women with contiguous pathologic Q waves.

Cardiopulmonary Exercise Testing in Patients with Heart Failure: Impact of Gender in Predictive Value for Heart Transplantation Listing

BACKGROUND

Exercise testing is key in the risk stratification of patients with heart failure (HF). There are scarce data on its prognostic power in women. Our aim was to assess the predictive value of the heart transplantation (HTx) thresholds in HF in women and in men.

METHODS

Prospective evaluation of HF patients who underwent cardiopulmonary exercise testing (CPET) from 2009 to 2018 for the composite endpoint of cardiovascular mortality and urgent HTx.

RESULTS

A total of 458 patients underwent CPET, with a composite endpoint frequency of 10.5% in females vs. 16.0% in males in 36-month follow-up. Peak VO2 (pVO2), VE/VCO2 slope and percent of predicted pVO2 were independent discriminators of the composite endpoint, particularly in women. The International Society for Heart Lung Transplantation recommended values of pVO2 ≤ 12 mL/kg/min or ≤14 if the patient is intolerant to β-blockers, VE/VCO2 slope > 35, and percent of predicted pVO2 ≤ 50% showed a higher diagnostic effectiveness in women. Specific pVO2, VE/VCO2 slope and percent of predicted pVO2 cut-offs in each sex group presented a higher prognostic power than the recommended thresholds.

CONCLUSION

Individualized sex-specific thresholds may improve patient selection for HTx. More evidence is needed to address sex differences in HF risk stratification.

FGF-23 is a biomarker of RV dysfunction and congestion in patients with HFrEF

There is no biomarker reflecting right ventricular dysfunction in HFrEF patients used in clinical practice. We have aimed to look for a circulating marker of RV dysfunction employing a quantitative proteomic strategy. The Olink Proteomics Multiplex panels (Cardiovascular Disease II, III, Cardiometabolic, and Inflammation Target Panels) identified FGF-23 to be the most differentially abundant (more than 2.5-fold) in blood plasma of HF patients with severe RV dysfunction (n = 30) compared to those with preserved RV function (n = 31). A subsequent ELISA-based confirmatory analysis of circulating FGF-23 in a large cohort of patients (n = 344, 72.7% NYHA III/IV, LVEF 22.5%, 54.1% with moderate/severe RV dysfunction), followed by multivariable regression analysis, revealed that the plasma FGF-23 level was most significantly associated with RV dysfunction grade (p = 0.0004) and congestion in the systemic circulation (p = 0.03), but not with LV-ejection fraction (p = 0.69) or estimated glomerular filtration rate (eGFR, p = 0.08). FGF-23 was associated with the degree of RV dysfunction in both sub-cohorts (i.e. in patients with and without congestion, p < 0.0001). The association between FGF-23 and RV-dysfunction remained significant after the adjustment for BNP (p = 0.01). In contrast, when adjusted for BNP, FGF-23 was no longer associated with LV dysfunction (p = 0.59). The Cox proportional hazard model revealed that circulating FGF-23 was significantly associated with adverse outcomes even after adjusting for BNP, LVEF, RV dysfunction grade and eGFR. Circulating FGF-23 is thus a biomarker of right ventricular dysfunction in HFrEF patients regardless of congestion status.

Age Differences in Cardiopulmonary Exercise Testing Parameters in Heart Failure with Reduced Ejection Fraction

Background and Objectives: Cardiopulmonary exercise testing (CPET) is a cornerstone of risk stratification in heart failure with reduced ejection fraction (HFrEF). However, there is a paucity of evidence on its predictive power in older patients. The aim of this study was to evaluate the prognostic power of current heart transplantation (HTx) listing criteria in HFrEF stratified according to age groups. Materials and Methods: Consecutive patients with HFrEF undergoing CPET between 2009 and 2018 were followed-up for cardiac death and urgent HTx. Results: CPET was performed in 458 patients with HFrEF. The composite endpoint occurred in 16.8% of patients ≤50 years vs. 14.1% of patients ≥50 years in a 36-month follow-up. Peak VO2 (pVO2), VE/VCO2 slope and percentage of predicted pVO2 were strong independent predictors of outcomes. The International Society for Heart and Lung Transplantation thresholds of pVO2 ≤ 12 mL/kg/min (≤14 if intolerant to β-blockers), VE/VCO2 slope > 35 and percentage of predicted pVO2 ≤ 50% presented a higher overall diagnostic effectiveness in younger patients (≤50 years). Specific thresholds for each age subgroup outperformed the traditional cut-offs. Conclusions: Personalized age-specific thresholds may contribute to an accurate risk stratification in HFrEF. Further studies are needed to address the gap in evidence between younger and older patients.

The ABC of Heart Transplantation-Part 1: Indication, Eligibility, Donor Selection, and Surgical Technique

Cardiac transplantation represents the gold standard of treatment for selected patients with advanced heart failure who have poor functional capacity and prognosis despite guideline-directed medical therapy and device-based therapy. Proper patient selection and appropriate referral of patients to centers for the treatment of advanced heart failure are the first but decisive steps for screening patients eligible for cardiac transplantation. The eligibility and the decision to list for cardiac transplantation, even for patients with relative contraindications, are based on a multidisciplinary evaluation of a transplant team. This review will discuss the practical indications, the process of patient eligibility for cardiac transplantation, the principle of donor selection, as well as the surgical technique.

Cardiopulmonary Exercise Testing in the Age of New Heart Failure Therapies: Still a Powerful Tool?

BACKGROUND

New therapies with prognostic benefits have been recently introduced in heart failure with reduced ejection fraction (HFrEF) management. The aim of this study was to evaluate the prognostic power of current listing criteria for heart transplantation (HT) in an HFrEF cohort submitted to cardiopulmonary exercise testing (CPET) between 2009 and 2014 (group A) and between 2015 and 2018 (group B).

METHODS

Consecutive patients with HFrEF who underwent CPET were followed-up for cardiac death and urgent HT.

RESULTS

CPET was performed in 487 patients. The composite endpoint occurred in 19.4% of group A vs. 7.4% of group B in a 36-month follow-up. Peak VO2 (pVO2) and VE/VCO2 slope were the strongest independent predictors of mortality. International Society for Heart and Lung Transplantation (ISHLT) thresholds of pVO2 ≤ 12 mL/kg/min (≤14 if intolerant to β-blockers) and VE/VCO2 slope > 35 presented a similar and lower Youden index, respectively, in group B compared to group A, and a lower positive predictive value. pVO2 ≤ 10 mL/kg/min and VE/VCO2 slope > 40 outperformed the traditional cut-offs. An ischemic etiology subanalysis showed similar results.

CONCLUSION

ISHLT thresholds showed a lower overall prognostic effectiveness in a contemporary HFrEF population. Novel parameters may be needed to improve risk stratification.

Right ventricular global dysfunction score: a new concept of right ventricular function assessment in patients with heart failure with reduced ejection fraction (HFrEF)

Background

Right ventricular (RV) function is currently being evaluated solely according to the properties of RV myocardium. We have tested a concept that in patients with heart failure with reduced ejection fraction (HFrEF), RV assessment should integrate the information about both RV function as well as size.

Methods

A total of 836 stable patients with HFrEF (LVEF 23.6 ± 5.8%, 82.8% males, 68% NYHA III/IV) underwent echocardiographic evaluation and were prospectively followed for a median of 3.07 (IQRs 1.11; 4.89) years for the occurrence of death, urgent heart transplantation or implantation of mechanical circulatory support.

Results

RV size (measured as RV-basal diameter, RVD1) was significantly associated with an adverse outcome independent of RV dysfunction grade (p = 0.0002). The prognostic power of RVD1 was further improved by indexing to body surface area (RVD1i, p < 0.05 compared to non-indexed value). A novel parameter named RV global dysfunction score (RVGDs) was calculated as a product of RVD1i and the degree of RV dysfunction (1-4 for preserved RV function, mild, moderate and severe dysfunction, respectively). RVGDs showed a superior prognostic role compared to RV dysfunction grade alone (ΔAUC >0.03, p < 0.0001). In every subgroup of RVGDs (<20, 20-40, 40-60, >60), patients with milder degree of RV dysfunction but more dilated RV had similar outcome as those with more severe degree of RV dysfunction but smaller RV size (all p > 0.50), independent of tricuspid regurgitation severity and degree of pulmonary hypertension.

Conclusion

RV dilatation is a manifestation of RV dysfunction. The evaluation of RV performance should integrate the information about both RV size and function.

The First Dedicated Comprehensive Heart Failure Program in the United States: The Division of Circulatory Physiology at Columbia Presbyterian (1992-2004)

The first dedicated multidisciplinary heart failure program in the United States was founded as the Division of Circulatory Physiology at the Columbia University College of Physicians & Surgeons in 1992. The Division was administratively and financially independent of the Division of Cardiology and grew to 24 faculty members at its peak. Its administrative innovations included (1) a comprehensive full-integrated service line, with 2 differentiated clinical teams, one devoted to drug therapy and the other to heart transplantation and ventricular assist devices; (2) a nurse specialist/physician assistant-led clinical service; and (3) a financial structure independent of (and not supported by) other cardiovascular medical or surgical services. The division had 3 overarching missions: (1) to promote a unique career development path for each faculty member to be linked to recognition in a specific area of heart failure expertise; (2) to change the trajectory and enhance the richness of intellectual discourse in the discipline of heart failure, so as to foster an understanding of fundamental mechanisms and to develop new therapeutics; and (3) to provide optimal medical care to patients and to promote the ability of other physicians to provide optimal care. The major research achievements of the division included (1) the development of beta-blockers for heart failure, from initial hemodynamic assessments to proof-of-concept studies to large-scale international trials; (2) the development and definitive assessment of flosequinan, amlodipine, and endothelin antagonists; (3) initial clinical trials and concerns with nesiritide; (4) large-scale trials evaluating dosing of angiotensin converting-enzyme inhibitors and the efficacy and safety of neprilysin inhibition; (5) identification of key mechanisms in heart failure, including neurohormonal activation, microcirculatory endothelial dysfunction, deficiencies in peripheral vasodilator pathways, noncardiac factors in driving dyspnea, and the first identification of subphenotypes of heart failure and a preserved ejection fraction; (6) the development of a volumetric approach to the assessment of myocardial shortening; (7) conceptualization and early studies of cardiac contractility modulation as a treatment for heart failure; (8) novel approaches to the identification of cardiac allograft rejection and new therapeutics to prevent allograft vasculopathy; and (9) demonstration of the effect of left ventricular assist devices to induce reverse remodeling, and the first randomized trial showing a survival benefit with ventricular assist devices. Above all, the division served as an exceptional incubator for a generation of leaders in the field of heart failure.

How to select a patient for LVAD

Left ventricular assist device (LVAD) implantation leads to improvement in symptoms and survival in patients with advanced heart failure. An important factor in improving outcomes post-LVAD implantation is optimal preoperative patient selection and optimization. In this review, we highlight the latest on the evaluation of patients with advanced heart failure for LVAD candidacy, including discussion of patient selection, implantation timing, laboratory and other testing considerations, and the importance of psychosocial evaluation. Such thorough evaluation by multidisciplinary team can serve to improve the outcomes of a complex group of patients with advanced heart failure being evaluated for LVAD.

Prognostic Role of Metabolic Exercise Testing in Heart Failure

Heart failure is a clinical syndrome with significant heterogeneity in presentation and severity. Serial risk-stratification and prognostication can guide management decisions, particularly in advanced heart failure, when progression toward advanced therapies or end-of-life care is warranted. Each currently utilized prognostic marker carries its own set of challenges in acquisition, reproducibility, accuracy, and significance. Left ventricular ejection fraction is foundational for heart failure syndrome classification after clinical diagnosis and remains the primary parameter for inclusion in most clinical trials; however, it does not consistently correlate with symptoms and functional capacity, which are also independently prognostic in this patient population. Utilizing the left ventricular ejection fraction as the sole basis of prognostication provides an incomplete characterization of this condition and is prone to misguide medical decision-making when used in isolation. In this review article, we survey and exposit the important role of metabolic exercise testing across the heart failure spectrum, as a complementary diagnostic and prognostic modality. Metabolic exercise testing, also known as cardiopulmonary exercise testing, provides a comprehensive evaluation of the multisystem (i.e., neurological, respiratory, circulatory, and musculoskeletal) response to exercise performance. These differential responses can help identify the predominant contributors to exercise intolerance and exercise symptoms. Additionally, the aerobic exercise capacity (i.e., oxygen consumption during exercise) is directly correlated with overall life expectancy and prognosis in many disease states. Specifically in heart failure patients, metabolic exercise testing provides an accurate, objective, and reproducible assessment of the overall circulatory sufficiency and circulatory reserve during physical stress, being able to isolate the concurrent chronotropic and stroke volume responses for a reliable depiction of the circulatory flow rate in real time.

Effects of cardiac rehabilitation on cardiopulmonary test parameters in heart failure: A real world experience

Background

Cardio-Pulmonary Exercise Test (CPET) is the gold standard for evaluation of patients with heart failure (HF); however, its use is limited in everyday practice. We analyzed the use of CPET for HF management in the real world.

Methods

From 2009 to 2022, 341 patients with HF underwent 12-16 weeks of rehabilitation in our Centre. We present data from 203 patients (60%), excluding those unable to perform CPET, those with anaemia and severe pulmonary disease. Before and after rehabilitation, we performed CPET, blood tests and echocardiography, tailoring individual physical training to the results of baseline test. The following variables were considered: peak Respiratory Equivalent Ratio (RER), peakVO₂ (ml/Kg/min), VO₂ at aerobic threshold (VO₂AT,% maximal), VE/VCO₂ slope, P_(ET)CO₂, VO₂ /Work ratio (ΔVO₂/ΔWork).

Results

Rehabilitation improved peak VO₂, pulse O₂, VO₂ AT and ΔVO₂/ΔWork in all patients by about 13% (p < 0.01). Most patients (126, 62%) showed a reduced left ventricular ejection fraction (HFrEF), but rehabilitation was effective also in patients with mildly reduced (HFmrEF: n = 55, 27%) or preserved ejection fraction (HFpEF: n = 22, 11%).

Conclusions

Rehabilitation in patients with heart failure induces a significant recovery of cardiorespiratory performance easily assessed by CPET, that is applicable to the majority of them and should be used routinely in the programming and evaluating of cardiac rehabilitation programs.

Cardiopulmonary exercise testing and heart failure: a tale born from oxygen uptake

Since 50 years, cardiopulmonary exercise testing (CPET) plays a central role in heart failure (HF) assessment. Oxygen uptake (VO2) is one of the main HF prognostic indicators, then paralleled by ventilation to carbon dioxide (VE/VCO2) relationship slope. Also anaerobic threshold retains a strong prognostic power in severe HF, especially if expressed as a percent of maximal VO2 predicted value. Moving beyond its absolute value, a modern approach is to consider the percentage of predicted value for peak VO2 and VE/VCO2 slope, thus allowing a better comparison between genders, ages, and races. Several VO2 equations have been adopted to predict peak VO2, built considering different populations. A step forward was made possible by the introduction of reliable non-invasive methods able to calculate cardiac output during exercise: the inert gas rebreathing method and the thoracic electrical bioimpedance. These techniques made possible to calculate the artero-venous oxygen content differences (ΔC(a-v)O2), a value related to haemoglobin concentration, pO2, muscle perfusion, and oxygen extraction. The role of haemoglobin, frequently neglected, is however essential being anaemia a frequent HF comorbidity. Finally, peak VO2 is traditionally obtained in a laboratory setting while performing a standardized physical effort. Recently, different wearable ergo-spirometers have been developed to allow an accurate metabolic data collection during different activities that better reproduce HF patients' everyday life. The evaluation of exercise performance is now part of the holistic approach to the HF syndrome, with the inclusion of CPET data into multiparametric prognostic scores, such as the MECKI score.

Development and validation of explainable machine learning models for risk of mortality in transcatheter aortic valve implantation: TAVI risk machine scores

Aims

Identification of high-risk patients and individualized decision support based on objective criteria for rapid discharge after transcatheter aortic valve implantation (TAVI) are key requirements in the context of contemporary TAVI treatment. This study aimed to predict 30-day mortality following TAVI based on machine learning (ML) using data from the German Aortic Valve Registry.

Methods and results

Mortality risk was determined using a random forest ML model that was condensed in the newly developed TAVI Risk Machine (TRIM) scores, designed to represent clinically meaningful risk modelling before (TRIMpre) and in particular after (TRIMpost) TAVI. Algorithm was trained and cross-validated on data of 22 283 patients (729 died within 30 days post-TAVI) and generalisation was examined on data of 5864 patients (146 died). TRIMpost demonstrated significantly better performance than traditional scores [C-statistics value, 0.79; 95% confidence interval (CI)] [0.74; 0.83] compared to Society of Thoracic Surgeons (STS) with C-statistics value 0.69; 95%-CI [0.65; 0.74]). An abridged (aTRIMpost) score comprising 25 features (calculated using a web interface) exhibited significantly higher performance than traditional scores (C-statistics value, 0.74; 95%-CI [0.70; 0.78]). Validation on external data of 6693 patients (205 died within 30 days post-TAVI) of the Swiss TAVI Registry confirmed significantly better performance for the TRIMpost (C-statistics value 0.75, 95%-CI [0.72; 0.79]) compared to STS (C-statistics value 0.67, CI [0.63; 0.70]).

Conclusion

TRIM scores demonstrate good performance for risk estimation before and after TAVI. Together with clinical judgement, they may support standardised and objective decision-making before and after TAVI.

The Ability of Comorbidity Indices to Predict Mortality After Heart Transplantation: A Validation of the Danish Comorbidity Index for Acute Myocardial Infarction, Charlson Comorbidity Index, and Elixhauser Comorbidity Index

Advanced heart failure patients often have comorbidities of prognostic importance. However, whether total pretransplantation comorbidity burden predicts mortality in patients treated with heart transplantation (HTx) is unknown. We used population-based hospital and prescription data to examine the ability of the Danish Comorbidity Index for Acute Myocardial Infarction (DANCAMI), DANCAMI restricted to noncardiovascular diseases, Charlson Comorbidity Index, and Elixhauser Comorbidity Index to predict 30-d, 1-y, 5-y, and 10-y all-cause and cardiovascular mortality after HTx.

Methods

We identified all adult Danish patients with incident HTx from the Scandiatransplant Database between March 1, 1995, and December 31, 2018 (n = 563). We calculated Harrell's C-Statistics to examine discriminatory performance.

Results

The C-Statistic for predicting 1-y all-cause mortality after HTx was 0.58 (95% confidence interval [CI], 0.50-0.65) for a baseline model including age and sex. Adding comorbidity score to the baseline model did not increase the C-Statistics for DANCAMI (0.58; 95% CI, 0.50-0.65), DANCAMI restricted to noncardiovascular diseases (0.57; 95% CI, 0.50-0.64), Charlson Comorbidity Index (0.59; 95% CI, 0.51-0.66), or Elixhauser Comorbidity Index (0.58; 95% CI, 0.51-0.65). The results for 30-d, 5-y, and 10-y all-cause and cardiovascular mortality were consistent.

Conclusions

After accounting for patient age and sex, none of the commonly used comorbidity indices added predictive value to short- or long-term all-cause or cardiovascular mortality after HTx.

Optimizing exercise testing‐based risk stratification to predict poor prognosis after acute heart failure

AIMS

The timely selection of severe heart failure (HF) patients for cardiac transplantation and advanced HF therapy is challenging. Peak oxygen consumption (VO₂ ) values obtained by the cardiopulmonary exercise testing are used to determine the transplant recipient list. This study reassessed the prognostic predictability of peak VO₂ and compared it with the Heart Failure Survival Score (HFSS) in the modern optimized guideline-directed medical therapy (GDMT) era.

METHODS AND RESULTS

We retrospectively selected 377 acute HF patients discharged from the hospital. The primary outcome was a composite of all-cause mortality, or urgent cardiac transplantation. We divided these patients into the more GDMT (two or more types of GDMT) and less GDMT groups (fewer than two types of GDMT) and compared the performance of their peak VO₂ and HFSS in predicting primary outcomes. The median follow-up period was 3.3 years. The primary outcome occurred in 57 participants. Peak VO₂ outperformed HFSS when predicting 1 year (0.81 vs. 0.61; P = 0.017) and 2 year (0.78 vs. 0.58; P < 0.001) major outcomes. The cutoff peak VO₂ for predicting a 20% risk of a major outcome within 2 years was 10.2 (11.8-7.0) for the total cohort. Multivariate Cox regression analyses showed that peak VO₂ , sodium, previous implantable cardioverter defibrillator (ICD) implantation, and estimated glomerular filtration rate were significant predictors of major outcomes.

CONCLUSIONS

Optimizing the cutoff value of peak VO₂ is required in the current GDMT era for advanced HF therapy. Other clinical factors such as ICD use, hyponatraemia, and chronic kidney disease could also be used to predict poor prognosis. The improvement of resource allocation and patient outcomes could be achieved by careful selection of appropriate patients for advanced HF therapies, such as cardiac transplantation.

Hyponatremia Is a Powerful Predictor of Poor Prognosis in Left Ventricular Assist Device Patients

Serum sodium is an established prognostic marker in heart failure (HF) patients and is associated with an increased risk of morbidity and mortality. We sought to study the prognostic value of serum sodium in left ventricular assist device (LVAD) patients and whether hyponatremia reflects worsening HF or an alternative mechanism. We identified HF patients that underwent LVAD implantation between 2008 and 2019. Hyponatremia was defined as Na ≤134 mEq/L at 3 months after implantation. We assessed for differences in hyponatremia before and after LVAD implantation. We also evaluated the association of hyponatremia with all-cause mortality and recurrent HF hospitalizations. There were 342 eligible LVAD patients with a sodium value at 3 months. Among them, there was a significant improvement in serum sodium after LVAD implantation compared to preoperatively (137.2 vs. 134.7 mEq/L, P < 0.0001). Patients with and without hyponatremia had no significant differences in echocardiographic and hemodynamic measurements. In a multivariate analysis, hyponatremia was associated with a markedly increased risk of all-cause mortality (HR 3.69, 95% CI, 1.93-7.05, P < 0.001) when accounting for age, gender, co-morbidities, use of loop diuretics, and B-type natriuretic peptide levels. Hyponatremia was also significantly associated with recurrent HF hospitalizations (HR 2.11, 95% CI, 1.02-4.37, P = 0.04). Hyponatremia in LVAD patients is associated with significantly higher risk of all-cause mortality and recurrent HF hospitalizations. Hyponatremia may be a marker of ongoing neurohormonal activation that is more sensitive than other lab values, echocardiography parameters, and hemodynamic measurements.

The MAGGIC risk score in the prediction of death or hospitalization in patients with heart failure: Comparison with natriuretic peptides

BACKGROUND

The MAGGIC risk score has been validated to predict mortality in patients with heart failure (HF).

OBJECTIVES

To assess the score ability to predict hospitalization and death and to compare with natriuretic peptides.

METHODS

Ninety-three consecutive patients (mean age 62±10 years) with chronic HF and left ventricular ejection fraction (EF) <50% were studied. The MAGGIC score was applied at baseline and the patients were followed for 219±86 days. MAGGIC score was compared with NT-proBNP in the prediction of events. The primary end point was the time to the first event, which was defined as cardiovascular death or hospitalization for HF.

RESULTS

There were 23 (24.7%) events (3 deaths and 20 hospitalizations). The median score in patients with and without events was, respectively, 20 [interquartile range 14.2-22] vs. 15.5 [11/21], p=0.16. A ROC curve was performed and a cutoff point of 12 points showed a sensitivity of 87% and specificity of 37% with an area under the curve of 0.59 (95% CI 0.48-0.69) which was lower than that of NT-proBNP (AUC 0.67; 95% CI 0.56-0.76). The mean event-free survival time for patients above and below this cutpoint was 248.8±13 vs. 290±13.7 days (log rank test with p=0.044). Using the COX proportional hazard model, age (p=0.004), NT-proBNP >1000 pg/mL (p=0.014) and the MAGGIC score (p=0.025) were independently associated with the primary outcome.

CONCLUSION

The MAGGIC risk score was an independent predictor of events, including heart failure hospitalization. The addition of biomarkers improved the accuracy of the score.

Amino acid profiling to predict prognosis in patients with heart failure: an expert review

Heart failure is a complex disease with a poor prognosis. A number of widely used prognostic tools have limitations, so efforts to identify novel predictive markers and measures are important. As a metabolomics tool, amino acid profiling has shown promise in predicting heart failure prognosis; however, the evidence has not yet been sufficiently evaluated. We describe the utilization of amino acids in the healthy heart and in heart failure before reviewing the literature on amino acid profiling for prognostic prediction. We expertly interpret the findings and provide suggestions for future research to advance the understanding of the prognostic potential of amino acid profiling in heart failure. Our analysis revealed correlations between amino acid biomarkers and traditional prognostic factors, the additional prognostic value of amino acid biomarkers over traditional prognostic factors, and the successful use of amino acid biomarkers to distinguish heart failure aetiology. Although certain amino acid biomarkers have demonstrated additional prognostic value over traditional measures, such as New York Heart Association functional class, these measures are deeply rooted in clinical practice; thus, amino acid biomarkers may be best placed as additional prognostic tools to improve current risk stratification rather than as surrogate tools. Once the metabolic profiles of different heart failure aetiologies have been clearly delineated, the amino acid biomarkers with the most value in prognostic prediction should be determined. Amino acid profiling could be useful to evaluate the pathophysiology and metabolic status of different heart failure cohorts, distinguish heart failure aetiologies, and improve risk stratification and prognostic prediction.

Prognosis value of Forrester's classification in advanced heart failure patients awaiting heart transplantation

AIMS

The value of Forrester's perfusion/congestion profiles assessed by invasive catheter evaluation in non-inotrope advanced heart failure patients listed for heart transplant (HT) is unclear. We aimed to assess the value of haemodynamic evaluation according to Forrester's profiles to predict events on the HT waitlist.

METHODS AND RESULTS

All non-inotrope patients (n = 837, 79% ambulatory at listing) registered on the French national HT waiting list between 1 January 2013 and 31 December 2019 with right heart catheterization (RHC) were included. The primary outcome was a combined criteria of waitlist death, delisting for aggravation, urgent HT or left ventricular assist device implantation. Secondary outcome was waitlist death. The 'warm-dry', 'cold-dry', 'warm-wet', and 'cold-wet' profiles represented 27%, 18%, 27%, and 28% of patients, respectively. At 12 months, the respective rates of primary outcome were 15%, 17%, 25%, and 29% (P = 0.008). Taking the 'warm-dry' category as reference, a significant increase in the risk of primary outcome was observed only in the 'wet' categories, irrespectively of 'warm/cold' status: hazard ratios, 1.50; 1.06-2.13; P = 0.024 in 'warm-wet' and 1.77; 1. 25-2.49; P = 0.001 in 'cold-wet'.

CONCLUSIONS

Haemodynamic assessment of advanced HF patients using perfusion/congestion profiles predicts the risk of the combine endpoint of waitlist death, delisting for aggravation, urgent heart transplantation, or left ventricular assist device implantation. 'Wet' patients had the worst prognosis, independently of perfusion status, thus placing special emphasis on the cardinal prominence of persistent congestion in advanced HF.

Identifying novel phenotypes of elevated left ventricular end diastolic pressure using hierarchical clustering of features derived from electromechanical waveform data

Introduction

Elevated left ventricular end diastolic pressure (LVEDP) is a consequence of compromised left ventricular compliance and an important measure of myocardial dysfunction. An algorithm was developed to predict elevated LVEDP utilizing electro-mechanical (EM) waveform features. We examined the hierarchical clustering of selected features developed from these EM waveforms in order to identify important patient subgroups and assess their possible prognostic significance.

Materials and methods

Patients presenting with cardiovascular symptoms (N = 396) underwent EM data collection and direct LVEDP measurement by left heart catheterization. LVEDP was classified as non-elevated ( ≤ 12 mmHg) or elevated (≥25 mmHg). The 30 most contributive features to the algorithm output were extracted from EM data and input to an unsupervised hierarchical clustering algorithm. The resultant dendrogram was divided into five clusters, and patient metadata overlaid.

Results

The cluster with highest LVEDP (cluster 1) was most dissimilar from the lowest LVEDP cluster (cluster 5) in both clustering and with respect to clinical characteristics. In contrast to the cluster demonstrating the highest percentage of elevated LVEDP patients, the lowest was predominantly non-elevated LVEDP, younger, lower BMI, and males with a higher rate of significant coronary artery disease (CAD). The next adjacent cluster (cluster 2) to that of the highest LVEDP (cluster 1) had the second lowest LVEDP of all clusters. Cluster 2 differed from Cluster 1 primarily based on features extracted from the electrical data, and those that quantified predictability and variability of the signal. There was a low predictability and high variability in the highest LVEDP cluster 1, and the opposite in adjacent cluster 2.

Conclusion

This analysis identified subgroups of patients with varying degrees of LVEDP elevation based on waveform features. An approach to stratify movement between clusters and possible progression of myocardial dysfunction may include changes in features that differentiate clusters; specifically, reductions in electrical signal predictability and increases in variability. Identification of phenotypes of myocardial dysfunction evidenced by elevated LVEDP and knowledge of factors promoting transition to clusters with higher levels of left ventricular filling pressures could permit early risk stratification and improve patient selection for novel therapeutic interventions.

Pretransplant survival of patients with end-stage heart failure under competing risks

Heart transplantation is the gold standard of care for end-stage heart failure in the United States. Donor hearts are a scarce resource, however the current allocation policy—proposed in 2016 and implemented in 2018—has not addressed certain disparities. Between 2005 and 2016, the number of active candidates increased 127%, whereas transplant rates decreased 27.8%. Pretransplant mortality rates declined steadily for that period from 14.6 to 9.7, especially for candidates with mechanical circulatory assistive devices (MCSDs). This study reports survival analyses of candidates for heart transplantation list under competing events of transplantation and MCSD implantation. We queried the transplant data for a cohort of adult patients (age ≥ 16) without MCSDs prior to listing for transplantation between 2005 and 2014 (n = 23,373). We used cause-specific and subdistribution hazards models as multivariate regressions for all competing events. Patients listed as low priority for transplantation are less likely to require implantation but less likely to survive after 1,000 days of listing than patients listed at higher priorities. The current policy does not address this disparity as it focuses on stratifying patients with different types of MCSD. Clinical characteristics must be considered in prioritization.

Quality of life in patients with heart failure and improved ejection fraction: one-year changes and prognosis

AIMS

The criteria for patients with heart failure (HF) and improved ejection fraction (HFimpEF) are a baseline left ventricular ejection fraction (LVEF) ≤40%, a ≥10-point increase from baseline LVEF, and a second LVEF measurement >40%. We aimed to (i) assess patients with HF and reduced LVEF (HFrEF) at baseline and compare quality of life (QoL) changes between those that fulfilled and those that did not fulfil the HFimpEF criteria 1 year later and (ii) assess the prognostic role of QoL in patients with HFimpEF.

METHODS

We reviewed data from a prospective registry of real-world outpatients with HF that were assessed for LVEF and QoL at a first visit to the HF clinic and 1 year later. QoL was evaluated with the Minnesota Living with Heart Failure Questionnaire (MLWHFQ). The primary prognostic endpoint was the composite of all-cause death or HF hospitalization.

RESULTS

Baseline and 1-year LVEF and MLWFQ scores were available for 1040 patients with an initial LVEF ≤40% (mean age, 65.2 ± 11.7 years; 75.9% men). The main aetiology was ischaemic heart disease (52.9%), and patients were mostly in New York heart Association Classes II (71.1%) and III (21.6%). At baseline, the mean LVEF was 28.5% ± 7.3, and the mean MLWHFQ score was 30.2 ± 19.5. After 1 year, the mean LVEF increased to 38.0% ± 12.2, and the MLWHFQ scores improved to 17.4 ± 16.0. In 361 patients that fulfilled the HFimpEF criteria (34.7%), significant improvements were observed in both LVEF (from 28.7% ± 6.6 to 50.9% ± 7.6, P < 0.001) and QoL (from 32.9 ± 20.6 to 16.9 ± 16.0, P < 0.001). Patients that did not fulfil the HFimpEF criteria also showed significant improvements in LVEF (from 28.4% ± 7.6 to 31.1% ± 7.9, P < 0.001) and QoL (from 28.7 ± 18.8 to 17.6 ± 15.9, P < 0.001). However, the QoL improvement was significantly higher in the HFimpEF group (-16.0 ± 23.8 vs. -11.1 ± 20.3, P = 0.001), despite the worse mean baseline MLWHFQ score, compared with the non-HFimpEF group (P = 0.001). The 1-year QoL was similar between groups (P = 0.50). The 1-year MLWHFQ score was independently associated with outcomes; the hazard ratio for the composite endpoint was 1.02 (95% CI: 1.01-1.03, P = 0.006). In contrast, the QoL improvement (with a cut-off ≥5 points) was not independently associated with the composite outcome.

CONCLUSIONS

Patients with HFrEF showed improved QoL after 1 year, regardless of whether they met the HFimpEF criteria. The similar 1-year QoL perception between groups suggested that factors other than LVEF influenced QoL perception. The 1-year QoL was superior to the QoL change from baseline for predicting prognosis in patients with HFimpEF.

Validation of R-hf risk score for risk stratification in ischemic heart failure patients: A prospective cohort study

Background

The aim of this study was to validate R-heart failure (R-hf) risk score in ischemic heart failure patients.

Methods

We prospectively recruited a cohort of 179 ischemic and 107 non-ischemic heart failure patients. This study mainly focused on ischemic heart failure patients. Non-ischemic heart failure patients were included for the purpose of validation of the risk score in various heart failure groups. Patients were stratified in high risk, moderate risk and low risk groups according to R-hf risk score.

Results

A total of 179 participants with ischemic heart failure were included. Based on R-hf risk score, 82 had high risk, 50 had moderate risk and 47 had low risk heart failure scores. More than half of the patients having R-hf score of <5 had renal failure (n = 91, 50.8%) and anemia (n = 99, 55.3%). Notably, HFrEF was more prevalent in patients with high risk score (74, 90.2%). Patients with high risk score had significantly higher creatinine (2.63 ± 1.96, p < 0.001), Troponin-T HS (59.9 ± 38.0, p < 0.001) and PRO BNP (17842 ± 6684, p < 0.001) when compared to patients with low and moderate risk score. Patients with low risk score had significantly higher Hb (13.2 ± 1.85, p < 0.001), Albumin (3.69 ± 0.42, p < 0.001) and GFR (90.0 ± 8.04, p < 0.001). A R-hf score of <5 was a significant predictor of mortality in ischemic (OR = 50.34; 95% CI [16.94-194.00, p < 0.001) and non-ischemic (OR = 46.34; 95% CI [12.97-225.39], p < 0.001) heart failure patients.

Conclusions

Lower R-hf risk score is a significant predictor of mortality in ischemic and non-ischemic heart failure patients. Risk score can be accessed at https://www.hfriskcalc.in.

Metformin treatment is associated with improved outcome in patients with diabetes and advanced heart failure (HFrEF)

The role of metformin (MET) in the treatment of patients with advanced HFrEF and type 2 diabetes mellitus (DM) is not firmly established. We studied the impact of MET on metabolic profile, quality of life (QoL) and survival in these patients. A total of 847 stable patients with advanced HFrEF (57.4 ± 11.3 years, 67.7% NYHA III/IV, LVEF 23.6 ± 5.8%) underwent clinical and laboratory evaluation and were prospectively followed for a median of 1126 (IQRs 410; 1781) days for occurrence of death, urgent heart transplantation or mechanical circulatory support implantation. A subgroup of 380 patients (44.9%) had DM, 87 of DM patients (22.9%) were treated with MET. Despite worse insulin sensitivity and more severe DM (higher BMI, HbA1c, worse insulin resistance), MET-treated patients exhibited more stable HF marked by lower BNP level (400 vs. 642 ng/l), better LV and RV function, lower mitral and tricuspid regurgitation severity, were using smaller doses of diuretics (all p < 0.05). Further, they had higher eGFR (69.23 vs. 63.34 ml/min/1.73 m2) and better QoL (MLHFQ: 36 vs. 48 points, p = 0.002). Compared to diabetics treated with other glucose-lowering agents, MET-treated patients had better event-free survival even after adjustment for BNP, BMI and eGFR (p = 0.035). Propensity score-matched analysis with 17 covariates yielded 81 pairs of patients and showed a significantly better survival for MET-treated subgroup (p = 0.01). MET treatment in patients with advanced HFrEF and DM is associated with improved outcome by mechanisms beyond the improvement of blood glucose control.

A survey-based triage tool to identify patients potentially eligible for referral to an advanced heart failure centre

AIMS

Accurate prevalence data for ambulatory advanced heart failure (HF) in European countries remains limited. This study was designed to identify the population of patients potentially eligible for referral for assessment for advanced surgical HF therapies to a National advanced HF and cardiac transplant centre.

METHODS AND RESULTS

A survey comprising 13 potential clinical markers of advanced HF was developed, modified from the 'I NEED HELP' tool from the 2018 position statement of the Heart Failure Association of the European Society of Cardiology, and distributed to all HF clinic services (secondary and tertiary units) nationwide. Each HF clinic unit was asked to complete the survey on consecutive patients over a 3 month period fulfilling the following three criteria: (i) age <65 years; (ii) ejection fraction <40% and (iii) HF of >3 months duration. As a comparison, the number of actual referrals to the advanced HF clinic were also audited over a 9 month period. In all, 21 of 26 HF clinic units participated in the survey. Across the period of inclusion, 4950 all-comer HF patients were seen across all sites. Of these, 375 (7.5%) fulfilled the inclusion criteria and were surveyed (74.4% male, median age 57 years [IQR: 11 years]). In total, 246 (66%) of the surveyed patients had ≥1 potential markers for advanced HF, representing just under 5% of the total all-comer HF population seen across the same time period. Of these, 67 patients (27%) had ≥2, 48 (20%) had 3 and 40 (16%) had ≥4 potential markers. The most frequently noted markers were ≥1 HF hospitalization or unscheduled clinic review (56%), intolerance to renin-angiotensin-aldosterone system inhibitors due to hypotension or renal dysfunction (29%) and intolerance to beta-blockers due to hypotension (27%). Almost one-quarter of patients reported NYHA Class III or IV symptoms. During the advanced HF clinic audit, the number of patients actually referred to the advanced HF clinic during the same time period was <5% of this potentially eligible cohort.

CONCLUSIONS

In this index prospective National survey, approximately 5% of an all-comer routine HF clinic population and two-thirds of a pre-selected HF with reduced EF under 65 years cohort were found to have at least one clinical or biochemical marker suggesting advanced or impending advanced HF. Almost one-quarter of patients in this chronic outpatient 'snapshot' population have NYHA III-IV symptoms. This simple one-page triage survey-modified from the 'I NEED HELP' tool-is useful to identify a population potentially eligible for referral to an advanced HF centre for assessment for advanced surgical therapies, thereby aiding resource and service planning.

Long-Term Outcomes and Risk Stratification of Patients With Heart Failure With Recovered Ejection Fraction

This study aimed to understand the long-term outcomes of patients with heart failure with recovered ejection fraction, identify predictors of adverse events, and develop a risk stratification model. From an academic healthcare system, we retrospectively identified 133 patients (median age 66, 38% female, 30% ischemic etiology) who had an improvement in left ventricular ejection fraction (LVEF) from <40% to ≥53%. Significant predictors of all-cause mortality, hospitalization, and future reduction in LVEF were identified through Cox regression analysis. Kaplan-Meier survival was 70% at 5 years. Freedom from hospitalization was 58% at 1 year, and the risk of future LVEF reduction to <40% was 28% at 3 years. Diuretic dose and B-type natriuretic peptide (BNP) at the time of LVEF recovery were the strongest predictors of mortality and hospitalization in multivariate-adjusted analysis (BNP hazard ratio 1.13 per 100 pg/ml increase [p <0.01]; furosemide-equivalent dose hazard ratio 1.19 per 40 mg increase [p = 0.02]). An all-cause mortality Cox proportional hazard risk model incorporating New York Heart Association functional class, BNP and diuretic dose at the time of recovery showed excellent risk discrimination (c-statistic 0.79) and calibration. In conclusion, patients with heart failure with recovered ejection fraction have heterogenous clinical outcomes and are not "cured." A risk model using New York Heart Association functional class, BNP, and diuretic dose can accurately stratify mortality risk.

Predicting major events in ambulatory patients with advanced heart failure awaiting heart transplantation: a pilot study

AIMS

In heart failure (HF), prognostic risk scores focus on all-cause mortality prediction. However, in advanced HF (AdHF) ambulatory patients awaiting heart transplantation (HTx), hospitalizations for acutely decompensated/worsening HF are relevant to clinical decision-making, but unpredicted by common risk functions.

METHODS

Among consecutive ambulatory patients added to the waitlist for HTx, event discriminators within 2 years from recruitment were assessed prospectively by area under the curve from receiver-operating characteristic curves, and by Cox proportional hazards models. Primary composite end points included the first between all-cause mortality and acutely decompensated/worsening HF requiring hospitalization and specific treatments.

RESULTS

In 89 patients, 36 primary composite events were recorded in a 2-year follow-up (40% of the study sample), and associated with nonischemic etiology and nonsinus rhythm, with lower systolic blood pressure (BP), lower plasma sodium and hemoglobin concentrations, and with higher N-terminal pro-brain natriuretic peptide (NT-proBNP), larger left ventricular (LV) dimensions and lower LV ejection fraction, greater proportion of significant mitral regurgitation, lower tricuspid annulus peak systolic excursion (TAPSE), lower percentage of predicted distance at 6-minute walking test (%p6MWT) and lower global symptoms burden by the Kansas City Cardiomyopathy Questionnaire, lower peak oxygen uptake by cardiopulmonary exercise, and higher wedge pressure by right heart catheterization, as compared with those with no events (P < 0.05). Only Metabolic Exercise Cardiac Kidney Index (MECKI) at recruitment was higher with patients reporting events, which predicted composite end points in addition to and independently of NT-proBNP, and lower systolic BP (all P < 0.05). In an alternative risk model, severe mitral regurgitation and lower TAPSE replaced MECKI and BP but not NT-proBNP (all P < 0.01).

CONCLUSION

Higher NT-pro-BNP, lower systolic BP and higher MECKI may contribute to predicting all-cause death and acutely decompensated/worsening HF among ambulatory patients awaiting HTx, with lower TAPSE and severe mitral regurgitation representing further alternative independent prognosticators.

Effects of omecamtiv mecarbil in heart failure with reduced ejection fraction according to blood pressure: the GALACTIC-HF trial

AIM

Patients with heart failure with reduced ejection fraction and low systolic blood pressure (SBP) have high mortality, hospitalizations, and poorly tolerate evidence-based medical treatment. Omecamtiv mecarbil may be particularly helpful in such patients. This study examined its efficacy and tolerability in patients with SBP ≤100 mmHg enrolled in the Global Approach to Lowering Adverse Cardiac outcomes Through Improving Contractility in Heart Failure (GALACTIC-HF).

METHODS AND RESULTS

The GALACTIC-HF enrolled patients with baseline SBP ≥85 mmHg with a primary outcome of time to cardiovascular death or first heart failure event. In this analysis, patients were divided according to their baseline SBP (≤100 vs. >100 mmHg). Among the 8232 analysed patients, 1473 (17.9%) had baseline SBP ≤100 mmHg and 6759 (82.1%) had SBP >100 mmHg. The primary outcome occurred in 715 (48.5%) and 2415 (35.7%) patients with SBP ≤100 and >100 mmHg, respectively. Patients with lower SBP were at higher risk of adverse outcomes. Omecamtiv mecarbil, compared with placebo, appeared to be more effective in reducing the primary composite endpoint in patients with SBP ≤100 mmHg [hazard ratio (HR), 0.81; 95% confidence interval (CI), 0.70-0.94] compared with those with SBP >100 mmHg (HR, 0.95; 95% CI, 0.88-1.03; P-value for interaction = 0.051). In both groups, omecamtiv mecarbil did not change SBP values over time and did not increase the risk of adverse events, when compared with placebo.

CONCLUSION

In GALACTIC-HF, risk reduction of heart failure outcomes with omecamtiv mecarbil compared with placebo was large and significant in patients with low SBP. Omecamtiv mecarbil did not affect SBP and was well tolerated independent of SBP values.

Prognostic risk scores for patients with heart failure

Heart failure has many causes. Although new drugs, devices and technologies are available, the survival rate and prognosis of patients with heart failure remain poor, placing a significant burden on individuals and society. Attempts to improve outcomes for patients with heart failure include developing prognostic risk scores. With medical advances, however, previous heart failure risk scores are not fully applicable to current practice, particularly because of the classification as heart failure with reduced ejection fraction, heart failure with mildly reduced ejection fraction, and heart failure with preserved ejection fraction. This article describes the use of risk prediction scores for heart failure patients with different clinical status and discusses their clinical applicability.

The Utility of Pentraxin and Modified Prognostic Scales in Predicting Outcomes of Patients with End-Stage Heart Failure

Risk stratification is an important element of management in patients with heart failure (HF). We aimed to determine factors associated with predicting outcomes in end-stage HF patients listed for heart transplantation (HT), with particular emphasis placed on pentraxin-3 (PXT-3). In addition, we investigated whether the combination of PTX-3 with the Heart Failure Survival Score (HFSS), the Seattle Heart Failure Model (SHFM), or the Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC) improved the prognostic strength of these scales in the study population. We conducted a prospective analysis of 343 outpatients with end-stage HF who accepted the HT waiting list between 2015 and 2018. HFSS, SHFM, and MAGGIC scores were calculated for all patients. PTX3 was measured by sandwich enzyme-linked immunosorbent assay with a commercially available kit. The endpoints were death, left ventricular assist device implantation, and HT during the one-year follow-up. The median age was 56 (50−60) years, and 86.6% were male. During the follow-up period, 173 patients reached the endpoint. Independent risk factors associated with outcomes were ischemic etiology of HF [HR 1.731 (1.227−2.441), p = 0.0018], mean arterial pressure (MAP) [1.026 (1.010−1.042), p = 0.0011], body mass index (BMI) [1.055 (1.014−1.098), p = 0.0083], sodium [1.056 [(1.007−1.109), p = 0.0244] PTX-3 [1.187 (1.126−1.251, p < 0.0001) and N-terminal pro-brain natriuretic peptide (NT-proBNP) [HR 1.004 (1.000−1.008), p = 0.0259]. The HFSS-PTX-3, SHFM-PTX-3 and MAGGIC-PTX-3 scores had significantly higher predictive power [AUC = 0.951, AUC = 0.973; AUC = 0.956, respectively] than original scores [AUC for HFSS = 0.8481, AUC for SHFM = 0.7976, AUC for MAGGIC = 0.7491]. Higher PTX-3 and NT-proBNP concentrations, lower sodium concentrations, lower MAP and BMI levels, and ischemic etiology of HF are associated with worse outcomes in patients with end-stage HF. The modified SHFM-PTX-3, HFSS-PTX-3, and MAGGIC-PTX-3 scores provide effective methods of assessing the outcomes in the analyzed group.

2022 AHA/ACC/HFSA Guideline for the Management of Heart Failure: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines

AIM

The "2022 AHA/ACC/HFSA Guideline for the Management of Heart Failure" replaces the "2013 ACCF/AHA Guideline for the Management of Heart Failure" and the "2017 ACC/AHA/HFSA Focused Update of the 2013 ACCF/AHA Guideline for the Management of Heart Failure." The 2022 guideline is intended to provide patient-centric recommendations for clinicians to prevent, diagnose, and manage patients with heart failure.

METHODS

A comprehensive literature search was conducted from May 2020 to December 2020, encompassing studies, reviews, and other evidence conducted on human subjects that were published in English from MEDLINE (PubMed), EMBASE, the Cochrane Collaboration, the Agency for Healthcare Research and Quality, and other relevant databases. Additional relevant clinical trials and research studies, published through September 2021, were also considered. This guideline was harmonized with other American Heart Association/American College of Cardiology guidelines published through December 2021. Structure: Heart failure remains a leading cause of morbidity and mortality globally. The 2022 heart failure guideline provides recommendations based on contemporary evidence for the treatment of these patients. The recommendations present an evidence-based approach to managing patients with heart failure, with the intent to improve quality of care and align with patients' interests. Many recommendations from the earlier heart failure guidelines have been updated with new evidence, and new recommendations have been created when supported by published data. Value statements are provided for certain treatments with high-quality published economic analyses.

2022 AHA/ACC/HFSA Guideline for the Management of Heart Failure

AIM

The "2022 AHA/ACC/HFSA Guideline for the Management of Heart Failure" replaces the "2013 ACCF/AHA Guideline for the Management of Heart Failure" and the "2017 ACC/AHA/HFSA Focused Update of the 2013 ACCF/AHA Guideline for the Management of Heart Failure." The 2022 guideline is intended to provide patient-centric recommendations for clinicians to prevent, diagnose, and manage patients with heart failure.

METHODS

A comprehensive literature search was conducted from May 2020 to December 2020, encompassing studies, reviews, and other evidence conducted on human subjects that were published in English from MEDLINE (PubMed), EMBASE, the Cochrane Collaboration, the Agency for Healthcare Research and Quality, and other relevant databases. Additional relevant clinical trials and research studies, published through September 2021, were also considered. This guideline was harmonized with other American Heart Association/American College of Cardiology guidelines published through December 2021.

STRUCTURE

Heart failure remains a leading cause of morbidity and mortality globally. The 2022 heart failure guideline provides recommendations based on contemporary evidence for the treatment of these patients. The recommendations present an evidence-based approach to managing patients with heart failure, with the intent to improve quality of care and align with patients' interests. Many recommendations from the earlier heart failure guidelines have been updated with new evidence, and new recommendations have been created when supported by published data. Value statements are provided for certain treatments with high-quality published economic analyses.

Heart Transplantation: A Bibliometric Review From 1990-2021

BACKGROUND

As the rapidly aging population and the rising incidence of end-stage heart failure (HF), extensive research has been conducted on heart transplantation (HTx). Bibliometrics harbors the function for describing the relationships of knowledge structures in different research fields and predicting the growth trend .

METHODS

The publications were searched and filtered based on the WOS core database. The target literature was visualized and analyzed by CiteSpace or VOSviewer .

RESULTS

In total, 19,998 published papers were obtained. There is a wave-like growth in HTx development. Most advanced research results are concentrated in a few developed countries, while the interactions with developing countries are still in infancy. The United States occupies a strong dominant position among active countries on HTx. Early research hotpots mostly focused on primary disease, survival risk factors, and complications. In recent years, the research frontiers have shifted steadily to clinical evaluation of immunosuppressants and diagnosis of acute rejection, cardiac re-injury with COVID-19, innovations in ventricular assist devices(VAD), and donation allocation strategies. The research directions of HTx are gradually shifting from observational studies to intervention research.