Serial cardiac biomarkers, pulmonary artery pressures and traditional parameters of fluid status in relation to prognosis in patients with chronic heart failure: Design and rationale of the BioMEMS study.

AIMS: Heart failure (HF), a global pandemic affecting millions of individuals, calls for adequate predictive guidance for improved therapy. Congestion, a key factor in HF-related hospitalizations, further underscores the need for timely interventions. Proactive monitoring of intracardiac pressures, guided by pulmonary artery (PA) pressure, offers opportunities for efficient early-stage intervention, since haemodynamic congestion precedes clinical symptoms. METHODS: The BioMEMS study, a substudy of the MONITOR-HF trial, proposes a multifaceted approach integrating blood biobank data with traditional and novel HF parameters. Two additional blood samples from 340 active participants in the MONITOR-HF trial were collected at baseline, 3-, 6-, and 12-month visits and stored for the BioMEMS biobank. The main aims are to identify the relationship between temporal biomarker patterns and PA pressures derived from the CardioMEMS-HF system, and to identify the biomarker profile(s) associated with the risk of HF events and cardiovascular death. CONCLUSION: Since the prognostic value of single baseline measurements of biomarkers like N-terminal pro-B-type natriuretic peptide is limited, with the BioMEMS study we advocate a dynamic, serial approach to better capture HF progression. We will substantiate this by relating repeated biomarker measurements to PA pressures. This design rationale presents a comprehensive review on cardiac biomarkers in HF, and aims to contribute valuable insights into personalized HF therapy and patient risk assessment, advancing our ability to address the evolving nature of HF effectively.

Photoplethysmography and intracardiac pressures: early insights from a pilot study.

Aims: Invasive haemodynamic monitoring of heart failure (HF) is used to detect deterioration in an early phase thereby preventing hospitalizations. However, this invasive approach is costly and presently lacks widespread accessibility. Hence, there is a pressing need to identify an alternative non-invasive method that is reliable and more readily available. In this pilot study, we investigated the relation between wrist-derived photoplethysmography (PPG) signals and the invasively measured pulmonary capillary wedge pressure (PCWP). Methods and results: Fourteen patients with aortic valve stenosis who underwent transcatheter aortic valve replacement with concomitant right heart catheterization and PPG measurements were included. Six unique features of the PPG signals [heart rate, heart rate variability, systolic amplitude (SA), diastolic amplitude, crest time (CT), and large artery stiffness index (LASI)] were extracted. These features were used to estimate the continuous PCWP values and the categorized PCWP (low < 12 mmHg vs. high ≥ 12 mmHg). All PPG features resulted in regression models that showed low correlations with the invasively measured PCWP. Classification models resulted in higher performances: the model based on the SA and the model based on the LASI both resulted in an area under the curve (AUC) of 0.86 and the model based on the CT resulted in an AUC of 0.72. Conclusion: These results demonstrate the capability to non-invasively classify patients into clinically meaningful categories of PCWP using PPG signals from a wrist-worn wearable device. To enhance and fully explore its potential, the relationship between PPG and PCWP should be further investigated in a larger cohort of HF patients.

Machine learning-based analysis of non-invasive measurements for predicting intracardiac pressures.

Aims: Early detection of congestion has demonstrated to improve outcomes in heart failure (HF) patients. However, there is limited access to invasively haemodynamic parameters to guide treatment. This study aims to develop a model to estimate the invasively measured pulmonary capillary wedge pressure (PCWP) using non-invasive measurements with both traditional statistics and machine learning (ML) techniques. Methods and results: The study involved patients undergoing right-sided heart catheterization at Erasmus MC, Rotterdam, from 2017 to 2022. Invasively measured PCWP served as outcomes. Model features included non-invasive measurements of arterial blood pressure, saturation, heart rate (variability), weight, and temperature. Various traditional and ML techniques were used, and performance was assessed using R2 and area under the curve (AUC) for regression and classification models, respectively. A total of 853 procedures were included, of which 31% had HF as primary diagnosis and 49% had a PCWP of 12 mmHg or higher. The mean age of the cohort was 59 ± 14 years, and 52% were male. The heart rate variability had the highest correlation with the PCWP with a correlation of 0.16. All the regression models resulted in low R2 values of up to 0.04, and the classification models resulted in AUC values of up to 0.59. Conclusion: In this study, non-invasive methods, both traditional and ML-based, showed limited correlation to PCWP. This highlights the weak correlation between traditional HF monitoring and haemodynamic parameters, also emphasizing the limitations of single non-invasive measurements. Future research should explore trend analysis and additional features to improve non-invasive haemodynamic monitoring, as there is a clear demand for further advancements in this field.

Cost-effectiveness of remote haemodynamic monitoring by an implantable pulmonary artery pressure monitoring sensor (CardioMEMS-HF system) in chronic heart failure in the Netherlands.

AIMS: Remote haemodynamic monitoring with an implantable pulmonary artery (PA) sensor has been shown to reduce heart failure (HF) hospitalizations and improve quality of life. Cost-effectiveness analyses studying the value of remote haemodynamic monitoring in a European healthcare system with a contemporary standard care group are lacking. METHODS AND RESULTS: A Markov model was developed to estimate the cost-effectiveness of PA-guided therapy compared to the standard of care based upon patient-level data of the MONITOR-HF trial performed in the Netherlands in patients with chronic HF (New York Heart Association class III and at least one previous HF hospitalization). Cost-effectiveness was measured as the incremental cost per quality-adjusted life year (QALY) gained from the Dutch societal perspective with a lifetime horizon which encompasses a wide variety of costs including costs of hospitalizations, monitoring time, telephone contacts, laboratory assessments, and drug changes in both treatment groups. In the base-case analysis, PA-guided therapy increased costs compared to standard of care by €12 121. The QALYs per patient for PA-guided therapy and standard of care was 4.07 and 3.481, respectively, reflecting a gain of 0.58 QALYs. The resulting incremental cost-effectiveness ratio was €20 753 per QALY, which is below the Dutch willingness-to-pay threshold of €50 000 per QALY gained for HF. CONCLUSIONS: The current cost-effectiveness study suggests that remote haemodynamic monitoring with PA-guided therapy on top of standard care is likely to be cost-effective for patients with symptomatic moderate-to-severe HF in the Netherlands.

[Options for home telemonitoring in heart failure patients]. / Opties voor thuismonitoring bij patiënten met hartfalen.

OBJECTIVE: To provide an overview of the effectiveness of various forms of home telemonitoring systems for heart failure and the potential role these systems may have in decreasing the burden on healthcare through reduction in heart failure hospitalizations. DESIGN: Meta-analysis of randomized and observational studies. METHODS: A meta-analysis of randomized and observational studies was carried out to assess the effect on mortality and heart failure-related hospitalizations, comparing standard heart failure care with the use of home monitoring systems. RESULTS: The pooled results from 92 studies demonstrate a reduction in the risk of both mortality and heart failure admissions. CONCLUSION: The results of this meta-analysis support the use of home telemonitoring systems. Determining which form of home telemonitoring is most suitable for which patient requires further research.

Embedding routine health care data in clinical trials: with great power comes great responsibility.

Randomised clinical trials (RCTs) are vital for medical progress. Unfortunately, 'traditional' RCTs are expensive and inherently slow. Moreover, their generalisability has been questioned. There is considerable overlap in routine health care data (RHCD) and trial-specific data. Therefore, integration of RHCD in an RCT has great potential, as it would reduce the effort and costs required to collect data, thereby overcoming some of the major downsides of a traditional RCT. However, use of RHCD comes with other challenges, such as privacy issues, as well as technical and practical barriers. Here, we give a current overview of related initiatives on national cardiovascular registries (Netherlands Heart Registration, Heart4Data), showcasing the interrelationships between and the relevance of the different registries for the practicing physician. We then discuss the benefits and limitations of RHCD use in the setting of a pragmatic RCT from a cardiovascular perspective, illustrated by a case study in heart failure.

Guideline implementation, drug sequencing, and quality of care in heart failure: design and rationale of TITRATE-HF.

AIMS: Current heart failure (HF) guidelines recommend to prescribe four drug classes in patients with HF with reduced ejection fraction (HFrEF). A clear challenge exists to adequately implement guideline-directed medical therapy (GDMT) regarding the sequencing of drugs and timely reaching target dose. It is largely unknown how the paradigm shift from a serial and sequential approach for drug therapy to early parallel application of the four drug classes will be executed in daily clinical practice, as well as the reason clinicians may not adhere to new guidelines. We present the design and rationale for the real-world TITRATE-HF study, which aims to assess sequencing strategies for GDMT initiation, dose titration patterns (order and speed), intolerance for GDMT, barriers for implementation, and long-term outcomes in patients with de novo, chronic, and worsening HF. METHODS AND RESULTS: A total of 4000 patients with HFrEF, HF with mildly reduced ejection fraction, and HF with improved ejection fraction will be enrolled in >40 Dutch centres with a follow-up of at least 3 years. Data collection will include demographics, physical examination and vital parameters, electrocardiogram, laboratory measurements, echocardiogram, medication, and quality of life. Detailed information on titration steps will be collected for the four GDMT drug classes. Information will include date, primary reason for change, and potential intolerances. The primary clinical endpoints are HF-related hospitalizations, HF-related urgent visits with a need for intravenous diuretics, all-cause mortality, and cardiovascular mortality. CONCLUSIONS: TITRATE-HF is a real-world multicentre longitudinal registry that will provide unique information on contemporary GDMT implementation, sequencing strategies (order and speed), and prognosis in de novo, worsening, and chronic HF patients.

The different risk of new-onset, chronic, worsening, and advanced heart failure: A systematic review and meta-regression analysis.

AIMS: Heart failure (HF) is a chronic and progressive syndrome associated with a poor prognosis. While it may seem intuitive that the risk of adverse outcomes varies across the different stages of HF, an overview of these risks is lacking. This study aims to determine the risk of all-cause mortality and HF hospitalizations associated with new-onset HF, chronic HF (CHF), worsening HF (WHF), and advanced HF. METHODS AND RESULTS: We performed a systematic review of observational studies from 2012 to 2022 using five different databases. The primary outcomes were 30-day and 1-year all-cause mortality, as well as 1-year HF hospitalization. Studies were pooled using random effects meta-analysis, and mixed-effects meta-regression was used to compare the different HF groups. Among the 15 759 studies screened, 66 were included representing 862 046 HF patients. Pooled 30-day mortality rates did not reveal a significant distinction between hospital-admitted patients, with rates of 10.13% for new-onset HF and 8.11% for WHF (p = 0.10). However, the 1-year mortality risk differed and increased stepwise from CHF to advanced HF, with a rate of 8.47% (95% confidence interval [CI] 7.24-9.89) for CHF, 21.15% (95% CI 17.78-24.95) for new-onset HF, 26.84% (95% CI 23.74-30.19) for WHF, and 29.74% (95% CI 24.15-36.10) for advanced HF. Readmission rates for HF at 1 year followed a similar trend. CONCLUSIONS: Our meta-analysis of observational studies confirms the different risk for adverse outcomes across the distinct HF stages. Moreover, it emphasizes the negative prognostic value of WHF as the first progressive stage from CHF towards advanced HF.

Sex-specific cardiovascular protein levels and their link with clinical outcome in heart failure.

AIMS: This study aims to provide insight into sex-specific cardiovascular protein profiles and their associations with adverse outcomes, which may contribute to a better understanding of heart failure (HF) pathophysiology and the optimal use of circulating proteins for prognostication in women and men. METHODS AND RESULTS: In 250 stable patients with HF with reduced ejection fraction (HFrEF), we performed trimonthly blood sampling (median follow-up: 26 [17-30] months). We selected all baseline samples and two samples closest to the primary endpoint (PEP; composite of cardiovascular death, heart transplantation, left ventricular assist device implantation, and HF hospitalization) or one sample closest to censoring and applied the Olink Cardiovascular III panel. We used linear regression to study sex-based differences in baseline levels and joint models to study differences in the prognostic value of serially measured proteins. In 66 women and 184 men (mean age of 66 and 67 years, respectively), 21% and 28% reached the PEP, respectively. Mean baseline levels of fatty acid-binding protein 4, secretoglobin family 3A member 2, paraoxonase 3, and trefoil factor 3 were higher in women (Pinteraction : 0.001, 0.007, 0.018, and 0.049, respectively), while matrix metalloproteinase-3, interleukin 1 receptor-like 1, and myoglobin were higher in men (Pinteraction : <0.001, 0.001, and 0.049, respectively), independent of clinical characteristics. No significant differences between sexes were observed in the longitudinal associations of proteins with the PEP. Only peptidoglycan recognition protein 1 showed a suggestive interaction with sex for the primary outcome (Pinteraction  = 0.028), without multiple testing correction, and was more strongly associated with adverse outcome in women {hazard ratio [HR] 3.03 [95% confidence interval (CI), 1.42 to 6.68], P = 0.008} compared with men [HR 1.18 (95% CI, 0.84 to 1.66), P = 0.347]. CONCLUSIONS: Although multiple cardiovascular-related proteins show sex differences at baseline, temporal associations with the adverse outcome do not differ between women and men with HFrEF.

Machine learning-based biomarker profile derived from 4210 serially measured proteins predicts clinical outcome of patients with heart failure.

Aims: Risk assessment tools are needed for timely identification of patients with heart failure (HF) with reduced ejection fraction (HFrEF) who are at high risk of adverse events. In this study, we aim to derive a small set out of 4210 repeatedly measured proteins, which, along with clinical characteristics and established biomarkers, carry optimal prognostic capacity for adverse events, in patients with HFrEF. Methods and results: In 382 patients, we performed repeated blood sampling (median follow-up: 2.1 years) and applied an aptamer-based multiplex proteomic approach. We used machine learning to select the optimal set of predictors for the primary endpoint (PEP: composite of cardiovascular death, heart transplantation, left ventricular assist device implantation, and HF hospitalization). The association between repeated measures of selected proteins and PEP was investigated by multivariable joint models. Internal validation (cross-validated c-index) and external validation (Henry Ford HF PharmacoGenomic Registry cohort) were performed. Nine proteins were selected in addition to the MAGGIC risk score, N-terminal pro-hormone B-type natriuretic peptide, and troponin T: suppression of tumourigenicity 2, tryptophanyl-tRNA synthetase cytoplasmic, histone H2A Type 3, angiotensinogen, deltex-1, thrombospondin-4, ADAMTS-like protein 2, anthrax toxin receptor 1, and cathepsin D. N-terminal pro-hormone B-type natriuretic peptide and angiotensinogen showed the strongest associations [hazard ratio (95% confidence interval): 1.96 (1.17-3.40) and 0.66 (0.49-0.88), respectively]. The multivariable model yielded a c-index of 0.85 upon internal validation and c-indices up to 0.80 upon external validation. The c-index was higher than that of a model containing established risk factors (P = 0.021). Conclusion: Nine serially measured proteins captured the most essential prognostic information for the occurrence of adverse events in patients with HFrEF, and provided incremental value for HF prognostication beyond established risk factors. These proteins could be used for dynamic, individual risk assessment in a prospective setting. These findings also illustrate the potential value of relatively 'novel' biomarkers for prognostication. Clinical Trial Registration: https://clinicaltrials.gov/ct2/show/NCT01851538?term=nCT01851538&draw=2&rank=1 24.

Use of loop diuretics in patients with chronic heart failure: an observational overview.

INTRODUCTION: This study aimed to evaluate the use and dose of loop diuretics (LDs) across the entire ejection fraction (EF) spectrum in a large, 'real-world' cohort of chronic heart failure (HF) patients. METHODS: A total of 10 366 patients with chronic HF from 34 Dutch outpatient HF clinics were analysed regarding diuretic use and diuretic dose. Data regarding daily diuretic dose were stratified by furosemide dose equivalent (FDE)>80 mg or ≤80 mg. Multivariable logistic regression models were used to assess the association between diuretic dose and clinical features. RESULTS: In this cohort, 8512 (82.1%) patients used diuretics, of which 8179 (96.1%) used LDs. LD use was highest among HF with reduced EF (HFrEF) patients (81.1%) followed by HF with mild-reduced EF (76.1%) and HF with preserved ejection fraction EF (73.8%, p<0.001). Among all LDs users, the median FDE was 40 mg (IQR: 40-80). The results of the multivariable analysis showed that New York Heart Association classes III and IV and diabetes mellitus were one of the strongest determinants of an FDE >80 mg, across all HF categories. Renal impairment was associated with a higher FDE across the entire EF spectrum. CONCLUSION: In this large registry of real-world HF patients, LD use was highest among HFrEF patients. Advanced symptoms, diabetes mellitus and worse renal function were significantly associated with a higher diuretic dose regardless of left ventricular ejection fraction.

Association between epicardial adipose tissue and cardiac dysfunction in subjects with severe obesity.

AIM: Epicardial adipose tissue (EAT) plays a role in obesity-related heart failure with preserved ejection fraction. However, the association of EAT thickness with the development of cardiac dysfunction in subjects with severe obesity without known cardiovascular disease is unclear. The aim of this study was to determine the association between EAT thickness and cardiac dysfunction and describe the potential value of EAT as an early marker of cardiac dysfunction. METHODS AND RESULTS: Subjects with body mass index ≥35 kg/m2 aged 35 to 65 years, who were referred for bariatric surgery, without suspicion of or known cardiac disease, were enrolled. Conventional transthoracic echocardiography and strain analyses were performed. A total of 186 subjects were divided into tertiles based on EAT thickness, of whom 62 were in EAT-1 (EAT <3.8 mm), 63 in EAT-2 (EAT 3.8-5.4 mm), and 61 in EAT-3 (EAT >5.4 mm). Parameters of systolic and diastolic function were comparable between tertiles. Patients in EAT-3 had the lowest global longitudinal strain (GLS) and left atrial contractile strain (LASct). Linear regression showed that a one-unit increase in EAT thickness (mm) was independently associated with a decrease in GLS (%) (ß coefficient -0.404, p = 0.002), and a decrease in LASct (%) (ß coefficient -0.544, p = 0.027). Furthermore, EAT-3 independently predicted cardiac dysfunction as defined by a GLS <18% (odds ratio 2.8, p = 0.013) and LASct <14% (odds ratio 2.5, p = 0.045). CONCLUSIONS: Increased EAT thickness in subjects with obesity without known cardiac disease was independently associated with subclinical cardiac dysfunction. Our findings suggest that EAT might play a role in the early stages of cardiac dysfunction in obesity before this may progress to overt clinical disease.

Management of Worsening Heart Failure With Reduced Ejection Fraction: JACC Focus Seminar 3/3.

Despite worsening heart failure (HF) being extremely common, expensive, and associated with substantial risk of death, there remain no dedicated clinical practice guidelines for the specific management of these patients. The lack of a management guideline is despite a rapidly evolving evidence-base, as a number of recent clinical trials have demonstrated multiple therapies to be safe and efficacious in this high-risk population. Herein, we propose a framework for treating worsening HF with reduced ejection fraction with the sense of urgency it deserves. This includes treating congestion; managing precipitants; and establishing a foundation of rapid-sequence, simultaneous, and/or in-hospital initiation of quadruple medical therapy for HF with reduced ejection fraction, with the top priority being at least low doses of all 4 medications. Moreover, to maximally reduce residual clinical risk, we further propose consideration of upfront simultaneous use of vericiguat (ie, quintuple medical therapy) and administration of intravenous iron for those who are iron deficient.

Discovering Distinct Phenotypical Clusters in Heart Failure Across the Ejection Fraction Spectrum: a Systematic Review.

REVIEW PURPOSE: This systematic review aims to summarise clustering studies in heart failure (HF) and guide future clinical trial design and implementation in routine clinical practice. FINDINGS: 34 studies were identified (n = 19 in HF with preserved ejection fraction (HFpEF)). There was significant heterogeneity invariables and techniques used. However, 149/165 described clusters could be assigned to one of nine phenotypes: 1) young, low comorbidity burden; 2) metabolic; 3) cardio-renal; 4) atrial fibrillation (AF); 5) elderly female AF; 6) hypertensive-comorbidity; 7) ischaemic-male; 8) valvular disease; and 9) devices. There was room for improvement on important methodological topics for all clustering studies such as external validation and transparency of the modelling process. The large overlap between the phenotypes of the clustering studies shows that clustering is a robust approach for discovering clinically distinct phenotypes. However, future studies should invest in a phenotype model that can be implemented in routine clinical practice and future clinical trial design. HF = heart failure, EF = ejection fraction, HFpEF = heart failure with preserved ejection fraction, HFrEF = heart failure with reduced ejection fraction, CKD = chronic kidney disease, AF = atrial fibrillation, IHD = ischaemic heart disease, CAD = coronary artery disease, ICD = implantable cardioverter-defibrillator, CRT = cardiac resynchronization therapy, NT-proBNP = N-terminal pro b-type natriuretic peptide, BMI = Body Mass Index, COPD = Chronic obstructive pulmonary disease.

Guideline-directed medical therapy for HFrEF: sequencing strategies and barriers for life-saving drug therapy.

Multiple landmark trials have helped to advance the treatment of heart failure with reduced ejection fraction (HFrEF) significantly over the past decade. These trials have led to the introduction of four main drug classes into the 2021 ESC guideline, namely angiotensin-receptor neprilysin inhibitors/angiotensin-converting-enzyme inhibitors, beta-blockers, mineralocorticoid receptor antagonists, and sodium-glucose cotransporter-2 inhibitors. The life-saving effect of these therapies has been shown to be additive and becomes apparent within weeks, which is why maximally tolerated or target doses of all drug classes should be strived for as quickly as possible. Recent evidence, such as the STRONG-HF trial, demonstrated that rapid drug implementation and up-titration is superior to the traditional and more gradual step-by-step approach where valuable time is lost to up-titration. Accordingly, multiple rapid drug implementation and sequencing strategies have been proposed to significantly reduce the time needed for the titration process. Such strategies are urgently needed since previous large-scale registries have shown that guideline-directed medical therapy (GDMT) implementation is a challenge. This challenge is reflected by generally low adherence rates, which can be attributed to factors considering the patient, health care system, and local hospital/health care provider. This review of the four medication classes used to treat HFrEF seeks to present a thorough overview of the data supporting current GDMT, discuss the obstacles to GDMT implementation and up-titration, and identify multiple sequencing strategies that could improve GDMT adherence. Sequencing strategies for GDMT implementation. GDMT: guideline-directed medical therapy; ACEi: angiotensin-converting enzyme inhibitor; ARB: Angiotensin II receptor blocker; ARNi: angiotensin receptor-neprilysin inhibitor; BB: beta-blocker; MRA: mineralocorticoid receptor antagonist; SGLT2i: sodium-glucose co-transporter 2 inhibitor.

HFrEF subphenotypes based on 4210 repeatedly measured circulating proteins are driven by different biological mechanisms.

BACKGROUND: HFrEF is a heterogenous condition with high mortality. We used serial assessments of 4210 circulating proteins to identify distinct novel protein-based HFrEF subphenotypes and to investigate underlying dynamic biological mechanisms. Herewith we aimed to gain pathophysiological insights and fuel opportunities for personalised treatment. METHODS: In 382 patients, we performed trimonthly blood sampling during a median follow-up of 2.1 [IQR:1.1-2.6] years. We selected all baseline samples and two samples closest to the primary endpoint (PEP; composite of cardiovascular mortality, HF hospitalization, LVAD implantation, and heart transplantation) or censoring, and applied an aptamer-based multiplex proteomic approach. Using unsupervised machine learning methods, we derived clusters from 4210 repeatedly measured proteomic biomarkers. Sets of proteins that drove cluster allocation were analysed via an enrichment analysis. Differences in clinical characteristics and PEP occurrence were evaluated. FINDINGS: We identified four subphenotypes with different protein profiles, prognosis and clinical characteristics, including age (median [IQR] for subphenotypes 1-4, respectively:70 [64, 76], 68 [60, 79], 57 [47, 65], 59 [56, 66]years), EF (30 [26, 36], 26 [20, 38], 26 [22, 32], 33 [28, 37]%), and chronic renal failure (45%, 65%, 36%, 37%). Subphenotype allocation was driven by subsets of proteins associated with various biological functions, such as oxidative stress, inflammation and extracellular matrix organisation. Clinical characteristics of the subphenotypes were aligned with these associations. Subphenotypes 2 and 3 had the worst prognosis compared to subphenotype 1 (adjHR (95%CI):3.43 (1.76-6.69), and 2.88 (1.37-6.03), respectively). INTERPRETATION: Four circulating-protein based subphenotypes are present in HFrEF, which are driven by varying combinations of protein subsets, and have different clinical characteristics and prognosis. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT01851538https://clinicaltrials.gov/ct2/show/NCT01851538. FUNDING: EU/EFPIA IMI2JU BigData@Heart grant n°116074, Jaap Schouten Foundation and Noordwest Academie.

Remote haemodynamic monitoring of pulmonary artery pressures in patients with chronic heart failure (MONITOR-HF): a randomised clinical trial.

BACKGROUND: The effect of haemodynamic monitoring of pulmonary artery pressure has predominantly been studied in the USA. There is a clear need for randomised trial data from patients treated with contemporary guideline-directed-medical-therapy with long-term follow-up in a different health-care system. METHODS: MONITOR-HF was an open-label, randomised trial, done in 25 centres in the Netherlands. Eligible patients had chronic heart failure of New York Heart Association class III and a previous heart failure hospitalisation, irrespective of ejection fraction. Patients were randomly assigned (1:1) to haemodynamic monitoring (CardioMEMS-HF system, Abbott Laboratories, Abbott Park, IL, USA) or standard care. All patients were scheduled to be seen by their clinician at 3 months and 6 months, and every 6 months thereafter, up to 48 months. The primary endpoint was the mean difference in the Kansas City Cardiomyopathy Questionnaire (KCCQ) overall summary score at 12 months. All analyses were by intention-to-treat. This trial was prospectively registered under the clinical trial registration number NTR7673 (NL7430) on the International Clinical Trials Registry Platform. FINDINGS: Between April 1, 2019, and Jan 14, 2022, we randomly assigned 348 patients to either the CardioMEMS-HF group (n=176 [51%]) or the control group (n=172 [49%]). The median age was 69 years (IQR 61-75) and median ejection fraction was 30% (23-40). The difference in mean change in KCCQ overall summary score at 12 months was 7·13 (95% CI 1·51-12·75; p=0·013) between groups (+7·05 in the CardioMEMS group, p=0·0014, and -0·08 in the standard care group, p=0·97). In the responder analysis, the odds ratio (OR) of an improvement of at least 5 points in KCCQ overall summary score was OR 1·69 (95% CI 1·01-2·83; p=0·046) and the OR of a deterioration of at least 5 points was 0·45 (0·26-0·77; p=0·0035) in the CardioMEMS-HF group compared with in the standard care group. The freedom of device-related or system-related complications and sensor failure were 97·7% and 98·8%, respectively. INTERPRETATION: Haemodynamic monitoring substantially improved quality of life and reduced heart failure hospitalisations in patients with moderate-to-severe heart failure treated according to contemporary guidelines. These findings contribute to the aggregate evidence for this technology and might have implications for guideline recommendations and implementation of remote pulmonary artery pressure monitoring. FUNDING: The Dutch Ministry of Health, Health Care Institute (Zorginstituut), and Abbott Laboratories.

Identifying distinct clinical clusters in heart failure with mildly reduced ejection fraction.

INTRODUCTION: Heart failure (HF) is a heterogeneous syndrome, and the specific sub-category HF with mildly reduced ejection fraction (EF) range (HFmrEF; 41-49% EF) is only recently recognised as a distinct entity. Cluster analysis can characterise heterogeneous patient populations and could serve as a stratification tool in clinical trials and for prognostication. The aim of this study was to identify clusters in HFmrEF and compare cluster prognosis. METHODS AND RESULTS: Latent class analysis to cluster HFmrEF patients based on their characteristics was performed in the Swedish HF registry (n = 7316). Identified clusters were validated in a Dutch cross-sectional HF registry-based dataset CHECK-HF (n = 1536). In Sweden, mortality and hospitalisation across the clusters were compared using a Cox proportional hazard model, with a Fine-Gray sub-distribution for competing risks and adjustment for age and sex. Six clusters were discovered with the following prevalence and hazard ratio with 95% confidence intervals (HR [95%CI]) vs. cluster 1: 1) low-comorbidity (17%, reference), 2) ischaemic-male (13%, HR 0.9 [95% CI 0.7-1.1]), 3) atrial fibrillation (20%, HR 1.5 [95% CI 1.2-1.9]), 4) device/wide QRS (9%, HR 2.7 [95% CI 2.2-3.4]), 5) metabolic (19%, HR 3.1 [95% CI 2.5-3.7]) and 6) cardio-renal phenotype (22%, HR 2.8 [95% CI 2.2-3.6]). The cluster model was robust between both datasets. CONCLUSION: We found robust clusters with potential clinical meaning and differences in mortality and hospitalisation. Our clustering model could be valuable as a clinical differentiation support and prognostic tool in clinical trial design.