Model-based comorbidity clusters in patients with heart failure: association with clinical outcomes and healthcare utilization

Causes and predictors of recurrent unplanned hospital admissions in heart failure patients: a cohort study

Despite progress in therapy, heart failure (HF) inflicts a heavy burden of hospital admissions. In this study, we identified among 1360 community-dwelling HF patients (mean age 70.7 ± 11.3 years, 72.5% men) subgroups sharing similar profiles of unplanned hospital admissions, based on the admission causes and frequency of each cause. Hospital discharge summaries were reviewed for the main admission cause. Patient subgroups were identified via cluster analysis. We investigated baseline predictors associated with these subgroups, using multinomial logistic models. During 3421 patient-years, there were 5192 hospital admissions, of which 4252 (82%) were unplanned. We identified five patient subgroups (clusters 1-5) with distinctive hospitalization profiles. HF accounted for approximately one-third of admissions in the first patient cluster (23% of the patient sample). In contrast, patients in the second cluster (39% of the patient sample) were hospitalized for various reasons, with no single prominent admission cause identified. The other three clusters, comprising 16% of the patient sample, accounted for 42% of all unplanned hospitalizations. While patients in the third cluster were hospitalized mainly due to ischemic heart disease and arrhythmia, patients in the fourth and fifth clusters shared a high burden of recurrent HF admissions. The five patient clusters differed by baseline predictors, including age, functional capacity, comorbidity burden, hemoglobin, and cause of HF. HF patients differ significantly in the causes and overall burden of unplanned hospitalizations. The patient subgroups identified and predictors for these subgroups may guide personalized interventions to reduce the burden of unplanned hospitalizations among HF patients. Trial registration: ClinicalTrials.gov, NCT00533013. Registered 20 September 2007. https://clinicaltrials.gov/study/NCT00533013 .

Distinct Comorbidity Clusters in Patients With Acute Heart Failure: Data From RELAX-AHF-2

BACKGROUND

Multimorbidity frequently occurs in patients with acute heart failure (AHF). The co-occurrence of comorbidities often follows specific patterns.

OBJECTIVES

This study investigated multimorbidity subtypes and their associations with clinical outcomes.

METHODS

From the prospective RELAX-AHF-2 (Relaxin for the Treatment of Acute Heart Failure-2) trial, 6,545 patients (26% with HF with preserved ejection fraction, defined as LVEF ≥50%) were classified into multimorbidity groups using latent class analysis. The association between subgroups and clinical outcomes was examined. Validation of these findings was conducted in the RELAX-AHF trial, which comprised 1,161 patients.

RESULTS

Five distinct multimorbidity groups emerged: 1) diabetes and chronic kidney disease (CKD) (often male, high prevalence of CKD and diabetes mellitus); 2) ischemic (ischemic HF); 3) elderly/atrial fibrillation (AF) (oldest, high prevalence of AF); 4) metabolic (obese, hypertensive, more often HF with preserved ejection fraction); and 5) young (fewest comorbidities). After adjusting for confounders, patients in the diabetes and CKD (HR: 1.80; 95% CI: 1.50-2.20), elderly/AF (HR: 1.42; 95% CI: 1.20-1.70), and metabolic (HR: 1.40; 95% CI: 1.20-1.80) groups had higher rates of the composite outcome than patients in the young group, primarily driven by differences in rehospitalization. Treatment allocation (placebo or serelaxin) modified these associations (Pinteraction <0.001). Serelaxin-treated patients in the young group were associated with a lower risk for all-cause mortality (HR: 0.59; 95% CI: 0.40-0.90). Similarly, patients from the RELAX-AHF trial clustered in 5 multimorbidity groups. The clinical characteristics and associations with outcomes could also be validated.

CONCLUSIONS

Comorbidities naturally clustered into 5 mutually exclusive groups in RELAX-AHF-2, showing variations in clinical outcomes. These data emphasize that the specific combination of comorbidities can influence adverse outcomes and treatment responses in patients with AHF.

Can we effectively manage chronic kidney disease with a precision-based pharmacotherapy plan? Where are we?

INTRODUCTION

In recent years, thanks to significant advances in basic science and biotechnologies, nephrology has witnessed a deeper understanding of the mechanisms leading to various conditions associated with or causing kidney disease, opening new perspectives for developing specific treatments. These new possibilities have brought increased challenges to physicians, who face with a new complexity in disease characterization and selection the right treatment for individual patients.

AREAS COVERED

We chose four therapeutic situations: anaemia in chronic kidney disease (CKD), heart failure in CKD, IgA nephropathy (IgAN) and membranous nephropathy (MN). The literature search was made through PubMed.

EXPERT OPINION

Anaemia management remains challenging in CKD; a personalized therapeutic approach is often needed. Identifying patients who could benefit from a specific therapy is also an important goal for patients with CKD and heart failure with reduced ejection fraction. Several new treatments are under clinical development for IgAN; interestingly, they target specifically the pathogenetic mechanisms of the disease. The understanding of MN pathogenesis as an autoimmune disease and the discovery of several autoantibodies allows a better characterization of patients. High-sensible techniques for lymphocyte counting open the possibility of more personalized use of anti CD20 therapies.

Identifying subgroups in heart failure patients with multimorbidity by clustering and network analysis

This study presents a workflow for identifying and characterizing patients with Heart Failure (HF) and multimorbidity utilizing data from Electronic Health Records. Multimorbidity, the co-occurrence of two or more chronic conditions, poses a significant challenge on healthcare systems. Nonetheless, understanding of patients with multimorbidity, including the most common disease interactions, risk factors, and treatment responses, remains limited, particularly for complex and heterogeneous conditions like HF. We conducted a clustering analysis of 3745 HF patients using demographics, comorbidities, laboratory values, and drug prescriptions. Our analysis revealed four distinct clusters with significant differences in multimorbidity profiles showing differential prognostic implications regarding unplanned hospital admissions. These findings underscore the considerable disease heterogeneity within HF patients and emphasize the potential for improved characterization of patient subgroups for clinical risk stratification through the use of EHR data.

Cost of Care for Patients With Cirrhosis

INTRODUCTION

There are limited longitudinal data on the cost of treating patients with cirrhosis, which hampers value-based improvement initiatives.

METHODS

We conducted a retrospective cohort study of patients with cirrhosis seen in the Veterans Affairs health care system from 2011 to 2015. Patients were followed up through 2019. We identified a sex-matched and age-matched control cohort without cirrhosis. We estimated incremental annual health care costs attributable to cirrhosis for 4 years overall and in subgroups based on severity (compensated, decompensated), cirrhosis complications (ascites, encephalopathy, varices, hepatocellular cancer, acute kidney injury), and comorbidity (Deyo index).

RESULTS

We compared 39,361 patients with cirrhosis with 138,964 controls. The incremental adjusted costs for caring of patients with cirrhosis were $35,029 (95% confidence interval $32,473-$37,585) during the first year and ranged from $14,216 to $17,629 in the subsequent 3 years. Cirrhosis complications accounted for most of these costs. Costs of managing patients with hepatic encephalopathy (year 1 cost, $50,080) or ascites ($50,364) were higher than the costs of managing patients with varices ($20,488) or hepatocellular cancer ($37,639) in the first year. Patients with acute kidney injury or those who had multimorbidity were the most costly at $64,413 and $66,653 in the first year, respectively.

DISCUSSION

Patients with cirrhosis had substantially higher health care costs than matched controls and multimorbid patients had even higher costs. Cirrhosis complications accounted for most of the excess cost, so preventing complications has the largest potential for cost saving and could serve as targets for improvement.

The Interplay of Comorbidities in Chronic Heart Failure: Challenges and Solutions

BACKGROUND

Chronic heart failure (HF) is frequently associated with various comorbidities. These comorbid conditions, such as anemia, diabetes mellitus, renal insufficiency, and sleep apnea, can significantly impact the prognosis of patients with HF.

OBJECTIVE

This review aims to synthesize current evidence on the prevalence, impact, and management of comorbidities in patients with chronic HF.

METHODS

A comprehensive review was conducted, with a rigorous selection process. Out of an initial pool of 59,030 articles identified across various research modalities, 134 articles were chosen for inclusion. The selection spanned various research methods, from randomized controlled trials to observational studies.

RESULTS

Comorbidities are highly prevalent in patients with HF and contribute to increased hospitalization rates and mortality. Despite advances in therapies for HF with reduced ejection fraction, options for treating HF with preserved ejection fraction remain sparse. Existing treatment protocols often lack standardization, reflecting a limited understanding of the intricate relationships between HF and associated comorbidities.

CONCLUSION

There is a pressing need for a multidisciplinary, tailored approach to manage HF and its intricate comorbidities. This review underscores the importance of ongoing research efforts to devise targeted treatment strategies for HF patients with various comorbid conditions.

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.

Common non-cardiovascular multimorbidity groupings and clinical outcomes in older adults with major cardiovascular disease

BACKGROUND

Among older adults, non-cardiovascular multimorbidity often coexists with cardiovascular disease (CVD) but their clinical significance is uncertain. We identified common non-cardiovascular comorbidity patterns and their association with clinical outcomes in Medicare fee-for-service beneficiaries with acute myocardial infarction (AMI), congestive heart failure (CHF), or atrial fibrillation (AF).

METHODS

Using 2015-2016 Medicare data, we took 1% random sample to create 3 cohorts of beneficiaries diagnosed with AMI (n = 24,808), CHF (n = 57,285), and AF (n = 36,277) prior to 1/1/2016. Within each cohort, we applied latent class analysis to classify beneficiaries based on 9 non-cardiovascular comorbidities (anemia, cancer, chronic kidney disease, chronic lung disease, dementia, depression, diabetes, hypothyroidism, and musculoskeletal disease). Mortality, cardiovascular and non-cardiovascular hospitalizations, and home time lost over a 1-year follow-up period were compared across non-cardiovascular multimorbidity classes.

RESULTS

Similar non-cardiovascular multimorbidity classes emerged from the 3 CVD cohorts: (1) minimal, (2) depression-lung, (3) chronic kidney disease (CKD)-diabetes, and (4) multi-system class. Across CVD cohorts, multi-system class had the highest risk of mortality (hazard ratio [HR], 2.7-3.9), cardiovascular hospitalization (HR, 1.6-3.3), non-cardiovascular hospitalization (HR, 3.1-7.2), and home time lost (rate ratio, 2.7-5.4). Among those with AMI, the CKD-diabetes class was more strongly associated with all the adverse outcomes than the depression-lung class. In CHF and AF, differences in risk between the depression-lung and CKD-diabetes classes varied per outcome; and the depression-lung and multi-system classes had double the rates of non-cardiovascular hospitalizations than cardiovascular hospitalizations.

CONCLUSION

Four non-cardiovascular multimorbidity patterns were found among Medicare beneficiaries with CHF, AMI, or AF. Compared to the minimal class, the multi-system, CKD-diabetes, and depression-lung classes were associated with worse outcomes. Identification of these classes offers insight into specific segments of the population that may benefit from more than the usual cardiovascular care.

Comprehensive characterization of non-cardiac comorbidities in acute heart failure: an analysis of ESC-HFA EURObservational Research Programme Heart Failure Long-Term Registry

AIMS

To evaluate the prevalence and associations of non-cardiac comorbidities (NCCs) with in-hospital and post-discharge outcomes in acute heart failure (AHF) across the ejection fraction (EF) spectrum.

METHODS AND RESULTS

The 9326 AHF patients from European Society of Cardiology (ESC)-Heart Failure Association (HFA)-EURObservational Research Programme Heart Failure Long-Term Registry had complete information for the following 12 NCCs: anaemia, chronic obstructive pulmonary disease (COPD), diabetes, depression, hepatic dysfunction, renal dysfunction, malignancy, Parkinson's disease, peripheral vascular disease (PVD), rheumatoid arthritis, sleep apnoea, and stroke/transient ischaemic attack (TIA). Patients were classified by number of NCCs (0, 1, 2, 3, and ≥4). Of the AHF patients, 20.5% had no NCC, 28.5% had 1 NCC, 23.1% had 2 NCC, 15.4% had 3 NCC, and 12.5% had ≥4 NCC. In-hospital and post-discharge mortality increased with number of NCCs from 3.0% and 18.5% for 1 NCC to 12.5% and 36% for ≥4 NCCs.Anaemia, COPD, PVD, sleep apnoea, rheumatoid arthritis, stroke/TIA, Parkinson, and depression were more prevalent in HF with preserved EF (HFpEF). The hazard ratio (95% confidence interval) for post-discharge death for each NCC was for anaemia 1.6 (1.4-1.8), diabetes 1.2 (1.1-1.4), kidney dysfunction 1.7 (1.5-1.9), COPD 1.4 (1.2-1.5), PVD 1.2 (1.1-1.4), stroke/TIA 1.3 (1.1-1.5), depression 1.2 (1.0-1.5), hepatic dysfunction 2.1 (1.8-2.5), malignancy 1.5 (1.2-1.8), sleep apnoea 1.2 (0.9-1.7), rheumatoid arthritis 1.5 (1.1-2.1), and Parkinson 1.4 (0.9-2.1). Anaemia, kidney dysfunction, COPD, and diabetes were associated with post-discharge mortality in all EF categories, PVD, stroke/TIA, and depression only in HF with reduced EF, and sleep apnoea and malignancy only in HFpEF.

CONCLUSION

Multiple NCCs conferred poor in-hospital and post-discharge outcomes. Ejection fraction categories had different prevalence and risk profile associated with individual NCCs.

A network medicine approach to study comorbidities in heart failure with preserved ejection fraction

BACKGROUND

Comorbidities are expected to impact the pathophysiology of heart failure (HF) with preserved ejection fraction (HFpEF). However, comorbidity profiles are usually reduced to a few comorbid disorders. Systems medicine approaches can model phenome-wide comorbidity profiles to improve our understanding of HFpEF and infer associated genetic profiles.

METHODS

We retrospectively explored 569 comorbidities in 29,047 HF patients, including 8062 HFpEF and 6585 HF with reduced ejection fraction (HFrEF) patients from a German university hospital. We assessed differences in comorbidity profiles between HF subtypes via multiple correspondence analysis. Then, we used machine learning classifiers to identify distinctive comorbidity profiles of HFpEF and HFrEF patients. Moreover, we built a comorbidity network (HFnet) to identify the main disease clusters that summarized the phenome-wide comorbidity. Lastly, we predicted novel gene candidates for HFpEF by linking the HFnet to a multilayer gene network, integrating multiple databases. To corroborate HFpEF candidate genes, we collected transcriptomic data in a murine HFpEF model. We compared predicted genes with the murine disease signature as well as with the literature.

RESULTS

We found a high degree of variance between the comorbidity profiles of HFpEF and HFrEF, while each was more similar to HFmrEF. The comorbidities present in HFpEF patients were more diverse than those in HFrEF and included neoplastic, osteologic and rheumatoid disorders. Disease communities in the HFnet captured important comorbidity concepts of HF patients which could be assigned to HF subtypes, age groups, and sex. Based on the HFpEF comorbidity profile, we predicted and recovered gene candidates, including genes involved in fibrosis (COL3A1, LOX, SMAD9, PTHL), hypertrophy (GATA5, MYH7), oxidative stress (NOS1, GSST1, XDH), and endoplasmic reticulum stress (ATF6). Finally, predicted genes were significantly overrepresented in the murine transcriptomic disease signature providing additional plausibility for their relevance.

CONCLUSIONS

We applied systems medicine concepts to analyze comorbidity profiles in a HF patient cohort. We were able to identify disease clusters that helped to characterize HF patients. We derived a distinct comorbidity profile for HFpEF, which was leveraged to suggest novel candidate genes via network propagation. The identification of distinctive comorbidity profiles and candidate genes from routine clinical data provides insights that may be leveraged to improve diagnosis and identify treatment targets for HFpEF patients.

Machine Learning-Based Phenomapping in Patients with Heart Failure and Secondary Prevention Implantable Cardioverter-Defibrillator Implantation: A Proof-of-Concept Study

Background

Previous studies have failed to implement risk stratification in patients with heart failure (HF) who are eligible for secondary implantable cardioverter-defibrillator (ICD) implantation. We aimed to evaluate whether machine learning-based phenomapping using routinely available clinical data can identify subgroups that differ in characteristics and prognoses.

Methods

A total of 389 patients with chronic HF implanted with an ICD were included, and forty-four baseline variables were collected. Phenomapping was performed using hierarchical k-means clustering based on factor analysis of mixed data (FAMD). The utility of phenomapping was validated by comparing the baseline features and outcomes of the first appropriate shock and all-cause death among the phenogroups.

Results

During a median follow-up of 2.7 years for device interrogation and 5.1 years for survival status, 142 (36.5%) first appropriate shocks and 113 (29.0%) all-cause deaths occurred. The first 12 principal components extracted using the FAMD, explaining 60.5% of the total variability, were left for phenomapping. Three mutually exclusive phenogroups were identified. Phenogroup 1 comprised the oldest patients with ischemic cardiomyopathy; had the highest proportion of diabetes mellitus, hypertension, and hyperlipidemia; and had the most favorable cardiac structure and function among the phenogroups. Phenogroup 2 included the youngest patients, mostly those with non-ischemic cardiomyopathy, who had intermediate heart dimensions and function, and the fewest comorbidities. Phenogroup 3 had the worst HF progression. Kaplan-Meier curves revealed significant differences in the first appropriate shock (p = 0.002) and all-cause death (p < 0.001) across the phenogroups. After adjusting for medications in Cox regression, phenogroups 2 and 3 displayed a graded increase in appropriate shock risk (hazard ratio [HR] 1.54, 95% confidence interval [CI] 1.03-2.28, p = 0.033; HR 2.21, 95% CI 1.42-3.43, p < 0.001, respectively; p for trend < 0.001) compared to phenogroup 1. Regarding mortality risk, phenogroup 3 was associated with an increased risk (HR 2.25, 95% CI 1.45-3.49, p < 0.001). In contrast, phenogroup 2 had a risk (p = 0.124) comparable with phenogroup 1.

Conclusions

Machine-learning-based phenomapping can identify distinct phenotype subgroups in patients with clinically heterogeneous HF with secondary prophylactic ICD therapy. This novel strategy may aid personalized medicine for these patients.

Heart Failure and Multimorbidity in Asia

PURPOSE OF THE REVIEW

Multimorbidity, the presence of two or more comorbidities, is common in patients with heart failure (HF) and worsens clinical outcomes. In Asia, multimorbidity has become the norm rather than the exception. Therefore, we evaluated the burden and unique patterns of comorbidities in Asian patients with HF.

RECENT FINDINGS

Asian patients with HF are almost a decade younger than Western Europe and North American patients. However, over two in three patients have multimorbidity. Comorbidities usually cluster due to the close and complex links between chronic medical conditions. Elucidating these links may guide public health policies to address risk factors. In Asia, barriers in treating comorbidities at the patient, healthcare system and national level hamper preventative efforts. Asian patients with HF are younger yet have a higher burden of comorbidities than Western patients. A better understanding of the unique co-occurrence of medical conditions in Asia can improve the prevention and treatment of HF.

Clusters of Comorbidities in the Short-Term Prognosis of Acute Heart Failure among Elderly Patients: A Retrospective Cohort Study

Background and Objectives: Elderly patients affected by acute heart failure (AHF) often show different patterns of comorbidities. In this paper, we aimed to evaluate how chronic comorbidities cluster and which pattern of comorbidities is more strongly related to in-hospital death in AHF. Materials and Methods: All patients admitted for AHF to an Internal Medicine Department (01/2015−01/2019) were retrospectively evaluated; the main outcome of this study was in-hospital death during an admission for AHF; age, sex, the Charlson comorbidity index (CCI), and 17 different chronic pathologies were investigated; the association between the comorbidities was studied with Pearson’s bivariate test, considering a level of p ≤ 0.10 significant, and considering p < 0.05 strongly significant. Thus, we identified the clusters of comorbidities associated with the main outcome and tested the CCI and each cluster against in-hospital death with logistic regression analysis, assessing the accuracy of the prediction with ROC curve analysis. Results: A total of 459 consecutive patients (age: 83.9 ± 8.02 years; males: 56.6%). A total of 55 (12%) subjects reached the main outcome; the CCI and 16 clusters of comorbidities emerged as being associated with in-hospital death from AHF. Of these, CCI and six clusters showed an accurate prediction of in-hospital death. Conclusions: Both the CCI and specific clusters of comorbidities are associated with in-hospital death from AHF among elderly patients. Specific phenotypes show a greater association with a worse short-term prognosis than a more generic scale, such as the CCI.

Identifying novel subgroups in heart failure patients with unsupervised machine learning: A scoping review

Background

Heart failure is currently divided into three main forms, HFrEF, HFpEF, and HFmrEF, but its etiology is diverse and highly heterogeneous. Many studies reported a variety of novel subgroups in heart failure patients, with unsupervised machine learning methods. The aim of this scoping review is to provide insights into how these techniques can diagnose and manage HF faster and better, thus providing direction for future research and facilitating its routine use in clinical practice.

Methods

The review was performed following PRISMA-SCR guideline. We searched the PubMed database for eligible publications. Studies were included if they defined new subgroups in HF patients using clustering analysis methods, and excluded if they are (1) Reviews, commentary, or editorials, (2) Studies not about defining new sub-types, or (3) Studies not using unsupervised algorithms. All study screening and data extraction were conducted independently by two investigators and narrative integration of data extracted from included studies was performed.

Results

Of the 498 studies identified, 47 were included in the analysis. Most studies (61.7%) were published in 2020 and later. The largest number of studies (46.8%) coming from the United States, and most of the studies were authored and included in the same country. The most commonly used machine learning method was hierarchical cluster analysis (46.8%), the most commonly used cluster variable type was comorbidity (61.7%), and the least used cluster variable type was genomics (12.8%). Most of the studies used data sets of less than 500 patients (48.9%), and the sample size had negative correlation with the number of clustering variables. The majority of studies (85.1%) assessed the association between cluster grouping and at least one outcomes, with death and hospitalization being the most commonly used outcome measures.

Conclusion

This scoping review provides an overview of recent studies proposing novel HF subgroups based on clustering analysis. Differences were found in study design, study population, clustering methods and variables, and outcomes of interests, and we provided insights into how these studies were conducted and identify the knowledge gaps to guide future research.

The HUNT study: Association of comorbidity clusters with long-term survival and incidence of exacerbation in a population-based Norwegian COPD cohort

BACKGROUND AND OBJECTIVE

Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease often viewed as part of a multimorbidity complex. There is a need for better phenotyping of the disease, characterization of its interplay with other comorbidities and its association with long-term outcomes. This study aims to examine how clusters of comorbidities are associated with severe exacerbations and mortality in COPD.

METHODS

Participants with potential COPD were recruited from the second (1995-1997) and third (2006-2008) survey of the HUNT Study and followed up until April 2020. Ten objectively identified comorbidities were clustered using self-organizing maps. Severe COPD exacerbations requiring hospitalization were assessed using hospital data. All-cause mortality was collected from national registries. Multivariable Cox regression was used to calculate hazard ratios (HRs) with 95% CIs for the association between comorbidity clusters and all-cause mortality. Poisson regression was used to calculate incidence rate ratios (IRRs) with 95% CI for the cumulative number of severe exacerbations for each cluster.

RESULTS

Five distinct clusters were identified, including 'less comorbidity', 'psychological', 'cardiovascular', 'metabolic' and 'cachectic' clusters. Using the less comorbidity cluster as reference, the psychological and cachectic clusters were associated with all-cause mortality (HR 1.23 [1.04-1.45] and HR 1.83 [1.52-2.20], adjusted for age and sex). The same clusters also had increased risk of exacerbations (unadjusted IRR of 1.24 [95% CI 1.04-1.48] and 1.50 [95% CI 1.23-1.83], respectively).

CONCLUSION

During 25 years of follow-up, individuals in the psychological and cachectic clusters had increased mortality. Furthermore, these clusters were associated with increased risk of severe COPD exacerbations.

Differences in Outcomes between Heart Failure Phenotypes in Patients with Coexistent COPD: A Cohort Study

RATIONALE

Differences in clinical presentation and outcomes between HF phenotypes in patients with COPD have not been assessed.

OBJECTIVES

The aim of this study was to compare clinical outcomes and healthcare resource use (HRU) between patients with COPD and HF with preserved (HFpEF), mildly-reduced (HFmrEF), and reduced ejection fraction (HFrEF).

METHODS

Patients with COPD and HF were identified in the United States (US) administrative claims database OptumLabs® DataWarehouse between 2008-2018. All-cause and cause-specific (HF) hospitalization, acute exacerbation of COPD (AECOPD, severe and moderate combined), mortality and HRU were compared between HF phenotypes.

RESULTS

From 5,419 patients with COPD, 70% had HFpEF, 20% had HFrEF and 10% had HFmrEF. All-cause hospitalization did not differ across groups, however patients with COPD and HFrEF had a greater risk of HF-specific hospitalization (HR 1.54, 95%CI 1.29-1.84) and mortality (HR: 1.17, 95%CI 1.03-1.33) compared to patients with COPD and HFpEF. Conversely, patients with COPD and HFrEF had a lower risk of AECOPD compared with those with COPD and HFpEF (HR 0.75, 95%CI 0.66-0.87). Rates of long-term stays (in skilled-nursing facilities) and emergency room visits were lower for those with COPD and HFrEF than for those with COPD and HFpEF.

CONCLUSION

Outcomes in patients with comorbid COPD and HFpEF are largely driven by COPD. Given the paucity in treatments for HFpEF, better differentiation between cardiac and respiratory symptoms may provide an opportunity to reduce the risk of AECOPD. Risk of death and HF hospitalization were highest among patients with COPD and HFrEF, emphasizing the importance of optimizing guideline-recommended HFrEF therapies in this group.

Disease evolution and risk-based disease trajectories in congestive heart failure patients

Congestive Heart Failure (CHF) is among the most prevalent chronic diseases worldwide, and is commonly associated with comorbidities and complex health conditions. Consequently, CHF patients are typically hospitalized frequently, and are at a high risk of premature death. Early detection of an envisaged patient disease trajectory is crucial for precision medicine. However, despite the abundance of patient-level data, cardiologists currently struggle to identify disease trajectories and track the evolution patterns of the disease over time, especially in small groups of patients with specific disease subtypes. The present study proposed a five-step method that allows clustering CHF patients, detecting cluster similarity, and identifying disease trajectories, and promises to overcome the existing difficulties. This work is based on a rich dataset of patients' records spanning ten years of hospital visits. The dataset contains all the health information documented in the hospital during each visit, including diagnoses, lab results, clinical data, and demographics. It utilizes an innovative Cluster Evolution Analysis (CEA) method to analyze the complex CHF population where each subject is potentially associated with numerous variables. We have defined sub-groups for mortality risk levels, which we used to characterize patients' disease evolution by refined data clustering in three points in time over ten years, and generating patients' migration patterns across periods. The results elicited 18, 23, and 25 clusters respective to the first, second, and third visits, uncovering clinically interesting small sub-groups of patients. In the following post-processing stage, we identified meaningful patterns. The analysis yielded fine-grained patient clusters divided into several finite risk levels, including several small-sized groups of high-risk patients. Significantly, the analysis also yielded longitudinal patterns where patients' risk levels changed over time. Four types of disease trajectories were identified: decline, preserved state, improvement, and mixed-progress. This stage is a unique contribution of the work. The resulting fine partitioning and longitudinal insights promise to significantly assist cardiologists in tailoring personalized interventions to improve care quality. Cardiologists could utilize these results to glean previously undetected relationships between symptoms and disease evolution that would allow a more informed clinical decision-making and effective interventions.

Hierarchical management of chronic heart failure: a perspective based on the latent structure of comorbidities

AIMS

Chronic heart failure (CHF) has an increasing burden of comorbidities, which affect clinical outcomes. Few studies have focused on the clustering and hierarchical management of patients with CHF based on comorbidity. This study aimed to explore the cluster model of CHF patients based on comorbidities and to verify their relationship with clinical outcomes.

METHODS AND RESULTS

Electronic health records of patients hospitalized with CHF from January 2014 to April 2019 were collected, and 12 common comorbidities were included in the latent class analysis. The Fruchterman-Reingold layout was used to draw the comorbidity network, and analysis of variance was used to compare the weighted degrees among them. The incidence of clinical outcomes among different clusters was presented on Kaplan-Meier curves and compared using the log-rank test, and the hazard ratio was calculated using the Cox proportional risk model. Sensitivity analysis was performed according to the left ventricular ejection fraction. Four different clinical clusters from 4063 total patients were identified: metabolic, ischaemic, high comorbidity burden, and elderly-atrial fibrillation. Compared with the metabolic cluster, patients in the high comorbidity burden cluster had the highest adjusted risk of combined outcome and all-cause mortality {1.67 [95% confidence interval (CI), 1.40-1.99] and 2.87 [95% CI, 2.17-3.81], respectively}, followed by the elderly-atrial fibrillation and ischaemic clusters. The adjusted readmission risk of patients with ischaemic, high comorbidity burden, and elderly-atrial fibrillation clusters were 1.35 (95% CI, 1.08-1.68), 1.39 (95% CI, 1.13-1.72), and 1.42 (95% CI, 1.14-1.77), respectively. The comorbidity network analysis found that patients in the high comorbidity burden cluster had more and higher comorbidity correlations than those in other clusters. Sensitivity analysis revealed that patients in the high comorbidity burden cluster had the highest risk of combined outcome and all-cause mortality (P < 0.05).

CONCLUSIONS

The difference in adverse outcomes among clusters confirmed the heterogeneity of CHF and the importance of hierarchical management. This study can provide a basis for personalized treatment and management of patients with CHF, and provide a new perspective for clinical decision making.

Redefining β-blocker response in heart failure patients with sinus rhythm and atrial fibrillation: a machine learning cluster analysis

BACKGROUND

Mortality remains unacceptably high in patients with heart failure and reduced left ventricular ejection fraction (LVEF) despite advances in therapeutics. We hypothesised that a novel artificial intelligence approach could better assess multiple and higher-dimension interactions of comorbidities, and define clusters of β-blocker efficacy in patients with sinus rhythm and atrial fibrillation.

METHODS

Neural network-based variational autoencoders and hierarchical clustering were applied to pooled individual patient data from nine double-blind, randomised, placebo-controlled trials of β blockers. All-cause mortality during median 1·3 years of follow-up was assessed by intention to treat, stratified by electrocardiographic heart rhythm. The number of clusters and dimensions was determined objectively, with results validated using a leave-one-trial-out approach. This study was prospectively registered with ClinicalTrials.gov (NCT00832442) and the PROSPERO database of systematic reviews (CRD42014010012).

FINDINGS

15 659 patients with heart failure and LVEF of less than 50% were included, with median age 65 years (IQR 56-72) and LVEF 27% (IQR 21-33). 3708 (24%) patients were women. In sinus rhythm (n=12 822), most clusters demonstrated a consistent overall mortality benefit from β blockers, with odds ratios (ORs) ranging from 0·54 to 0·74. One cluster in sinus rhythm of older patients with less severe symptoms showed no significant efficacy (OR 0·86, 95% CI 0·67-1·10; p=0·22). In atrial fibrillation (n=2837), four of five clusters were consistent with the overall neutral effect of β blockers versus placebo (OR 0·92, 0·77-1·10; p=0·37). One cluster of younger atrial fibrillation patients at lower mortality risk but similar LVEF to average had a statistically significant reduction in mortality with β blockers (OR 0·57, 0·35-0·93; p=0·023). The robustness and consistency of clustering was confirmed for all models (p<0·0001 vs random), and cluster membership was externally validated across the nine independent trials.

INTERPRETATION

An artificial intelligence-based clustering approach was able to distinguish prognostic response from β blockers in patients with heart failure and reduced LVEF. This included patients in sinus rhythm with suboptimal efficacy, as well as a cluster of patients with atrial fibrillation where β blockers did reduce mortality.

FUNDING

Medical Research Council, UK, and EU/EFPIA Innovative Medicines Initiative BigData@Heart.

Global Prevalence of Depression among Heart Failure Patients: A Systematic Review and Meta-Analysis

The present study aimed to evaluate the prevalence of depression among heart failure (HF) patients. Depression is one of the main risk factors of mortality and reduction in quality of life in patients with HR. Despite individual studies, there is no comprehensive study on depression in HF patients. In the present systematic review and meta-analysis, databases (Web of Science, Scopus, and PubMed) were searched from January 1, 2000, to December 15, 2020. The keywords used included: depression and heart failure. The research stages including search, screening, quality evaluation, and extraction of study data were performed separately by two researchers. A total of 149 studies performed on 305,407 HF patients entered the final stage. The global prevalence of any severity and moderate to severe severity of depression was 41.9% and 28.1%, respectively. The results of the subgroup analysis showed that the prevalence of depression was higher in women (45.5%) than in men. Also, according to the NHYA classification, the prevalence of depression in patients in stages three and four (54.7%) was higher than stages 1 and 2. The prevalence of depression was higher in the EMRO region (70.1%) and lower economic status countries (56.7%).The high prevalence of depression among HF patients indicates the importance of paying more attention and providing the necessary training for patients to reduce depression.