A personalized BEST: characterization of latent clinical classes of nonischemic heart failure that predict outcomes and response to bucindolol

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.

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.

Multimorbidity in Heart Failure: Leveraging Cluster Analysis to Guide Tailored Treatment Strategies

REVIEW PURPOSE

This review summarises key findings on treatment effects within phenotypical clusters of patients with heart failure (HF), making a distinction between patients with preserved ejection fraction (HFpEF) and reduced ejection fraction (HFrEF).

FINDINGS

Treatment response differed among clusters; ACE inhibitors were beneficial in all HFrEF phenotypes, while only some studies show similar beneficial prognostic effects in HFpEF patients. Beta-blockers had favourable effects in all HFrEF patients but not in HFpEF phenotypes and tended to worsen prognosis in older, cardiorenal patients. Mineralocorticoid receptor antagonists had more favourable prognostic effects in young, obese males and metabolic HFpEF patients. While a phenotype-guided approach is a promising solution for individualised treatment strategies, there are several aspects that still require improvements before such an approach could be implemented in clinical practice. Stronger evidence from clinical trials and real-world data may assist in establishing a phenotype-guided treatment approach for patient with HF in the future.

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.

Oncologist phenotypes and associations with response to a machine learning-based intervention to increase advance care planning: Secondary analysis of a randomized clinical trial

BACKGROUND

While health systems have implemented multifaceted interventions to improve physician and patient communication in serious illnesses such as cancer, clinicians vary in their response to these initiatives. In this secondary analysis of a randomized trial, we identified phenotypes of oncology clinicians based on practice pattern and demographic data, then evaluated associations between such phenotypes and response to a machine learning (ML)-based intervention to prompt earlier advance care planning (ACP) for patients with cancer.

METHODS AND FINDINGS

Between June and November 2019, we conducted a pragmatic randomized controlled trial testing the impact of text message prompts to 78 oncology clinicians at 9 oncology practices to perform ACP conversations among patients with cancer at high risk of 180-day mortality, identified using a ML prognostic algorithm. All practices began in the pre-intervention group, which received weekly emails about ACP performance only; practices were sequentially randomized to receive the intervention at 4-week intervals in a stepped-wedge design. We used latent profile analysis (LPA) to identify oncologist phenotypes based on 11 baseline demographic and practice pattern variables identified using EHR and internal administrative sources. Difference-in-differences analyses assessed associations between oncologist phenotype and the outcome of change in ACP conversation rate, before and during the intervention period. Primary analyses were adjusted for patients' sex, age, race, insurance status, marital status, and Charlson comorbidity index. The sample consisted of 2695 patients with a mean age of 64.9 years, of whom 72% were White, 20% were Black, and 52% were male. 78 oncology clinicians (42 oncologists, 36 advanced practice providers) were included. Three oncologist phenotypes were identified: Class 1 (n = 9) composed primarily of high-volume generalist oncologists, Class 2 (n = 5) comprised primarily of low-volume specialist oncologists; and 3) Class 3 (n = 28), composed primarily of high-volume specialist oncologists. Compared with class 1 and class 3, class 2 had lower mean clinic days per week (1.6 vs 2.5 [class 3] vs 4.4 [class 1]) a higher percentage of new patients per week (35% vs 21% vs 18%), higher baseline ACP rates (3.9% vs 1.6% vs 0.8%), and lower baseline rates of chemotherapy within 14 days of death (1.4% vs 6.5% vs 7.1%). Overall, ACP rates were 3.6% in the pre-intervention wedges and 15.2% in intervention wedges (11.6 percentage-point difference). Compared to class 3, oncologists in class 1 (adjusted percentage-point difference-in-differences 3.6, 95% CI 1.0 to 6.1, p = 0.006) and class 2 (adjusted percentage-point difference-in-differences 12.3, 95% confidence interval [CI] 4.3 to 20.3, p = 0.003) had greater response to the intervention.

CONCLUSIONS

Patient volume and time availability may be associated with oncologists' response to interventions to increase ACP. Future interventions to prompt ACP should prioritize making time available for such conversations between oncologists and their patients.

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

BACKGROUND

Comorbidities affect outcomes in heart failure (HF), but are not reflected in current HF classification. The aim of this study is to characterize HF groups that account for higher-order interactions between comorbidities and to investigate the association between comorbidity groups and outcomes.

METHODS

Latent class analysis (LCA) was performed on 12 comorbidities from patients with HF identified from administrative claims data in the USA (OptumLabs Data Warehouse®) between 2008 and 2018. Associations with admission to hospital and mortality were assessed with Cox regression. Negative binomial regression was used to examine rates of healthcare use.

RESULTS

In a population of 318,384 individuals, we identified five comorbidity clusters, named according to their dominant features: low-burden, metabolic-vascular, anemic, ischemic, and metabolic. Compared to the low-burden group (minimal comorbidities), patients in the metabolic-vascular group (exhibiting a pattern of diabetes, obesity, and vascular disease) had the worst prognosis for admission (HR 2.21, 95% CI 2.17-2.25) and death (HR 1.87, 95% CI 1.74-2.01), followed by the ischemic, anemic, and metabolic groups. The anemic group experienced an intermediate risk of admission (HR 1.49, 95% CI 1.44-1.54) and death (HR 1.46, 95% CI 1.30-1.64). Healthcare use also varied: the anemic group had the highest rate of outpatient visits, compared to the low-burden group (IRR 2.11, 95% CI 2.06-2.16); the metabolic-vascular and ischemic groups had the highest rate of admissions (IRR 2.11, 95% CI 2.08-2.15, and 2.11, 95% CI 2.07-2.15) and healthcare costs.

CONCLUSIONS

These data demonstrate the feasibility of using LCA to classify HF based on comorbidities alone and should encourage investigation of multidimensional approaches in comorbidity management to reduce admission and mortality risk among patients with HF.

Phenotyping physician practice patterns and associations with response to a nudge in the electronic health record for influenza vaccination: A quasi-experimental study

BACKGROUND

Health systems routinely implement changes to the design of electronic health records (EHRs). Physician behavior may vary in response and methods to identify this variation could help to inform future interventions. The objective of this study was to phenotype primary care physician practice patterns and evaluate associations with response to an EHR nudge for influenza vaccination.

METHODS AND FINDINGS

During the 2016-2017 influenza season, 3 primary care practices at Penn Medicine implemented an active choice intervention in the EHR that prompted medical assistants to template influenza vaccination orders for physicians to review during the visit. We used latent class analysis to identify physician phenotypes based on 9 demographic, training, and practice pattern variables, which were obtained from the EHR and publicly available sources. A quasi-experimental approach was used to evaluate response to the intervention relative to control practices over time in each of the physician phenotype groups. For each physician latent class, a generalized linear model with logit link was fit to the binary outcome of influenza vaccination at the patient visit level. The sample comprised 45,410 patients with a mean (SD) age of 58.7 (16.3) years, 67.1% were white, and 22.1% were black. The sample comprised 56 physicians with mean (SD) of 24.6 (10.2) years of experience and 53.6% were male. The model segmented physicians into groups that had higher (n = 41) and lower (n = 15) clinical workloads. Physicians in the higher clinical workload group had a mean (SD) of 818.8 (429.1) patient encounters, 11.6 (4.7) patient appointments per day, and 4.0 (1.1) days per week in clinic. Physicians in the lower clinical workload group had a mean (SD) of 343.7 (129.0) patient encounters, 8.0 (2.8) patient appointments per day, and 3.1 (1.2) days per week in clinic. Among the higher clinical workload group, the EHR nudge was associated with a significant increase in influenza vaccination (adjusted difference-in-difference in percentage points, 7.9; 95% CI, 0.4-9.0; P = .01). Among the lower clinical workload group, the EHR nudge was not associated with a significant difference in influenza vaccination rates (adjusted difference-in-difference in percentage points, -1.0; 95% CI, -5.3-5.8; P = .90).

CONCLUSIONS

A model-based approach categorized physician practice patterns into higher and lower clinical workload groups. The higher clinical workload group was associated with a significant response to an EHR nudge for influenza vaccination.

Pharmacological interventions for heart failure in people with chronic kidney disease

BACKGROUND

Approximately half of people with heart failure have chronic kidney disease (CKD). Pharmacological interventions for heart failure in people with CKD have the potential to reduce death (any cause) or hospitalisations for decompensated heart failure. However, these interventions are of uncertain benefit and may increase the risk of harm, such as hypotension and electrolyte abnormalities, in those with CKD.

OBJECTIVES

This review aims to look at the benefits and harms of pharmacological interventions for HF (i.e., antihypertensive agents, inotropes, and agents that may improve the heart performance indirectly) in people with HF and CKD.

SEARCH METHODS

We searched the Cochrane Kidney and Transplant Register of Studies through 12 September 2019 in consultation with an Information Specialist and using search terms relevant to this review. Studies in the Register are identified through searches of CENTRAL, MEDLINE, and EMBASE, conference proceedings, the International Clinical Trials Register (ICTRP) Search Portal and ClinicalTrials.gov.

SELECTION CRITERIA

We included randomised controlled trials of any pharmacological intervention for acute or chronic heart failure, among people of any age with chronic kidney disease of at least three months duration.

DATA COLLECTION AND ANALYSIS

Two authors independently screened the records to identify eligible studies and extracted data on the following dichotomous outcomes: death, hospitalisations, worsening heart failure, worsening kidney function, hyperkalaemia, and hypotension. We used random effects meta-analysis to estimate treatment effects, which we expressed as a risk ratio (RR) with 95% confidence intervals (CI). We assessed the risk of bias using the Cochrane tool. We applied the GRADE methodology to rate the certainty of evidence.

MAIN RESULTS

One hundred and twelve studies met our selection criteria: 15 were studies of adults with CKD; 16 studies were conducted in the general population but provided subgroup data for people with CKD; and 81 studies included individuals with CKD, however, data for this subgroup were not provided. The risk of bias in all 112 studies was frequently high or unclear. Of the 31 studies (23,762 participants) with data on CKD patients, follow-up ranged from three months to five years, and study size ranged from 16 to 2916 participants. In total, 26 studies (19,612 participants) reported disaggregated and extractable data on at least one outcome of interest for our review and were included in our meta-analyses. In acute heart failure, the effects of adenosine A1-receptor antagonists, dopamine, nesiritide, or serelaxin on death, hospitalisations, worsening heart failure or kidney function, hyperkalaemia, hypotension or quality of life were uncertain due to sparse data or were not reported. In chronic heart failure, the effects of angiotensin-converting enzyme inhibitors (ACEi) or angiotensin receptor blockers (ARB) (4 studies, 5003 participants: RR 0.85, 95% CI 0.70 to 1.02; I² = 78%; low certainty evidence), aldosterone antagonists (2 studies, 34 participants: RR 0.61 95% CI 0.06 to 6.59; very low certainty evidence), and vasopressin receptor antagonists (RR 1.26, 95% CI 0.55 to 2.89; 2 studies, 1840 participants; low certainty evidence) on death (any cause) were uncertain. Treatment with beta-blockers may reduce the risk of death (any cause) (4 studies, 3136 participants: RR 0.69, 95% CI 0.60 to 0.79; I² = 0%; moderate certainty evidence). Treatment with ACEi or ARB (2 studies, 1368 participants: RR 0.90, 95% CI 0.43 to 1.90; I² = 97%; very low certainty evidence) had uncertain effects on hospitalisation for heart failure, as treatment estimates were consistent with either benefit or harm. Treatment with beta-blockers may decrease hospitalisation for heart failure (3 studies, 2287 participants: RR 0.67, 95% CI 0.43 to 1.05; I² = 87%; low certainty evidence). Aldosterone antagonists may increase the risk of hyperkalaemia compared to placebo or no treatment (3 studies, 826 participants: RR 2.91, 95% CI 2.03 to 4.17; I² = 0%; low certainty evidence). Renin inhibitors had uncertain risks of hyperkalaemia (2 studies, 142 participants: RR 0.86, 95% CI 0.49 to 1.49; I² = 0%; very low certainty). We were unable to estimate whether treatment with sinus node inhibitors affects the risk of hyperkalaemia, as there were few studies and meta-analysis was not possible. Hyperkalaemia was not reported for the CKD subgroup in studies investigating other therapies. The effects of ACEi or ARB, or aldosterone antagonists on worsening heart failure or kidney function, hypotension, or quality of life were uncertain due to sparse data or were not reported. Effects of anti-arrhythmic agents, digoxin, phosphodiesterase inhibitors, renin inhibitors, sinus node inhibitors, vasodilators, and vasopressin receptor antagonists were very uncertain due to the paucity of studies.

AUTHORS' CONCLUSIONS

The effects of pharmacological interventions for heart failure in people with CKD are uncertain and there is insufficient evidence to inform clinical practice. Study data for treatment outcomes in patients with heart failure and CKD are sparse despite the potential impact of kidney impairment on the benefits and harms of treatment. Future research aimed at analysing existing data in general population HF studies to explore the effect in subgroups of patients with CKD, considering stage of disease, may yield valuable insights for the management of people with HF and CKD.

National Health and Wellness Survey exploratory cluster analysis of males 40-70 years old focused on erectile dysfunction and associated risk factors across the USA, Italy, Brazil and China

OBJECTIVES

Prior studies of erectile dysfunction (ED) tend to narrowly focus on relationships with specific comorbidities, rather than evaluating a more comprehensive array of risk factors and assessing naturalistic patterns among them. This study identifies natural clusters of male characteristics from a general population sample per country, quantifies ED dynamics in these profiles and compares profiles across the US, Italy, Brazil and China samples.

METHODS

National Health and Wellness Survey 2015 and 2016 patient-reported data on men aged 40-70 years (USA n = 15,652; Italy n = 2,521; Brazil n = 2,822; China n = 5,553) were analysed. Hierarchical agglomerative clustering identified clusters where predictors included demographics, health characteristics/behaviours, ED risk factors and provider visits in the past 6 months. Multinomial logistic regression assessed the independent utility of variables in predicting cluster membership, compared with the healthiest control cluster per country.

RESULTS

Different natural clusters were found across countries, with four clusters for the USA, Italy and China and three clusters for Brazil. Age, income, employment, health behaviours and ED risk factors predicted different cluster membership across countries. In the USA, Italy and Brazil, younger clusters were predicted by ED, unhealthy behaviours and ED risk factors. Unique cluster profiles were identified in China, with ED and ED risk factors (aside from hypertension) not predicting cluster membership, while socio-demographics and health behaviours were strongly predictive.

CONCLUSIONS

Natural cluster profiles revealed notable ED rates among adult males of age 40-70 in four different countries. Clusters were mainly predicted by unhealthy behaviours, ED risk factors and ED, regardless of level or presence of positive health characteristics and behaviours. This analysis identified meaningful subgroups of men with heightened ED risk factors, which can help healthcare providers to better recognise specific populations with the greatest need for intervention.

Data-Driven Approach to Identify Subgroups of Heart Failure With Reduced Ejection Fraction Patients With Different Prognoses and Aldosterone Antagonist Response Patterns

BACKGROUND

Patients with heart failure with reduced ejection fraction have a poor prognosis. The identification of subgroups with different outcomes and treatment response patterns may help to tailor strategies to each individual patient. We present an exploratory study of patients enrolled in the EMPHASIS-HF trial (Eplerenone in Patients With Systolic Heart Failure and Mild Symptoms) using latent class analysis with validation using the EPHESUS trial (Eplerenone, a Selective Aldosterone Blocker, in Patients With Left Ventricular Dysfunction After Myocardial Infarction) to identify subgroups of patients with different prognosis and response to eplerenone therapy.

METHODS AND RESULTS

Latent class analysis identifies mutually exclusive groups of individuals maximizing within-group similarities and between-group differences. In the EMPHASIS-HF trial, 2279 heart failure with reduced ejection fraction patients were randomized to eplerenone or placebo and were characterized according to 18 clinical features. Subgroup definitions were applied to 6472 patients enrolled in the EPHESUS trial to validate observations. Event-free survival and effect of eplerenone on the composite of cardiovascular death and heart failure hospitalization were determined for each subgroup. Four subgroups were identified with significant differences in event-free survival (P=0.002). The subgroup C had the worst event-free survival in both studies and was characterized by older age, lower body mass index, worse renal function, higher baseline potassium levels, high prevalence of anemia, diabetes mellitus, previous revascularization and higher rates of eplerenone discontinuation, and hyperkalemia during follow-up. Two subgroups (B and C) showed a poorer response to eplerenone in both studies and these groups shared common features such as lower body mass index and high prevalence of anemia. Clinical profiles, prognosis, and treatment response patterns of the 4 subgroups applied in EPHESUS trial presented similarities to those observed in EMPHASIS.

CONCLUSIONS

Using a data-driven approach, we identified heart failure with reduced ejection fraction subgroups with significantly different prognoses and potentially different responses to eplerenone. However, these data should be regarded as hypothesis-generating and prospective validation is warranted, to assess the potential clinical implications of these subgroups.

CLINICAL TRIAL REGISTRATION

URL: https://www.clinicaltrials.gov. Unique identifier: NCT00232180.

Clinical profiles in acute heart failure: an urgent need for a new approach

Acute heart failure (HF) is a major public health concern, responsible for >26 million hospitalizations per year worldwide. Many trials have investigated new therapeutic options for acute HF, with most revealing equivocal results. Successful innovations in therapy for acute HF have remained limited, and standard of care has remained largely unchanged over the past decade, suggesting the need for a new approach for therapeutic decision making and clinical trial design in acute HF. This manuscript focuses on one approach that could prove useful in the development and application of novel therapies: classification of patients based on clinical profiles. While previous attempts at developing clinical profiles were successful in stratifying patients based on clinical and laboratory variables, they have not been utilized for personalized treatment strategies that improve patient outcomes. We suggest a new approach to the creation of clinical profiles that could stratify patients based on their underlying aetiology and their response to novel interventions. We also investigate novel analytic approaches to the creation of new clinical profiles that both investigators and clinicians alike could utilize to inform clinical trial design and the application of new therapies. Despite a large number of clinical trials for new therapeutic options, the treatment of acute HF has seen few advances over the past decades. Innovative approaches to patient selection through the use of clinical profiles could help to identify patients most likely to benefit from novel interventions and lead to the discovery of new therapeutic options.

Multimorbidity in patients with heart failure from 11 Asian regions: A prospective cohort study using the ASIAN-HF registry

BACKGROUND

Comorbidities are common in patients with heart failure (HF) and complicate treatment and outcomes. We identified patterns of multimorbidity in Asian patients with HF and their association with patients' quality of life (QoL) and health outcomes.

METHODS AND FINDINGS

We used data on 6,480 patients with chronic HF (1,204 with preserved ejection fraction) enrolled between 1 October 2012 and 6 October 2016 in the Asian Sudden Cardiac Death in Heart Failure (ASIAN-HF) registry. The ASIAN-HF registry is a prospective cohort study, with patients prospectively enrolled from in- and outpatient clinics from 11 Asian regions (Hong Kong, Taiwan, China, Japan, Korea, India, Malaysia, Thailand, Singapore, Indonesia, and Philippines). Latent class analysis was used to identify patterns of multimorbidity. The primary outcome was defined as a composite of all-cause mortality or HF hospitalization within 1 year. To assess differences in QoL, we used the Kansas City Cardiomyopathy Questionnaire. We identified 5 distinct multimorbidity groups: elderly/atrial fibrillation (AF) (N = 1,048; oldest, more AF), metabolic (N = 1,129; obesity, diabetes, hypertension), young (N = 1,759; youngest, low comorbidity rates, non-ischemic etiology), ischemic (N = 1,261; ischemic etiology), and lean diabetic (N = 1,283; diabetic, hypertensive, low prevalence of obesity, high prevalence of chronic kidney disease). Patients in the lean diabetic group had the worst QoL, more severe signs and symptoms of HF, and the highest rate of the primary combined outcome within 1 year (29% versus 11% in the young group) (p for all <0.001). Adjusting for confounders (demographics, New York Heart Association class, and medication) the lean diabetic (hazard ratio [HR] 1.79, 95% CI 1.46-2.22), elderly/AF (HR 1.57, 95% CI 1.26-1.96), ischemic (HR 1.51, 95% CI 1.22-1.88), and metabolic (HR 1.28, 95% CI 1.02-1.60) groups had higher rates of the primary combined outcome compared to the young group. Potential limitations include site selection and participation bias.

CONCLUSIONS

Among Asian patients with HF, comorbidities naturally clustered in 5 distinct patterns, each differentially impacting patients' QoL and health outcomes. These data underscore the importance of studying multimorbidity in HF and the need for more comprehensive approaches in phenotyping patients with HF and multimorbidity.

TRIAL REGISTRATION

ClinicalTrials.gov NCT01633398.

Validity of the Seattle Heart Failure Model for prognosis in a population at low coronary heart disease risk

AIM

Validation of the Seattle Heart Failure Model (SHFM) for predicting the risk of death in a population different than the derivation cohort.

METHODS

In a retrospective analysis of a cohort of chronic heart failure patients with left ventricular systolic dysfunction, consecutively referred between 2000 and 2011, we computed the score, according to characteristics at referral. We compared the observed risk of death with that predicted by the model, using receiver operating characteristic (ROC) curves to assess discrimination and a goodness-of-fit test for the comparison of predicted and observed risks.

RESULTS

In 565 patients, 68.5% were men, the median age was 70 years, 46.0% had ischemic cause, 89.7% moderate-severe left ventricular systolic dysfunction and 61.2% New York Heart Association class II. The risk of death increased progressively with the model's score, with an area under the ROC curve between 0.69 and 0.72 when considering different follow-up periods. The model underestimated the risk of death (observed vs. predicted: 12.2 vs. 10.4%, P < 0.001; 28.1 vs. 25.1%, P < 0.001; and 43.4 vs. 35.7%, P < 0.001 at 1, 3 and 5 years, respectively). Accurate predictions, with nonsignificant differences between observed and predicted risks in a goodness-of-fit test, were obtained after recalibration.

CONCLUSION

In this study, the SHFM substantially underestimated the absolute risk of death in ambulatory chronic heart failure patients, mostly nonischemic and elderly. After adjustment for sample-specific circumstances, the recalibrated model demonstrated to be credible in clinical practice and may provide useful information to physicians.

Cluster Individuals Based on Phenotype and Determine the Risk for Atrial Fibrillation in the PREVEND and Framingham Heart Study Populations

Background

Risk prediction of atrial fibrillation (AF) is of importance to improve the early diagnosis and treatment of AF. Latent class analysis takes into account the possible existence of classes of individuals each with shared risk factors, and maybe a better method of incorporating the phenotypic heterogeneity underlying AF.

Methods and findings

Two prospective community-based cohort studies from Netherlands and United States were used. Prevention of Renal and Vascular End-stage Disease (PREVEND) study, started in 1997, and the Framingham Heart Study (FHS) Offspring cohort started in 1971, both with 10-years follow-up. The main objective was to determine the risk of AF using a latent class analysis, and compare the discrimination and reclassification performance with traditional regression analysis. Mean age in PREVEND was 49±13 years, 49.8% were men. During follow-up, 250(3%) individuals developed AF. We built a latent class model based on 18 risk factors. A model with 7 distinct classes (ranging from 341 to 1517 individuals) gave the optimum tradeoff between a high statistical model-likelihood and a low number of model parameters. All classes had a specific profile. The incidence of AF varied; class 1 0.0%, class 2 0.3%, class 3 7.5%, class 4 0.2%, class 5 1.3%, class 6 4.2%, class 7 21.7% (p<0.001). The discrimination (C-statistic 0.830 vs. 0.842, delta-C -0.013, p = 0.22) and reclassification (IDI -0.028, p<0.001, NRI -0.090, p = 0.049, and category-less-NRI -0.049, p = 0.495) performance of both models was comparable. The results were successfully replicated in a sample of the FHS study (n = 3162; mean age 58±9 years, 46.3% men).

Conclusions

Latent class analysis to build an AF risk model is feasible. Despite the heterogeneity in number and severity of risk factors between individuals at risk for AF, latent class analysis produces distinguishable groups.

Identifying Patterns of Multimorbidity in Older Americans: Application of Latent Class Analysis

OBJECTIVES

To define multimorbidity "classes" empirically based on patterns of disease co-occurrence in older Americans and to examine how class membership predicts healthcare use.

DESIGN

Retrospective cohort study.

SETTING

Nationally representative sample of Medicare beneficiaries in file years 1999-2007.

PARTICIPANTS

Individuals aged 65 and older in the Medicare Beneficiary Survey who had data available for at least 1 year after index interview (N = 14,052).

MEASUREMENTS

Surveys (self-report) were used to assess chronic conditions, and latent class analysis (LCA) was used to define multimorbidity classes based on the presence or absence of 13 conditions. All participants were assigned to a best-fit class. Primary outcomes were hospitalizations and emergency department visits over 1 year.

RESULTS

The primary LCA identified six classes. The largest portion of participants (32.7%) was assigned to the minimal disease class, in which most persons had fewer than two of the conditions. The other five classes represented various degrees and patterns of multimorbidity. Usage rates were higher in classes with greater morbidity, but many individuals could not be assigned to a particular class with confidence (sample misclassification error estimate = 0.36). Number of conditions predicted outcomes at least as well as class membership.

CONCLUSION

Although recognition of general patterns of disease co-occurrence is useful for policy planning, the heterogeneity of persons with significant multimorbidity (≥3 conditions) defies neat classification. A simple count of conditions may be preferable for predicting usage.

Four Distinct Health Profiles in Older Patients With Cancer: Latent Class Analysis of the Prospective ELCAPA Cohort

BACKGROUND

Several studies have evaluated the independent prognostic value of impairments in single geriatric-assessment (GA) components in elderly cancer patients. None identified homogeneous subgroups. Our aims were to identify such subgroups based on combinations of GA components and to assess their associations with treatment decisions, admission, and death.

METHODS

We prospectively included 1,021 patients aged ≥70 years who had solid or hematologic malignancies and who underwent a GA in one of two French teaching hospitals. Two geriatricians independently selected candidate GA parameters for latent class analysis, which was then performed on the 821 cases without missing data. Age, gender, tumor site, metastatic status, and inpatient versus outpatient status were used as active covariates and predictors of class membership. Outcomes were cancer treatment decisions, overall 1-year mortality, and 6-month unscheduled admissions. Sensitivity analyses were performed on the overall population of 1,021 patients and on 375 newly enrolled patients.

RESULTS

We identified four classes: relatively healthy (LC1, 28%), malnourished (LC2, 36%), cognitive and mood impaired (LC3, 15%), and globally impaired (LC4, 21%). Tumor site, metastatic status, age, and in/outpatient status independently predicted class membership (p < .001). In adjusted pairwise comparisons, compared to LC1, the three other LCs were associated with higher risks of palliative treatment, death, and unscheduled admission (p ≤ .05). LC4 was associated with 1-year mortality and palliative treatment compared to LC2 and LC3 (p ≤ .05).

CONCLUSION

We identified four health profiles that may help physicians select cancer treatments and geriatric interventions. Researchers may find these profiles useful for stratifying patients in clinical trials.

Characterization of subgroups of heart failure patients with preserved ejection fraction with possible implications for prognosis and treatment response

BACKGROUND

Patients with heart failure and preserved ejection fraction (HFpEF) have a poor prognosis, and no therapies have been proven to improve outcomes. It has been proposed that heart failure, including HFpEF, represents overlapping syndromes that may have different prognoses. We present an exploratory study of patients enrolled in the Irbesartan in Heart Failure with Preserved Ejection Fraction Study (I-PRESERVE) using latent class analysis (LCA) with validation using the Candesartan in Heart failure: Assessment of Reduction in Mortality and morbidity (CHARM)-Preserved study to identify HFpEF subgroups.

METHODS AND RESULTS

In total, 4113 HFpEF patients randomized to irbesartan or placebo were characterized according to 11 clinical features. The HFpEF subgroups were identified using LCA. Event-free survival and effect of irbesartan on the composite of all-cause mortality and cardiovascular hospitalization were determined for each subgroup. Subgroup definitions were applied to 3203 patients enrolled in CHARM-Preserved to validate observations regarding prognosis and treatment response. Six subgroups were identified with significant differences in event-free survival (P < 0.001). Clinical profiles and prognoses of the six subgroups were similar in CHARM-Preserved. The two subgroups with the worst event-free survival in both studies were characterized by a high prevalence of obesity, hyperlipidaemia, diabetes mellitus, anaemia, and renal insufficiency (Subgroup C) and by female predominance, advanced age, lower body mass index, and high rates of atrial fibrillation, valvular disease, renal insufficiency, and anaemia (Subgroup F).

CONCLUSION

Using a data-driven approach, we identified HFpEF subgroups with significantly different prognoses. Further development of this approach for characterizing HFpEF subgroups is warranted.

GPCR signaling and cardiac function

G protein-coupled receptors (GPCRs), such as β-adrenergic and angiotensin II receptors, located in the membranes of all three major cardiac cell types, i.e. myocytes, fibroblasts and endothelial cells, play crucial roles in regulating cardiac function and morphology. Their importance in cardiac physiology and disease is reflected by the fact that, collectively, they represent the direct targets of over a third of the currently approved cardiovascular drugs used in clinical practice. Over the past few decades, advances in elucidation of their structure, function and the signaling pathways they elicit, specifically in the heart, have led to identification of an increasing number of new molecular targets for heart disease therapy. Here, we review these signaling modalities employed by GPCRs known to be expressed in the cardiac myocyte membranes and to directly modulate cardiac contractility. We also highlight drugs and drug classes that directly target these GPCRs to modulate cardiac function, as well as molecules involved in cardiac GPCR signaling that have the potential of becoming novel drug targets for modulation of cardiac function in the future.

Current and future G protein-coupled receptor signaling targets for heart failure therapy

Although there have been significant advances in the therapy of heart failure in recent decades, such as the introduction of β-blockers and antagonists of the renin–angiotensin–aldosterone system, this devastating disease still carries tremendous morbidity and mortality in the western world. G protein-coupled receptors, such as β-adrenergic and angiotensin II receptors, located in the membranes of all three major cardiac cell types, ie, myocytes, fibroblasts, and endothelial cells, play crucial roles in regulation of cardiac function in health and disease. Their importance is reflected by the fact that, collectively, they represent the direct targets of over one-third of the currently approved cardiovascular drugs used in clinical practice. Over the past few decades, advances in elucidation of the signaling pathways they elicit, specifically in the heart, have led to identification of an increasing number of new molecular targets for heart failure therapy. Here, we review these possible targets for heart failure therapy that have emerged from studies of cardiac G protein-coupled receptor signaling in health and disease, with a particular focus on the main cardiac G protein-coupled receptor types, ie, the β-adrenergic and the angiotensin II type 1 receptors. We also highlight key issues that need to be addressed to improve the chances of success of novel therapies directed against these targets.