Incorporation of natriuretic peptides with clinical risk scores to predict heart failure among individuals with dysglycaemia

Machine learning in the prevention of heart failure

Heart failure (HF) is a global pandemic with a growing prevalence and is a growing burden on the healthcare system. Machine learning (ML) has the potential to revolutionize medicine and can be applied in many different forms to aid in the prevention of symptomatic HF (stage C). HF prevention currently has several challenges, specifically in the detection of pre-HF (stage B). HF events are missed in contemporary models, limited therapeutic options are proven to prevent HF, and the prevention of HF with preserved ejection is particularly lacking. ML has the potential to overcome these challenges through existing and future models. ML has limitations, but the many benefits of ML outweigh these limitations and risks in most scenarios. ML can be applied in HF prevention through various strategies such as refinement of incident HF risk prediction models, capturing diagnostic signs from available tests such as electrocardiograms, chest x-rays, or echocardiograms to identify structural/functional cardiac abnormalities suggestive of pre-HF (stage B HF), and interpretation of biomarkers and epigenetic data. Altogether, ML is able to expand the screening of individuals at risk for HF (stage A HF), identify populations with pre-HF (stage B HF), predict the risk of incident stage C HF events, and offer the ability to intervene early to prevent progression to or decline in stage C HF. In this narrative review, we discuss the methods by which ML is utilized in HF prevention, the benefits and pitfalls of ML in HF risk prediction, and the future directions.

Electronic Health Record Alert With Heart Failure Risk and Sodium Glucose Cotransporter 2 Inhibitor Prescriptions in Diabetes: A Randomized Clinical Trial

BACKGROUND

Sodium glucose cotransporter 2 inhibitors (SGLT2i) prevent heart failure (HF) in patients with type 2 diabetes mellitus (T2DM) but prescription rates are low. The effect of an electronic health record (EHR) alert notifying providers of patients' estimated risk of developing HF on SGTL2i prescriptions is unknown.

METHODS

This was a pragmatic, randomized clinical trial that compared an EHR alert and usual care among patients with T2DM and no history of HF or SGLT2i use at a single center. The EHR alert notified providers of their patient's HF risk and recommended HF prevention strategies. Randomization was performed at the provider level across general and subspecialty internal medicine as well as family medicine outpatient clinics. The primary outcome was proportion of SGLT2i prescriptions within 30 days. Proportion of natriuretic peptide (NP) tests within 90 days was also assessed.

RESULTS

A total of 1524 patients (median age 75 years, 45% women, 23% Black) were enrolled between September 28, 2021, and April 29, 2022 from 189 outpatient clinics. SGLT2i were prescribed to 1.2% (9/780) of patients in the EHR alert group and 0% (0/744) of those in the usual care group (P value = 0.009). Natriuretic peptide testing was performed within 90 days among 10.8% (84/780) of patients in the EHR alert group and 7.3% (54/744) of patients in the usual care group (P value = 0.02).

CONCLUSIONS

In a single-center trial with low overall SGLT2i use, an EHR alert incorporating HF risk information significantly increased SGLT2i prescriptions and NP testing although the absolute rates were low.

Association of High-Density Lipoprotein Parameters and Risk of Heart Failure: A Multicohort Analysis

BACKGROUND

High-density lipoprotein (HDL) is commonly characterized by its cholesterol concentration (HDL-C) and inverse association with atherosclerotic cardiovascular disease.

OBJECTIVES

The authors sought to evaluate the association of HDL particle concentration (HDL-P), HDL particle size (HDL-size), HDL-C, and cholesterol content per particle (HDL-C/HDL-P) with risk of overall heart failure (HF) and subtypes.

METHODS

Participants from the Atherosclerosis Risk In Communities Study, Dallas Heart Study, Multi-Ethnic Study of Atherosclerosis, and Prevention of Renal and Vascular End-stage Disease studies without HF history were included. Associations of HDL-P, HDL-size, HDL-C, and HDL-C/HDL-P with risk of overall HF, HF with reduced and preserved ejection fraction were assessed using adjusted Cox models.

RESULTS

Among 16,925 participants (53.5% women; 21.8% Black), there were 612 incident HF events (3.6%) (HF with reduced ejection fraction, 309 [50.5%]; HF preserved ejection fraction, 303 [49.5%]) over median follow-up of 11.4 years. In adjusted models, higher HDL-P was significantly associated with lower HF risk (HR of highest vs lowest tertile of HDL-P: 0.76 [95% CI: 0.62-0.93]). Larger HDL-size was significantly associated with higher overall HF risk (HR of largest vs smallest tertile of HDL-size: 1.27 [95% CI: 1.03-1.58]). HF risk associated with HDL-P and HDL-size was similar for HF subtypes. In adjusted analyses, there was no significant association between HDL-C and HF risk. Higher HDL-C/HDL-P was significantly associated with higher overall HF risk (HR of highest vs lowest tertile of HDL-C/HDL-P: 1.29 [95% CI: 1.04-1.60]).

CONCLUSIONS

Higher HDL-P was associated with a lower risk of HF. In contrast, larger HDL-size was associated with higher risk of HF and there was no significant association observed between HDL-C and HF risk after accounting for cardiovascular risk factors.

2024 update in heart failure

In the last years, major progress has occurred in heart failure (HF) management. The 2023 ESC focused update of the 2021 HF guidelines introduced new key recommendations based on the results of the last years of science. First, two drugs, sodium-glucose co-transporter-2 (SGLT2) inhibitors and finerenone, a novel nonsteroidal, selective mineralocorticoid receptor antagonist (MRA), are recommended for the prevention of HF in patients with diabetic chronic kidney disease (CKD). Second, SGLT2 inhibitors are now recommended for the treatment of HF across the entire left ventricular ejection fraction spectrum. The benefits of quadruple therapy in patients with HF with reduced ejection fraction (HFrEF) are well established. Its rapid and early up-titration along with a close follow-up with frequent clinical and laboratory re-assessment after an episode of acute HF (the so-called 'high-intensity care' strategy) was associated with better outcomes in the STRONG-HF trial. Patients experiencing an episode of worsening HF might require a fifth drug, vericiguat. In the STEP-HFpEF-DM and STEP-HFpEF trials, semaglutide 2.4 mg once weekly administered for 1 year decreased body weight and significantly improved quality of life and the 6 min walk distance in obese patients with HF with preserved ejection fraction (HFpEF) with or without a history of diabetes. Further data on safety and efficacy, including also hard endpoints, are needed to support the addition of acetazolamide or hydrochlorothiazide to a standard diuretic regimen in patients hospitalized due to acute HF. In the meantime, PUSH-AHF supported the use of natriuresis-guided diuretic therapy. Further options and most recent evidence for the treatment of HF, including specific drugs for cardiomyopathies (i.e., mavacamten in hypertrophic cardiomyopathy and tafamidis in transthyretin cardiac amyloidosis), device therapies, cardiac contractility modulation and percutaneous treatment of valvulopathies, with the recent finding from the TRILUMINATE Pivotal trial, are also reviewed in this article.

Methods to predict heart failure in diabetes patients

INTRODUCTION

Type 2 diabetes mellitus (T2DM) is one of the leading causes of cardiovascular disease and powerful predictor for new-onset heart failure (HF).

AREAS COVERED

We focus on the relevant literature covering evidence of risk stratification based on imaging predictors and circulating biomarkers to optimize approaches to preventing HF in DM patients.

EXPERT OPINION

Multiple diagnostic algorithms based on echocardiographic parameters of cardiac remodeling including global longitudinal strain/strain rate are likely to be promising approach to justify individuals at higher risk of incident HF. Signature of cardiometabolic status may justify HF risk among T2DM individuals with low levels of natriuretic peptides, which preserve their significance in HF with clinical presentation. However, diagnostic and predictive values of conventional guideline-directed biomarker HF strategy may be non-optimal in patients with obesity and T2DM. Alternative biomarkers affecting cardiac fibrosis, inflammation, myopathy, and adipose tissue dysfunction are plausible tools for improving accuracy natriuretic peptides among T2DM patients at higher HF risk. In summary, risk identification and management of the patients with T2DM with established HF require conventional biomarkers monitoring, while the role of alternative biomarker approach among patients with multiple CV and metabolic risk factors appears to be plausible tool for improving clinical outcomes.

Cardiovascular Disease Management With Sodium-Glucose Cotransporter-2 Inhibitors in Patients With Type 2 Diabetes: A Cardiology Primer

Patients with type 2 diabetes face an elevated risk of cardiovascular disease. This review centers on sodium-glucose cotransporter-2 (SGLT2) inhibitors, a class of drugs that, according to a growing body of evidence, may have major potential for managing cardiovascular disease in patients with type 2 diabetes. This review presents findings from multiple clinical trials suggesting that SGLT2 inhibitors can not only serve as preventive therapeutic agents but also play a role in the active management of heart failure. The discussion includes the mechanism of action of SGLT2 inhibitors, emphasizing that they enhance urinary glucose excretion, which could lead to improved glycemic control and contribute to metabolic shifts beneficial to cardiac function. Alongside these cardiometabolic effects, safety concerns and practical considerations for prescribing these agents are addressed, taking into account potential adverse effects such as genitourinary infections and diabetic ketoacidosis as well as the financial implications for patients. Despite these drawbacks, therapeutic indications for SGLT2 inhibitors continue to expand, including for kidney protection, although further research is necessary to fully understand the mechanisms driving the cardioprotective and kidney-protective effects of SGLT2 inhibitors. By synthesizing current knowledge, this review intends to inform and guide clinical decision-making, thereby enhancing cardiovascular disease outcomes in patients with type 2 diabetes.

Optimal Screening for Predicting and Preventing the Risk of Heart Failure Among Adults With Diabetes Without Atherosclerotic Cardiovascular Disease: A Pooled Cohort Analysis

BACKGROUND

The optimal approach to identify individuals with diabetes who are at a high risk for developing heart failure (HF) to inform implementation of preventive therapies is unknown, especially in those without atherosclerotic cardiovascular disease (ASCVD).

METHODS

Adults with diabetes and no HF at baseline from 7 community-based cohorts were included. Participants without ASCVD who were at high risk for developing HF were identified using 1-step screening strategies: risk score (WATCH-DM [Weight, Age, Hypertension, Creatinine, HDL-C, Diabetes Control, QRS Duration, MI, and CABG] ≥12), NT-proBNP (N-terminal pro-B-type natriuretic peptide ≥125 pg/mL), hs-cTn (high-sensitivity cardiac troponin T ≥14 ng/L; hs-cTnI ≥31 ng/L), and echocardiography-based diabetic cardiomyopathy (echo-DbCM; left atrial enlargement, left ventricular hypertrophy, or diastolic dysfunction). High-risk participants were also identified using 2-step screening strategies with a second test to identify residual risk among those deemed low risk by the first test: WATCH-DM/NT-proBNP, NT-proBNP/hs-cTn, NT-proBNP/echo-DbCM. Across screening strategies, the proportion of HF events identified, 5-year number needed to treat and number needed to screen to prevent 1 HF event with an SGLT2i (sodium-glucose cotransporter 2 inhibitor) among high-risk participants, and cost of screening were estimated.

RESULTS

The initial study cohort included 6293 participants (48.2% women), of whom 77.7% without prevalent ASCVD were evaluated with different HF screening strategies. At 5-year follow-up, 6.2% of participants without ASCVD developed incident HF. The 5-year number needed to treat to prevent 1 HF event with an SGLT2i among participants without ASCVD was 43 (95% CI, 29-72). In the cohort without ASCVD, high-risk participants identified using 1-step screening strategies had a low 5-year number needed to treat (22 for NT-proBNP to 37 for echo-DbCM). However, a substantial proportion of HF events occurred among participants identified as low risk using 1-step screening approaches (29% for echo-DbCM to 47% for hs-cTn). Two-step screening strategies captured most HF events (75-89%) in the high-risk subgroup with a comparable 5-year number needed to treat as the 1-step screening approaches (30-32). The 5-year number needed to screen to prevent 1 HF event was similar across 2-step screening strategies (45-61). However, the number of tests and associated costs were lowest for WATCH-DM/NT-proBNP ($1061) compared with other 2-step screening strategies (NT-proBNP/hs-cTn: $2894; NT-proBNP/echo-DbCM: $16 358).

CONCLUSIONS

Selective NT-proBNP testing based on the WATCH-DM score efficiently identified a high-risk primary prevention population with diabetes expected to derive marked absolute benefits from SGLT2i to prevent HF.

The WATCH-DM risk score estimates clinical outcomes in type 2 diabetic patients with heart failure with preserved ejection fraction

The coexistence of heart failure is frequent and associated with higher mortality in patients with type 2 diabetes (T2DM), and its management is a critical issue. The WATCH-DM risk score is a tool to predict heart failure in patients with type 2 diabetes mellitus (T2DM). We investigated whether it could estimate outcomes in T2DM patients with heart failure with preserved ejection fraction (HFpEF). The WATCH-DM risk score was calculated in 418 patients with T2DM hospitalized for HFpEF (male 49.5%, age 80 ± 9 years, HbA1c 6.8 ± 1.0%), and they were divided into the "average or lower" (≤ 10 points), "high" (11-13 points) and "very high" (≥ 14 points) risk groups. We followed patients to observe all-cause death for 386 days (median). We compared the area under the curve (AUC) of the WATCH-DM score for predicting 1-year mortality with that of the Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC) score and of the Barcelona Bio-Heart Failure Risk (BCN Bio-HF). Among the study patients, 108 patients (25.8%) had average or lower risk scores, 147 patients (35.2%) had high risk scores, and 163 patients (39.0%) had very high risk scores. The Cox proportional hazard model selected the WATCH-DM score as an independent predictor of all-cause death (HR per unit 1.10, 95% CI 1.03 to 1.19), and the "average or lower" risk group had lower mortality than the other groups (p = 0.047 by log-rank test). The AUC of the WATCH-DM for 1-year mortality was 0.64 (95% CI 0.45 to 0.74), which was not different from that of the MAGGIC score (0.72, 95% CI 0.63 to 0.80, p = 0.08) or that of BCN Bio-HF (0.70, 0.61 to 0.80, p = 0.25). The WATCH-DM risk score can estimate prognosis in T2DM patients with HFpEF and can identify patients at higher risk of mortality.

Prioritizing the primary prevention of heart failure: Measuring, modifying and monitoring risk

With the rising incidence of heart failure (HF) and increasing burden of morbidity, mortality, and healthcare expenditures, primary prevention of HF targeting individuals in at-risk HF (Stage A) and pre-HF (Stage B) Stages has become increasingly important with the goal to decrease progression to symptomatic (Stage C) HF. Identification of risk based on traditional risk factors (e.g., cardiovascular health which can be assessed with the American Heart Association's Life's Essential 8 framework), adverse social determinants of health, inherited risk of cardiomyopathies, and identification of risk-enhancing factors, such as patients with viral disease, exposure to cardiotoxic chemotherapy, and history of adverse pregnancy outcomes should be the first step in evaluation for HF risk. Next, use of guideline-endorsed risk prediction tools such as Pooled Cohort Equations to Prevent Heart Failure provide quantification of absolute risk of HF based in traditional risk factors. Risk reduction through counseling on traditional risk factors is a core focus of implementation of prevention and may include the use of novel therapeutics that target specific pathways to reduce risk of HF, such as mineralocorticoid receptor agonists (e.g., fineronone), angiotensin-receptor/neprolysin inhibitors, and sodium glucose co-transporter-2 inhibitors. These interventions may be limited in at-risk populations who experience adverse social determinants and/or individuals who reside in rural areas. Thus, strategies like telemedicine may improve access to preventive care. Gaps in the current knowledge base for risk-based prevention of HF are highlighted to outline future research that may target approaches for risk assessment and risk-based prevention with the use of artificial intelligence, genomics-enhanced strategies, and pragmatic trials to develop a guideline-directed medical therapy approach to reduce risk among individuals with Stage A and Stage B HF.

Novel Prediction Equations for Absolute Risk Assessment of Total Cardiovascular Disease Incorporating Cardiovascular-Kidney-Metabolic Health: A Scientific Statement From the American Heart Association

Cardiovascular-kidney-metabolic (CKM) syndrome is a novel construct recently defined by the American Heart Association in response to the high prevalence of metabolic and kidney disease. Epidemiological data demonstrate higher absolute risk of both atherosclerotic cardiovascular disease (CVD) and heart failure as an individual progresses from CKM stage 0 to stage 3, but optimal strategies for risk assessment need to be refined. Absolute risk assessment with the goal to match type and intensity of interventions with predicted risk and expected treatment benefit remains the cornerstone of primary prevention. Given the growing number of therapies in our armamentarium that simultaneously address all 3 CKM axes, novel risk prediction equations are needed that incorporate predictors and outcomes relevant to the CKM context. This should also include social determinants of health, which are key upstream drivers of CVD, to more equitably estimate and address risk. This scientific statement summarizes the background, rationale, and clinical implications for the newly developed sex-specific, race-free risk equations: PREVENT (AHA Predicting Risk of CVD Events). The PREVENT equations enable 10- and 30-year risk estimates for total CVD (composite of atherosclerotic CVD and heart failure), include estimated glomerular filtration rate as a predictor, and adjust for competing risk of non-CVD death among adults 30 to 79 years of age. Additional models accommodate enhanced predictive utility with the addition of CKM factors when clinically indicated for measurement (urine albumin-to-creatinine ratio and hemoglobin A1c) or social determinants of health (social deprivation index) when available. Approaches to implement risk-based prevention using PREVENT across various settings are discussed.

Prognostic models for heart failure in patients with type 2 diabetes: a systematic review and meta-analysis

OBJECTIVE

To provide a systematic review, critical appraisal, assessment of performance and generalisability of all the reported prognostic models for heart failure (HF) in patients with type 2 diabetes (T2D).

METHODS

We performed a literature search in Medline, Embase, Central Register of Controlled Trials, Cochrane Database of Systematic Reviews and Scopus (from inception to July 2022) and grey literature to identify any study developing and/or validating models predicting HF applicable to patients with T2D. We extracted data on study characteristics, modelling methods and measures of performance, and we performed a random-effects meta-analysis to pool discrimination in models with multiple validation studies. We also performed a descriptive synthesis of calibration and we assessed the risk of bias and certainty of evidence (high, moderate, low).

RESULTS

Fifty-five studies reporting on 58 models were identified: (1) models developed in patients with T2D for HF prediction (n=43), (2) models predicting HF developed in non-diabetic cohorts and externally validated in patients with T2D (n=3), and (3) models originally predicting a different outcome and externally validated for HF (n=12). RECODe (C-statistic=0.75 95% CI (0.72, 0.78), 95% prediction interval (PI) (0.68, 0.81); high certainty), TRS-HFDM (C-statistic=0.75 95% CI (0.69, 0.81), 95% PI (0.58, 0.87); low certainty) and WATCH-DM (C-statistic=0.70 95% CI (0.67, 0.73), 95% PI (0.63, 0.76); moderate certainty) showed the best performance. QDiabetes-HF demonstrated also good discrimination but was externally validated only once and not meta-analysed.

CONCLUSIONS

Among the prognostic models identified, four models showed promising performance and, thus, could be implemented in current clinical practice.

Heart failure diagnosis in the general community - Who, how and when? A clinical consensus statement of the Heart Failure Association (HFA) of the European Society of Cardiology (ESC)

A significant proportion of patients experience delays in the diagnosis of heart failure due to the non-specific signs and symptoms of the syndrome. Diagnostic tools such as measurement of natriuretic peptide concentrations are fundamentally important when screening for heart failure, yet are frequently under-utilized. This clinical consensus statement provides a diagnostic framework for general practitioners and non-cardiology community-based physicians to recognize, investigate and risk-stratify patients presenting in the community with possible heart failure.

Biomarkers for the Diagnosis of Heart Failure in People with Diabetes: A Consensus Report from Diabetes Technology Society

Diabetes Technology Society assembled a panel of clinician experts in diabetology, cardiology, clinical chemistry, nephrology, and primary care to review the current evidence on biomarker screening of people with diabetes (PWD) for heart failure (HF), who are, by definition, at risk for HF (Stage A HF). This consensus report reviews features of HF in PWD from the perspectives of 1) epidemiology, 2) classification of stages, 3) pathophysiology, 4) biomarkers for diagnosing, 5) biomarker assays, 6) diagnostic accuracy of biomarkers, 7) benefits of biomarker screening, 8) consensus recommendations for biomarker screening, 9) stratification of Stage B HF, 10) echocardiographic screening, 11) management of Stage A and Stage B HF, and 12) future directions. The Diabetes Technology Society panel recommends 1) biomarker screening with one of two circulating natriuretic peptides (B-type natriuretic peptide or N-terminal prohormone of B-type natriuretic peptide), 2) beginning screening five years following diagnosis of type 1 diabetes (T1D) and at the diagnosis of type 2 diabetes (T2D), 3) beginning routine screening no earlier than at age 30 years for T1D (irrespective of age of diagnosis) and at any age for T2D, 4) screening annually, and 5) testing any time of day. The panel also recommends that an abnormal biomarker test defines asymptomatic preclinical HF (Stage B HF). This diagnosis requires follow-up using transthoracic echocardiography for classification into one of four subcategories of Stage B HF, corresponding to risk of progression to symptomatic clinical HF (Stage C HF). These recommendations will allow identification and management of Stage A and Stage B HF in PWD to prevent progression to Stage C HF or advanced HF (Stage D HF).

Predicting and preventing heart failure in type 2 diabetes

The burden of heart failure among people with type 2 diabetes is increasing globally. People with comorbid type 2 diabetes and heart failure often have worse outcomes than those with only one of these conditions-eg, higher hospitalisation and mortality rates. Therefore, it is essential to implement optimal heart failure prevention strategies for people with type 2 diabetes. A detailed understanding of the pathophysiology underlying the occurrence of heart failure in type 2 diabetes can aid clinicians in identifying relevant risk factors and lead to early interventions that can help prevent heart failure. In this Review, we discuss the pathophysiology and risk factors of heart failure in type 2 diabetes. We also review the risk assessment tools for predicting heart failure incidence in people with type 2 diabetes as well as the data from clinical trials that have assessed the efficacy of lifestyle and pharmacological interventions. Finally, we discuss the potential challenges in implementing new management approaches and offer pragmatic recommendations to help overcome these challenges.

WATCH-DM risk score predicts the prognosis of diabetic phenotype patients with heart failure and preserved ejection fraction

BACKGROUND

Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous syndrome. Diabetes may identify an essential phenotype that significantly affects the prognosis of these patients. The WATCH-DM risk score has been validated for predicting the risk of heart failure in outpatients with type 2 diabetes mellitus (T2DM), but its ability to predict clinical outcomes in HFpEF patients with T2DM is unknown. We aimed to assess whether this risk score could predict the prognosis of diabetic phenotype patients with heart failure and preserved ejection fraction.

METHODS

We enrolled retrospectively 414 patients with HFpEF (70.03 ± 8.654 years, 58.70% female), including 203 (49.03%) type 2 diabetics. Diabetic HFpEF patients were stratified by baseline WATCH-DM risk score.

RESULTS

Diabetic HFpEF patients exhibited a trend toward more concentric remodeling/hypertrophy than nondiabetic HFpEF patients. When analyzed as a continuous variable, per 1-point increase in the WATCH-DM risk score was associated with increased risks of all-cause death (HR 1.181), cardiovascular death (HR 1.239), any hospitalization (HR 1.082), and HF hospitalization (HR 1.097). The AUC for the WATCH-DM risk score in predicting incident cardiovascular death (0.7061, 95% CI 0.6329-0.7792) was higher than that of all-cause death, any hospitalization, or HF hospitalization.

CONCLUSIONS

As a high-risk phenotype for heart failure, diabetic HFpEF necessitates early risk stratification and specific treatment. To the best of our knowledge, the current study is the first to demonstrate that the WATCH-DM score predicts poor outcomes in diabetic HFpEF patients. Its convenience may allow for quick risk assessments in busy clinical settings.

Effect of Canagliflozin on Heart Failure Hospitalization in Diabetes According to Baseline Heart Failure Risk

BACKGROUND

In the CANVAS (Canagliflozin Cardiovascular Assessment Study) program, canagliflozin reduced the risk of heart failure (HF) hospitalization among individuals with type 2 diabetes mellitus (T2DM).

OBJECTIVES

The purpose of this study was to evaluate heterogeneity in absolute and relative treatment effects of canagliflozin on HF hospitalization according to baseline HF risk as assessed by diabetes-specific HF risk scores (WATCH-DM [Weight (body mass index), Age, hyperTension, Creatinine, HDL-C, Diabetes control (fasting plasma glucose) and QRS Duration, MI and CABG] and TRS-HF_DM [TIMI Risk Score for HF in Diabetes]).

METHODS

Participants in the CANVAS trial were categorized into low, medium, and high risk for HF using the WATCH-DM score (for participants without prevalent HF) and the TRS-HF_DM score (for all participants). The outcome of interest was time to first HF hospitalization. The treatment effect of canagliflozin vs placebo for HF hospitalization was compared across risk strata.

RESULTS

Among 10,137 participants with available HF data, 1,446 (14.3%) had HF at baseline. Among participants without baseline HF, WATCH-DM risk category did not modify the treatment effect of canagliflozin (vs placebo) on HF hospitalization (P interaction = 0.56). However, the absolute and relative risk reduction with canagliflozin was numerically greater in the high-risk group (cumulative incidence, canagliflozin vs placebo: 8.1% vs 12.7%; HR: 0.62 [95% CI: 0.37-0.93]; P = 0.03; number needed to treat: 22) than in the low- and intermediate-risk groups. When overall study participants were categorized according to the TRS-HF_DM score, a statistically significant difference in the treatment effect of canagliflozin across risk strata was observed (P interaction = 0.04). Canagliflozin significantly reduced the risk of HF hospitalization by 39% in the high-risk group (HR: 0.61 [95% CI: 0.48-0.78]; P < 0.001; number needed to treat: 20) but not in the intermediate- or low-risk groups.

CONCLUSIONS

Among participants with T2DM, the WATCH-DM and TRS-HF_DM can reliably identify those at high risk for HF hospitalization and most likely to benefit from canagliflozin.

Global historical retrospect and future prospects on biomarkers of heart failure: A bibliometric analysis and science mapping

Introduction

Heart failure is a rapidly growing public health problem and has become a major cause of hospitalization in middle-aged and older adults. Biomarkers are clinically important in managing heart failure and have attracted more attention from researchers in recent years. This study aimed to evaluate the global research of heart failure biomarkers by bibliometrics and to identify the hot spots and perspectives for further advancement.

Methods

Selection of relevant documents was from the Web of Science Core Collection. Microsoft Excel, VOSviewer, SciMA, and CiteSpace software were used for bibliometric analysis.

Results

As of October 29, 2021, 5,978 documents for heart failure biomarkers have been identified from 1989 to 2021. European Journal of Heart Failure and Circulation respectively ranked first in terms of the number of publications and the number of co-citations. A total of 5,698 institutions from 90 countries participated in these publications, with the USA leading with 2,045 documents. The most productive institution was Harvard University. Januzzi, J.L. and Maisel, A.S. were the most productive and most cited authors respectively. Natriuretic peptide, copeptin, valsartan, ferric carboxymaltose, empagliflozin, preserved ejection fraction, myocardial fibrosis, and heart transplantation were hot themes.

Conclusions

Extensive national and inter-institutional collaboration should be enhanced to bridge the gap between developed and less developed countries in heart failure biomarkers research. The research in this field seems to have reached a relatively mature stage, with a decrease in research fervor in recent years. The study of the natriuretic peptide family still has high centrality, with advances in the study of expression products and inflammatory markers. Cardiac fibrosis, cardiac remodeling, and therapies regarding heart failure have become hot spots.

Heart failure: an update from the last years and a look at the near future

In the last years, major progress occurred in heart failure (HF) management. Quadruple therapy is now mandatory for all the patients with HF with reduced ejection fraction. Whilst verciguat is becoming available across several countries, omecamtiv mecarbil is waiting to be released for clinical use. Concurrent use of potassium-lowering agents may counteract hyperkalaemia and facilitate renin-angiotensin-aldosterone system inhibitor implementations. The results of the EMPagliflozin outcomE tRial in Patients With chrOnic heaRt Failure With Preserved Ejection Fraction (EMPEROR-Preserved) trial were confirmed by the Dapagliflozin in Heart Failure with Mildly Reduced or Preserved Ejection Fraction (DELIVER) trial, and we now have, for the first time, evidence for treatment of also patients with HF with preserved ejection fraction. In a pre-specified meta-analysis of major randomized controlled trials, sodium-glucose co-transporter-2 inhibitors reduced all-cause mortality, cardiovascular (CV) mortality, and HF hospitalization in the patients with HF regardless of left ventricular ejection fraction. Other steps forward have occurred in the treatment of decompensated HF. Acetazolamide in Acute Decompensated Heart Failure with Volume Overload (ADVOR) trial showed that the addition of intravenous acetazolamide to loop diuretics leads to greater decongestion vs. placebo. The addition of hydrochlorothiazide to loop diuretics was evaluated in the CLOROTIC trial. Torasemide did not change outcomes, compared with furosemide, in TRANSFORM-HF. Ferric derisomaltose had an effect on the primary outcome of CV mortality or HF rehospitalizations in IRONMAN (rate ratio 0.82; 95% confidence interval 0.66-1.02; P = 0.070). Further options for the treatment of HF, including device therapies, cardiac contractility modulation, and percutaneous treatment of valvulopathies, are summarized in this article.

Optimal cardiometabolic health and risk of heart failure in type 2 diabetes: an analysis from the Look AHEAD trial

AIMS

To evaluate the contribution of baseline and longitudinal changes in cardiometabolic health (CMH) towards heart failure (HF) risk among adults with type 2 diabetes (T2D).

METHODS AND RESULTS

Participants of the Look AHEAD trial with T2D and without prevalent HF were included. Adjusted Cox models were used to create a CMH score incorporating target levels of parameters weighted based on relative risk for HF. The associations of baseline and changes in the CMH score with risk of overall HF, HF with preserved (HFpEF) and reduced ejection fraction (HFrEF) were assessed using Cox models. Among the 5080 participants, 257 incident HF events occurred over 12.4 years of follow-up. The CMH score included 2 points each for target levels of waist circumference, glomerular filtration rate, urine albumin-to-creatinine ratio, and 1 point each for blood pressure and glycated haemoglobin at target. High baseline CMH score (6-8) was significantly associated with lower overall HF risk (adjusted hazard ratio [HR], ref = low score (0-3): 0.31, 95% confidence interval [CI] 0.21-0.47) with similar associations observed for HFpEF and HFrEF. Improvement in CMH was significantly associated with lower risk of overall HF (adjusted HR per 1-unit increase in score at 4 years: 0.80, 95% CI 0.70-0.91). In the ACCORD validation cohort, the baseline CMH score performed well for predicting HF risk with adequate discrimination (C-index 0.70), calibration (chi-square 5.53, p = 0.70), and risk stratification (adjusted HR [high (6-8) vs. low score (0-3)]: 0.35, 95% CI 0.26-0.46). In the Look AHEAD subgroup with available biomarker data, incorporating N-terminal pro-B-type natriuretic peptide to the baseline CMH score improved model discrimination (C-index 0.79) and risk stratification (adjusted HR [high (8-10) vs. low score (0-4)]: 0.18, 95% CI 0.09-0.35).

CONCLUSIONS

Achieving target levels of more CMH parameters at baseline and sustained improvements were associated with lower HF risk in T2D.

The Texas Health Resources Clinical Scholars Program: Learning healthcare system workforce development through embedded translational research

Introduction

Texas Health Resources (THR), a large, nonprofit health care system based in the Dallas-Fort Worth area, has collaborated with the University of Texas Southwestern Medical Center (UTSW) to develop and operate a unique, integrated approach for Learning Health System (LHS) workforce development. This training model centers on academic health system faculty members conducting later-stage translational research within a partnering regional care delivery organization.

Methods

The THR Clinical Scholars Program engages early career UTSW faculty members to conduct studies that are likely to have an impact on care delivery at the health system level. Interested candidates submit formal applications to the program. A joint committee comprised of senior research faculty from UTSW and THR clinical leadership reviews proposals with a focus on the shared LHS needs of both institutions-developing high quality research output that can be applied to enhance care delivery. A key prioritization criterion for funding is the degree to which the research addresses a question relevant to THR as a high-volume network with multiple channels for consumers to access care. The program emphasis is on supporting embedded research initiatives using health system data to generate knowledge that will improve the quality and efficiency of care for the patient populations served by the participant organizations.

Results

We discuss specific strategic and tactical components of the THR Clinical Scholars Program including an overview of the academic affiliation agreement between the collaborating organizations, criteria for successful program applications, data sharing, and funding. We also share project summaries from selected clinical scholars as examples of the LHS research done in the program to date.

Conclusion

This experience report provides an implementation framework for other academic health systems interested in adopting similar LHS workforce training models with community partners.

Biomarkers for the prediction of heart failure and cardiovascular events in patients with type 2 diabetes: a position statement from the Heart Failure Association of the European Society of Cardiology

Knowledge on risk predictors of incident heart failure (HF) in patients with type 2 diabetes (T2D) is crucial given the frequent coexistence of the two conditions and the fact that T2D doubles the risk of incident HF. In addition, HF is increasingly being recognized as an important endpoint in trials in T2D. On the other hand, the diagnostic and prognostic performance of established cardiovascular biomarkers may be modified by the presence of T2D. The present position paper, derived by an expert panel workshop organized by the Heart Failure Association of the European Society of Cardiology, summarizes the current knowledge and gaps in evidence regarding the use of a series of different biomarkers, reflecting various pathogenic pathways, for the prediction of incident HF and cardiovascular events in patients with T2D and in those with established HF and T2D.

Validation of the WATCH-DM and TRS-HFDM Risk Scores to Predict the Risk of Incident Hospitalization for Heart Failure Among Adults With Type 2 Diabetes: A Multicohort Analysis

Background The WATCH-DM (weight [body mass index], age, hypertension, creatinine, high-density lipoprotein cholesterol, diabetes control [fasting plasma glucose], ECG QRS duration, myocardial infarction, and coronary artery bypass grafting) and TRS-HF_DM (Thrombolysis in Myocardial Infarction [TIMI] risk score for heart failure in diabetes) risk scores were developed to predict risk of heart failure (HF) among individuals with type 2 diabetes. WATCH-DM was developed to predict incident HF, whereas TRS-HF_DM predicts HF hospitalization among patients with and without a prior HF history. We evaluated the model performance of both scores to predict incident HF events among patients with type 2 diabetes and no history of HF hospitalization across different cohorts and clinical settings with varying baseline risk. Methods and Results Incident HF risk was estimated by the integer-based WATCH-DM and TRS-HF_DM scores in participants with type 2 diabetes free of baseline HF from 2 randomized clinical trials (TECOS [Trial Evaluating Cardiovascular Outcomes With Sitagliptin], N=12 028; and Look AHEAD [Look Action for Health in Diabetes] trial, N=4867). The integer-based WATCH-DM score was also validated in electronic health record data from a single large health care system (N=7475). Model discrimination was assessed by the Harrell concordance index and calibration by the Greenwood-Nam-D'Agostino statistic. HF incidence rate was 7.5, 3.9, and 4.1 per 1000 person-years in the TECOS, Look AHEAD trial, and electronic health record cohorts, respectively. Integer-based WATCH-DM and TRS-HF_DM scores had similar discrimination and calibration for predicting 5-year HF risk in the Look AHEAD trial cohort (concordance indexes=0.70; Greenwood-Nam-D'Agostino P>0.30 for both). Both scores had lower discrimination and underpredicted HF risk in the TECOS cohort (concordance indexes=0.65 and 0.66, respectively; Greenwood-Nam-D'Agostino P<0.001 for both). In the electronic health record cohort, the integer-based WATCH-DM score demonstrated a concordance index of 0.73 with adequate calibration (Greenwood-Nam-D'Agostino P=0.96). TRS-HF_DM score could not be validated in the electronic health record because of unavailability of data on urine albumin/creatinine ratio in most patients in the contemporary clinical practice. Conclusions The WATCH-DM and TRS-HF_DM risk scores can discriminate risk of HF among intermediate-risk populations with type 2 diabetes.

Team-based strategies to prevent heart failure

PURPOSE OF REVIEW

The burden of heart failure (HF) in the United States and worldwide is projected to rise. Prevention of HF can curb the burden of this chronic syndrome, but current approaches are limited. This review discusses team-based strategies aimed to prevent HF.

RECENT FINDINGS

Individuals at high risk for developing HF can be identified using HF risk scores, biomarkers, and cardiac imaging. Electronic medical records (EMR) can integrate clinical data to estimate HF risk and identify individuals who may benefit most from preventive therapies. Team-based interventions can lead to enhanced adherence to medications, optimization of medical management, and control of risk factors. Multifaceted interventions involve EMR-based strategies, pharmacist- and nurse-led initiatives, involvement of community personnel, polypills, and digital solutions.

SUMMARY

Team-based strategies aimed to prevent HF incorporate a broad group of personnel and tools. Despite implementation challenges, existing resources can be efficiently utilized to facilitate team-based approaches to potentially reduce the burden of HF.