Prevalence of heart failure phenotypes and current use of therapies in primary care: results from a nationwide study

Distribution and prognostic impact of different heart failure etiologies in patients with heart failure with mildly reduced ejection fraction: Etiology in heart failure with mildly reduced ejection fraction

OBJECTIVE

The study investigates the characteristics and prognostic impact of different heart failure (HF) etiologies in patients with heart failure with mildly reduced ejection fraction (HFmrEF).

BACKGROUND

Data regarding the characterization of patients with HFmrEF and their outcomes is scarce.

METHODS

Consecutive patients with HFmrEF (i.e., left ventricular ejection fraction 41-49 % and signs and/or symptoms of HF) were retrospectively included at one institution from 2016 to 2022. Patients with ischemic cardiomyopathy (ICM) were compared to patients without ischemic cardiomyopathy (non-ICM). The primary endpoint was all-cause mortality at 30 months (median follow-up). Statistical analyses included Kaplan-Meier, multivariable Cox proportional regression analyses and propensity score matching.

RESULTS

From a total of 1,832 patients hospitalized with HFmrEF, ICM was the most common HF etiology in 68.7 %, followed by hypertensive (9.7 %) and primary non-ischemic cardiomyopathies (NICM) (8.1 %). Within the entire study cohort, the presence of ICM was not associated with the risk of all-cause mortality (HR = 0.864; 95 % CI 0.723 - 1.031), however after multivariable adjustment (HR = 0.792; 95 % CI 0.646 - 0.972; p = 0.026) and propensity score matching (25.7% vs. 31.4 %; log rank p = 0.050), the presence of ICM was associated with lower risk of all-cause mortality at 30 months compared to patients without ICM.

CONCLUSION

ICM is the most common etiology of HF in HFmrEF and may be associated with favorable outcomes. This may be related to better adherence to pharmacological treatment and improved revascularization strategies for HFmrEF patients with ICM.

Age-specific mortality trends in heart failure over 25 years: a retrospective Danish nationwide cohort study

BACKGROUND

Despite advances in heart failure care reducing mortality in clinical trials, it remains unclear whether real-life cohorts have had similar improvements in life expectancy across the age spectrum. We aimed to investigate how mortality trends changed in patients with heart failure over the past 25 years, stratified by age groups.

METHODS

Using Danish nationwide registries, we identified patients with new-onset heart failure aged 18-95 years. The 5-year all-cause mortality risk and the absolute risk difference of mortality between patients with heart failure and age-matched and sex-matched heart failure-free controls were assessed using Kaplan-Meier estimates and multivariable Cox regression models. Mortality trends were analysed across five calendar periods (1996-2000, 2001-05, 2006-10, 2011-15, and 2016-20) and three age groups (<65 years, 65-79 years, and ≥80 years).

FINDINGS

194 997 patients with heart failure were included. Mortality significantly decreased from 1996-2000 (66% [95% CI 65·5-66·4]) to 2016-20 (43% [42·1-43·4]), with similar results shown in all age groups (<65 years: 35% [33·9-36·1] to 15% [14·6-16·3]; 65-79 years: 64% [63·1-64·5] to 39% [37·6-39·6]; and ≥80 years: 84% [83·1-84·3] to 73% [71·7-73·9]). Adjusted mortality rates supported these associations. The absolute risk difference declined notably in younger age groups (<65 years: 29·9% [28·8-31·0] to 12·7% [12·0-13·4] and 65-79 years: 41·1% [40·3-41·9] to 25·1% [24·4-25·8]), remaining relatively stable in those aged 80 years or older (30·6% [29·9-31·3] to 28% [27·2-28·8]).

INTERPRETATION

Over 25 years, there has been a consistent decrease in mortality among patients with heart failure across age groups, albeit less prominently in patients aged 80 years or older. Further insight is needed to identify effective strategies for improving disease burden in older patients with heart failure.

FUNDING

None.

TRANSLATION

For the Danish translation of the abstract see Supplementary Materials section.

Annual evolution of the prescription of drugs with prognostic implications in acute decompensated heart failure with reduced ejection fraction

BACKGROUND

Quadruple therapy (renin angiotensin system inhibitors, beta-blockers, mineralocorticoid receptor antagonists and sodium/glucose cotransporter type 2 inhibitors [SGLT2i]) has become the current prognostic modifying treatment for heart failure (HF) with reduced ejection fraction (HFrEF). This study aimed to analyse the prescription´s evolution of this combination therapy, the analysis of each pharmacological group and the differences according to HF subgroups.

METHODS

Retrospective analysis of consecutive patients admitted for cardiac decompensation. Inclusion period: from 1-1-2020 to 12-31-2022. Patients with left ventricular ejection fraction > 40% and deceased during admission were excluded. Finally, 602 patients were included. These were divided into: (a) de novo HF without previous heart disease (n:108), (b) de novo with previous heart disease (n:107), and (c) non-de novo (n:387).

RESULTS

Over the study time, all pharmacological groups experienced an increase in drugs prescription (p < 0.001). The group with the largest prescription rate increase was SGLT2i (2020:20%, 2021:42.9%, 2022:70.4%; mean increase 47.2%). The discharge rate prescription of quadruple therapy increased progressively (2020:7.4%, 2021:21.1%, 2022:32.5%; mean increase 21.9%). The subgroup with the highest combined prescription in 2022 was de novo with previous heart disease (43.9%).

CONCLUSION

The pharmacological group with the largest prescription´s rate increase was SGLT2i. The percentage of patients discharged on quadruple therapy has progressed significantly in recent years, although it remains low. The most optimised subgroup at discharge was that of de novo HF with previous heart disease.

Artificial intelligence methods for improved detection of undiagnosed heart failure with preserved ejection fraction

AIM

Heart failure with preserved ejection fraction (HFpEF) remains under-diagnosed in clinical practice despite accounting for nearly half of all heart failure (HF) cases. Accurate and timely diagnosis of HFpEF is crucial for proper patient management and treatment. In this study, we explored the potential of natural language processing (NLP) to improve the detection and diagnosis of HFpEF according to the European Society of Cardiology (ESC) diagnostic criteria.

METHODS AND RESULTS

In a retrospective cohort study, we used an NLP pipeline applied to the electronic health record (EHR) to identify patients with a clinical diagnosis of HF between 2010 and 2022. We collected demographic, clinical, echocardiographic and outcome data from the EHR. Patients were categorized according to the left ventricular ejection fraction (LVEF). Those with LVEF ≥50% were further categorized based on whether they had a clinician-assigned diagnosis of HFpEF and if not, whether they met the ESC diagnostic criteria. Results were validated in a second, independent centre. We identified 8606 patients with HF. Of 3727 consecutive patients with HF and LVEF ≥50% on echocardiogram, only 8.3% had a clinician-assigned diagnosis of HFpEF, while 75.4% met ESC criteria but did not have a formal diagnosis of HFpEF. Patients with confirmed HFpEF were hospitalized more frequently; however the ESC criteria group had a higher 5-year mortality, despite being less comorbid and experiencing fewer acute cardiovascular events.

CONCLUSIONS

This study demonstrates that patients with undiagnosed HFpEF are an at-risk group with high mortality. It is possible to use NLP methods to identify likely HFpEF patients from EHR data who would likely then benefit from expert clinical review and complement the use of diagnostic algorithms.

Machine learning profiles of cardiovascular risk in patients with diabetes mellitus: the Silesia Diabetes-Heart Project

AIMS

As cardiovascular disease (CVD) is a leading cause of death for patients with diabetes mellitus (DM), we aimed to find important factors that predict cardiovascular (CV) risk using a machine learning (ML) approach.

METHODS AND RESULTS

We performed a single center, observational study in a cohort of 238 DM patients (mean age ± SD 52.15 ± 17.27 years, 54% female) as a part of the Silesia Diabetes-Heart Project. Having gathered patients' medical history, demographic data, laboratory test results, results from the Michigan Neuropathy Screening Instrument (assessing diabetic peripheral neuropathy) and Ewing's battery examination (determining the presence of cardiovascular autonomic neuropathy), we managed use a ML approach to predict the occurrence of overt CVD on the basis of five most discriminative predictors with the area under the receiver operating characteristic curve of 0.86 (95% CI 0.80-0.91). Those features included the presence of past or current foot ulceration, age, the treatment with beta-blocker (BB) and angiotensin converting enzyme inhibitor (ACEi). On the basis of the aforementioned parameters, unsupervised clustering identified different CV risk groups. The highest CV risk was determined for the eldest patients treated in large extent with ACEi but not BB and having current foot ulceration, and for slightly younger individuals treated extensively with both above-mentioned drugs, with relatively small percentage of diabetic ulceration.

CONCLUSIONS

Using a ML approach in a prospective cohort of patients with DM, we identified important factors that predicted CV risk. If a patient was treated with ACEi or BB, is older and has/had a foot ulcer, this strongly predicts that he/she is at high risk of having overt CVD.

Prevalence of heart failure phenotypes and current use of therapies in primary care: results from a nationwide study

AIMS

Heart failure (HF) is an increasing concern worldwide. A rising HF burden is expected due to the prospected future demographic changes with aging populations. Consequently, the long-term follow-up and treatment will be performed increasingly by primary care physicians in the future. Contemporary data on HF patients in primary care are needed to plan and ensure an effective and safe follow-up of future patients.

METHODS AND RESULTS

The electronic patient journals of 148 primary care clinics in Denmark were searched in a standardized manner to identify patients with HF [code K77 of the International Classification of Primary Care, Second Edition]. Prespecified variables including demographic information, clinical variables, co-morbidities, prescribed medications, and setting of follow-up were recorded. In total, 1111 patients were included in the study. The mean timepoint for the HF diagnosis was August 2018. In 95% of cases, the diagnosis of HF was made in a specialized setting. The echocardiogram data used for phenotyping were available in 1042 (94%) of the 1111 patients. HF with reduced ejection fraction (HFrEF) was present in 43%, recovered HFrEF in 31%, and HF with mildly reduced (HFmrEF) or preserved ejection fraction (HFpEF) in 26%. In patients with HFrEF or recovered HFrEF, fundamental treatments were prescribed in 86% for angiotensin converting enzyme inhibitor (ACEI), angiotensin receptor blocker (ARB), or angiotensin receptor neprilysin inhibitor (ARNI), in 82% for beta-blocker, in 38% for mineralocorticoid receptor antagonist (MRA), and in 12% for sodium-glucose co-transporter-2 inhibitor (SGLT2i). Older patients were treated to a significantly lesser extent than young patients for all drug classes [odds ratio (OR) point estimates 0.50 to 0.69, all P-values < 0.05]. In patients with HFmrEF or HFpEF, an ACEI, ARB, or ARNI was prescribed in 67%, beta-blocker in 67%, MRA in 22%, and SGLT2i in 7.4% with significantly lower probability of treatment compared to patients with HFrEF or recovered HFrEF [OR point estimates 0.33 to 0.57, all P-values < 0.05]. The setting of follow-up was available in 96% of patients. Irrespective of HF phenotype, follow-up was performed solely in primary care in 64%. These patients were generally treated to a lesser extent with HF therapies compared with patients where follow-up included specialized care, yet differences were generally small.

CONCLUSIONS

HFrEF is the most common phenotype of HF in primary care followed by recovered HFrEF and fundamental therapies are markedly underutilized. Initiatives to increase the use of recommended therapies are needed to improve the future care of patients with HF.