Novel Mechanisms in Heart Failure With Preserved, Midrange, and Reduced Ejection Fraction

Development and Validation of R-hf Risk Score in Acute Heart Failure Patients in the Middle East

Objectives

The Rajan's heart failure (R-hf) score was proposed to aid risk stratification in heart failure patients. The aim of this study was to validate R-hf risk score in patients with acute decompensated heart failure.

Methods

R-hf risk score is derived from the product estimated glomerular filtration rate (mL/min), left ventricular ejection fraction (%), and hemoglobin levels (g/dL) divided by N-terminal pro-brain natriuretic peptide (pg/mL). This was a multinational, multicenter, prospective registry of heart failure from seven countries in the Middle East. Univariable and multivariable logistic regression was applied.

Results

A total of 776 patients (mean age = 62.0±14.0 years, 62.4% males; mean left ventricular ejection fraction = 33.0±14.0%) were included. Of these, 459 (59.1%) presented with acute decompensated chronic heart failure. The R-hf risk score group (≤ 5) was marginally associated with a higher risk of all-cause cumulative mortality at three months (adjusted odds ratio (aOR) = 4.28; 95% CI: 0.90-20.30; p =0.067) and significantly at 12 months (aOR = 3.84; 95% CI: 1.23-12.00; p =0.021) when compared to those with the highest R score group (≥ 50).

Conclusions

Lower R-hf risk scores are associated with increased risk of all-cause cumulative mortality at three and 12 months.

Efficacy of guideline-directed medical treatment in heart failure with mildly reduced ejection fraction

AIMS

Heart failure with mildly reduced ejection fraction (HFmrEF) has received increasing attention following the publication of the latest ESC guidelines in 2021. However, it remains unclear whether patients with HFmrEF could benefit from guideline-directed medical treatment (GDMT), referring the combination of ACEI/ARB/ARNI, β-blockers, and MRAs, which are recommended for those with reduced ejection fraction. This study explored the efficacy of GDMT in HFmrEF patients.

METHODS

This was a retrospective cohort study of HFmrEF patients admitted to The First Affiliated Hospital of Dalian Medical University between 1 September 2015 and 30 November 2019. Propensity score matching (1:2) between patients receiving triple-drug therapy (TT) and non-triple therapy (NTT) based on age and sex was performed. The primary outcome was all cause death, cardiac death, rehospitalization from any cause, and rehospitalization due to worsening heart failure.

RESULTS

Of the 906 patients enrolled in the matched cohort (TT group, n = 302; NTT group, N = 604), 653 (72.08%) were male, and mean age was 61.1 ± 11.92. Survival analysis suggested that TT group experienced a significantly lower incidence of prespecified primary endpoints than NTT group. Multivariable Cox regression showed that TT group had a lower risk of all-cause mortality (HR 0.656, 95% CI 0.447-0.961, P = 0.030), cardiac death (HR 0.599, 95% CI 0.380-0.946, P = 0.028), any-cause rehospitalization (HR 0.687, 95% CI 0.541-0.872, P = 0.002), and heart failure rehospitalization (HR 0.732, 95% CI 0.565-0.948, P = 0.018).

CONCLUSIONS

In patients with HFmrEF, combined use of neurohormonal antagonists produces remarkable effects in reducing the occurrence of the primary outcome of rehospitalization and death. Thus, the treatment of HFmrEF should be categorized as HFrEF due to the similar benefit of neurohormonal blocking therapy in HFrEF and HFmrEF.

P-wave durations from automated electrocardiogram analysis to predict atrial fibrillation and mortality in heart failure

BACKGROUND

P-wave indices have been used to predict incident atrial fibrillation (AF), stroke, and mortality. However, such indices derived from automated ECG measurements have not been explored for their predictive values in heart failure (HF). We investigated whether automated P-wave indices can predict adverse outcomes in HF.

METHODS

This study included consecutive Chinese patients admitted to a single tertiary centre, presenting with HF but without prior AF, and with at least one baseline ECG, between 1 January 2010 and 31 December 2016, with last follow-up of 31 December 2019.

RESULTS

A total of 2718 patients were included [median age: 77.4, interquartile range (IQR): (66.9-84.3) years; 47.9 males]. After a median follow-up of 4.8 years (IQR: 1.9-9.0 years), 1150 patients developed AF (8.8/year), 339 developed stroke (2.6/year), 563 developed cardiovascular mortality (4.3/year), and 1972 had all-cause mortality (15.1/year). Compared with 101-120 ms as a reference, maximum P-wave durations predicted new-onset AF at ≤90 ms [HR: 1.17(1.11, 1.50), P < 0.01], 131-140 ms [HR: 1.29(1.09, 1.54), P < 0.001], and ≥141 ms [HR: 1.52(1.32, 1.75), P < 0.001]. Similarly, they predicted cardiovascular mortality at ≤90 ms [HR: 1.50(1.08, 2.06), P < 0.001] or ≥141 ms [HR: 1.18(1.15, 1.45), P < 0.001], and all-cause mortality at ≤90 ms [HR: 1.26(1.04, 1.51), P < 0.001], 131-140 ms [HR: 1.15(1.01, 1.32), P < 0.01], and ≥141 ms [HR: 1.31(1.18, 1.46), P < 0.001]. These remained significant after adjusting for significant demographics, past co-morbidities, P-wave dispersion, and maximum P-wave amplitude.

CONCLUSIONS

Extreme values of maximum P-wave durations (≤90 ms and ≥141 ms) were significant predictors of new-onset AF, cardiovascular mortality, and all-cause mortality.

Fragmented Vortex in Heart Failure With Reduced Ejection Fraction: A Prospective Vector Flow Mapping Study

OBJECTIVE

Heart failure with reduced ejection fraction (HFrEF) is associated with structural and functional left ventricular changes. We compared intracardiac vortices between patients with HFrEF and normal participants using echocardiographic vector flow mapping, a novel intracardiac vortex analysis technology.

METHODS

Transthoracic echocardiography was performed on 20 patients with HFrEF (age: 61 ± 15 y, 15 men) and 20 normal participants (age: 59 ± 12 y, 12 men) age- and sex-balanced at the cohort level. Systolic and diastolic energy loss, area (indexed by left ventricular end-diastolic diameter), circulation (reflects vortex strength) and relative positions of the largest vortex during systole (S-vortex), early (E-vortex) and late (A-vortex) diastole and maximal number of vortices in a single frame (MNV) were assessed.

DISCUSSION

Patients with HFrEF had disproportionately sized vortices with smaller indexed vortex areas (p < 0.0001), and more fragmented vortices with higher MNV during both systole (p = 0.030) and diastole (p < 0.0001). These accompanied higher diastolic energy loss (p = 0.001). Additionally, the E-vortex (p = 0.002) and A-vortex (p < 0.0001) were more apically positioned, and the S-vortex was weaker (p = 0.033) in patients with HFrEF. More severe fragmentation (higher MNV) correlated with worse energy efficiency (higher energy loss).

CONCLUSION

Patients with HFrEF had more fragmented intracardiac vortices and lower energy efficiency predominantly during diastole.

Reverse Atrial Remodeling in Heart Failure With Recovered Ejection Fraction

Background Heart failure with recovered ejection fraction (HFrecEF) has been a newly recognized entity since 2020. However, the concept has primarily focused on left ventricular ejection fraction improvement, with less focus on the recovery of the left atrium. In this study, we investigated changes in left atrial (LA) echocardiographic indices in HFrecEF. Methods and Results An inpatient cohort with heart failure with reduced ejection fraction (HFrEF) was identified retrospectively and followed up prospectively in a single tertiary hospital. The enrolled patients were classified into HFrecEF and persistent HFrEF groups. Alternations in LA parameters by echocardiography were calculated. The primary outcome was a composite of cardiovascular death or heart failure rehospitalization. A total of 699 patients were included (HFrecEF: n=228; persistent HFrEF: n=471). Compared with persistent HFrEF, the HFrecEF group had greater reductions in LA diameter, LA transverse diameter, LA superior-inferior diameter, LA volume, and LA volume index but not in LA sphericity index. Cox regression analysis showed that the HFrecEF group experienced lower risks of prespecified end points than the persistent HFrEF group after adjusting for confounders. Additionally, 136 (59.6%) and 62 (13.0%) patients showed LA reverse remodeling (LARR) for the HFrecEF and persistent HFrEF groups, respectively. Among the HFrecEF subgroup, patients with LARR had better prognosis compared with those without LARR. Multivariate logistic analysis demonstrated that age and coronary heart disease were 2 independent negative predictors for LARR. Conclusions In HFrecEF, both left ventricular systolic function and LA structure remodeling were improved. Patients with HFrecEF with LARR had improved clinical outcomes, indicating that the evaluation of LA size provides a useful biomarker for risk stratification of heart failure.

Electrocardiogram-based artificial intelligence for the diagnosis of heart failure: a systematic review and meta-analysis

BACKGROUND

The electrocardiogram (ECG) is an inexpensive and easily accessible investigation for the diagnosis of cardiovascular diseases including heart failure (HF). The application of artificial intelligence (AI) has contributed to clinical practice in terms of aiding diagnosis, prognosis, risk stratification and guiding clinical management. The aim of this study is to systematically review and perform a meta-analysis of published studies on the application of AI for HF detection based on the ECG.

METHODS

We searched Embase, PubMed and Web of Science databases to identify literature using AI for HF detection based on ECG data. The quality of included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) criteria. Random-effects models were used for calculating the effect estimates and hierarchical receiver operating characteristic curves were plotted. Subgroup analysis was performed. Heterogeneity and the risk of bias were also assessed.

RESULTS

A total of 11 studies including 104,737 subjects were included. The area under the curve for HF diagnosis was 0.986, with a corresponding pooled sensitivity of 0.95 (95% CI: 0.86-0.98), specificity of 0.98 (95% CI: 0.95-0.99) and diagnostic odds ratio of 831.51 (95% CI: 127.85-5407.74). In the patient selection domain of QUADAS-2, eight studies were designated as high risk.

CONCLUSIONS

According to the available evidence, the incorporation of AI can aid the diagnosis of HF. However, there is heterogeneity among machine learning algorithms and improvements are required in terms of quality and study design.

Triglyceride-glucose index and the risk of heart failure: Evidence from two large cohorts and a mendelian randomization analysis

BACKGROUND

The relationship between triglyceride-glucose (TyG) index, an emerging marker of insulin resistance, and the risk of incident heart failure (HF) was unclear. This study thus aimed to investigate this relationship.

METHODS

Subjects without prevalent cardiovascular diseases from the prospective Kailuan cohort (recruited during 2006-2007) and a retrospective cohort of family medicine patients from Hong Kong (recruited during 2000-2003) were followed up until December 31st, 2019 for the outcome of incident HF. Separate adjusted hazard ratios (aHRs) summarizing the relationship between TyG index and HF risk in the two cohorts were combined using a random-effect meta-analysis. Additionally, a two-sample Mendelian randomization (MR) of published genome-wide association study data was performed to assess the causality of observed associations.

RESULTS

In total, 95,996 and 19,345 subjects from the Kailuan and Hong Kong cohorts were analyzed, respectively, with 2,726 cases of incident HF in the former and 1,709 in the latter. Subjects in the highest quartile of TyG index had the highest risk of incident HF in both cohorts (Kailuan: aHR 1.23 (95% confidence interval: 1.09-1.39), PTrend <0.001; Hong Kong: aHR 1.21 (1.04-1.40), PTrend =0.007; both compared with the lowest quartile). Meta-analysis showed similar results (highest versus lowest quartile: HR 1.22 (1.11-1.34), P < 0.001). Findings from MR analysis, which included 47,309 cases and 930,014 controls, supported a causal relationship between higher TyG index and increased risk of HF (odds ratio 1.27 (1.15-1.40), P < 0.001).

CONCLUSION

A higher TyG index is an independent and causal risk factor for incident HF in the general population.

CLINICAL TRIAL REGISTRATION

URL: https://www.chictr.org.cn ; Unique identifier: ChiCTR-TNRC-11,001,489.

The Value of IGF-1 and IGFBP-1 in Patients With Heart Failure With Reduced, Mid-range, and Preserved Ejection Fraction

Background

Previous studies have reported inconsistent results regarding the implications of deranged insulin-like growth factor 1 (IGF-1)/insulin-like growth factor-binding protein 1 (IGFBP-1) axis in patients with heart failure (HF). This study evaluates the roles of IGF1/IGFBP-1 axis in patients with HF with reduced ejection fraction (HFrEF), mid-range ejection fraction (HFmrEF), or preserved ejection fraction (HFpEF).

Methods

Consecutive patients with HFrEF, HFmrEF, and HFpEF who underwent comprehensive cardiac assessment were included. The primary endpoint was the composite endpoint of all-cause death and HF rehospitalization at one year.

Results

A total of 151 patients with HF (HFrEF: n = 51; HFmrEF: n = 30; HFpEF: n = 70) and 50 control subjects were included. The concentrations of IGFBP-1 (p < 0.001) and IGFBP-1/IGF-1 ratio (p < 0.001) were significantly lower in patients with HF compared to controls and can readily distinguish patients with and without HF (IGFBP-1: areas under the curve (AUC): 0.725, p < 0.001; IGFBP-1/IGF-1 ratio: AUC:0.755, p < 0.001; respectively). The concentrations of IGF-1, IGFBP-1, and IGFBP-1/IGF-1 ratio were similar among HFpEF, HFmrEF, and HFrEF patients. IGFBP-1 and IGFBP-1/IGF-1 ratio positively correlated with N-terminal probrain natriuretic peptide (NT-proBNP) levels (r = 0.255, p = 0.002; r = 0.224, p = 0.007, respectively). IGF-1, IGFBP-1, and IGFBP-1/IGF-1 ratio did not predict the primary endpoint at 1 year for the whole patients with HF and HF subtypes on both univariable and multivariable Cox regression.

Conclusion

The concentrations of plasma IGFBP-1 and IGFBP-1/IGF-1 ratio can distinguish patients with and without HF. In HF, IGFBP-1 and IGFBP-1/IGF-1 ratio positively correlated with NT-proBNP levels.

Predictive Value of HFA-PEFF Score in Patients With Heart Failure With Preserved Ejection Fraction

Aims: HFA-PEFF score has been proposed for diagnosing heart failure with preserved ejection fraction (HFpEF). Currently, there are only a limited number of tools for predicting the prognosis. In this study, we evaluated whether the HFA-PEFF score can predict mortality in patients with HFpEF.

Methods: This single-center, retrospective observational study enrolled patients diagnosed with HFpEF at the First Affiliated Hospital of Dalian Medical University between January 1, 2015, and April 30, 2018. The subjects were divided according to their HFA-PEFF score into low (0–2 points), intermediate (3–4 points), and high (5–6 points) score groups. The primary outcome was all-cause mortality.

Results: A total of 358 patients (mean age: 70.21 ± 8.64 years, 58.1% female) were included. Of these, 63 (17.6%), 156 (43.6%), and 139 (38.8%) were classified into the low, intermediate, and high score groups, respectively. Over a mean follow-up of 26.9 months, 46 patients (12.8%) died. The percentage of patients who died in the low, intermediate, and high score groups were 1 (1.6%), 18 (11.5%), and 27 (19.4%), respectively. A multivariate Cox regression identified HFA-PEFF score as an independent predictor of all-cause mortality [hazard ratio (HR):1.314, 95% CI: 1.013–1.705, P = 0.039]. A Cox analysis demonstrated a significantly higher rate of mortality in the intermediate (HR: 4.912, 95% CI 1.154–20.907, P = 0.031) and high score groups (HR: 5.291, 95% CI: 1.239–22.593, P = 0.024) than the low score group. A receiver operating characteristic (ROC) analysis indicated that the HFA-PEFF score can effectively predict all-cause mortality after adjusting for age and New York Heart Association (NYHA) class [area under the curve (AUC) 0.726, 95% CI 0.651–0.800, P = 0.000]. With an HFA-PEFF score cut-off value of 3.5, the sensitivity and specificity were 78.3 and 54.8%, respectively. The AUC on ROC analysis for the biomarker component of the score was similar to that of the total score.

Conclusions: The HFA-PEFF score can be used both to diagnose HFpEF and predict the prognosis. The higher scores are associated with higher all-cause mortality.

Characteristics and outcomes of heart failure with recovered left ventricular ejection fraction

Aims

There is an emerging interest in elucidating the natural history and prognosis for patients with heart failure with reduced ejection fraction (HFrEF) in whom left ventricular ejection fraction (LVEF) subsequently improves. The characteristics and outcomes were compared between heart failure with recovered ejection fraction (HFrecEF) and persistent HFrEF.

Methods and results

This is a retrospective study of adults who underwent at least two echocardiograms 3 months apart between 1 November 2015 and 31 October 2019 with an initial diagnosis of HFrEF. The subjects were divided into HFrecEF group (second LVEF > 40%, ≥10% absolute improvement in LVEF) and persistent HFrEF group (<10% absolute improvement in LVEF) according to the second LVEF. To further study the characteristics of HFrecEF patients, the cohort was further divided into LVEF improvement of 10–20% and >20% subgroups. The primary outcomes were all‐cause mortality and rehospitalization. A total of 1160 HFrEF patients were included [70.2% male, mean (standard deviation) age: 62 ± 13 years]. On the second echocardiogram, 284 patients (24.5%) showed HFrecEF and 876 patients (75.5%) showed persistent HFrEF. All‐cause mortality was identified in 23 (8.10%) HFrecEF and 165 (18.84%) persistent HFrEF, whilst 76 (26.76%) and 426 (48.63%) showed rehospitalizations, respectively. Survival analysis showed that the persistent HFrEF subgroup experienced a significantly higher mortality at 12 and 24 months and a higher hospitalization at 12, 24, 48, and more than 48 months following discharge. Multivariate Cox regression showed that persistent HFrEF had a higher risk of all‐cause mortality [hazard ratio (HR) 2.30, 95% confidence interval (CI) 1.49–3.56, P = 0.000] and rehospitalization (HR 1.85, 95% CI 1.45–2.36, P = 0.000) than the HFrecEF group. Subgroup analysis showed that the LVEF ≥ 20% improvement subgroup had lower rates of adverse outcomes compared with those with less improvement of 10–20%.

Conclusions

Heart failure with recovered ejection fraction is a distinct HF phenotype with better clinical outcomes compared with those with persistent HFrEF. HFrecEF patients have a relatively better short‐term mortality at 24 months but not thereafter.

Heart Failure With Midrange Ejection Fraction: Prior Left Ventricular Ejection Fraction and Prognosis

Aims: Evidence-based guidelines for heart failure management depend mainly on current left ventricular ejection fraction (LVEF). However, fewer studies have examined the impact of prior LVEF. Patients may enter the heart failure with midrange ejection fraction (HFmrEF) category when heart failure with preserved ejection fraction (HFpEF) deteriorates or heart failure with reduced ejection fraction (HFrEF) improves. In this study, we examined the association between change in LVEF and adverse outcomes.

Methods: HFmrEF patients with at least two or more echocardiograms 3 months apart at the First Affiliated Hospital of Dalian Medical University between September 1, 2015 and November 30, 2019 were identified. According to the prior LVEF, the subjects were divided into improved group (prior LVEF < 40%), stable group (prior LVEF between 40 and 50%), and deteriorated group (prior LVEF ≥ 50%). The primary outcomes were cardiovascular death, all-cause mortality, hospitalization for worsening heart failure, and composite event of all-cause mortality or all-cause hospitalization.

Results: A total of 1,168 HFmrEF patients (67.04% male, mean age 63.60 ± 12.18 years) were included. The percentages of improved, stable, and deteriorated group were 310 (26.54%), 334 (28.60%), and 524 (44.86%), respectively. After a period of follow-up, 208 patients (17.81%) died and 500 patients met the composite endpoint. The rates of all-cause mortality were 35 (11.29%), 55 (16.47%), and 118 (22.52%), and the composite outcome was 102 (32.90%), 145 (43.41%), and 253 (48.28%) for the improved, stable, and deteriorated groups, respectively. Cox regression analysis showed that the deterioration group had higher risk of cardiovascular death (HR: 1.707, 95% CI: 1.064–2.739, P = 0.027), all-cause death (HR 1.948, 95% CI 1.335–2.840, P = 0.001), and composite outcome (HR 1.379, 95% CI 1.096–1.736, P = 0.006) compared to the improvement group. The association still remained significant after fully adjusted for both all-cause mortality (HR = 1.899, 95% CI 1.247–2.893, P = 0.003) and composite outcome (HR: 1.324, 95% CI: 1.020–1.718, P = 0.035).

Conclusion: HFmrEF patients are heterogeneous with three different subsets identified, each with different outcomes. Strategies for managing HFmrEF should include previously measured LVEF to allow stratification based on direction changes in LVEF to better optimize treatment.

Derivation of an electronic frailty index for predicting short-term mortality in heart failure: a machine learning approach

Aims

Frailty may be found in heart failure patients especially in the elderly and is associated with a poor prognosis. However, assessment of frailty status is time‐consuming, and the electronic frailty indices developed using health records have served as useful surrogates. We hypothesized that an electronic frailty index developed using machine learning can improve short‐term mortality prediction in patients with heart failure.

Methods and results

This was a retrospective observational study that included patients admitted to nine public hospitals for heart failure from Hong Kong between 2013 and 2017. Age, sex, variables in the modified frailty index, Deyo's Charlson co‐morbidity index (≥2), neutrophil‐to‐lymphocyte ratio (NLR), and prognostic nutritional index at baseline were analysed. Gradient boosting, which is a supervised sequential ensemble learning algorithm with weak prediction submodels (typically decision trees), was applied to predict mortality. Variables were ranked in the order of importance with a total score of 100 and used to build the frailty models. Comparisons were made with decision tree and multivariable logistic regression. A total of 8893 patients (median: age 81, Q1–Q3: 71–87 years old) were included, in whom 9% had 30 day mortality and 17% had 90 day mortality. Prognostic nutritional index, age, and NLR were the most important variables predicting 30 day mortality (importance score: 37.4, 32.1, and 20.5, respectively) and 90 day mortality (importance score: 35.3, 36.3, and 14.6, respectively). Gradient boosting significantly outperformed decision tree and multivariable logistic regression. The area under the curve from a five‐fold cross validation was 0.90 for gradient boosting and 0.87 and 0.86 for decision tree and logistic regression in predicting 30 day mortality. For the prediction of 90 day mortality, the area under the curve was 0.92, 0.89, and 0.86 for gradient boosting, decision tree, and logistic regression, respectively.

Conclusions

The electronic frailty index based on co‐morbidities, inflammation, and nutrition information can readily predict mortality outcomes. Their predictive performances were significantly improved by gradient boosting techniques.

β-blocker and 1-year outcomes among patients hospitalized for heart failure with mid-range ejection fraction

Aims

The beneficial effect of β-blocker on heart failure with reduced ejection fraction is well established. However, its effect on the 1-year outcome of heart failure with mid-range ejection fraction (HFmrEF) remains unclear.

Methods and results

We analysed the data of the patients with left ventricular ejection fraction (LVEF) between 40% and 49% in China Patient-centred Evaluative Assessment of Cardiac Events Prospective Heart Failure Study (China PEACE 5p-HF Study), in which patients hospitalized for heart failure from 52 Chinese hospitals were recruited from 2016 to 2018. Two primary outcomes were all-cause death and all-cause hospitalization. The associations between β-blocker use at discharge and outcomes were assessed by inverse probability of treatment weighting (IPTW)-weighted Cox regression analyses. To assess consistency, IPTW adjusting medications analyses, multivariable analyses and dose-effect analyses were performed. A total of 1035 HFmrEF patients were included in the analysis. The mean age was 65.5 ± 12.7 years and 377 (36.4%) were female. The median (interquartile range) of LVEF was 44% (42–47%). Six hundred and sixty-one (63.8%) were treated with β-blocker. Patients using β-blocker were younger with better cardiac function, and more likely to use renin–angiotensin system inhibitor and mineralocorticoid receptor antagonist. During the 1-year follow-up, death occurred in 84 (12.7%) treated and 85 (22.7%) untreated patients (P < 0.0001); all-cause hospitalization occurred in 298 (45.1%) treated and 188 (50.3%) untreated patients (P = 0.04). After IPTW-weighted adjustment, β-blocker use was significantly associated with lower risk of all-cause death [hazard ratio (HR): 0.70; 95% confidence interval (CI): 0.51–0.96, P = 0.03], but not with lower all-cause hospitalization (HR, 0.92, 95% CI, 0.76–1.10, P = 0.36). Consistency analyses showed consistent favourable effect of β-blocker on all-cause death, but not on all-cause hospitalization.

Conclusions

Among patients with HFmrEF, β-blocker use was associated with lower risk of all-cause death, but not with lower risk of all-cause hospitalization.

Evaluation of the prescribing practice of guideline-directed medical therapy among ambulatory chronic heart failure patients

BACKGROUND

Studies have demonstrated that heart failure (HF) patients who receive direct pharmacist input as part of multidisciplinary care have better clinical outcomes. This study evaluated/compared the difference in prescribing practices of guideline-directed medical therapy (GDMT) for chronic HF patients between two multidisciplinary clinics-with and without the direct involvement of a pharmacist.

METHODS

A retrospective audit of chronic HF patients, presenting to two multidisciplinary outpatient clinics between March 2005 and January 2017, was performed; a Multidisciplinary Ambulatory Consulting Service (MACS) with an integrated pharmacist model of care and a General Cardiology Heart Failure Service (GCHFS) clinic, without the active involvement of a pharmacist.

RESULTS

MACS clinic patients were significantly older (80 vs. 73 years, p < .001), more likely to be female (p < .001), and had significantly higher systolic (123 vs. 112 mmHg, p < .001) and diastolic (67 vs. 60 mmHg, p < .05) blood pressures compared to the GCHF clinic patients. Moreover, the MACS clinic patients showed more polypharmacy and higher prevalence of multiple comorbidities. Both clinics had similar prescribing rates of GDMT and achieved maximal tolerated doses of angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) in HFrEF. However, HFpEF patients in the MACS clinic were significantly more likely to be prescribed ACEIs/ARBs (70.5% vs. 56.2%, p = 0.0314) than the GCHFS patients. Patients with both HFrEF and HFpEF (MACS clinic) were significantly less likely to be prescribed β-blockers and mineralocorticoid receptor antagonists. Use of digoxin in chronic atrial fibrillation (AF) in MACS clinic was significantly higher in HFrEF patients (82.5% vs. 58.5%, p = 0.004), but the number of people anticoagulated in presence of AF (27.1% vs. 48.0%, p = 0.002) and prescribed diuretics (84.0% vs. 94.5%, p = 0.022) were significantly lower in HFpEF patients attending the MACS clinic. Age, heart rate, systolic blood pressure (SBP), anemia, chronic renal failure, and other comorbidities were the main significant predictors of utilization of GDMT in a multivariate binary logistic regression.

CONCLUSIONS

Lower prescription rates of some medications in the pharmacist-involved multidisciplinary team were found. Careful consideration of demographic and clinical characteristics, contraindications for use of medications, polypharmacy, and underlying comorbidities is necessary to achieve best practice.

Predictive value of H2 FPEF score in patients with heart failure with preserved ejection fraction

Aims

The H₂FPEF score is a convenient risk stratification tool for diagnosing heart failure with preserved ejection fraction (HFpEF). This study examined the value of the H₂FPEF score for predicting all‐cause mortality and rehospitalization in HFpEF patients.

Methods and results

This was a retrospective cohort study of patients diagnosed with HFpEF by echocardiography at a single tertiary centre between 1 January 2015 and 30 April 2018. According to the H₂FPEF score, the subjects were divided into low (0–1 points), intermediate (2–5 points), and high (6–9 points) score groups. The primary outcomes were all‐cause mortality and rehospitalization. A total of 476 patients (mean age: 70.5 ± 8.4 years, 60.7% female) were included. Of these, 47 (9.9%), 262 (55.0%), and 167 (35.1%) were classified into the low, intermediate, and high score groups, respectively. Over a mean follow‐up of 27.5 months, 63 patients (13.2%) died, and 311 patients (65.3%) were rehospitalized. The mortality rates were 3 (6.4%), 29 (11.1%), and 31 (18.6%), and the number of patients with rehospitalization was 28 (59.6%), 159 (60.7%), and 124 (74.3%) for the low, intermediate, and high score groups, respectively. Multivariate Cox regression identified H₂FPEF score as an independent predictor of all‐cause mortality (hazard ratio [HR]: 1.46, 95% CI: 1.23–1.73, P < 0.0001) and rehospitalization (HR: 1.15, 95% CI: 1.08–1.22, P < 0.0001). Receiver operating characteristic (ROC) analysis demonstrated the H₂FPEF score can effectively predict all‐cause mortality (AUC 0.67, 95% CI: 0.60–0.73, P < 0.0001) and rehospitalization (AUC 0.59, 95% CI: 0.54–0.65, P = 0.001) after adjusting for age and NYHA class. With a cut‐off value of 5.5, the sensitivity and specificity were 68.3% and 55.4% for all‐cause mortality and 50.5% and 66.7% for rehospitalization.

Conclusions

The H₂FPEF score can be used to predict prognosis in HFpEF patients. Higher scores are associated with higher all‐cause mortality and rehospitalization.

Heart Failure With Midrange Ejection Fraction-What Is It, If Anything?

The patient cohort with left ventricular ejection fractions (LVEFs) of 41%-49%, which has been defined as heart failure with midrange ejection fraction (HFmrEF), represent a significant proportion of the heart failure (HF) population. Despite the clear cutoffs established by different society guidelines, confusion remains regarding the exact significance of midrange LVEF within the HF syndrome. Patients with LVEF 41%-49% represent a heterogeneous group of patients sharing pathophysiologic mechanisms, biomarker profiles, comorbidities, and clinical characteristics with patients with preserved and reduced LVEF. In this clinical review, we discuss the underlying pathophysiologic mechanisms that culminate in the clinical syndrome of HF and contribute to the disparities observed between HFpEF, HFrEF, and HFmrEF. We highlight differences and similarities in clinical characteristics and imaging features between HFpEF and HFrEF in an effort to disentangle the heterogeneous group of patients with midrange LVEF, but ultimately we conclude that LVEF should be seen as simply one important element of a continuum throughout the HF syndrome, and that although is useful, it is an oversimplification, because HF syndrome is more of a continuum. The underlying pathophysiology, etiology, and comorbidities of patients presenting with HF is becoming ever more important as the limitations of a classification solely based on LVEF are being better recognised, and as patient-specific personalisation of care is becoming ever more important.

Diagnostic and prognostic value of serum C-reactive protein in heart failure with preserved ejection fraction: a systematic review and meta-analysis

Heart failure (HF) is a major epidemic with rising morbidity and mortality rates that encumber global healthcare systems. While some studies have demonstrated the value of CRP in predicting (i) the development of HFpEF and (ii) long-term clinical outcomes in HFpEF patients, others have shown no such correlation. As a result, we conducted the following systematic review and meta-analysis to assess both the diagnostic and prognostic role of CRP in HFpEF. PubMed and Embase were searched for studies that assess the relationship between CRP and HFpEF using the following search terms: (((C-reactive protein) AND ((preserved ejection fraction) OR (diastolic heart failure))). The search period was from the start of database to August 6, 2019, with no language restrictions. A total of 312 and 233 studies were obtained from PubMed and Embase respectively, from which 19 studies were included. Our meta-analysis demonstrated the value of a high CRP in predicting the development of not only new onset HFpEF (HR: 1.08; 95% CI: 1.00-1.16; P = 0.04; I² = 22%), but also an increased risk of cardiovascular mortality when used as a categorical (HR: 2.52; 95% CI: 1.61-3.96; P < 0.0001; I² = 19%) or a continuous variable (HR: 1.24; 95% CI: 1.04-1.47; P = 0.01; I² = 28%), as well as all-cause mortality when used as a categorical (HR: 1.78; 95% CI: 1.53-2.06; P < 0.00001; I² = 0%) or a continuous variable: (HR: 1.06; 95% CI: 1.02-1.06; P = 0.003; I² = 61%) in HFpEF patients. CRP can be used as a biomarker to predict the development of HFpEF and long-term clinical outcomes in HFpEF patients, in turn justifying its use as a simple, accessible parameter to guide clinical management in this patient population. However, more prospective studies are still required to not only explore the utility and dynamicity of CRP in HFpEF but also to determine whether risk stratification algorithms incorporating CRP actually provide a material benefit in improving patient prognosis.