Pedro Moliner

Medical resource use and expenditure in patients with chronic heart failure: a population-based analysis of 88 195 patients.

Heart failure (HF) is one of the diseases with greater healthcare expenditure. However, little is known about the cost of HF at a population level. Hence, our aim was to study the population‐level distribution and predictors of healthcare expenditure in patients with HF.

Recovered heart failure with reduced ejection fraction and outcomes: a prospective study

Significant recovery of left ventricular ejection fraction (LVEF) occurs in a proportion of patients with heart failure (HF) and reduced ejection fraction (HFrEF). We analysed outcomes, including mortality [all‐cause, cardiovascular (CV), HF‐related, and sudden death], and HF‐related hospitalizations in this HF‐recovered group. The primary endpoint was a composite of CV death or HF hospitalization.

Medical resource use and expenditure in patients with chronic heart failure: a population-based analysis of 88 195 patients: a population-based analysis of 88 195 patients

Heart failure (HF) is one of the diseases with greater healthcare expenditure. However, little is known about the cost of HF at a population level. Hence, our aim was to study the population‐level distribution and predictors of healthcare expenditure in patients with HF.

Differences in neurohormonal activity partially explain the obesity paradox in patients with heart failure: The role of sympathetic activation

BACKGROUND Obese patients with chronic Heart Failure (HF) have better outcome than their lean counterparts, although little is known about the pathophysiology of this obesity paradox. Our aim was to evaluate the hypothesis that patients with chronic HF and obesity (defined as body mass index (BMI)≥30kg/m(2)), may have an attenuated neurohormonal activation in comparison with non-obese patients. METHODS AND RESULTS The present study is the post-hoc analysis of a cohort of 742 chronic HF patients from a single-center study evaluating sympathetic activation by measuring baseline levels of norepinephrine (NE). Obesity was present in 33% of patients. Higher BMI and obesity were significantly associated with lower NE levels in multivariable linear regression models adjusted for covariates (p<0.001). Addition to NE in multivariate Cox proportional hazard models attenuated the prognostic impact of BMI in terms of outcomes. Finally, when we explored the prognosis impact of raised NE levels (>70th percentile) carrying out a separate analysis in obese and non-obese patients we found that in both groups NE remained a significant independent predictor of poorer outcomes, despite the lower NE levels in patients with chronic HF and obesity: all-cause mortality hazard ratio=2.37 (95% confidence interval, 1.14-4.94) and hazard ratio=1.59 (95% confidence interval, 1.05-2.4) in obese and non-obese respectively; and cardiovascular mortality hazard ratio=3.08 (95% confidence interval, 1.05-9.01) in obese patients and hazard ratio=2.08 (95% confidence interval, 1.42-3.05) in non-obese patients. CONCLUSION Patients with chronic HF and obesity have significantly lower sympathetic activation. This finding may partially explain the obesity paradox described in chronic HF patients.

Real world heart failure epidemiology and outcome: A population-based analysis of 88,195 patients

Background Heart failure (HF) is frequent and its prevalence is increasing. We aimed to evaluate the epidemiologic features of HF patients, the 1-year follow-up outcomes and the independent predictors of those outcomes at a population level. Methods and results Population-based longitudinal study including all prevalent HF cases in Catalonia (Spain) on December 31st, 2012. Patients were divided in 3 groups: patients without a previous HF hospitalization, patients with a remote (>1 year) HF hospitalization and patients with a recent (<1 year) HF admission. We analyzed 1year all-cause and HF hospitalizations, and all-cause mortality. Logistic regression was used to identify the independent predictors of each of those outcomes. A total of 88,195 patients were included. Mean age was 77 years, 55% were women. Comorbidities were frequent. Fourteen percent of patients had never been hospitalized, 71% had a remote HF hospitalization and 15% a recent hospitalization. At 1-year follow-up, all-cause and HF hospitalization were 53% and 8.8%, respectively. One-year all-cause mortality rate was 14%, and was higher in patients with a recent HF hospitalization (24%). The presence of diabetes mellitus, atrial fibrillation or chronic kidney disease was independently associated with all-cause and HF hospitalization and all-cause mortality. Hospital admissions and emergency department visits the previous year were also found to be independently associated with the three study outcomes. Conclusions Outcomes are different depending on the HF population studied. Some comorbidity, an all-cause hospitalization or emergency department visit the previous year were associated with a worse outcome.

Early Postdischarge STOP-HF-Clinic Reduces 30-day Readmissions in Old and Frail Patients With Heart Failure

INTRODUCTION AND OBJECTIVES Heart failure (HF) is associated with a high rate of readmissions within 30 days postdischarge. Strategies to lower readmission rates generally show modest results. To reduce readmission rates, we developed a STructured multidisciplinary outpatient clinic for Old and frail Postdischarge patients hospitalized for HF (STOP-HF-Clinic). METHODS This prospective all-comers study enrolled patients discharged from internal medicine or geriatric wards after HF hospitalization. The intervention involved a face-to-face early visit (within 7 days), HF nurse education, treatment titration, and intravenous medication when needed. Thirty-day readmission risk was calculated using the CORE-HF risk score. We also studied the impact of 30-day readmission burden on regional health care by comparing the readmission rate in the STOP-HF-Clinic Referral Area (∼250000 people) with that of the rest of the Catalan Health Service (CatSalut) (∼7.5 million people) during the pre-STOP-HF-Clinic (2012-2013) and post-STOP-HF-Clinic (2014-2015) time periods. RESULTS From February 2014 to June 2016, 518 consecutive patients were included (age, 82 years; Barthel score, 70; Charlson index, 5.6, CORE-HF 30-day readmission risk, 26.5%). The observed all-cause 30-day readmission rate was 13.9% (47.5% relative risk reduction) and the observed HF-related 30-day readmission rate was 7.5%. The CatSalut registry included 65131 index HF admissions, with 9267 all-cause and 6686 HF-related 30-day readmissions. The 30-day readmission rate was significantly reduced in the STOP-HF-Clinic Referral Area in 2014-2015 compared with 2012-2013 (P < .001), mainly driven by fewer HF-related readmissions. CONCLUSIONS The STOP-HF-Clinic, an approach that could be promptly implemented elsewhere, is a valuable intervention for reducing the global burden of early readmissions among elder and vulnerable patients with HF.

Clinical correlates and prognostic impact of impaired iron storage versus impaired iron transport in an international cohort of 1821 patients with chronic heart failure

AIMS To define iron deficiency in chronic heart failure (CHF), both, ferritin<100μg/L (indicating reduced iron storage) and transferrin saturation (TSAT)<20% (indicating reduced iron transport) are used. The aim of the study was to evaluate clinical outcomes and prognosis of either low ferritin or low TSAT in patients with CHF. METHODS AND RESULTS We evaluated the clinical impact of impaired iron storage (IIS) and impaired iron transport (IIT) either alone or in combination compared to patients with normal iron status (NIS), in an international cohort of 1821 patients with CHF with a mean age of 66±13years and mean left ventricular ejection fraction of 35%±15. Isolated IIS was observed in 219 patients (12%), isolated IIT in 454 (25%) and coexistence of both conditions (IIS+IIT) were seen in 389 (21%). In adjusted models we found that patients with IIS+IIT and patients with isolated IIT had higher NT-proBNP levels (OR 2.2 [1.6-3.1] and OR 2.1 [1.5-2.9] respectively) and worse quality of life (OR 1.8 [1.2-2.7] and OR 1.7 [1.2-2.5] respectively) compared with isolated IIS. Multivariate Cox analyses showed that IIS+IIT and isolated IIT were independently associated with all-cause mortality (OR 1.41 [1.06-1.86] and OR 1.47 [1.13-1.92] respectively). Patients with isolated IIS did not differ from NIS patients in terms of severity or outcomes. CONCLUSIONS Impaired iron transport alone or in combination with impaired iron storage is associated with worse clinical profile and increased risk of mortality in patients with CHF. Patients with isolated impaired iron storage may have a milder form of iron deficiency.

Barcelona Bio-HF Calculator Version 2.0: incorporation of angiotensin II receptor blocker neprilysin inhibitor (ARNI) and risk for heart failure hospitalization

Estimating risk for an individual with heart failure (HF) is routine for the practising physician. This may sometimes be done using experience and clinical acumen, or by using a risk model. A number of prediction models with broad variation in terms of validation and output have been developed, but only a few are freely available as online calculators.1 The Barcelona (BCN) Bio-HF Calculator (www.bcnbiohfcalculator.org) (Figure 1),2 developed 3 years ago and discussed in the 2016 European Society of Cardiology HF guidelines,3 incorporates three biomarkers that reflect different facets of HF pathophysiology: N-terminal pro-B-type natriuretic peptide (NT-proBNP), a marker of myocardial stretch; high-sensitivity cardiac troponin T (hs-cTnT), a marker of myocyte injury, and high-sensitivity soluble ST2, which reflects myocardial fibrosis and remodelling. The calculator estimates the risk for all-cause death,2 has been externally validated,4 and was highlighted by Levy and Anand5 as a reference for the appropriate methodology for adding single or multiple variables to a risk model. The combination of clinical and treatment data plus routine laboratory data and biomarkers is also valuable for predicting HF-related hospitalization. Furthermore, the incorporation of novel drugs and devices into the HF armamentarium, notably sacubitril– valsartan, which have strong impacts on death and HF hospitalization,6 prompted an update of the BCN Bio-HF Calculator. The BCN Bio-HF Calculator Version 2.0 was derived from a cohort of 864 consecutive treated HF outpatients [72% men; mean age 68.2±12 years; New York Heart Association (NYHA) class I–II/III–IV 73%/27%, left ventricular ejection fraction (LVEF) 36%, ischaemic aetiology 52.2%].3 During followup of up to 5 years, 363 deaths and 210 first HF-related hospitalizations were recorded; 430 patients suffered at least one event of the composite endpoint. In the update, three new clinical variables (duration of HF in months, number of HF-related hospitalizations in the preceding year, and diabetes mellitus) and four new treatments [mineralocorticoid receptor antagonists, angiotensin II receptor blocker neprilysin inhibitors (ARNI), cardiac resynchronization therapy (CRT) and implantable cardioverter defibrillator (ICD)] were added to the original variables (age, sex, NYHA functional class, LVEF, serum sodium, estimated glomerular filtration rate, haemoglobin, loop diuretic dose, beta-blocker, angiotensin-converting enzyme inhibitor/angiotensin-II receptor blocker and statin treatments, and hs-cTnT, ST2 and NT-proBNP levels). Beta values for ARNI treatment were derived from the benefit observed in the PARADIGM-HF trial, which involved the largest and best characterized cohort of patients treated with ARNIs.6 HFrelated hospitalization was estimated taking into account competing risk for death. Model performance was evaluated using discrimination, calibration and reclassification tools. The C-statistics [area under the curve (AUC)] at 2 years for the model with biomarkers using logistic regression were 0.83 for all-cause death, 0.79 for HF-related hospitalization, and 0.80 for the composite endpoint. Discrimination was significantly better than that obtained in a model without biomarkers for risk for death (P= 0.001), risk for HF hospitalization (P< 0.05) and the composite endpoint (P= 0.001) (supplementary material online, Tables S1–S3). Calibration improved in the model with biomarkers, and reclassification with this model using continuous net reclassification improvement (NRI) and integrated discrimination improvement (IDI) was also highly significant (P< 0.001). Using NRI, the BCN Bio-HF Calculator Version 2.0 model with biomarkers reclassified in the correct direction 39% of patients for risk for death, and 42% for risk for the composite endpoint relative to the clinical model (supplementary material online, Tables S4 and S5). Validation for up to 2 years was possible in a subgroup of 1934 patients from the PARADIGM-HF study cohort6 for whom the three biomarkers were available. The C-statistics were 0.70 for both risk for death and risk for HF-related hospitalization at 2 years. Some variables and endpoints differed between the Barcelona derivation cohort and the PARADIGM-HF validation cohort. Indeed, risk prediction for the composite endpoint could not be validated because the composite endpoint in PARADIGM was cardiovascular death or HF-related hospitalization, rather than all-cause death. In a manner similar to the present efforts in a cohort of chronic ambulatory HF patients, the BIOSTAT-CHF study recently developed and validated three risk models to predict all-cause mortality, HF-related hospitalization and the composite endpoint in a cohort of worsening HF patients.7 These researchers obtained C-statistic values of 0.73, 0.69 and 0.71 for the three outcomes, respectively. Both methods are pioneers in their use of HF biomarkers and are appropriate in two different clinical scenarios. In conclusion, the updated version of the BCN Bio-HF Calculator incorporates new clinical variables and allows better individual prediction of all-cause death, HF-related hospitalization and the composite endpoint for up to 5 years. To the best of the present authors’ knowledge, this is the first online calculator to incorporate treatment with an ARNI in the prediction of risk in HF patients. Risk prediction is a cornerstone of HF management. Accurate prediction of risk for death and/or HF hospitalization may identify high-risk patients and candidates for intensified monitoring and treatment, such as drug dose increases, switches to ARNI,

Clinical characteristics, one-year change in ejection fraction and long-term outcomes in patients with heart failure with mid-range ejection fraction: a multicentre prospective observational study in Catalonia (Spain)

Objectives The aim of this study was to analyse baseline characteristics and outcome of patients with heart failure and mid-range left ventricular ejection fraction (HFmrEF, left ventricular ejection fraction (LVEF) 40%–49%) and the effect of 1-year change in LVEF in this group. Setting Multicentre prospective observational study of ambulatory patients with HF followed up at four university hospitals with dedicated HF units. Participants Fourteen per cent (n=504) of the 3580 patients included had HFmrEF. Interventions Baseline characteristics, 1-year LVEF and outcomes were collected. All-cause death, HF hospitalisation and the composite end-point were the primary outcomes. Results Median follow-up was 3.66 (1.69–6.04) years. All-cause death, HF hospitalisation and the composite end-point were 47%, 35% and 59%, respectively. Outcomes were worse in HF with preserved ejection fraction (HFpEF) (LVEF>50%), without differences between HF with reduced ejection fraction (HFrEF) (LVEF<40%) and HFmrEF (all-cause mortality 52.6% vs 45.8% and 43.8%, respectively, P=0.001). After multivariable Cox regression analyses, no differences in all-cause death and the composite end-point were seen between the three groups. HF hospitalisation and cardiovascular death were not statistically different between patients with HFmrEF and HFrEF. At 1-year follow-up, 62% of patients with HFmrEF had LVEF measured: 24% had LVEF<40%, 43% maintained LVEF 40%–49% and 33% had LVEF>50%. While change in LVEF as continuous variable was not associated with better outcomes, those patients who evolved from HFmrEF to HFpEF did have a better outcome. Those who remained in the HFmrEF and HFrEF groups had higher all-cause mortality after adjustment for age, sex and baseline LVEF (HR 1.96 (95% CI 1.08 to 3.54, P=0.027) and HR 2.01 (95% CI 1.04 to 3.86, P=0.037), respectively). Conclusions Patients with HFmrEF have a clinical profile in-between HFpEF and HFrEF, without differences in all-cause mortality and the composite end-point between the three groups. At 1 year, patients with HFmrEF exhibited the greatest variability in LVEF and this change was associated with survival.

Bio-profiling and bio-prognostication of chronic heart failure with mid-range ejection fraction

BACKGROUND Recent ESC guidelines on heart failure (HF) have introduced a new phenotype based on left ventricular ejection fraction (LVEF), called the mid-range (HFmrEF). This phenotype falls between the classical reduced (HFrEF) and preserved (HFpEF) HF phenotypes. We aimed to characterize the HFmrEF biomarker profile and outcomes. METHODS 1069 consecutive ambulatory patients were included in the study (age 66.2 ± 12.8 years); 800 with HFrEF (74.8%), 134 with HFmrEF (12.5%), and 135 with HFpEF (12.5%). We measured serum concentrations of N-terminal pro-brain natriuretic peptide (NT-proBNP), high-sensitivity troponin T (hs-TnT), soluble suppression of tumorigenicity (ST2), galectin-3, high-sensitivity C-reactive protein, cystatin-C, neprilysin, and soluble transferrin receptor, during 4.9 ± 2.8 years of follow-up. The primary end-point was the composite: cardiovascular death or HF-related hospitalization. We also examined all-cause, cardiovascular death, and the composite: all-cause death or HF-related hospitalization. RESULTS NTproBNP levels in HFmrEF were similar to levels in HFpEF, but significantly lower than levels in HFrEF. No other studied biomarkers were different between HFmrEF and HFrEF. All biomarkers, except neprilysin, showed higher risk prediction capabilities in HFmrEF than in HFrEF or HFpEF. The largest difference between HFrEF and HFmrEF was the hs-TnT level (hazard ratio [HR]: 4.72, 95% CI: 2.81-7.94 vs. HR: 1.67, 95%CI: 1.74-1.89; all p < 0.001). CONCLUSIONS Although HFmrEF is acknowledged as an intermediate phenotype between HFrEF and HFpEF, from a multi-biomarker point of view, HFmrEF was similar to HFrEF, except that NTproBNP levels were lower. Biomarkers commonly used for HFrEF risk prediction are more valuable for HFmrEF risk stratification.