ESC HEART FAILURE ORIGINAL RESEARCH ARTICLE ESC Heart Failure 2020; 7: 1007–1014 Published online 25 March 2020 in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/ehf2.12683 Rehospitalization burden and morbidity risk in patients with heart failure with mid-range ejection fraction Enrique Santas1†, Rafael de la Espriella1†, Patricia Palau2, Gema Miñana1, Martina Amiguet1, Juan Sanchis1, Josep Lupón3, Antoni Bayes-Genís3, Francisco Javier Chorro1 and Julio Núñez Villota1* 1 Servicio de Cardiología, Hospital Clínico Universitario, Universitat de València, INCLIVA, CIBERCV, Avenida Blasco Ibáñez 17, 46010, Valencia, Spain; 2Servicio de Cardiología, Hospital General de Castellón, Universitat Jaume I, Castellón, Spain; 3Servicio de Cardiología, Hospital Germans Trias i Pujol, Universitat Autònoma de Barcelona, CIBERCV, Barcelona, Spain Abstract Aims Heart failure with mid-range ejection fraction (HFmrEF) has been proposed as a distinct HF phenotype, but whether patients on this category fare worse, similarly, or better than those with HF with reduced EF (HFrEF) or preserved EF (HFpEF) in terms of rehospitalization risks over time remains unclear. Methods and results We prospectively included 2961 consecutive patients admitted for acute HF (AHF) in our institution. Of them, 158 patients died during the index admission, leaving the sample size to be 2803 patients. Patients were categorized according to their EF: HFrEF if EF ≤ 40% (n = 908, 32.4%); HFmrEF if EF = 41–49% (n = 449, 16.0%); and HFpEF if EF ≥ 50% (n = 1446, 51.6%). Covariate-adjusted incidence rate ratios (IRRs) were used to evaluate the association between EF status and recurrent all-cause and HF-related admissions. At a median follow-up of 2.6 years (inter-quartile range: 1.0–5.3), 1663 (59.3%) patients died, and 6035 all-cause readmissions were registered in 2026 patients (72.3%), 2163 of them HF related. Rates of all-cause readmission per 100 patients-years of follow-up were 150.1, 176.9, and 163.6 in HFrEF, HFmrEF, and HFpEF, respectively (P = 0.097). After multivariable adjustment, when compared with that of patients with HFrEF and HFpEF, HFmrEF status was not significantly associated with a different risk of all-cause readmissions (IRR = 0.99; 95% confidence interval [CI], 0.77–1.27; P = 0.926; and IRR = 0.93; 95% CI, 0.74–1.18; P = 0.621, respectively) or HF-related readmissions (IRR = 1.06; 95% CI, 0.77–1.46; P = 0.725; and IRR = 1.11; 95% CI, 0.82–1.50; P = 0.511, respectively). Conclusions Following an admission for AHF, patients with HFmrEF had a similar rehospitalization burden and a similar risk of recurrent all-cause and HF-related admissions than had patients with HFrEF or HFpEF. Regarding morbidity risk, HFmrEF seems not to be a distinct HF phenotype. Keywords Heart failure; Readmissions; Heart failure with mid-range ejection fraction; Recurrent events Received: 6 September 2019; Revised: 11 February 2020; Accepted: 1 March 2020 *Correspondence to: Julio Núñez Villota, Cardiology Department, Hospital Clínico Universitario, Universitat de Valencia, Avenida Blasco Ibáñez 17, 46010 Valencia, Spain. Tel: +34680862981; Fax: +34 963862658. Email: yulnunez@gmail.com †Both authors contributed equally to the manuscript. Introduction with an EF between 40% and 49%, which was HF with mid-range EF (HFmrEF), calling for research to fill the knowl- Rehospitalization burden in patients with heart failure (HF) re- edge gaps regarding this new entity.1 Several studies have ad- mains very high.1–3 These readmissions carry important and dressed the epidemiological, clinical, and prognostic negative consequences, being related to a poor quality of life differences between HFmrEF and the classical phenotypes of and an increased mortality risk, but also accounting for large HF ever since, but results have varied considerably across dif- health care expenditures.1–3 Overall, readmission burden is ferent studies, and very few of them have focused on the mor- similar between patients with HF and reduced ejection frac- bidity risk.6–16 tion (HFrEF) and patients with preserved ejection fraction Traditionally, the ‘time-to-first’ event approach has been (HFpEF).4,5 The latest 2016 European Society of Cardiology the classical method to evaluate the risk of adverse events (ESC) HF Guidelines proposed a distinct category for patients in HF, including readmission risks. However, in recent years, © 2020 The Authors. ESC Heart Failure published by John Wiley & Sons Ltd on behalf of the European Society of Cardiology This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. 1008 E. Santas et al. some experts have argued in favour of replacing classical analysis. Researchers in charge of endpoints adjudications time-to-first analyses with recurrent event methodology.17– were all blinded to the HF category. 19 . This longitudinal approach would better quantify the hos- pitalization burden over lifetime in a chronic disease such as HF, and it is being increasingly adopted in recent years, as it Ethical concerns is the case of important recent clinical trials in HF, such as the PARAGON-HF.20 The study was prospectively designed, conformed to the prin- In this study, we sought to evaluate the readmission bur- ciples outlined in the 1975 Declaration of Helsinki, and ap- den in patients with HFmrEF and to analyse whether patients proved by the institutional local review ethics committee. in this new category fare worse, similarly, or better in terms All patients gave informed consent. of rehospitalization risks than do patients with HFrEF and HFpEF, by means of a recurrent event analysis. Statistical analysis Continuous variables were expressed as mean ± standard Methods deviation (SD) or median (inter-quartile range), whenever appropriate. Discrete variables were summarized as per- Study population centages. Baseline continuous variables were compared across HF categories with ANOVA, adopting Kruskal–Wallis We prospectively include a cohort of 2961 consecutive pa- test for non-parametric variables. A descriptive analysis of tients admitted for acute HF in the cardiology department recurrent events was performed by counting the number of a tertiary teaching hospital (Hospital Clínico Universitario, of hospitalizations during the entire follow-up. Crude inci- Valencia-Spain), from 2004 to 2017. Of them, 158 patients dence rates (expressed as number of readmissions per died during the index admission, leaving the final sample size 100 person-years) were calculated for each readmission to be 2803 patients. Patients with either new-onset or wors- endpoint across HF categories. The following aspects were ening HF were included in the registry. A comprehensive set important on how to analyse the data: (i) a concern about of variables was routinely recorded at baseline using informative censoring in HF, meaning that an increase in pre-established registry questionnaires. Left ventricular EF readmission risks would lead to an associated increase in (LVEF) was assessed by two-dimensional transthoracic echo- the risk of subsequent death; and (ii) patient’s follow-up cardiogram in every patient during index hospitalization is usually truncated at death as a terminal event, preclud- (96 ± 24 h after admission). LVEF was calculated by the bi- ing new hospitalizations and potentially introducing bias re- plane Simpson method. Three commercially available systems garding recurrent event analyses. To overcome these were used throughout the study, Agilent Sonos 5500 ie33 and pitfalls, we used bivariate negative binomial regression EPIQ 7C (Philips, Massachusetts, USA). HF categories were (NBreg) models that simultaneously model the number of defined according to the 2016 HF ESC Guidelines: HFrEF readmissions (as counts) and mortality (as terminal event). (EF < 40%), HFmrEF (EF = 41–49%), and HFpEF (EF ≥ 50%). Regression estimates for both outcomes are mutually ad- Treatment with angiotensin-converting enzyme inhibitors, justed by means of shared frailty (accounting for the posi- angiotensin receptor blockers, beta-blockers, aldosterone an- tive correlation between the two outcomes).21 tagonist, diuretics, and other therapeutic strategies were in- Completeness of follow-up was calculated with the Clark dividualized by physicians’ criteria in clinical daily practice or completeness index C (92.5%). The independent associa- according to established guidelines that were operating by tion between HFmrEF and recurrent hospitalizations was the time patients were included in the registry. assessed with either HFrEF or HFpEF as reference catego- ries. Estimates of risk were expressed as incidence rate ra- tios (IRRs). All variables listed in Table 1 were evaluated as Follow-up and endpoints potential covariates in the NBreg models, independently of their P value. A backward stepwise selection, with a P value The incidence of recurrent all-cause and HF-related of 0.157 (Akaike information criterion) for variable inclu- rehospitalizations during follow-up was selected as the pri- sion, was used to achieve parsimonious models and pre- mary endpoint. Only unplanned readmissions were included. vent model’s overfitting.22,23 The covariates included in HF-related readmissions were those in which worsening or the multivariable clinical models were as follows: age, sex, acute HF was the main diagnosis at discharge. Hospitaliza- no prior HF admission, Charlson co-morbidity index, heart tions were adjudicated based on the paper-written and elec- rate at admission, systolic blood pressure at admission, tronic discharges records from every hospital in our health blood urea nitrogen, haemoglobin, New York Heart Associ- care system. All-cause mortality was evaluated as a sensitivity ation (NYHA) functional class prior at admission, treatment ESC Heart Failure 2020; 7: 1007–1014 DOI: 10.1002/ehf2.12683 Morbidity risk in patients with HFmrEF 1009 Table 1 Baseline characteristics in heart failure patients stratified according to ejection fraction HFrEF (n = 908) HFmrEF (n = 449) HFpEF (n = 1446) P value Demographics Age, years 70 ± 12 75 ± 10 76 ± 10 <0.001 Female, n (%) 273 (30.1) 176 (39.2) 932 (64.4) <0.001 Medical history Prior NYHA class III–IV, n (%) 138 (15.2) 74 (16.5) 229 (15,8) 0.556 No prior HF admission, n (%) 481 (53.0) 223 (49.7) 806 (53.9) 0.063 Hypertension, n (%) 657 (72.4) 377 (83.9) 1168 (80.8) <0.001 Diabetes mellitus, n (%) 400 (44.1) 236 (52.6) 612 (42.3) <0.001 Current smoker, n (%) 175 (19.3) 63 (14.0) 105 (7.3) <0.001 Ischaemic heart disease, n (%) 407 (44.8) 203 (45.2) 383 (26.5) <0.001 ICD carrier, n (%) 60 (6.9%) 11 (2.3%) 5 (0.4%) <0.001 CCI > 2, n (%) 323 (35.6) 180 (40.1) 440 (30.4) <0.001 QRS > 120 ms, n (%) 384 (42.3) 176 (39.2) 323 (22.3) <0.001 Atrial fibrillation, n (%) 293 (32.3) 186 (41.4) 764 (52.8) <0.001 Vital signs at admission Heart rate, b.p.m. 101 ± 26 99 ± 27 98 ± 30 0.042 Systolic blood pressure, mmHg 140 ± 31 150 ± 34 150 ± 33 <0.001 Diastolic blood pressure, mmHg 82 ± 19 84 ± 21 80 ± 19 <0.001 Echocardiography LVEF, % 31.3 ± 6.3 44.9 ± 2.5 61.6 ± 7.4 <0.001 LV diastolic diameter, mm 63.0 ± 7.9 57.7 ± 8.1 49.9 ± 7.0 <0.001 Left atrium diameter, mm 44.0 ± 7.9 43.9 ± 8.4 43.9 ± 8.0 0.453 Deceleration time, ms 185 ± 55.6 209.4 ± 66.8 223.1 ± 58.5 <0.001 E/e′ ratio 18.7 ± 8.5 17.4 ± 7.2 18.5 ± 11.2 0.263 TAPSE, mm 17.6 ± 8.4 18.1 ± 3.0 19.5 ± 6.0 <0.001 PASP, mmHg 42.8 ± 10.2 45.0 ± 31.3 47.0 ± 16.0 <0.001 Laboratory BUN, mg/dL 58.9 ± 31.6 61.3 ± 30.6 60.1 ± 30.5 0.377 Haemoglobin, g/dL 12.9 ± 1.9 12.7 ± 1.9 12.1 ± 1.9 <0.001 Sodium, mEq/L 138.5 ± 4.2 138.9 ± 4.3 138.4 ± 4.7 0.121 a NT-proBNP, pg/mL 5923 (8109) 5612 (7913) 3530 (5230) <0.001 Serum creatinine at admission, mg/dL 1.31 ± 0.6 1.36 ± 0.7 1.23 ± 0.6 <0.001 Treatment at discharge Furosemide dose, mg 71.7 ± 43.5 69.5 ± 45.5 63.1 ± 43.1 <0.001 Beta-blockers, n (%) 691 (76.1) 316 (70.4) 937 (64.8) <0.001 ACEI or ARB, n (%) 668 (71.9) 298 (64.9) 917 (61.8) <0.001 MRA, n (%) 519 (54.2) 132 (27.9) 220 (14.3) <0.001 ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin-II receptor blockers; b.p.m., beats per minute; BUN, blood urea nitro- gen; CCI, Charlson co-morbidity index; E/e′, ratio of mitral peak velocity of early filling (E) to early diastolic mitral annular velocity (e′); HF, heart failure; HFrEF, heart failure with reduced ejection fraction; HFmrEF, heart failure with mid-range ejection fraction; HFpEF, heart failure with preserved ejection fraction; ICD, implantable cardiac defibrillator; LV, left ventricular; LVEF, left ventricular ejection fraction; MRA, mineralocorticoid receptor antagonist; NT-proBNP, N-terminal pro-brain natriuretic peptide; NYHA, New York Heart Association functional class; PASP, pulmonary artery systolic pressure; TAPSE, tricuspid annular plane systolic excursion. Values are mean ± standard deviation or n (%). a Values are median (inter-quartile range). with beta-blockers, treatment with mineral receptor antag- Results onists, and the N-terminal pro-brain natriuretic peptide (NT-proBNP). All the covariates included in the model were Baseline characteristics 100% complete except for Charlson index, prior NYHA class, and NT-proBNP, that were available in 2785 (98.1%), 2751 Mean age of the cohort was 73.6 ± 11.1 years, 1381 (49%) (98.1%), and 2612 (93.2%) of the cases. In these cases, were women and 1293 (46%) had been previously admitted we performed a multiple imputation, avoiding dropping for acute HF. The distribution of the cohort across HF catego- such cases. ries was as follows: HFrEF, n = 908 (32.4%); HFmrEF, n = 449 A two-sided P value of <0.05 was considered to be statis- (16.0%); and HFpEF, n = 1446 (51.6%). Table 1 summarizes tically significant for all analyses. All survival analyses were baseline characteristics stratified by the HF category accord- performed using STATA 15.1 (StataCorp. 2015. Stata Statisti- ing to EF status. Overall, patients with HFmrEF had mostly cal Software: Release 14.1. College Station, TX: StataCorp an intermediate clinical profile when compared to patients LP). The ‘Bivcnto’ Stata module was used in the multivariable with HFrEF or HFpEF. For instance, age, prevalence of female regression models for bivariate count outcomes. sex, atrial fibrillation, echocardiographic signs of right ESC Heart Failure 2020; 7: 1007–1014 DOI: 10.1002/ehf2.12683 1010 E. Santas et al. ventricular systolic dysfunction or pulmonary hypertension, Heart failure-related readmissions NT-proBNP levels, or the use of HF medications were in-between in HFmrEF when compared with the classical phe- A cumulative total of 2245 readmissions were registered in notypes. In some other clinical features, such as ischaemic 1144 patients (40.8%). Crude incidence rates of HF-related heart disease or renal dysfunction, HFmrEF patients resem- readmissions per 100 person-years of follow-up are shown in bled more HFrEF. HFmrEF patients showed the highest prev- Figure 1. These rates were numerically slightly higher in alence of cardiovascular risk factors such as hypertension or HFmrEF when compared with those in the other categories, diabetes, and a high profile of co-morbidities, as expressed but these differences were not significant (P = 0.071). After with the Charlson co-morbidity index. multivariable adjustment, estimates of risk remained unal- tered. Thus, no significant differences were found in the risk of recurrent HF-related hospitalizations in HFmrEF when com- pared with either HFrEF or HFpEF (IRR = 1.06; 95% CI, 0.77– All-cause readmissions 1.46; P = 0.578; and IRR = 1.11; 95% CI, 0.82–1.50; P = 0.511, respectively) (Figure 2). All the IRRs for the different compari- At a median follow-up of 2.6 years (1.0–5.3), a cumulative to- sons among the three HF categories are shown in Table 2. In tal of 6035 all-cause readmissions were registered in 2026 pa- addition, there were again no differences in the risk of HF tients (72.3%). Of note, 865 (42.7%) and 379 (18.7%) patients readmissions between HFrEF and HFpEF patients (Table 2). were readmitted ≥3 or ≥5 times along the follow-up, respec- All the covariates and their estimates in the multivariate tively. No significant differences in the crude incidence rates NBreg models for both hospitalizations endpoints are pre- of all-cause readmissions were found across HF categories sented in Table 3. (P = 0.097). Rates per 100 person-years of follow-up for re- current all-cause hospitalizations across HF categories are shown in Figure 1. However, after multivariate adjustment, All-cause mortality and when compared with that in patients with either HFrEF or HFpEF, HFmrEF status was not significantly associated with A total of 1663 patients died (59.3%) in the follow-up. By a different risk of all-cause readmissions (IRR = 0.99: 95% con- Kaplan–Meier analysis, patients with HFrEF showed the fidence interval [CI], 0.77–1.27; P = 0.926; and IRR = 0.93: highest risk of long-term all-cause mortality (Figure S1). How- 95% CI, 0.74–1.19; P = 0.578, respectively) (Figure 2). All the ever, following multivariate adjustment, no significant differ- IRRs for the different comparisons among the three HF cate- ences in the risk of death across HF categories were found gories are shown in Table 2. Interestingly, no differences (HFmrEF vs. HFrEF: IRR = 0.96; 95% CI, 0.72–1.23; P = 0.779; were observed in the risk of all-cause readmissions between and HFpEF vs. HFrEF: IRR = 0.96; 95% CI, 0.75–1.23; P = 0.758). the two classical HF phenotypes (HFrEF vs. HFpEF, Table 2). Discussion In this large single-centre registry, we found that all-cause re- admission rates of HFmrEF patients were similar to those seen in patients with HFrEF and HFpEF. Accordingly, HFmrEF status, when compared with HFrEF or HFpEF, was not associ- ated with a different risk of recurrent all-cause hospitaliza- tions. With regard to HF-related readmissions, incidence rates and the risk of HF-related recurrent events were also comparable in the three HF categories. A need for evaluating the risk of rehospitalizations in heart failure with mid-range ejection fraction Figure 1 Crude incidence of all-cause and HF-related readmission rates in HFmrEF was officially launched as a distinct entity in the patients with HFrEF, HFmrEF, and HFpEF. HFmrEF, heart failure with 2016 ESC HF Guidelines.1 One of the main objectives of this mid-range ejection fraction; HFpEF, heart failure with preserved ejection new phenotype was to encourage research to better eluci- fraction; HFrEF, heart failure with reduced ejection fraction. Complete- date clinical characteristics, prognosis, and treatment for ness of follow-up, using Clark C index, was 92.5%. patients with an EF between 41% and 49%. As ESC Heart Failure 2020; 7: 1007–1014 DOI: 10.1002/ehf2.12683 Morbidity risk in patients with HFmrEF 1011 Figure 2 Risk of recurrent all-cause and HF-related hospitalizations in HFmrEF when compared with HFrEF or HFpEF in the multivariable regression models for bivariate count outcomes. HFmrEF, heart failure with mid-range ejection fraction; HFpEF, heart failure with preserved ejection fraction; HFrEF, heart failure with reduced ejection fraction. Table 2 Risk of all-cause and heart failure-related recurrent admissions in patients with heart failure with mid-range ejection fraction when compared with those with heart failure with reduced ejection fraction and heart failure with preserved ejection fraction in the mul- tivariate models NBreg models IRR (95% CI) P value IRR (95% CI) P value All-cause recurrent admissions HFrEF as reference HFpEF as reference HFmrEF 0.99 (0.77–1.27) 0.926 0.93 (0.74–1.19) 0.578 a HFpEF/HFrEF 1.06 (0.85–1.32) 0.621 0.95 (0–76-1.18) 0.621 Heart failure-related recurrent admissions HFrEF as reference HFpEF as reference HFmrEF 1.06 (0.77–1.46) 0.578 1.11 (0.82–1.50) 0.511 a HFpEF/HFrEF 0.96 (0.72–1.27) 0.761 1.04 (0.79–1.38) 0.761 CI, confidence interval; HFrEF, heart failure with reduced ejection fraction; HFmrEF, heart failure with mid-range ejection fraction; HFpEF, heart failure with preserved ejection fraction; IRR, incidence rate ratio; NBreg, negative binomial regression. a HFpEF vs. HFrEF and HFrEF vs. HFpEF, as indicated. Table 3 All the covariates and their estimates in the multivariate negative binomial regression models for all-cause and heart failure-related recurrent admissions IRR (CI 95%) P value IRR (CI 95%) P value All-cause recurrent admissions HF-related recurrent admissions Age 1.03 (1.02–1.04) <0.001 1.04 (1.03–1.05) <0.001 Male sex 1.25 (1.05–1.50) 0.013 1.03 (0.82–1.30) 0.770 No prior HF admission 0.55 (0.47–0.66) <0.001 0.13 (0.10–0.16) <0.001 Charlson index 1.16 (1.10–1.22) <0.001 1.12 (1.10–1.25) <0.001 SBP 0.99 (0.99–0.99) <0.001 0.99 (0.99–0.99) 0.001 Heart rate 0.99 (0.99–1.00) 0.126 0.99 (0.99–1.00) 0.148 Haemoglobin (g/dL) 0.92 (0.88–0.96) <0.001 0.92 (0.87–0.98) 0.006 BUN (g/dL) 1.00 (0.99–1.00) 0.119 1.00 (0.99–1.00) 0.156 Serum sodium 0.99 (0.96–0.99) 0.007 0.96 (0.94–0.98) 0.001 NT-proBNP (pg/mL) 1.00 (1.00–1.01) 0.002 1.00 (1.00–1.01) 0.002 Prior NYHA class 1.23 (1.08–1.39) 0.001 1.13 (0.96–1.33) 0.138 Beta-blockers 0.81 (0.68–0.97) 0.020 0.82 (0.66–1.03) 0.089 MRA 0.83 (0.69–1.01) 0.072 0.86 (0.67–1.11) 0.252 BUN, blood urea nitrogen; CI, confidence interval; HF, heart failure; IRR, incidence rate ratio; MRA, mineralocorticoid receptor antagonist; NT-proBNP, N-terminal pro-brain natriuretic peptide; NYHA, New York Heart Association; SBP, systolic blood pressure. recommended in the Guidelines, many studies had ad- controversial.26,27 In addition, despite that many studies dressed all these open issues ever since,6,7,9,11–16,24,25 but have focused on the clinical profile or the mortality risk the adoption of this new category has been of HFmrEF, none of them have primarily focused on ESC Heart Failure 2020; 7: 1007–1014 DOI: 10.1002/ehf2.12683 1012 E. Santas et al. readmission risks. 6,7,9,11–16,24,25 Overall, prior studies on re- surprising that HFmrEF status was associated with the low- admission risks in HFmrEF have shown conflicting results. est risk of readmission. CHARM included stable HF patients, For instance, two meta-analyses assessing the prognosis in whereas our patients were included following an admission HFmrEF were published in 2018.7,11 In the meta-analysis for acute HF in ‘real-world’ clinical daily practice. In our co- by Altaie and Khalife, the risk of an HF-related readmission hort, HFmrEF patients were considerably older and showed was similar in HFmrEF when compared with that in HFpEF a higher prevalence of co-morbidities than those from or HFrEF.11 On the contrary, Lauritsen et al. reported that CHARM. Along this line, in the ESC-HF Long-Term (ESC-HF- the risk of an HF rehospitalization was lower in HFmrEF LT) registry, the incidence of all-cause and HF readmissions than in HFrEF but was significantly higher than in HFpEF.7 was also much lower in HFmrEF than in HFrEF.9 These data Nowadays, hospitalizations in HF are a matter of great in- had a deep impact on the results from the meta-analysis of terest. Hospital admissions are increasing worldwide, affect- Lauritsen et al., resulting in a lower risk of HF readmissions ing patients’ quality of life and increasing the risk of further in HFmrEF than in HFrEF.7 As in CHARM, patients included adverse events.1–3 In this context, further research into re- in the ESC-HF-LT were stable, were relatively young, and hospitalization burden in HFmrEF seemed to be showed a lower prevalence of co-morbidities than did pa- appropriate. tients included in our registry (e.g. diabetes: 32.3% vs. 52.5%; atrial fibrillation: 18.3% vs. 41.7%, respectively). Conversely, patients in both the TIME-CHF trial and the Risk of readmission in heart failure with study by Gómez-Otero were more similar to our mid-range ejection fraction: a distinct cohort,13,24 resulting in similar readmission risks in the phenotype? three HF categories. We believe that such differences in the clinical profile of the patients may help to explain the As previously stated, prior studies have reported somewhat different outcomes across studies and the conflicting results conflicting results in HFmrEF. In data from the ‘Get with the of previous meta-analyses. Guidelines’ registry, Shah et al. reported that patients with HFmrEF is a heterogeneous and controversial category. It HFmrEF had slightly higher readmission rates than patients has been stated that many patients in this category are in- with HFpEF, whereas HF readmissions rates were intermedi- deed in transition from one classical HF phenotype to the ate between the two classical phenotypes.14 On the contrary, other, mostly from HFrEF to the so-called HF with improved survival free of all-cause and HF readmissions was similar re- or recovered EF.28–30 We hypothesize that the clinical pro- gardless of EF in the Trial of Intensified versus standard Med- file of patients with HFmrEF may be different depending ical Therapy in Elderly patients with Congestive Heart Failure on the clinical scenario. This ‘transitional’ HFmrEF pheno- (TIME-CHF),13 and along the same line, no differences were type may be more dominant in stable HF cohorts than in found in the incidence of HF readmissions across the three patients admitted for acute HF. For instance, 34.8% pa- HF categories in a multicentre national cohort of 1241 pa- tients with an LVEF = 40–49% from the ESC-HF-LT registry tients discharged from acute HF in Spain,24 in concordance were implantable cardiac defibrillator carriers.9 The labelling with our results. of those patients with improved EF as HFmrEF may intro- It is important to highlight that in all of these previous duce significant bias, as patients with improved EF are a studies, readmission risks have been evaluated as a time- low-risk cohort,28,29 and this could help to explain the low- to-first event. Although this methodology is well recog- est risk attributed to HFmrEF in some of the previous nized, it probably does not accurately reflect the hospitali- studies. zation burden in HF. Nowadays, in order to capture the In summary, our findings confirm the prohibitive burden of morbidity burden following an admission for acute HF in hospitalizations following an episode of acute HF regardless a more comprehensive way, some experts recommend to LVEF status. HFmrEF seems not to be a distinct phenotype evaluate every readmission that occur during follow-up, be- with regard to readmission risks. In our opinion, we should cause recurrent events are the typical feature of HF disease move forward, leaving back the LVEF-centric way of evaluat- progression.17–19 The only study in which morbidity risk of ing the HF-associated morbidity. There is an unmet need for HFmrEF patients has been reported according to this meth- reducing the readmission burden in every HF patient, beyond odology has been conducted by Lund et al. on patients in- their LVEF status. cluded in the Candesartan in Heart Failure: Assessment of Reduction in Mortality and Morbidity (CHARM) programme.25 Patients with LVEF = 40–49%, when com- Limitations pared with HFpEF patients, showed a non-significant trend to a higher risk of recurrent HF admissions. Such an analy- There are some limitations of our study that need to be ac- sis was not conducted for all-cause rehospitalizations, in knowledged. First, this is a single-centre observational study which, by a time-to-first analysis, it was somewhat in which hidden bias might be operating. Second, data on ESC Heart Failure 2020; 7: 1007–1014 DOI: 10.1002/ehf2.12683 Morbidity risk in patients with HFmrEF 1013 LVEF prior to the index episode are scarce and preclude us to HFmrEF exhibit crucial pathophysiological, clinical, or thera- state definite conclusions on the percentage of patients with peutic singularities in HF are warranted. HF and improved EF in our cohort. In addition, longitudinal assessment of LVEF was out of the scope of the present study, so transitional changes in HF categories during the Conflict of interest follow-up were not evaluated. Fourth, we did not focus on any specific rehospitalization clustering in the follow-up. None declared. Finally, the adoption of recurrent event methodology in the HF field is relatively new, so there might be areas still sub- jected to controversy. However, it seems to portray a more accurate evaluation of the morbidity burden than the tradi- Supporting information tional time-to-first event endpoints. Additional supporting information may be found online in the Supporting Information section at the end of the article. Conclusions Figure S1. Kaplan–Meier all-cause mortality curves according to left ventricular ejection fraction. HFmrEF: heart failure with Following an admission for acute HF, patients with HFmrEF mid-range ejection fraction; HFpEF: heart failure with pre- had a similar risk of recurrent all-cause and HF-related served ejection fraction; HFrEF: heart failure with reduced hospitalizations than had patients with either HFrEF or HFpEF. ejection fraction. Further works aiming to unravel whether patients with References 1. Ponikowski P, Voors AA, Anker SD, of patients newly admitted with heart and preserved, mid-range and reduced Bueno H, Cleland JG, Coats AJ, Falk V, failure. Circ Heart Fail 2012; 5: 414–421. ejection fraction: an analysis of the ESC González-Juanatey JV, Harjola VP, 6. Bambhani V, Kizer JR, Lima JAC, van der Heart Failure Long-Term Registry. 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Heart fail- W, Anand IS, Ge J, Lam CSP, Maggioni ure with mid-range fraction in CHARM: ESC Heart Failure 2020; 7: 1007–1014 DOI: 10.1002/ehf2.12683