Congestion Phenotypes in Elderly Patients with Acute Heart Failure: Distinct Patterns in Preserved vs. Reduced Ejection Fraction

Congestion Phenotypes in Elderly Patients with Acute Heart Failure: Distinct Patterns in Preserved vs. Reduced Ejection Fraction | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Congestion Phenotypes in Elderly Patients with Acute Heart Failure: Distinct Patterns in Preserved vs. Reduced Ejection Fraction Pau Llàcer, François Croset, Alberto Pérez Nieva, Jorge Campos, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8544161/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background Congestion is the main driver of worsening acute heart failure (AHF), yet whether congestion phenotypes differ by left ventricular ejection fraction (LVEF) in the elderly remains uncertain. This study aimed to characterize clinical, echocardiographic, and biomarker congestion profiles by LVEF phenotype (HFpEF ≥ 50% vs. HFrEF < 50%) in older patients hospitalized with AHF and to examine their associations with prognosis. Methods We conducted a retrospective cohort study of 830 consecutive patients admitted with AHF. Congestion was assessed clinically, by echocardiography, and through biomarkers (BNP, CA125) and point-of-care ultrasound. Outcomes included HF rehospitalization and all-cause mortality over a median follow-up of 310 days (IQR 62–543). Cox models adjusted for multiple variables. Results Median age was 87 years, 65.6% were women, and 81.7% had HFpEF. Traditional congestion signs and NYHA class were similar across phenotypes. HFrEF showed greater structural and functional remodeling, while diastolic indices and ultrasound congestion markers were comparable. BNP and CA125 concentrations were significantly higher in HFrEF. Overall, 301 patients (36.3%) were rehospitalized and 418 (50.4%) died. LVEF phenotype was not associated with rehospitalization or mortality. As a continuous variable, LVEF showed a modest positive association with rehospitalization (HR 1.013 per 1%; p = 0.019), but was unrelated to mortality. Conclusions In very elderly patients hospitalized with AHF, HFpEF predominated and exhibited distinct biomarker and echocardiographic patterns despite similar clinical congestion. Prognosis was not determined by EF category but was mainly driven by age and congestion and myocardial stress biomarkers (CA125, BNP), with higher LVEF independently predicting rehospitalization. congestion phenotypes ejection fraction older adults acute heart failure Figures Figure 1 Figure 2 Figure 3 Figure 4 KEY SUMMARY POINTS Aim To characterize clinical, echocardiographic, and biomarker congestion phenotypes according to left ventricular ejection fraction (LVEF) in very elderly patients hospitalized with acute heart failure, and to assess their prognostic implications. Findings In a cohort of very elderly patients with acute heart failure, HFpEF was the predominant phenotype. Clinical signs of congestion were similar across LVEF categories, whereas HFrEF showed greater cardiac remodeling and higher BNP and CA125 levels. LVEF phenotype was not associated with rehospitalization or mortality, but when analyzed as a continuous variable, LVEF independently predicted rehospitalization risk. Age, BNP, and CA125 were the main predictors of mortality. Message In very elderly patients hospitalized with acute heart failure, HFpEF predominates and shows distinct biomarker and echocardiographic profiles despite similar clinical congestion. Prognosis is driven mainly by age and biomarkers of congestion and myocardial stress rather than EF category, with LVEF analyzed as a continuous variable providing additional prognostic information for rehospitalization. Introduction Congestion is the main driver of worsening heart failure 1 . Traditionally, congestion in heart failure (HF) has been considered a hemodynamic concept, defined as increased central filling pressures, which are typically the result or consequence of fluid accumulation in the intravascular and extravascular compartments 2 , 3 . However, congestion is not synonymous with fluid overload, as elevated intracardiac pressures do not always correlate with an expansion of total blood volume 2 , 3 , and vice versa, for example, the fluid redistribution is a type of congestion without fluid overload, common in the acute phase of decompensation, typical in acute pulmonary oedema 1 , in contrast, acutely decompensated HF is characterized for fluid overload mainly 1 . An optimal identification of congestion phenotypes through a multiparametric approach will allow for better treatment of HF and improve the prognosis 4 . Despite the well-established pathophysiological differences between heart failure with reduced ejection fraction (HFrEF) and heart failure with preserved ejection fraction (HFpEF) in chronic settings 5 , 6 , these distinctions become less clear during acute episodes, especially in the elderly. Currently, congestion management in the acute phase does not differ based on left ventricular ejection fraction (LVEF) 3 , 4 , 7 . The aim of this study is to characterize congestion phenotypes based on clinical, echocardiographic, and biomarker parameters in relation to LVEF in a cohort of elderly patients with acute heart failure (AHF), and, secondarily, to assess their associations with clinical outcomes. Methods Study design and patients This is a retrospective observational study of a cohort of patients admitted to the Internal Medicine department of the Hospital Ramón y Cajal with the diagnosis of AHF between July 2020 and May 2023. AHF was defined as the rapid onset of symptoms and signs secondary to abnormal cardiac function, along with objective evidence of structural or functional cardiac abnormalities at rest, according to current guidelines¹. An echocardiographic assessment of ejection fraction (EF) was performed either during hospitalization once the patient was clinically stable or, if available, using a study conducted within the previous year. Variables of study Demographic data, medical history, vital signs, 12-lead electrocardiogram, laboratory data and treatments were determined during hospitalization. Treatment with angiotensin converting enzyme inhibitors (ACEi), angiotensin receptor blocker (ARB), angiotensin receptor neprilysin inhibitors (ARNI), mineralocorticoid receptor antagonists (MRA), sodium-glucose co-transporter-2 inhibitors (SLGT2-i), beta blockers, furosemide and other therapeutic strategies were individualized following established guidelines. 1 This study was carried out in accordance with the Declaration of Helsinki and was approved by the ethics committee of the Hospital Ramón y Cajal. All participants signed an informed consent form before participating in this study. Echocardiographic parameters Echocardiographic examinations were conducted after initial patient stabilization during hospitalization index by experienced operators from the local echocardiography laboratory. Cardiac chamber dimensions and systolic and diastolic function were measured in accordance with the current echocardiography guidelines 8 . A conventional EF cut-off of 50% was used to classify patients into two groups: those with heart failure with reduced ejection fraction (HFrEF, EF < 50%) and those with heart failure with preserved ejection fraction (HFpEF, EF ≥ 50%). The analysis included the E/é ratio, left atrial volume index, left ventricular end-systolic and end-diastolic volumes, tricuspid annular plane systolic excursion (TAPSE), and systolic pulmonary artery pressure (sPAP). Clinical, biochemical and ultrasonographic parameters of congestion At admission, clinical signs of congestion such as edema, pulmonary rales, and jugular vein distension were assessed. Baseline blood tests were performed within 24 hours of admission and analyzed in the local laboratory, including measurements of B-type natriuretic peptide (BNP) and carbohydrate antigen 125 (CA125), using the Roche BNP and CA125 assays, respectively. Additionally, point-of-care ultrasound was used at admission to evaluate inferior vena cava (IVC) diameter and collapsibility, as well as the presence of pulmonary B-lines and pleural effusion. Endpoint The primary endpoint was the characterization of congestion phenotypes using clinical, biomarker, and echocardiographic data according to LVEF category (HFrEF < 50% vs. HFpEF ≥ 50%). The secondary endpoints were the associations between LVEF phenotype and clinical outcomes, including all-cause mortality during follow-up, heart failure rehospitalizations during follow-up and the composite of all-cause mortality or heart failure rehospitalization. Statistical analysis Continuous variables are presented as mean ± standard deviation (SD) or median with interquartile ranges (IQR) (percentile 25% to percentile 75%), and categorical variables are expressed as percentages. Baseline characteristics across groups, categorized in LVEF ≥ 50% vs < 50%, were compared using the χ² (chi-square) test for categorical variables. For continuous variables, ANOVA was used if the data followed a normal distribution or the Kruskal-Wallis test if the data were not normally distributed. Secondarily, an exploratory analysis evaluated the association between LVEF category (≥ 50% vs. <50%) and clinical outcomes. Survival analysis was performed using the Kaplan–Meier method and the log-rank test to compare the survival curves between patients according to the categories. Such analysis was carried out by a Cox proportional hazard regression and estimates of risk attributable were expressed as hazard ratios (HR) with 95% confidence intervals (CI). To quantify the association between LVEF and adverse outcomes, Cox proportional hazards regression models were applied. Risk estimates were expressed as hazard ratios (HR) with 95% confidence intervals (CI). A separate multivariable Cox model for clinical outcomes included age, sex, systolic blood pressure, creatinine, hemoglobin, pleural effusion, oedemas, LVEF, B-type Natriuretic Peptide (BNP), and carbohydrate antigen 125 (CA125) as covariates, selected based on prior evidence. LVEF was also analyzed as a continuous variable using restricted cubic splines (RCS) to assess the relationship with the risk of events. We set a two-sided p-value of < 0.05 as the threshold for statistical significance. Stata 18 (StataCorp. 2023. Stata Statistical Software: Release 18. College Station, TX: StataCorp LLC.) was used for these analyses. Results Baseline characteristics A total of 830 patients were included in the study. The median age was 87 years (IQR: 83–90), and women represented 65.6% of the cohort. A total of 677 patients (81.7%) had preserved ejection fraction (LVEF ≥ 50%) and 153 (18.3%) had reduced ejection fraction (LVEF < 50%). There was no significant difference in age distribution (p = 0.275) between groups. Female sex was significantly more frequent in HFpEF group (70.5%) compared to HFrEF group (44.1%) (p < 0.001). Prevalence of hypertension, diabetes mellitus, dyslipidemia, COPD, atrial fibrillation, and valvular heart disease was also similar between groups. However, ischemic etiology was more common in patients with HFrEF (38.6% vs. 20.4%, p < 0.001), while the index hospitalisation was the first HF hospitalization more frequently in the HFpEF group (58.4% vs. 48.0%, p = 0.020). Regarding treatment at admission, use of ACEi/ARBs, MRA and beta-blockers was similar across groups. However, sacubitril/valsartan use was markedly higher in the HFrEF group (17% vs. 0.3%, p < 0.001), and SGLT2i were also more frequently used (45.1% vs. 35.4%, p = 0.026). Use of diuretics, including furosemide and thiazides, did not differ significantly between groups. Congestion profile: HFpEF vs HFrEF Clinical Traditional signs of congestion, such as peripheral edema, pulmonary rales, and jugular venous distension, were similarly prevalent in HFpEF and HFrEF, with no major discriminatory value according to LVEF category. Likewise, NYHA functional class distribution did not differ significantly between groups (p = 0.518). Systolic blood pressure was slightly lower in HFrEF group (median 132 mmHg vs. 134 mmHg, p = 0.035). Echocardiographic and point-of-care ultrasound findings Echocardiographic assessment revealed marked structural and functional differences between groups. Patients with HFrEF had larger left ventricular end-diastolic and end-systolic diameters, and reduced TAPSE, indicating more advanced ventricular remodeling and right ventricular dysfunction. Indexed left atrial volumes were modestly higher in HFrEF (p = 0.039). By contrast, parameters of diastolic dysfunction (such as E/é ratio) did not significantly differ between groups. Point-of-care ultrasound findings did not differ significantly between HFpEF and HFrEF, with similar prevalence of dilated inferior vena cava (52% vs. 51%), pulmonary B-lines (42% vs. 42%), and pleural effusion (39% vs. 40%). Biomarkers Laboratory analysis showed higher median creatinine and hemoglobin levels in the HFrEF group (1.3 [1.0–1.7] mg/dL vs. 1.2 [0.9–1.6] mg/dL, p = 0.022; and 12.5 [11.0–13.8] g/dL vs. 11.9 [10.5–13.3] g/dL, p = 0.003), respectively. BNP levels were markedly elevated in patients with reduced EF (945 [584.5–1433.6] pg/mL vs. 548.7 [305.7–859.6] pg/mL, p < 0.001), as were CA125 concentrations (69.6 [33.4–132.9] U/mL vs. 55.4 [25.0–114.2] U/mL, p = 0.010). Association vs ejection fraction phenotype and prognosis Heart failure hospitalizations During a median (p25 to p75) follow-up of 310 (62–543) days, 301 (36.3%) patients presented rehospitalization. Rehospitalization rates were 32.7% for HFrEF and 37.1% for HFpEF (p = 0.307). Kaplan–Meier curves showed no significant difference in rehospitalization-free survival between groups (Fig. 1 ). In the multivariable Cox model adjusted for demographic, clinical, echocardiographic, and biomarker variables, two factors independently predicted rehospitalisation risk: higher LVEF (HR per 1% = 1.013, 95% CI 1.002–1.025, p = 0.013) and higher BNP at admission (HR per 1 pg/mL = 1.0002, 95% CI 1.00006–1.0004, p = 0.009). Other variables, including age, sex, creatinine, hemoglobin, heart rate, pleural effusion, peripheral edema, leukocyte count, and CA125, were not significantly associated with rehospitalisation. Modelling LVEF with a restricted cubic spline, the joint spline term was borderline (p = 0.065). (Fig. 2 ) All-cause mortality During the same follow-up, 418 (50.4%) patients died. Mortality rates did not differ between groups [54.9% in the HFrEF group and 49.3% in the HFpEF group (p = 0.213)], and survival curves did not show significant divergence (Fig. 3 ). In the adjusted Cox regression, higher age (HR per year = 1.054, 95% CI 1.033–1.075, p < 0.001), higher BNP (HR per 1 pg/mL = 1.0003, 95% CI 1.0001–1.0004, p < 0.001), and higher CA125 (HR per 1 U/mL = 1.0009, 95% CI 1.0002–1.002, p = 0.011) were associated with increased mortality. Female sex was associated with lower mortality risk (HR = 0.73, 95% CI 0.57–0.92, p = 0.009). LVEF as a continuous variable was not significantly related to mortality (HR per 1% = 1.004, p = 0.465). In a restricted cubic spline specification for LVEF, the overall spline term was not significant (p = 0.590). (Fig. 4 ) Discussion In this large, real-world cohort of elderly patients hospitalized with acute heart failure (AHF), we identified four key findings: (1) HFpEF was the predominant phenotype, more common in women, whereas HFrEF was more frequently linked to ischemic etiology and greater structural cardiac remodeling; (2) compared with HFpEF, HFrEF was associated with higher BNP and CA125 concentrations, larger LV dimensions, and lower TAPSE, while clinical signs of congestion were similar across groups; (3) LVEF phenotype was not associated with significant differences in rehospitalization or mortality during follow-up; and (4) in adjusted analyses, age, BNP, CA125, and LVEF were independent prognostic markers, with BNP predicting both rehospitalization and mortality, CA125 predicting mortality, and higher LVEF linked to greater rehospitalization risk. Despite similar clinical signs at presentation, we observed distinct congestion-related profiles in HFpEF versus HFrEF, reflected in differences in biomarker expression and echocardiographic parameters. The strengths of our study include the large sample size, the systematic multiparametric assessment of congestion (clinical, biomarker, and echocardiographic), and the focus on an elderly population often underrepresented in clinical trials. Clinical phenotype and comorbidities Consistent with previous reports 9 , HFpEF was the predominant phenotype in this elderly population, accounting for over 80% of cases. HFpEF patients were more frequently women and more often presented during their first HF hospitalization, whereas ischemic etiology was more prevalent among HFrEF patients. These differences reflect well-established epidemiological trends in chronic HF, where HFpEF is often associated with advanced age, female sex, and multiple comorbidities, while HFrEF is more frequently linked to ischemic heart disease 10 , 11 . The similarity in most comorbidity rates between groups suggests that, in advanced age, shared risk factors such as hypertension, atrial fibrillation, and diabetes mellitus contribute substantially to both HF phenotypes. Congestion signs and hemodynamics Although traditional clinical signs of congestion, such as peripheral edema, pulmonary rales, and jugular venous distension were similar in HFpEF and HFrEF, this is consistent with previous studies reporting no major clinical differences by LVEF 12 , 13 . In contrast, echocardiographic data revealed notable distinctions: HFrEF patients exhibited larger ventricular dimensions, lower TAPSE, and slightly greater left atrial volumes, suggesting more advanced structural remodeling and biventricular involvement, in line with the findings of Bosch et al. 14 Notably, systolic blood pressure at admission was marginally lower in HFrEF, which may reflect reduced forward output or the effects of more intensive neurohormonal blockade. Large-scale registry data, such as the OPTIMIZE-HF registry 15 , have similarly shown that patients admitted with HFrEF are more likely to present with lower systolic blood pressure, whereas those with HFpEF more frequently present with higher values. Biomarker profile BNP and CA125 concentrations were significantly higher in HFrEF patients, indicating a greater degree of myocardial wall stress and serosal involvement, respectively. BNP elevations are expected in HFrEF due to both increased wall tension and reduced EF 16 , but the parallel rise in CA125 suggests more extensive serosal inflammation and congestion in this subgroup 17 , 18 . These biomarker distinctions may help refine congestion phenotyping beyond purely clinical or imaging-based assessment, and they reinforce the concept that biochemical congestion may precede or outlast over physical signs. Prognosis and ejection fraction phenotype In our elderly AHF cohort, LVEF phenotype (HFrEF vs. HFpEF) was not associated with significant differences in either rehospitalization-free survival or overall survival at one year, consistent with findings from larger cohorts that likewise reported no prognostic differences between EF groups 19 , 20 . Although unadjusted Kaplan–Meier curves did not show differences between EF categories, the multivariable Cox model identified LVEF as an independent predictor of rehospitalization, with higher EF values associated with increased risk. These results suggest that, in advanced age, prognosis after an episode of acute decompensation is influenced not only by global disease burden and congestion severity but also by systolic function when assessed as a continuous variable. In the adjusted models, higher LVEF independently predicted an increased risk of rehospitalization, whereas it was not associated with mortality. BNP was an independent predictor of both rehospitalization and mortality, while CA125 predicted mortality. In contrast, traditional clinical signs of congestion, such as edema or pleural effusion, did not retain independent prognostic value. The counterintuitive association of higher LVEF with increased rehospitalization risk likely reflects the predominance of HFpEF in this very old population, where recurrent admissions are driven by comorbidities, diastolic dysfunction, and fluid redistribution rather than progressive systolic impairment. Taken together, these findings reinforce the need for multiparametric prognostic assessment in elderly AHF patients, incorporating objective biomarkers and clinical context, rather than relying solely on EF classification to guide follow-up intensity and therapeutic decision-making. Therapeutic differences between ejection fraction groups Treatment patterns reflected contemporary guideline-directed medical therapy 1 . As expected, the use of sacubitril/valsartan and SGLT2 inhibitors was higher among HFrEF patients, in line with current evidence-based guideline recommendations for sacubitril/valsartan therapy. However, the absence of major differences in diuretic use suggests that acute decongestion strategies remain largely uniform, irrespective of EF. This uniformity in congestion management aligns with prior observations that acute-phase treatment protocols rarely distinguish between HF phenotypes 3 , 4 , despite underlying pathophysiological differences. Pathophysiological considerations While the chronic-phase pathophysiology of HFpEF and HFrEF differs 21 , 22 , acute decompensation in both phenotypes appears to converge on a shared final pathway of elevated filling pressures, resulting in comparable prognostic trajectories. The similar prevalence of clinical congestion across phenotypes likely reflects this common hemodynamic endpoint during decompensation, irrespective of baseline EF. Nonetheless, the structural, hemodynamic, and biomarker differences we observed suggest that the underlying congestion mechanisms may diverge—driven more by volume overload and progressive remodeling in HFrEF, and by fluid redistribution, vascular stiffness, and impaired relaxation in HFpEF. Recognizing these nuances could have prognostic relevance and support further exploration of phenotype-tailored decongestion strategies. Clinical implications Our findings highlight that in elderly patients hospitalized with AHF, reliance on LVEF phenotype alone is insufficient for prognostic stratification or for guiding acute-phase management. While HFpEF and HFrEF share similar clinical congestion profiles during decompensation, they differ in structural remodeling patterns, biomarker expression, and potentially in the underlying mechanisms driving congestion. This suggests that acute-phase therapeutic strategies, currently applied uniformly, may benefit from phenotype-specific tailoring, particularly in terms of decongestion intensity and post-discharge monitoring. The strong prognostic role of BNP and CA125 reinforces the value of integrating objective biomarkers into routine risk assessment, especially in very old patients where comorbidities and diastolic dysfunction often dominate the clinical course. Moving toward a multiparametric approach that combines clinical, imaging, and biomarker data could improve individualized follow-up planning, optimize resource allocation, and potentially reduce recurrent hospitalizations in this high-risk population. Limitations This study has several limitations. First, its retrospective, single-center design may limit generalizability to other populations and care settings. Second, although echocardiography was performed in all patients, measurements were obtained after initial stabilization or from prior studies, which may not fully capture acute-phase hemodynamic status. Third, congestion assessment relied on clinical signs, biomarkers, and echocardiographic parameters measured only at admission, without serial evaluation to assess changes over time or response to therapy. Fourth, invasive hemodynamic measurements were not available to confirm filling pressures. Fifth, despite multivariable adjustment, residual confounding is possible given the advanced age and high comorbidity burden of the cohort. Finally, follow-up data were limited to all-cause mortality and HF rehospitalizations, without differentiation between cardiovascular and non-cardiovascular causes, which may be particularly relevant in elderly, multimorbid population. Conclusions In this large cohort of elderly patients with acute heart failure, HFpEF was the predominant phenotype, with distinct echocardiographic and biomarker profiles compared with HFrEF despite similar clinical signs. Although EF categories did not discriminate prognosis, LVEF as a continuous variable independently predicted rehospitalization risk. Overall, outcomes were mainly driven by age and biomarkers of congestion and myocardial stress (CA125 and BNP). These findings support a multiparametric risk assessment that integrates clinical, imaging, and biomarker data rather than relying solely on EF-based classification. Declarations Competing Interests The authors declare that they have no competing financial or non-financial interests directly or indirectly related to the work submitted for publication. Ethics Approval This study was conducted in accordance with the principles of the Declaration of Helsinki. Ethical approval was obtained from the Ethics Committee of Hospital Universitario Ramón y Cajal (Madrid, Spain). Informed Consent Written informed consent was obtained from all participants prior to inclusion in the study, in accordance with institutional requirements. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Data Availability The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request. References McDonagh TA, Metra M, Adamo M, Gardner RS, Baumbach A, Böhm M et al (2021) 2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure. Eur Heart J 42(36):3599–3726 Miller WL (2016) Fluid Volume Overload and Congestion in Heart Failure: Time to Reconsider Pathophysiology and How Volume Is Assessed. 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Am J Cardiol 113(4):691–696 Tromp J, Westenbrink BD, Ouwerkerk W, van Veldhuisen DJ, Samani NJ, Ponikowski P et al (2018) Identifying Pathophysiological Mechanisms in Heart Failure With Reduced Versus Preserved Ejection Fraction. J Am Coll Cardiol 72(10):1081–1090 Eidizadeh A, Schnelle M, Leha A, Edelmann F, Nolte K, Werhahn SM et al (2023) Biomarker profiles in heart failure with preserved vs. reduced ejection fraction: results from the DIAST-CHF study. ESC Heart Fail 10(1):200–210 Tables Table 1 is available in the Supplementary Files section. Supplementary Files Table1.docx Table 1: Baseline characteristics Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major revisions 18 Feb, 2026 Reviewers agreed at journal 19 Jan, 2026 Reviewers invited by journal 14 Jan, 2026 Editor assigned by journal 13 Jan, 2026 First submitted to journal 09 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8544161","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":574478154,"identity":"eaf28fb8-83f6-45f9-83a5-c15d809b8551","order_by":0,"name":"Pau 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Cajal","correspondingAuthor":false,"prefix":"","firstName":"Marina","middleName":"García","lastName":"Melero","suffix":""},{"id":574478159,"identity":"9855baf3-d798-4e93-b15b-1492b0175b6f","order_by":5,"name":"Carlos Pérez","email":"","orcid":"","institution":"Ramon y Cajal University Hospital: Hospital Universitario Ramon y Cajal","correspondingAuthor":false,"prefix":"","firstName":"Carlos","middleName":"","lastName":"Pérez","suffix":""},{"id":574478160,"identity":"ffdcc9f7-8898-4253-a1b5-b1629d1ee290","order_by":6,"name":"Marina Vergara","email":"","orcid":"","institution":"Ramon y Cajal University Hospital: Hospital Universitario Ramon y Cajal","correspondingAuthor":false,"prefix":"","firstName":"Marina","middleName":"","lastName":"Vergara","suffix":""},{"id":574478161,"identity":"e35d1af6-6326-492e-ad97-c401423e9977","order_by":7,"name":"Paul Cevallos Castro","email":"","orcid":"","institution":"Ramon y Cajal University Hospital: Hospital Universitario Ramon y Cajal","correspondingAuthor":false,"prefix":"","firstName":"Paul","middleName":"Cevallos","lastName":"Castro","suffix":""},{"id":574478162,"identity":"b23b4b2a-c4e3-4873-8a71-9e66a3810a48","order_by":8,"name":"Juanes Rodriguez","email":"","orcid":"","institution":"Ramon y Cajal University Hospital: Hospital Universitario Ramon y Cajal","correspondingAuthor":false,"prefix":"","firstName":"Juanes","middleName":"","lastName":"Rodriguez","suffix":""},{"id":574478163,"identity":"e2281884-93bb-419b-9482-72e0567aea8c","order_by":9,"name":"Cristina Fernández","email":"","orcid":"","institution":"Ramon y Cajal University Hospital: Hospital Universitario Ramon y Cajal","correspondingAuthor":false,"prefix":"","firstName":"Cristina","middleName":"","lastName":"Fernández","suffix":""},{"id":574478164,"identity":"0461d39b-8fa8-4ac9-aa45-f86d7a0592e1","order_by":10,"name":"María Pumares","email":"","orcid":"","institution":"Ramon y Cajal University Hospital: Hospital Universitario Ramon y Cajal","correspondingAuthor":false,"prefix":"","firstName":"María","middleName":"","lastName":"Pumares","suffix":""},{"id":574478165,"identity":"e49acf61-f283-42ba-bc30-f553c3eda1f1","order_by":11,"name":"Martin Fabregate","email":"","orcid":"","institution":"Ramon y Cajal University Hospital: Hospital Universitario Ramon y Cajal","correspondingAuthor":false,"prefix":"","firstName":"Martin","middleName":"","lastName":"Fabregate","suffix":""},{"id":574478166,"identity":"19707ee8-5cd9-4c57-9e2c-23e053e61f78","order_by":12,"name":"Beatriz Del Hoyo","email":"","orcid":"","institution":"Ramon y Cajal University Hospital: Hospital Universitario Ramon y Cajal","correspondingAuthor":false,"prefix":"","firstName":"Beatriz","middleName":"Del","lastName":"Hoyo","suffix":""},{"id":574478167,"identity":"65534682-c49e-4a5a-bb67-480bc5011791","order_by":13,"name":"Esteban Pérez Pisón","email":"","orcid":"","institution":"Ramon y Cajal University Hospital: Hospital Universitario Ramon y Cajal","correspondingAuthor":false,"prefix":"","firstName":"Esteban","middleName":"Pérez","lastName":"Pisón","suffix":""},{"id":574478168,"identity":"a8af2884-c7e7-4021-8be7-ab1a3020845b","order_by":14,"name":"Luis Manzano","email":"","orcid":"","institution":"Ramon y Cajal University Hospital: Hospital Universitario Ramon y Cajal","correspondingAuthor":false,"prefix":"","firstName":"Luis","middleName":"","lastName":"Manzano","suffix":""}],"badges":[],"createdAt":"2026-01-07 17:32:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8544161/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8544161/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":100567664,"identity":"35cd6bf3-c3a4-4418-87c0-3f8239fcf5bc","added_by":"auto","created_at":"2026-01-19 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09:12:31","extension":"html","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":113890,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8544161/v1/c15ce19abf854bf70c0713f2.html"},{"id":100594924,"identity":"83962d26-68c1-42ae-abcf-2298c55db27a","added_by":"auto","created_at":"2026-01-19 13:46:31","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":13382,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier Curves for heart failure rehospitalization by LVEF Category.\u003c/p\u003e","description":"","filename":"OnlineFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8544161/v1/25be387f9ba221f483cfacba.png"},{"id":100567663,"identity":"26df49cd-888e-4a41-a186-7917b0381cb0","added_by":"auto","created_at":"2026-01-19 09:12:30","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":9552,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between LVEF as a continuous variable (modeled using restricted cubic splines) and the risk of heart failure rehospitalization.\u003c/p\u003e","description":"","filename":"OnlineFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8544161/v1/b658522f9c720b3637136e06.png"},{"id":100567666,"identity":"1c1b5575-4484-43de-8d79-e855e03835ba","added_by":"auto","created_at":"2026-01-19 09:12:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":13914,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier Curves for for all-cause mortality by LVEF Category.\u003c/p\u003e","description":"","filename":"OnlineFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8544161/v1/718d532727cceb376282ff77.png"},{"id":100595173,"identity":"fc08a1fb-0ba7-44d7-8805-2af165ca452c","added_by":"auto","created_at":"2026-01-19 13:47:46","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":8616,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between LVEF as a continuous variable (modeled using restricted cubic splines) and the risk of all-cause mortality.\u003c/p\u003e","description":"","filename":"OnlineFigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8544161/v1/dae9fab6b759604ed33ba858.png"},{"id":100803966,"identity":"6b534854-911e-46b8-bc1d-b4c12b9dc3a9","added_by":"auto","created_at":"2026-01-21 14:32:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":921601,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8544161/v1/c8f301a6-c216-4f0a-9e27-da8fb3d30ff0.pdf"},{"id":100595736,"identity":"44d12a1b-d470-414e-80b8-af8ec5a2ea61","added_by":"auto","created_at":"2026-01-19 13:49:18","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":73674,"visible":true,"origin":"","legend":"\u003cp\u003eTable 1: Baseline characteristics\u003c/p\u003e","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8544161/v1/1158d0e044fc8172b03b16c2.docx"}],"financialInterests":"","formattedTitle":"Congestion Phenotypes in Elderly Patients with Acute Heart Failure: Distinct Patterns in Preserved vs. Reduced Ejection Fraction","fulltext":[{"header":"KEY SUMMARY POINTS","content":"\u003cp\u003e\u003cstrong\u003eAim\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo characterize clinical, echocardiographic, and biomarker congestion phenotypes according to left ventricular ejection fraction (LVEF) in very elderly patients hospitalized with acute heart failure, and to assess their prognostic implications.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFindings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn a cohort of very elderly patients with acute heart failure, HFpEF was the predominant phenotype. Clinical signs of congestion were similar across LVEF categories, whereas HFrEF showed greater cardiac remodeling and higher BNP and CA125 levels. LVEF phenotype was not associated with rehospitalization or mortality, but when analyzed as a continuous variable, LVEF independently predicted rehospitalization risk. Age, BNP, and CA125 were the main predictors of mortality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMessage\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn very elderly patients hospitalized with acute heart failure, HFpEF predominates and shows distinct biomarker and echocardiographic profiles despite similar clinical congestion. Prognosis is driven mainly by age and biomarkers of congestion and myocardial stress rather than EF category, with LVEF analyzed as a continuous variable providing additional prognostic information for rehospitalization.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eCongestion is the main driver of worsening heart failure\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Traditionally, congestion in heart failure (HF) has been considered a hemodynamic concept, defined as increased central filling pressures, which are typically the result or consequence of fluid accumulation in the intravascular and extravascular compartments\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. However, congestion is not synonymous with fluid overload, as elevated intracardiac pressures do not always correlate with an expansion of total blood volume\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, and vice versa, for example, the fluid redistribution is a type of congestion without fluid overload, common in the acute phase of decompensation, typical in acute pulmonary oedema\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e, in contrast, acutely decompensated HF is characterized for fluid overload mainly\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. An optimal identification of congestion phenotypes through a multiparametric approach will allow for better treatment of HF and improve the prognosis\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Despite the well-established pathophysiological differences between heart failure with reduced ejection fraction (HFrEF) and heart failure with preserved ejection fraction (HFpEF) in chronic settings\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e, these distinctions become less clear during acute episodes, especially in the elderly. Currently, congestion management in the acute phase does not differ based on left ventricular ejection fraction (LVEF)\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe aim of this study is to characterize congestion phenotypes based on clinical, echocardiographic, and biomarker parameters in relation to LVEF in a cohort of elderly patients with acute heart failure (AHF), and, secondarily, to assess their associations with clinical outcomes.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and patients\u003c/h2\u003e \u003cp\u003eThis is a retrospective observational study of a cohort of patients admitted to the Internal Medicine department of the Hospital Ram\u0026oacute;n y Cajal with the diagnosis of AHF between July 2020 and May 2023.\u003c/p\u003e \u003cp\u003e AHF was defined as the rapid onset of symptoms and signs secondary to abnormal cardiac function, along with objective evidence of structural or functional cardiac abnormalities at rest, according to current guidelines\u0026sup1;. An echocardiographic assessment of ejection fraction (EF) was performed either during hospitalization once the patient was clinically stable or, if available, using a study conducted within the previous year.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eVariables of study\u003c/h3\u003e\n\u003cp\u003eDemographic data, medical history, vital signs, 12-lead electrocardiogram, laboratory data and treatments were determined during hospitalization. Treatment with angiotensin converting enzyme inhibitors (ACEi), angiotensin receptor blocker (ARB), angiotensin receptor neprilysin inhibitors (ARNI), mineralocorticoid receptor antagonists (MRA), sodium-glucose co-transporter-2 inhibitors (SLGT2-i), beta blockers, furosemide and other therapeutic strategies were individualized following established guidelines.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e This study was carried out in accordance with the Declaration of Helsinki and was approved by the ethics committee of the Hospital Ram\u0026oacute;n y Cajal. All participants signed an informed consent form before participating in this study.\u003c/p\u003e\n\u003ch3\u003eEchocardiographic parameters\u003c/h3\u003e\n\u003cp\u003eEchocardiographic examinations were conducted after initial patient stabilization during hospitalization index by experienced operators from the local echocardiography laboratory. Cardiac chamber dimensions and systolic and diastolic function were measured in accordance with the current echocardiography guidelines \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. A conventional EF cut-off of 50% was used to classify patients into two groups: those with heart failure with reduced ejection fraction (HFrEF, EF\u0026thinsp;\u0026lt;\u0026thinsp;50%) and those with heart failure with preserved ejection fraction (HFpEF, EF\u0026thinsp;\u0026ge;\u0026thinsp;50%). The analysis included the E/\u0026eacute; ratio, left atrial volume index, left ventricular end-systolic and end-diastolic volumes, tricuspid annular plane systolic excursion (TAPSE), and systolic pulmonary artery pressure (sPAP).\u003c/p\u003e\n\u003ch3\u003eClinical, biochemical and ultrasonographic parameters of congestion\u003c/h3\u003e\n\u003cp\u003eAt admission, clinical signs of congestion such as edema, pulmonary rales, and jugular vein distension were assessed. Baseline blood tests were performed within 24 hours of admission and analyzed in the local laboratory, including measurements of B-type natriuretic peptide (BNP) and carbohydrate antigen 125 (CA125), using the Roche BNP and CA125 assays, respectively. Additionally, point-of-care ultrasound was used at admission to evaluate inferior vena cava (IVC) diameter and collapsibility, as well as the presence of pulmonary B-lines and pleural effusion.\u003c/p\u003e\n\u003ch3\u003eEndpoint\u003c/h3\u003e\n\u003cp\u003eThe primary endpoint was the characterization of congestion phenotypes using clinical, biomarker, and echocardiographic data according to LVEF category (HFrEF\u0026thinsp;\u0026lt;\u0026thinsp;50% vs. HFpEF\u0026thinsp;\u0026ge;\u0026thinsp;50%). The secondary endpoints were the associations between LVEF phenotype and clinical outcomes, including all-cause mortality during follow-up, heart failure rehospitalizations during follow-up and the composite of all-cause mortality or heart failure rehospitalization.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eContinuous variables are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) or median with interquartile ranges (IQR) (percentile 25% to percentile 75%), and categorical variables are expressed as percentages. Baseline characteristics across groups, categorized in LVEF\u0026thinsp;\u0026ge;\u0026thinsp;50% vs\u0026thinsp;\u0026lt;\u0026thinsp;50%, were compared using the χ\u0026sup2; (chi-square) test for categorical variables. For continuous variables, ANOVA was used if the data followed a normal distribution or the Kruskal-Wallis test if the data were not normally distributed.\u003c/p\u003e \u003cp\u003eSecondarily, an exploratory analysis evaluated the association between LVEF category (\u0026ge;\u0026thinsp;50% vs. \u0026lt;50%) and clinical outcomes. Survival analysis was performed using the Kaplan\u0026ndash;Meier method and the log-rank test to compare the survival curves between patients according to the categories. Such analysis was carried out by a Cox proportional hazard regression and estimates of risk attributable were expressed as hazard ratios (HR) with 95% confidence intervals (CI). To quantify the association between LVEF and adverse outcomes, Cox proportional hazards regression models were applied. Risk estimates were expressed as hazard ratios (HR) with 95% confidence intervals (CI). A separate multivariable Cox model for clinical outcomes included age, sex, systolic blood pressure, creatinine, hemoglobin, pleural effusion, oedemas, LVEF, B-type Natriuretic Peptide (BNP), and carbohydrate antigen 125 (CA125) as covariates, selected based on prior evidence. LVEF was also analyzed as a continuous variable using restricted cubic splines (RCS) to assess the relationship with the risk of events.\u003c/p\u003e \u003cp\u003eWe set a two-sided p-value of \u0026lt;\u0026thinsp;0.05 as the threshold for statistical significance. Stata 18 (StataCorp. 2023. Stata Statistical Software: Release 18. College Station, TX: StataCorp LLC.) was used for these analyses.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics\u003c/h2\u003e \u003cp\u003eA total of 830 patients were included in the study. The median age was 87 years (IQR: 83\u0026ndash;90), and women represented 65.6% of the cohort. A total of 677 patients (81.7%) had preserved ejection fraction (LVEF\u0026thinsp;\u0026ge;\u0026thinsp;50%) and 153 (18.3%) had reduced ejection fraction (LVEF\u0026thinsp;\u0026lt;\u0026thinsp;50%).\u003c/p\u003e \u003cp\u003eThere was no significant difference in age distribution (p\u0026thinsp;=\u0026thinsp;0.275) between groups. Female sex was significantly more frequent in HFpEF group (70.5%) compared to HFrEF group (44.1%) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003ePrevalence of hypertension, diabetes mellitus, dyslipidemia, COPD, atrial fibrillation, and valvular heart disease was also similar between groups. However, ischemic etiology was more common in patients with HFrEF (38.6% vs. 20.4%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while the index hospitalisation was the first HF hospitalization more frequently in the HFpEF group (58.4% vs. 48.0%, p\u0026thinsp;=\u0026thinsp;0.020).\u003c/p\u003e \u003cp\u003eRegarding treatment at admission, use of ACEi/ARBs, MRA and beta-blockers was similar across groups. However, sacubitril/valsartan use was markedly higher in the HFrEF group (17% vs. 0.3%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and SGLT2i were also more frequently used (45.1% vs. 35.4%, p\u0026thinsp;=\u0026thinsp;0.026). Use of diuretics, including furosemide and thiazides, did not differ significantly between groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCongestion profile: HFpEF vs HFrEF\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003eClinical\u003c/h2\u003e \u003cp\u003eTraditional signs of congestion, such as peripheral edema, pulmonary rales, and jugular venous distension, were similarly prevalent in HFpEF and HFrEF, with no major discriminatory value according to LVEF category. Likewise, NYHA functional class distribution did not differ significantly between groups (p\u0026thinsp;=\u0026thinsp;0.518). Systolic blood pressure was slightly lower in HFrEF group (median 132 mmHg vs. 134 mmHg, p\u0026thinsp;=\u0026thinsp;0.035).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eEchocardiographic and point-of-care ultrasound findings\u003c/h2\u003e \u003cp\u003eEchocardiographic assessment revealed marked structural and functional differences between groups. Patients with HFrEF had larger left ventricular end-diastolic and end-systolic diameters, and reduced TAPSE, indicating more advanced ventricular remodeling and right ventricular dysfunction. Indexed left atrial volumes were modestly higher in HFrEF (p\u0026thinsp;=\u0026thinsp;0.039). By contrast, parameters of diastolic dysfunction (such as E/\u0026eacute; ratio) did not significantly differ between groups.\u003c/p\u003e \u003cp\u003ePoint-of-care ultrasound findings did not differ significantly between HFpEF and HFrEF, with similar prevalence of dilated inferior vena cava (52% vs. 51%), pulmonary B-lines (42% vs. 42%), and pleural effusion (39% vs. 40%).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eBiomarkers\u003c/h2\u003e \u003cp\u003eLaboratory analysis showed higher median creatinine and hemoglobin levels in the HFrEF group (1.3 [1.0\u0026ndash;1.7] mg/dL vs. 1.2 [0.9\u0026ndash;1.6] mg/dL, p\u0026thinsp;=\u0026thinsp;0.022; and 12.5 [11.0\u0026ndash;13.8] g/dL vs. 11.9 [10.5\u0026ndash;13.3] g/dL, p\u0026thinsp;=\u0026thinsp;0.003), respectively. BNP levels were markedly elevated in patients with reduced EF (945 [584.5\u0026ndash;1433.6] pg/mL vs. 548.7 [305.7\u0026ndash;859.6] pg/mL, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as were CA125 concentrations (69.6 [33.4\u0026ndash;132.9] U/mL vs. 55.4 [25.0\u0026ndash;114.2] U/mL, p\u0026thinsp;=\u0026thinsp;0.010).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eAssociation vs ejection fraction phenotype and prognosis\u003c/h2\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003eHeart failure hospitalizations\u003c/h2\u003e \u003cp\u003eDuring a median (p25 to p75) follow-up of 310 (62\u0026ndash;543) days, 301 (36.3%) patients presented rehospitalization. Rehospitalization rates were 32.7% for HFrEF and 37.1% for HFpEF (p\u0026thinsp;=\u0026thinsp;0.307). Kaplan\u0026ndash;Meier curves showed no significant difference in rehospitalization-free survival between groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the multivariable Cox model adjusted for demographic, clinical, echocardiographic, and biomarker variables, two factors independently predicted rehospitalisation risk: higher LVEF (HR per 1% = 1.013, 95% CI 1.002\u0026ndash;1.025, p\u0026thinsp;=\u0026thinsp;0.013) and higher BNP at admission (HR per 1 pg/mL\u0026thinsp;=\u0026thinsp;1.0002, 95% CI 1.00006\u0026ndash;1.0004, p\u0026thinsp;=\u0026thinsp;0.009). Other variables, including age, sex, creatinine, hemoglobin, heart rate, pleural effusion, peripheral edema, leukocyte count, and CA125, were not significantly associated with rehospitalisation. Modelling LVEF with a restricted cubic spline, the joint spline term was borderline (p\u0026thinsp;=\u0026thinsp;0.065). (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eAll-cause mortality\u003c/h2\u003e \u003cp\u003eDuring the same follow-up, 418 (50.4%) patients died. Mortality rates did not differ between groups [54.9% in the HFrEF group and 49.3% in the HFpEF group (p\u0026thinsp;=\u0026thinsp;0.213)], and survival curves did not show significant divergence (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the adjusted Cox regression, higher age (HR per year\u0026thinsp;=\u0026thinsp;1.054, 95% CI 1.033\u0026ndash;1.075, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), higher BNP (HR per 1 pg/mL\u0026thinsp;=\u0026thinsp;1.0003, 95% CI 1.0001\u0026ndash;1.0004, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and higher CA125 (HR per 1 U/mL\u0026thinsp;=\u0026thinsp;1.0009, 95% CI 1.0002\u0026ndash;1.002, p\u0026thinsp;=\u0026thinsp;0.011) were associated with increased mortality. Female sex was associated with lower mortality risk (HR\u0026thinsp;=\u0026thinsp;0.73, 95% CI 0.57\u0026ndash;0.92, p\u0026thinsp;=\u0026thinsp;0.009). LVEF as a continuous variable was not significantly related to mortality (HR per 1% = 1.004, p\u0026thinsp;=\u0026thinsp;0.465). In a restricted cubic spline specification for LVEF, the overall spline term was not significant (p\u0026thinsp;=\u0026thinsp;0.590). (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this large, real-world cohort of elderly patients hospitalized with acute heart failure (AHF), we identified four key findings: (1) HFpEF was the predominant phenotype, more common in women, whereas HFrEF was more frequently linked to ischemic etiology and greater structural cardiac remodeling; (2) compared with HFpEF, HFrEF was associated with higher BNP and CA125 concentrations, larger LV dimensions, and lower TAPSE, while clinical signs of congestion were similar across groups; (3) LVEF phenotype was not associated with significant differences in rehospitalization or mortality during follow-up; and (4) in adjusted analyses, age, BNP, CA125, and LVEF were independent prognostic markers, with BNP predicting both rehospitalization and mortality, CA125 predicting mortality, and higher LVEF linked to greater rehospitalization risk.\u003c/p\u003e \u003cp\u003eDespite similar clinical signs at presentation, we observed distinct congestion-related profiles in HFpEF versus HFrEF, reflected in differences in biomarker expression and echocardiographic parameters. The strengths of our study include the large sample size, the systematic multiparametric assessment of congestion (clinical, biomarker, and echocardiographic), and the focus on an elderly population often underrepresented in clinical trials.\u003c/p\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eClinical phenotype and comorbidities\u003c/h2\u003e \u003cp\u003eConsistent with previous reports\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, HFpEF was the predominant phenotype in this elderly population, accounting for over 80% of cases. HFpEF patients were more frequently women and more often presented during their first HF hospitalization, whereas ischemic etiology was more prevalent among HFrEF patients. These differences reflect well-established epidemiological trends in chronic HF, where HFpEF is often associated with advanced age, female sex, and multiple comorbidities, while HFrEF is more frequently linked to ischemic heart disease\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. The similarity in most comorbidity rates between groups suggests that, in advanced age, shared risk factors such as hypertension, atrial fibrillation, and diabetes mellitus contribute substantially to both HF phenotypes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eCongestion signs and hemodynamics\u003c/h2\u003e \u003cp\u003eAlthough traditional clinical signs of congestion, such as peripheral edema, pulmonary rales, and jugular venous distension were similar in HFpEF and HFrEF, this is consistent with previous studies reporting no major clinical differences by LVEF\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. In contrast, echocardiographic data revealed notable distinctions: HFrEF patients exhibited larger ventricular dimensions, lower TAPSE, and slightly greater left atrial volumes, suggesting more advanced structural remodeling and biventricular involvement, in line with the findings of Bosch et al.\u003csup\u003e14\u003c/sup\u003e Notably, systolic blood pressure at admission was marginally lower in HFrEF, which may reflect reduced forward output or the effects of more intensive neurohormonal blockade. Large-scale registry data, such as the OPTIMIZE-HF registry\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, have similarly shown that patients admitted with HFrEF are more likely to present with lower systolic blood pressure, whereas those with HFpEF more frequently present with higher values.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eBiomarker profile\u003c/h2\u003e \u003cp\u003eBNP and CA125 concentrations were significantly higher in HFrEF patients, indicating a greater degree of myocardial wall stress and serosal involvement, respectively. BNP elevations are expected in HFrEF due to both increased wall tension and reduced EF\u003csup\u003e16\u003c/sup\u003e, but the parallel rise in CA125 suggests more extensive serosal inflammation and congestion in this subgroup\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. These biomarker distinctions may help refine congestion phenotyping beyond purely clinical or imaging-based assessment, and they reinforce the concept that biochemical congestion may precede or outlast over physical signs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003ePrognosis and ejection fraction phenotype\u003c/h2\u003e \u003cp\u003eIn our elderly AHF cohort, LVEF phenotype (HFrEF vs. HFpEF) was not associated with significant differences in either rehospitalization-free survival or overall survival at one year, consistent with findings from larger cohorts that likewise reported no prognostic differences between EF groups\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Although unadjusted Kaplan\u0026ndash;Meier curves did not show differences between EF categories, the multivariable Cox model identified LVEF as an independent predictor of rehospitalization, with higher EF values associated with increased risk. These results suggest that, in advanced age, prognosis after an episode of acute decompensation is influenced not only by global disease burden and congestion severity but also by systolic function when assessed as a continuous variable.\u003c/p\u003e \u003cp\u003eIn the adjusted models, higher LVEF independently predicted an increased risk of rehospitalization, whereas it was not associated with mortality. BNP was an independent predictor of both rehospitalization and mortality, while CA125 predicted mortality. In contrast, traditional clinical signs of congestion, such as edema or pleural effusion, did not retain independent prognostic value. The counterintuitive association of higher LVEF with increased rehospitalization risk likely reflects the predominance of HFpEF in this very old population, where recurrent admissions are driven by comorbidities, diastolic dysfunction, and fluid redistribution rather than progressive systolic impairment.\u003c/p\u003e \u003cp\u003eTaken together, these findings reinforce the need for multiparametric prognostic assessment in elderly AHF patients, incorporating objective biomarkers and clinical context, rather than relying solely on EF classification to guide follow-up intensity and therapeutic decision-making.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eTherapeutic differences between ejection fraction groups\u003c/h2\u003e \u003cp\u003eTreatment patterns reflected contemporary guideline-directed medical therapy\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. As expected, the use of sacubitril/valsartan and SGLT2 inhibitors was higher among HFrEF patients, in line with current evidence-based guideline recommendations for sacubitril/valsartan therapy. However, the absence of major differences in diuretic use suggests that acute decongestion strategies remain largely uniform, irrespective of EF. This uniformity in congestion management aligns with prior observations that acute-phase treatment protocols rarely distinguish between HF phenotypes\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, despite underlying pathophysiological differences.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003ePathophysiological considerations\u003c/h2\u003e \u003cp\u003eWhile the chronic-phase pathophysiology of HFpEF and HFrEF differs\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, acute decompensation in both phenotypes appears to converge on a shared final pathway of elevated filling pressures, resulting in comparable prognostic trajectories. The similar prevalence of clinical congestion across phenotypes likely reflects this common hemodynamic endpoint during decompensation, irrespective of baseline EF. Nonetheless, the structural, hemodynamic, and biomarker differences we observed suggest that the underlying congestion mechanisms may diverge\u0026mdash;driven more by volume overload and progressive remodeling in HFrEF, and by fluid redistribution, vascular stiffness, and impaired relaxation in HFpEF. Recognizing these nuances could have prognostic relevance and support further exploration of phenotype-tailored decongestion strategies.\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eClinical implications\u003c/h2\u003e \u003cp\u003eOur findings highlight that in elderly patients hospitalized with AHF, reliance on LVEF phenotype alone is insufficient for prognostic stratification or for guiding acute-phase management. While HFpEF and HFrEF share similar clinical congestion profiles during decompensation, they differ in structural remodeling patterns, biomarker expression, and potentially in the underlying mechanisms driving congestion. This suggests that acute-phase therapeutic strategies, currently applied uniformly, may benefit from phenotype-specific tailoring, particularly in terms of decongestion intensity and post-discharge monitoring. The strong prognostic role of BNP and CA125 reinforces the value of integrating objective biomarkers into routine risk assessment, especially in very old patients where comorbidities and diastolic dysfunction often dominate the clinical course. Moving toward a multiparametric approach that combines clinical, imaging, and biomarker data could improve individualized follow-up planning, optimize resource allocation, and potentially reduce recurrent hospitalizations in this high-risk population.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThis study has several limitations. First, its retrospective, single-center design may limit generalizability to other populations and care settings. Second, although echocardiography was performed in all patients, measurements were obtained after initial stabilization or from prior studies, which may not fully capture acute-phase hemodynamic status. Third, congestion assessment relied on clinical signs, biomarkers, and echocardiographic parameters measured only at admission, without serial evaluation to assess changes over time or response to therapy. Fourth, invasive hemodynamic measurements were not available to confirm filling pressures. Fifth, despite multivariable adjustment, residual confounding is possible given the advanced age and high comorbidity burden of the cohort. Finally, follow-up data were limited to all-cause mortality and HF rehospitalizations, without differentiation between cardiovascular and non-cardiovascular causes, which may be particularly relevant in elderly, multimorbid population.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this large cohort of elderly patients with acute heart failure, HFpEF was the predominant phenotype, with distinct echocardiographic and biomarker profiles compared with HFrEF despite similar clinical signs. Although EF categories did not discriminate prognosis, LVEF as a continuous variable independently predicted rehospitalization risk. Overall, outcomes were mainly driven by age and biomarkers of congestion and myocardial stress (CA125 and BNP). These findings support a multiparametric risk assessment that integrates clinical, imaging, and biomarker data rather than relying solely on EF-based classification.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCompeting Interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing financial or non-financial interests directly or indirectly related to the work submitted for publication.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthics Approval\u003c/strong\u003e \u003cp\u003e This study was conducted in accordance with the principles of the Declaration of Helsinki. Ethical approval was obtained from the Ethics Committee of Hospital Universitario Ram\u0026oacute;n y Cajal (Madrid, Spain).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eInformed Consent\u003c/strong\u003e \u003cp\u003e Written informed consent was obtained from all participants prior to inclusion in the study, in accordance with institutional requirements.\u003c/p\u003e \u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e \u003cp\u003eThe datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMcDonagh TA, Metra M, Adamo M, Gardner RS, Baumbach A, B\u0026ouml;hm M et al (2021) 2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure. Eur Heart J 42(36):3599\u0026ndash;3726\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiller WL (2016) Fluid Volume Overload and Congestion in Heart Failure: Time to Reconsider Pathophysiology and How Volume Is Assessed. Circ Heart Fail 9(8):e002922\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMullens W, Damman K, Harjola VP, Mebazaa A, Brunner-La Rocca HP, Martens P et al (2019) The use of diuretics in heart failure with congestion - a position statement from the Heart Failure Association of the European Society of Cardiology. Eur J Heart Fail 21(2):137\u0026ndash;155\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLl\u0026agrave;cer P, Romero G, Trull\u0026agrave;s JC, de la Espriella R, Cobo M, Quiroga B et al (2024) Consensus on the approach to hydrosaline overload in acute heart failure. SEMI/SEC/S.E.N. recommendations. Rev Esp Cardiol (Engl Ed) 77(7):556\u0026ndash;565\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTromp J, Westenbrink BD, Ouwerkerk W, van Veldhuisen DJ, Samani NJ, Ponikowski P et al (2018) Identifying Pathophysiological Mechanisms in Heart Failure With Reduced Versus Preserved Ejection Fraction. J Am Coll Cardiol 72(10):1081\u0026ndash;1090\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBorlaug BA, Sharma K, Shah SJ, Ho JE (2023) Heart Failure With Preserved Ejection Fraction: JACC Scientific Statement. J Am Coll Cardiol 81(18):1810\u0026ndash;1834\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKapłon-Cieślicka A, Benson L, Chioncel O, Crespo-Leiro MG, Coats AJS, Anker SD et al (2022) A comprehensive characterization of acute heart failure with preserved versus mildly reduced versus reduced ejection fraction - insights from the ESC-HFA EORP Heart Failure Long-Term Registry. Eur J Heart Fail. ;24(2):335\u0026ndash;350. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/ejhf.2408\u003c/span\u003e\u003cspan address=\"10.1002/ejhf.2408\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Epub 2022 Jan 10. Erratum in: Eur J Heart Fail. 2023;25(3):443\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLang RM, Badano LP, Mor-Avi V, Afilalo J, Armstrong A, Ernande L et al (2015) Recommendations for cardiac chamber quantification by echocardiography in adults: an update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging. Eur Heart J Cardiovasc Imaging 16(3):233\u0026ndash;270\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHamo CE, DeJong C, Hartshorne-Evans N, Lund LH, Shah SJ, Solomon S et al (2024) Heart failure with preserved ejection fraction. 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Eur J Heart Fail 20(4):738\u0026ndash;747\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmbrosy AP, Bhatt AS, Gallup D, Anstrom KJ, Butler J, DeVore AD et al (2017) Trajectory of Congestion Metrics by Ejection Fraction in Patients With Acute Heart Failure (from the Heart Failure Network). Am J Cardiol 120(1):98\u0026ndash;105\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBosch L, Lam CSP, Gong L, Chan SP, Sim D, Yeo D et al (2017) Right ventricular dysfunction in left-sided heart failure with preserved versus reduced ejection fraction. Eur J Heart Fail 19(12):1664\u0026ndash;1671. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/ejhf.873\u003c/span\u003e\u003cspan address=\"10.1002/ejhf.873\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003eEpub 2017 Jun 8. PMID: 285\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGheorghiade M, Abraham WT, Albert NM, Greenberg BH, O'Connor CM, She L et al (2006) Systolic blood pressure at admission, clinical characteristics, and outcomes in patients hospitalized with acute heart failure. JAMA 296(18):2217\u0026ndash;2226\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIwanaga Y, Nishi I, Furuichi S, Noguchi T, Sase K, Kihara Y et al (2006) B-type natriuretic peptide strongly reflects diastolic wall stress in patients with chronic heart failure: comparison between systolic and diastolic heart failure. J Am Coll Cardiol 47(4):742\u0026ndash;748\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eN\u0026uacute;\u0026ntilde;ez J, de la Espriella R, Mi\u0026ntilde;ana G, Santas E, Ll\u0026aacute;cer P, N\u0026uacute;\u0026ntilde;ez E et al (2021) Antigen carbohydrate 125 as a biomarker in heart failure: a narrative review. Eur J Heart Fail 23(9):1445\u0026ndash;1457\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLl\u0026agrave;cer P, Gallardo M\u0026Aacute;, Palau P, Moreno MC, Castillo C, Fern\u0026aacute;ndez C et al (2021) Comparison between CA125 and NT-proBNP for evaluating congestion in acute heart failure. Med Clin (Barc) 156(12):589\u0026ndash;594\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJavaloyes P, Mir\u0026oacute; \u0026Ograve;, Gil V, Mart\u0026iacute;n-S\u0026aacute;nchez FJ, Jacob J, Herrero P et al (2019) Clinical phenotypes of acute heart failure based on signs and symptoms of perfusion and congestion at emergency department presentation and their relationship with patient management and outcomes. Eur J Heart Fail 21(11):1353\u0026ndash;1365\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQuiroz R, Doros G, Shaw P, Liang CS, Gauthier DF, Sam F (2014) Comparison of characteristics and outcomes of patients with heart failure preserved ejection fraction versus reduced left ventricular ejection fraction in an urban cohort. Am J Cardiol 113(4):691\u0026ndash;696\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTromp J, Westenbrink BD, Ouwerkerk W, van Veldhuisen DJ, Samani NJ, Ponikowski P et al (2018) Identifying Pathophysiological Mechanisms in Heart Failure With Reduced Versus Preserved Ejection Fraction. J Am Coll Cardiol 72(10):1081\u0026ndash;1090\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEidizadeh A, Schnelle M, Leha A, Edelmann F, Nolte K, Werhahn SM et al (2023) Biomarker profiles in heart failure with preserved vs. reduced ejection fraction: results from the DIAST-CHF study. ESC Heart Fail 10(1):200\u0026ndash;210\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"european-geriatric-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"EGEM","sideBox":"Learn more about [European Geriatric Medicine](https://www.springer.com/journal/41999)","snPcode":"41999","submissionUrl":"https://www.editorialmanager.com/egem/default2.aspx","title":"European Geriatric Medicine","twitterHandle":"","acdcEnabled":false,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"congestion, phenotypes, ejection fraction, older adults, acute heart failure","lastPublishedDoi":"10.21203/rs.3.rs-8544161/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8544161/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eCongestion is the main driver of worsening acute heart failure (AHF), yet whether congestion phenotypes differ by left ventricular ejection fraction (LVEF) in the elderly remains uncertain. This study aimed to characterize clinical, echocardiographic, and biomarker congestion profiles by LVEF phenotype (HFpEF\u0026thinsp;\u0026ge;\u0026thinsp;50% vs. HFrEF\u0026thinsp;\u0026lt;\u0026thinsp;50%) in older patients hospitalized with AHF and to examine their associations with prognosis.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted a retrospective cohort study of 830 consecutive patients admitted with AHF. Congestion was assessed clinically, by echocardiography, and through biomarkers (BNP, CA125) and point-of-care ultrasound. Outcomes included HF rehospitalization and all-cause mortality over a median follow-up of 310 days (IQR 62\u0026ndash;543). Cox models adjusted for multiple variables.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eMedian age was 87 years, 65.6% were women, and 81.7% had HFpEF. Traditional congestion signs and NYHA class were similar across phenotypes. HFrEF showed greater structural and functional remodeling, while diastolic indices and ultrasound congestion markers were comparable. BNP and CA125 concentrations were significantly higher in HFrEF. Overall, 301 patients (36.3%) were rehospitalized and 418 (50.4%) died. LVEF phenotype was not associated with rehospitalization or mortality. As a continuous variable, LVEF showed a modest positive association with rehospitalization (HR 1.013 per 1%; p\u0026thinsp;=\u0026thinsp;0.019), but was unrelated to mortality.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eIn very elderly patients hospitalized with AHF, HFpEF predominated and exhibited distinct biomarker and echocardiographic patterns despite similar clinical congestion. Prognosis was not determined by EF category but was mainly driven by age and congestion and myocardial stress biomarkers (CA125, BNP), with higher LVEF independently predicting rehospitalization.\u003c/p\u003e","manuscriptTitle":"Congestion Phenotypes in Elderly Patients with Acute Heart Failure: Distinct Patterns in Preserved vs. Reduced Ejection Fraction","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-19 09:12:26","doi":"10.21203/rs.3.rs-8544161/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revisions","date":"2026-02-18T05:32:12+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2026-01-19T07:32:42+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-14T10:49:37+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-13T05:48:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Geriatric Medicine","date":"2026-01-09T06:16:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-geriatric-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"EGEM","sideBox":"Learn more about [European Geriatric Medicine](https://www.springer.com/journal/41999)","snPcode":"41999","submissionUrl":"https://www.editorialmanager.com/egem/default2.aspx","title":"European Geriatric Medicine","twitterHandle":"","acdcEnabled":false,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"4c5ea156-a80c-438e-8673-6377f31701cf","owner":[],"postedDate":"January 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-06T10:29:06+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-19 09:12:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8544161","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8544161","identity":"rs-8544161","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}