Impact of an intensive follow up program on the outcome of acute heart failure patients hospitalized in internal medicine versus cardiology units

Impact of an intensive follow up program on the outcome of acute heart failure patients hospitalized in internal medicine versus cardiology units | 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 Impact of an intensive follow up program on the outcome of acute heart failure patients hospitalized in internal medicine versus cardiology units Giulia Antonelli, Giuseppe Pinto, Alessandro Villaschi, Gaia Filiberti, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7620058/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Background This study evaluates the efficacy of a post-discharge follow-up program in patients recovering from acute heart failure (AHF) hospitalized in internal medicine (IM) and in cardiology (CA) wards. Methods Patients hospitalized for AHF between June 2020 and November 2022 at a third-level center were retrospectively analyzed according to their hospitalization ward in CA vs IM. The primary endpoint was a composite of time to first HF hospitalization or cardiovascular (CV) death at 6 months, while secondary endpoints were its individual components, all-cause death and a composite of time to first HF hospitalization or all-cause mortality at 6 months. Results Out of 230 patients, 122 were hospitalized in CA and 108 in IM wards. Patients hospitalized in CA were younger and less frequently affected by extra-cardiac comorbidities compared to patients managed in IM. At 6 months, no difference in the primary endpoint was registered in the two groups (IM 16.6% vs CA 13.1%, log-rank p = 0.425; IR 37.5 per 100 p/y CI 23.7-59.6 vs 28.4 per 100 p/y CI 17.4-46.5; p = 0.523). Moreover, the cohorts did not differ for any of the secondary endpoints. A secondary analysis according both to ward of hospitalization and ejection fraction (> 40% vs ≤ 40%) did not show any significant difference in the primary composite outcome between the subgroups. Conclusion No difference in the risk of major adverse CV events were found among patients hospitalized in CA and IM wards during mid-term follow-up after the inclusion in a post-AHF follow up program. acute heart failure follow-up heart failure hospitalization guidelines-directed medical therapy acute decompensated heart failure heart failure team Figures Figure 1 Figure 2 Figure 3 Background Acute heart failure (AHF) is a life-threatening condition associated with high in-hospital mortality and vulnerability to adverse event during early follow up ( 1 ). The post-discharge period is critical: approximately one fourth of patients experience HF-related hospitalizations, particularly within the first six months, while one-year mortality remains high, affecting 25–30% of patients ( 1 , 2 ). Recent evidence demonstrated that a strict follow-up including serial cardiological visits and rapid guideline-directed medical therapy (GDMT) uptitration after an AHF episode has the potential to reduce mid-term all-cause mortality and hospital readmissions for HF ( 3 ). Consistently, the latest European guidelines have recommended at least one rapid post-discharge HF visit (i.e. within 1–2 weeks) and frequent follow-up for the first 6 weeks following an AHF episode ( 4 , 5 ). However, generalizability of randomized clinical trials is frequently limited and results may not be consistent in everyday clinical practice, as many other factors may contribute to determine the prognosis of AHF patients ( 6 ). Among such factors, hospitalization setting, cardiology (CA) versus non-CA wards may impact on subsequent clinical outcomes. A significant proportion of HF patients are currently managed outside of CA settings, which may lead to suboptimal up-titration of GDMT at discharge and to an increased risk of morbidity and mortality ( 7 – 10 ). Younger patients suffering from HF with reduced ejection fraction (HFrEF), particularly if of ischemic origin, are more likely to receive a specialist cardiological care. On the contrary, older patients with numerous extra-cardiac comorbidities and HF with preserved ejection fraction (HFpEF) are more likely managed by internal medicine (IM) physicians ( 8 , 10 ). The application of an intensive CA follow-up, including a structured post-discharge program for AHF patients based on in-person visits and phone calls by dedicated nursing staff, have been proposed as a feasible and effective strategy to improve patient management and outcomes, but whether such strategies may be effective in both contexts remains to be determined ( 11 – 13 ). This real-word study aims to evaluate whether the benefits of a specialized post-AHF discharge program may be extended to patients hospitalized both in IM and in CA wards, regardless of the ejection fraction (EF). Methods This single-center retrospective study included consecutive patients admitted to a tertiary care hospital for AHF between October 2020 and November 2022, hospitalized either in CA or IM department, and included in a specialized post-AHF follow-up program. Study population. The AHF program consists in a pre-discharge consultation with a HF specialist, followed by a structured follow-up including biweekly scheduled phone-calls from a nurse of the HF team and cardiological visits at one, four and ten months after discharge. The follow-up involves close monitoring of laboratory tests, GDMT uptitration, and diuretic dosage adjustments by a HF specialist ( Central illustration ). In this analysis, patients were stratified according to their hospitalization ward in CA versus IM group. Patients were deemed eligible for inclusion if they were aged > 18 years, had a hospitalization for AHF defined as signs or symptoms of HF with brain natriuretic peptide (BNP) levels > 100 pg/ml. Only patients discharged alive were included in the post-AHF follow-up program. Data collection. De-identified individual patients’ data (medical history, clinical presentation, echocardiography and laboratory findings, medical therapy and clinical outcomes) were retrospectively collected. Follow-up was performed by means of medical records or telephone contact. The study complied with the Declaration of Helsinki. Institutional review board approval was waived for this registry because of its retrospective design with collection of anonymized data and without any study-specific intervention. Clinical outcomes. The primary endpoint was the composite of first HF hospitalization or cardiovascular (CV) death at 180 days. Secondary endpoints included the individual components of the primary endpoint, all-cause death and a composite of first HF hospitalization and all-cause death at 180 days. Statistical analysis. Continuous variables were reported as mean and standard deviation (SD) or median and interquartile range (IQR) and were compared using the Student t test or the Mann-Whitney U test, respectively, on the basis of normality of data distribution, verified using the Shapiro-Wilk test. Categorical variables were reported as absolute number and percentage and were compared using the χ 2 test or the Fisher exact test, as appropriate. Medical therapy dosages were reported as percentage (%) of guidelines’ recommended target dose ( 4 ). Kaplan-Meier curves were used to present time to event for the primary composite endpoint and all the secondary endpoints according to ward of hospitalization and EF, and compared via a long-rank test. Incidence rates (IR) of composite primary endpoint, HF rehospitalization, CV death and all-cause death were also calculated and are presented as cases/100 patient-years (95% confidence interval (CI)) and compared via Fisher exact test. A subgroup analysis was also conducted, further stratifying the two cohorts according to EF value (> 40% vs ≤ 40%). Statistical analyses were performed with STATA 18 (StataCorp, College Station, TX). A p-value < 0.05 was considered statistically significant. Results Baseline characteristics. Out of 230 patients enrolled, 108 were hospitalized in IM and 122 in CA ward. Baseline clinical characteristics are reported in Table 1 . The median age was 78 years old (71.7–82.6 years), with men accounting for 54.8% of the study population. Table 1 Baseline characteristics Internal medicine 108 Cardiology 122 Total 230 p-value Male 53 (49.1%) 73 (59.8%) 126 (54.8%) 0.102 Age (years) 80.9 (74.6–84.4) 75.6 (66.7–81.8) 78.0 (71.7–82.6) 0.001 Smoke history No Yes Previous 51 (47.2%) 16 (14.8%) 41 (38.0%) 64 (52.5%) 23 (18.9%) 35 (28.7%) 115 (50.0%) 39 (17.0%) 76 (33.0%) 0.308 Hypertension 98 (90.7%) 100 (81.9%) 198 (86.1%) 0.055 Dyslipidemia 72 (66.7%) 73 (60.3%) 145 (63.3%) 0.321 Diabetes 53 (49.1%) 41 (33.6%) 94 (40.9%) 0.017 PAD 48 (44.4%) 40 (32.8%) 88 (38.3%) 0.069 Prior stroke 17 (15.7%) 13 (10.7%) 30 (13.1%) 0.263 Prior AF 53 (49.1%) 67 (54.9%) 120 (52.2%) 0.376 Type_AF Paroxysmal Persistent Permanent 32 (55.2%) 3 (5.2%) 23 (39.7%) 56 (73.7%) 5 (6.6%) 15 (19.7%) 88 (65.7%) 8 (6.0%) 38 (28.4%) 0.040 Prior AF ablation 4 (3.8%) 8 (6.7%) 12 (5.3%) 0.341 Prior MI 19 (17.6%) 26 (21.3%) 45 (19.6%) 0.478 Prior CAD 33 (30.6%) 41 (33.6%) 74 (32.2%) 0.621 Prior PCI 25 (23.4%) 35 (28.7%) 60 (26.2%) 0.361 Prior CABG 8 (7.4%) 10 (8.2%) 18 (7.8%) 0.824 Prior surgical valve intervention 11 (10.2%) 11 (9.0%) 22 (9.6%) 0.764 Prior percutaneous valve intervention 1 (0.9%) 4 (3.3%) 5 (2.2%) 0.222 Prior myocarditis 0 (0.0%) 1 (0.8%) 1 (0.4%) 0.346 Ischemic etiology 33 (30.6%) 48 (39.3%) 81 (35.2%) 0.164 PM 17 (15.7%) 14 (11.5%) 31 (13.5%) 0.344 ICD 4 (3.7%) 6 (4.9%) 10 (4.3%) 0.652 CRT-D 5 (4.6%) 12 (9.8%) 17 (7.4%) 0.132 CRT-P 0 (0.0%) 2 (1.7%) 2 (0.9%) 0.180 COPD 34 (31.5%) 23 (18.9%) 57 (24.8%) 0.027 CKD 61 (56.5%) 52 (42.6%) 113 (49.1%) 0.036 Dialysis 1 (0.9%) 0 (0.0%) 1 (0.4%) 0.287 Cancer 25 (23.1%) 25 (20.5%) 50 (21.7%) 0.626 EF (%) 48 (35–55) 35.5 (30–52) 42 (32–55) 0.001 EF ≤ 40% (%) 38 (35.2%) 71 (58.2%) 109 (47.3%) 0.001 First HF hospitalization 62 (57.4%) 76 (62.3%) 138 (60%) 0.774 EHMRG Score 1.2 (0.038-3) 0.5 (0.027-2.3) 0.9 (0.03–2.5) 0.059 Abbreviations. PAD (peripheral artery disease); AF (atrial fibrillation); MI (myocardial infarction); CAD (coronary artery disease); PCI (percutaneous coronary intervention); CABG (coronary artery bypass graft); PM (pacemaker); ICD (implantable cardioverter defibrillator); CRT-D (cardiac resynchronization therapy with defibrillator); CRT-P (cardiac resynchronization therapy with pacemaker); COPD (chronic obstructive pulmonary disease); CKD (chronic kidney disease); EF (ejection fraction). CV risk factors and comorbidities were extremely prevalent among the global cohort. Overall, 86% of patients had hypertension, 63% had dyslipidemia, and 40% had diabetes (49.1% of IM group vs 33.6% of CA group; p = 0.017). Ischemic cardiomyopathy was the main etiology of HF in 30% of the IM group and in 39% of the CA group (p = 0.164). Approximately 60% of patients were experiencing their first hospitalization for AHF, with no significant differences between the two groups. IM patients were older (80.9 vs 75.6 years, p = 0.001) and more frequently affected by non-cardiac comorbidities, such as chronic kidney disease (56.5% vs 42.6%; p = 0.036) and chronic obstructive pulmonary disease (31.5% vs 18.9%; p = 0.027), as compared to CA patients. IM patients had a higher mean EF value [48% (35–55) vs 35.5% (30–52), p = 0.001] and lower HFrEF prevalence [38 (35.2%) vs 71 (58.1%), p = 0.001]. Characteristics at discharge, including blood test analysis, GDMT introduction and uptitration, and diuretic therapy are described in Table 2 . At discharge, no difference in beta-blockers (88% in IM vs 89.3% in CA group, p = 0.758), renin-angiotensin system inhibitors (49.1% in IM vs 54.9% in CA group, p = 0.376) and loop diuretics (97.2% in IM vs 92.6% in CA group, p = 0.117) assumption was found between the two groups. On the contrary, IM inpatients were less frequently treated with mineralocorticoid receptor antagonists (MRA) [75.9% (82) vs 89.3% (109); p = 0.007] and sodium-glucose transporter-2 inhibitors (SGLT2i) [13.9% ( 15 ) vs 27% (33); p = 0.014] compared to CA group. Moreover, IM patients less frequently achieved ≥ 50% of the recommended target dose of beta-blockers [50% (25-72.5) vs 72.5% (37.5–100); p = 0.002]. Table 2 Laboratory and therapeutical features of patients at discharge Internal Medicine Cardiology Total population p-value N 108 (47%) 122 (53%) 230 (100%) BNP (pg/ml) 301 (160–540) 383 (229–610) 342 (172.5–602) 0.874 Na+ mmol/L 140 (137–142) 138 (136–140) 139 (137–141) < 0.001 Creatinin mg/dL 1.37 (0.99–1.68) 1.3 (1-1.77) 1.35 (1-1.71) 0.926 Urea mg/dL 69 (53–95) 69 (52–99) 69 (53–99) 0.872 Hemoglobin g/dL 11 (9.95–12.75) 12 (10.4–13.6) 11.6 (10.2–13.2) 0.011 White blood cell 6.98 (5.92–8.94) 7.56 (6.34–9.1) 7.37 (6.14–9.1) 0.331 Platelets 213.5 (166–278) 212.5 (181–270) 212.5 (172–273) 0.670 AST 23 (19–36) 27 (21–50) 26 (20–44) 0.183 ALT 20 (14–30) 27.5 (17–59) 22 (15–49) 0.038 Beta-blocker 95 (88.0%) 108 (89.3%) 203 (88.6%) 0.758 Beta-blocker dosage (% of target dose) 50 (25-72.5) 72.5 (37.5–100) 50 (25–100) 0.002 RASi 53 (49.1%) 67 (54.9%) 120 (52.2%) 0.376 RASi dosage (% of target dose) 50 (33–100) 50 (33–50) 50 (33–66) 0.307 MRA 82 (75.9%) 109 (89.3%) 191 (83.0%) 0.007 MRA dosage (% of target dose) 50 (50–100) 50 (50–100) 50 (50–100) 0.672 SGLT2 inhibitors 15 (13.9%) 33 (27.0%) 48 (20.9%) 0.014 Loop diuretics 105 (97.2%) 113 (92.6%) 218 (94.8%) 0.117 Furosemide 18 (16.7%) 12 (9.8%) 30 (13.0%) 0.125 Torasemide 88 (81.5%) 106 (86.9%) 194 (84.3%) 0.290 Daily furosemide dosage (mg) 50 (25–100) 50 (25–100) 50 (25–100) 0.715 Daily torasemide dosage (mg) 15 ( 10 – 20 ) 10 ( 5 – 20 ) 10 ( 10 – 20 ) 0.135 Thiazidic diuretics 2 (1.9%) 1 (0.8%) 3 (1.3%) 0.491 Abbreviations. BNP (brain natriuretic peptide); ARNI (angiotensin receptor-neprilysin inhibitors); ACEi (angiotensin-converting enzyme inhibitors); ARB (angiotensin II receptor blockers); MRA (mineralocorticoid receptor antatagonists). Clinical outcomes. At 180 days, the primary endpoint occurred in 18 patients in the IM group and 16 in the CA group (16.6% vs. 13.1%; log-rank p = 0.425; IR 37.5 per 100 p/y, 95% CI 23.7–59.6 vs. IR 28.5 per 100 p/y 95% CI 17.4–46.5; p = 0.523) ( Fig. 1 ) ( Table 3 ). Table 3 Clinical outcomes of patients according to ward of hospitalization Internal Medicine (108) Cardiology (122) Total population (230) p-value N of events IR (p/y) 95% CI N of events IR (p/y) 95% CI N of events IR (p/y) 95% CI Primary endpoint 18 (16.6%) 37.5 23.7–59.6 16 (13.1%) 28.5 17.4–46.5 34 (14.7%) 32.6 23.3–45.7 0.523 HF hospitalization 13 (12%) 27.1 15.7–46.7 10 (8.2%) 17.8 9.6–33.0 23 (10%) 22.0 14.7–33.2 0.423 CV death 9 (8.3%) 17.7 9.2–34.0 10 (8.2%) 17.1 9.2–31.8 19 (8.3%) 17.4 11.1–27.2 1.00 All-cause death 14 (12.9%) 27.5 16.3–46.5 10 (8.2%) 17.1 9.2–31.8 24 (10.4%) 21.9 14.7–32.7 0.339 HF hospitalization + all-cause death 23 (21.3%) 47.96 31.9–72.2 17 (13.9%) 30.2 18.8–48.7 40 (17.4%) 38.39 28.2–52.3 0.195 Abbreviations . HF (heart failure); CV (cardiovascular). No significant difference in terms of CV death (8.3% vs. 8.2% log-rank p = 0.937; IR 17.7 per 100 p/y, 95% CI 9.2–34 vs. 17.1 per 100 p/y, 95% 9.2–31.8; p = 1.00) or first HF hospitalization (12% vs. 8.2% log-rank p = 0.323; IR 27.1 per 100 p/y, 95% CI 15.7–46.5 vs. 17.8 per 100 p/y, 95% CI 9.6–33.1 p = 0.423) was found between the two groups. All-cause death was reported in 12.9% of the IM group and 8.2% of the CA group (log-rank p = 0.247; IR 27.5 per 100 p/y, 95% CI 16.3–46.5 vs 17.1 per 100 p/y, 95% CI 9.2–31.8; p = 0.339). The composite secondary endpoint, including HF hospitalization and all-cause death, occurred in 21.3% of patients hospitalized in IM vs 13.9% in CA (log-rank p = 0.149; IR 48.0 per 100 p/y, 95% CI 31.9–72.2 vs IR 30.2 per 100 p/y, CI 18.8–48.7; p = 0.195) ( Fig. 2 ) ( Table 3 ). Subgroup analysis. After stratifying the two cohorts of patients according to EF value (> 40% vs. ≤40%), no difference in the primary endpoint was observed at 180 days [17.1% (IR 39.5 per 100 p/y, 95% CI 22.4–69.5) in IM with EF > 40%, 15.8% (IR 34.2 per 100 p/y 95%, CI 15.4–76.1) in IM with EF ≤ 40%, 17.7% (IR 39.3 per 100 p/y, 95% CI 20.5–75.5) in CA with EF > 40%, and 9.9% (IR 21.0 per 100 p/y, 95% CI 10.0-44.1) in CA with EF ≤ 40%; p = 0.551] ( Fig. 3 and Table 4 ). Table 4 Clinical outcomes of patients according to ward of hospitalizations and reduced ejection fraction IM and EF > 40% (70) IM and EF 40% (51) CA and EF < 40% (71) logrank p-value N of events IR (p/y) 95% CI N of events IR (p/y) 95% CI N of events IR (p/y) 95% CI N of events IR (p/y) 95% CI Primary endpoint 12 (17.1%) 39.5 22.4–69.5 6 (15.8%) 34.2 15.4–76.1 9 (17.7%) 39.3 20.5–75.5 7 (9.9%) 21.0 10.0-44.06 0.551 Abbreviations . IM (internal medicine); CA (cardiology); EF (ejection fraction); HF (heart failure); CV (cardiovascular). Discussion In this retrospective cohort study of 230 patients discharged after hospitalization for AHF and enrolled in a structured, cardiological follow-up program, no significant difference in mid-term clinical outcomes was found between patients admitted to CA versus IM wards. Despite clinically relevant differences in baseline characteristics, treatment implementation, and titration of GDMT, the incidence of HF rehospitalization, CV mortality, and all-cause mortality at 6 months was comparable between the two groups. Ward of hospitalization and patient characteristics AHF is a major health concern, associated with high morbidity and mortality driven by both the severity of the condition and the extreme frailty of patients generally affected ( 9 ). Disparities in the baseline characteristics of the patients with AHF appear to significantly influence decisions regarding hospital admission setting, and CA and IM wards appear to be treating distinct phenotypes of AHF ( 9 ). In our study, patients hospitalized in IM wards were significantly older and had a greater burden of non-cardiac comorbidities, including chronic kidney disease and chronic obstructive pulmonary disease. These features are known predictors of worse outcomes in HF and typically reflect higher clinical complexity ( 14 ). Conversely, patients in CA wards were more likely to have HFrEF, with higher prevalence of ischemic heart disease as the underlying etiology, which has been frequently associated with worse clinical outcomes ( 15 , 16 ). These findings do not solely apply to our institution: our data align with previous evidence reporting that older patients with more comorbidities and higher EF values are more frequently admitted to non-cardiological departments, while younger patients with HFrEF are commonly managed in CA units ( 17 , 18 ). Female gender and atrial fibrillation have also been associated to hospitalizations in IM rather than CA wards, but our study found no significant difference between the two groups in this regard ( 10 , 17 ). Although early initiation and uptitration of GDMT have been shown to reduce mortality and prevent rehospitalizations, its implementation is frequently delayed, and only a minority of patients achieve the recommended target doses within one year—primarily due to therapeutic inertia ( 19 ). Moreover, heterogeneity of GDMT prescription was observed across hospitalization units, with patients admitted to IM wards for AHF less frequently discharged on GDMT compared to patients managed in CA units ( 9 ). In agreement with previous evidence, our study demonstrated that IM patients were less likely to be discharged on key GDMT classes, particularly MRA and SGLT2i and had lower rates of beta-blocker uptitration. These findings reflect real-world challenges in optimizing GDMT in older and more comorbid populations, although differences in HF phenotypes might partially explain lower use of GDMT ( 20 , 21 ). Implementation of an AHF follow-up program and clinical outcomes Despite these differences, the primary outcome of time to first HF hospitalization or CV death at 180 days did not differ significantly between the groups. Similarly, no differences were found in the individual components of the primary endpoint, including HF readmission and CV mortality, or in the broader composite endpoint including all-cause death. Our data appear to be in contrast with previous evidence, despite similar patient characteristics. A prospective study including 302 consecutive patients admitted for ADHF − 45% of whom were in IM departments – identified IM hospitalization as an independent predictor of in-hospital mortality and 1-month hospital readmission (OR 3.74, CI 95% 1.72–8.12, p = 0.001) Moreover, patients discharged from IM departments had increased risk of HF rehospitalizations at six months compared to patients discharged from CA units (log-rank p = 0.001) ( 7 ). Similarly, in a retrospective multicenter study, Selim et al. found lower rates of both all-cause and HF readmissions at 30 days in CA patients; while a secondary analysis of the EAHFE (Epidemiology of Acute Heart Failure in Emergency Department) registry observed that HFpEF patients recovering from an AHF episode treated in IM wards had increased 30-days mortality risk compared to those hospitalized in CA ( 17 , 18 ). A possible explanation to these discrepancies may be found in the implementation of a CA-driven AHF follow-up program, that was not applied in any of the aforementioned studies. In our study, all patients discharged following an AHF episode received structured follow-up care managed by a CA team, which included regular telephone contact by a dedicated nursing staff and in-person evaluations by HF specialists. This comprehensive approach not only may explain the differences in clinical outcomes observed between our findings and those reported in earlier studies; but also underlies the potential efficacy of a dedicated post-AHF follow-up across different hospitalization settings. As recently demonstrated by the STRONG-HF trial, a structured follow-up —including frequent CA visits and rapid up-titration of GDMT—significantly reduces mid-term all-cause mortality and AHF-related readmissions in patients hospitalized for AHF, with such effect possibly explained by the intensity of follow-up rather than GDMT uptitration alone ( 3 , 22 ). Additionally, an HF-specific patient education by dedicated nurses is effective in reducing HF-related hospitalizations ( 23 ). However, the intensive follow-up regimen applied in randomized clinical trial is frequently difficult to implement in a real-world setting, contributing to the peculiar vulnerability of the post-discharge period. To address this gap, our post-AHF follow-up program was designed to reduce the disparities associated with the heterogeneous clinical management of AHF observed in clinical practice, particularly across different departments. By implementing intensive care after follow-up, as well as nurse-led patient education, it demonstrated the ability to mitigate such differences, with an effect that remained consistent across the spectrum of EF, confirming previous evidence.( 24 ) Limitations . This study has limitations that should be acknowledged. First, it reflects a single-center experience with a relatively small sample size, which limits the generalizability of the findings. Additionally, the retrospective design inherently carries methodological constraints, allowing only hypothesis generation. Moreover, to assess the effectiveness of the follow-up program, patients who died during the index hospitalization for AHF were excluded from the analysis, so that study population does not reflect the entirety of the HF population hospitalized at our center. Moreover, although we used incidence rates and Kaplan–Meier estimates, the relatively low number of events may reduce the power of subgroup analyses and a longer follow up may be needed to assess the long-term impact of in-hospital differences and follow up program. Conclusion In a real-world cohort of patients discharged after an episode of AHF, clinical outcomes at 6 months were comparable between patients admitted to CA versus IM wards, despite significant differences in baseline characteristics and GDMT implementation. These findings emphasize the potential of intensive follow-up programs to harmonize care and improve outcomes for AHF patients across hospital settings and advocates for future prospective evidence to validate these results. Declarations All authors disclose all relationships or interests that could have direct or potential influence or impart bias on the work. References Kimmoun A, Takagi K, Gall E, Ishihara S, Hammoum P, El Bèze N, et al. Temporal trends in mortality and readmission after acute heart failure: a systematic review and meta-regression in the past four decades. Eur J Heart Fail. 2021 Mar 1;23(3):420–31. Butt JH, Fosbøl EL, Gerds TA, Andersson C, McMurray JJV, Petrie MC, et al. Readmission and death in patients admitted with new-onset versus worsening of chronic heart failure: insights from a nationwide cohort. Eur J Heart Fail. 2020;22(10). Mebazaa A, Davison B, Chioncel O, Cohen-Solal A, Diaz R, Filippatos G, et al. Safety, tolerability and efficacy of up-titration of guideline-directed medical therapies for acute heart failure (STRONG-HF): a multinational, open-label, randomised, trial. The Lancet. 2022 Dec 3;400(10367):1938–52. McDonagh TA, Metra M, Adamo M, Baumbach A, Böhm M, Burri H, et al. 2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure. Vol. 42, European Heart Journal. Oxford University Press; 2021. p. 3599–726. McDonagh TA, Metra M, Adamo M, Gardner RS, Baumbach A, Böhm M, et al. 2023 Focused Update of the 2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure: Developed by the task force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC) With the special contribution of the Heart Failure Association (HFA) of the ESC. Eur J Heart Fail. 2024;26(1). Lim YMF, Molnar M, Vaartjes I, Savarese G, Eijkemans MJC, Uijl A, et al. Generalizability of randomized controlled trials in heart failure with reduced ejection fraction. Eur Heart J Qual Care Clin Outcomes. 2022;8(7). Bazmpani MA, Papanastasiou CA, Giampatzis V, Kamperidis V, Zegkos T, Zebekakis P, et al. Differences in Demographics, in-Hospital Management and Short-Term Prognosis in Admissions for Acutely Decompensated Heart Failure to Cardiology vs. Internal Medicine Departments: A Prospective Study. J Cardiovasc Dev Dis. 2023 Aug 1;10(8). Kapelios CJ, Canepa M, Benson L, Hage C, Thorvaldsen T, Dahlström U, et al. Non-cardiology vs. cardiology care of patients with heart failure and reduced ejection fraction is associated with lower use of guideline-based care and higher mortality: Observations from The Swedish Heart Failure Registry. Int J Cardiol. 2021 Nov 15;343:63–72. Maymon SL, Moravsky G, Marcus G, Shuvy M, Pereg D, Epstein D, et al. Disparities in the characteristics and outcomes of patients hospitalized with acute decompensated heart failure admitted to internal medicine and cardiology departments: a single-centre, retrospective cohort study. ESC Heart Fail. 2021 Feb 1;8(1):390–8. Ricciardi E, La Malfa G, Guglielmi G, Cenni E, Micali M, Corsello LM, et al. Characteristics of current heart failure patients admitted to internal medicine vs. cardiology hospital units: the VASCO study. Intern Emerg Med. 2020 Oct 1;15(7):1219–29. Atienza F, Anguita M, Martinez-Alzamora N, Osca J, Ojeda S, Almenar L, et al. Multicenter randomized trial of a comprehensive hospital discharge and outpatient heart failure management program. Eur J Heart Fail. 2004 Aug;6(5):643–52. Cyrille NB, Patel SR. Late In-Hospital Management of Patients Hospitalized with Acute Heart Failure. Vol. 60, Progress in Cardiovascular Diseases. W.B. Saunders; 2017. p. 198–204. Donaho EK, Hall AC, Gass JA, Elayda MA, Lee VV, Paire S, et al. Protocol-driven allied health post-discharge transition clinic to reduce hospital readmissions in heart failure. J Am Heart Assoc. 2015 Dec 1;4(12). Becher PM, Lindberg F, Benson L, Hage C, Dahlström U, Rosenkranz S, et al. Phenotyping patients with chronic obstructive pulmonary disease and heart failure. ESC Heart Fail. 2025 Apr 1;12(2):900–11. Villaschi A, Pagnesi M, Stolfo D, Baldetti L, Lombardi CM, Adamo M, et al. Ischemic Etiology in Advanced Heart Failure: Insight from the HELP-HF Registry. American Journal of Cardiology. 2023 Oct 1;204:268–75. Vedin O, Lam CSP, Koh AS, Benson L, Teng THK, Tay WT, et al. Significance of Ischemic Heart Disease in Patients with Heart Failure and Preserved, Midrange, and Reduced Ejection Fraction: A Nationwide Cohort Study. Circ Heart Fail. 2017 Jun 1;10(6). Selim AM, Mazurek JA, Iqbal M, Wang D, Negassa A, Zolty R. Mortality and Readmission Rates in Patients Hospitalized for Acute Decompensated Heart Failure: A Comparison between Cardiology and General-Medicine Service Outcomes in an Underserved Population. Clin Cardiol. 2015 Mar 1;38(3):131–8. Miró Ò, Gil V íctor, Martín-Sánchez FJ, Jacob J, Herrero P, Alquézar A, et al. Short-term outcomes of heart failure patients with reduced and preserved ejection fraction after acute decompensation according to the final destination after emergency department care. Clinical Research in Cardiology. 2018 Aug 1;107(8):698–710. Savarese G, Kishi T, Vardeny O, Adamsson Eryd S, Bodegård J, Lund LH, et al. Heart Failure Drug Treatment—Inertia, Titration, and Discontinuation: A Multinational Observational Study (EVOLUTION HF). JACC Heart Fail. 2023 Jan 1;11(1):1–14. Barry AR, Grewal M, Blain L. Use of Guideline-Directed Medical Therapy in Patients Aged 80 Years or Older With Heart Failure With Reduced Ejection Fraction. CJC Open. 2023 Apr 1;5(4):303–9. Villaschi A, Chiarito M, Pagnesi M, Stolfo D, Baldetti L, Lombardi CM, et al. Frailty according to the 2019 HFA-ESC definition in patients at risk for advanced heart failure: Insights from the HELP-HF registry. Eur J Heart Fail. 2024 Jun 1;26(6):1399–407. Reza N. High-Intensity Care Versus GDMT Titration: Which Rapidly Improves Health Status in Patients with Heart Failure? Vol. 17, Circulation: Heart Failure. Lippincott Williams and Wilkins; 2024. p. E011627. Mathew S, Thukha H. Pilot testing of the effectiveness of nurse-guided, patient-centered heart failure education for older adults. Geriatr Nurs (Minneap). 2018 Jul 1;39(4):376–81. Pagnesi M, Metra M, Cohen-Solal A, Edwards C, Adamo M, Tomasoni D, et al. Uptitrating Treatment After Heart Failure Hospitalization Across the Spectrum of Left Ventricular Ejection Fraction. J Am Coll Cardiol. 2023 Jun 6;81(22):2131–44. Supplementary Files Centralillustration.png Central illustration. A. Post-AHF follow-up structure comprising regular phone calls every two weeks from a dedicated HF team nurse and scheduled cardiological visits (1 month, 4 months and 10 months after discharge). B. Difference in patient characteristics hospitalized in internal medicine and cardiology wards. C. Kaplan-Meier curve of the primary endpoint according to ward of hospitalization at 180 days. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 28 Sep, 2025 Reviewers invited by journal 28 Sep, 2025 Editor assigned by journal 17 Sep, 2025 First submitted to journal 16 Sep, 2025 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-7620058","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":521842517,"identity":"5b010a51-103f-46ae-9834-a5bdfea90ce6","order_by":0,"name":"Giulia Antonelli","email":"","orcid":"","institution":"Humanitas Research Hospital","correspondingAuthor":false,"prefix":"","firstName":"Giulia","middleName":"","lastName":"Antonelli","suffix":""},{"id":521842518,"identity":"5ced2d6f-4137-40cd-aaf6-759c4c923a50","order_by":1,"name":"Giuseppe 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14:14:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":214475,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan-Meier curves of secondary endpoints according to ward of hospitalization at 180 days\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA \u003c/strong\u003eHeart failure hospitalization;\u003cstrong\u003e B \u003c/strong\u003eCardiovascular death;\u003cstrong\u003e C \u003c/strong\u003eAll-cause death;\u003cstrong\u003e D\u003c/strong\u003e Composite secondary endpoint (including heart failure hospitalization + all-cause death).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7620058/v1/c67d98daf69766e6bb273ec0.png"},{"id":93335283,"identity":"d8c09964-e77d-4f06-a806-e911d86a9596","added_by":"auto","created_at":"2025-10-12 13:58:18","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":155279,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan-Meier curves of the primary endpoint according to ward of hospitalization and ejection fraction (≤ or \u0026gt; 40%) at 180 days\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7620058/v1/3cd031e11f484d87b4a4e85f.png"},{"id":93340194,"identity":"a557a5f2-cf7b-4472-b0b3-eb17135e6fd1","added_by":"auto","created_at":"2025-10-12 14:30:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1482143,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7620058/v1/4a6a6a09-0259-4eab-8e96-c960533a7733.pdf"},{"id":93335285,"identity":"5857a475-1062-45ba-ac92-2b9d6610822e","added_by":"auto","created_at":"2025-10-12 13:58:18","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":205484,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCentral illustration. \u003c/strong\u003eA. Post-AHF follow-up structure comprising regular phone calls every two weeks from a dedicated HF team nurse and scheduled cardiological visits (1 month, 4 months and 10 months after discharge).\u003c/p\u003e\n\u003cp\u003eB. Difference in patient characteristics hospitalized in internal medicine and cardiology wards.\u003c/p\u003e\n\u003cp\u003eC. Kaplan-Meier curve of the primary endpoint according to ward of hospitalization at 180 days.\u003c/p\u003e","description":"","filename":"Centralillustration.png","url":"https://assets-eu.researchsquare.com/files/rs-7620058/v1/6e371e2b69451ae3dcbefe7a.png"}],"financialInterests":"","formattedTitle":"Impact of an intensive follow up program on the outcome of acute heart failure patients hospitalized in internal medicine versus cardiology units","fulltext":[{"header":"Background","content":"\u003cp\u003eAcute heart failure (AHF) is a life-threatening condition associated with high in-hospital mortality and vulnerability to adverse event during early follow up (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). The post-discharge period is critical: approximately one fourth of patients experience HF-related hospitalizations, particularly within the first six months, while one-year mortality remains high, affecting 25\u0026ndash;30% of patients (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eRecent evidence demonstrated that a strict follow-up including serial cardiological visits and rapid guideline-directed medical therapy (GDMT) uptitration after an AHF episode has the potential to reduce mid-term all-cause mortality and hospital readmissions for HF (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Consistently, the latest European guidelines have recommended at least one rapid post-discharge HF visit (i.e. within 1\u0026ndash;2 weeks) and frequent follow-up for the first 6 weeks following an AHF episode (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). However, generalizability of randomized clinical trials is frequently limited and results may not be consistent in everyday clinical practice, as many other factors may contribute to determine the prognosis of AHF patients (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAmong such factors, hospitalization setting, cardiology (CA) versus non-CA wards may impact on subsequent clinical outcomes. A significant proportion of HF patients are currently managed outside of CA settings, which may lead to suboptimal up-titration of GDMT at discharge and to an increased risk of morbidity and mortality (\u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eYounger patients suffering from HF with reduced ejection fraction (HFrEF), particularly if of ischemic origin, are more likely to receive a specialist cardiological care. On the contrary, older patients with numerous extra-cardiac comorbidities and HF with preserved ejection fraction (HFpEF) are more likely managed by internal medicine (IM) physicians (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe application of an intensive CA follow-up, including a structured post-discharge program for AHF patients based on in-person visits and phone calls by dedicated nursing staff, have been proposed as a feasible and effective strategy to improve patient management and outcomes, but whether such strategies may be effective in both contexts remains to be determined (\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis real-word study aims to evaluate whether the benefits of a specialized post-AHF discharge program may be extended to patients hospitalized both in IM and in CA wards, regardless of the ejection fraction (EF).\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e This single-center retrospective study included consecutive patients admitted to a tertiary care hospital for AHF between October 2020 and November 2022, hospitalized either in CA or IM department, and included in a specialized post-AHF follow-up program.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStudy population.\u003c/b\u003e The AHF program consists in a pre-discharge consultation with a HF specialist, followed by a structured follow-up including biweekly scheduled phone-calls from a nurse of the HF team and cardiological visits at one, four and ten months after discharge. The follow-up involves close monitoring of laboratory tests, GDMT uptitration, and diuretic dosage adjustments by a HF specialist (\u003cb\u003eCentral illustration\u003c/b\u003e).\u003c/p\u003e\u003cp\u003e In this analysis, patients were stratified according to their hospitalization ward in CA versus IM group. Patients were deemed eligible for inclusion if they were aged\u0026thinsp;\u0026gt;\u0026thinsp;18 years, had a hospitalization for AHF defined as signs or symptoms of HF with brain natriuretic peptide (BNP) levels\u0026thinsp;\u0026gt;\u0026thinsp;100 pg/ml. Only patients discharged alive were included in the post-AHF follow-up program.\u003c/p\u003e\u003cp\u003e\u003cb\u003eData collection.\u003c/b\u003e De-identified individual patients\u0026rsquo; data (medical history, clinical presentation, echocardiography and laboratory findings, medical therapy and clinical outcomes) were retrospectively collected. Follow-up was performed by means of medical records or telephone contact. The study complied with the Declaration of Helsinki. Institutional review board approval was waived for this registry because of its retrospective design with collection of anonymized data and without any study-specific intervention.\u003c/p\u003e\u003cp\u003e\u003cb\u003eClinical outcomes.\u003c/b\u003e The primary endpoint was the composite of first HF hospitalization or cardiovascular (CV) death at 180 days. Secondary endpoints included the individual components of the primary endpoint, all-cause death and a composite of first HF hospitalization and all-cause death at 180 days.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStatistical analysis.\u003c/b\u003e Continuous variables were reported as mean and standard deviation (SD) or median and interquartile range (IQR) and were compared using the Student \u003cem\u003et\u003c/em\u003e test or the Mann-Whitney \u003cem\u003eU\u003c/em\u003e test, respectively, on the basis of normality of data distribution, verified using the Shapiro-Wilk test. Categorical variables were reported as absolute number and percentage and were compared using the χ\u003csup\u003e2\u003c/sup\u003e test or the Fisher exact test, as appropriate. Medical therapy dosages were reported as percentage (%) of guidelines\u0026rsquo; recommended target dose (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eKaplan-Meier curves were used to present time to event for the primary composite endpoint and all the secondary endpoints according to ward of hospitalization and EF, and compared via a long-rank test. Incidence rates (IR) of composite primary endpoint, HF rehospitalization, CV death and all-cause death were also calculated and are presented as cases/100 patient-years (95% confidence interval (CI)) and compared via Fisher exact test. A subgroup analysis was also conducted, further stratifying the two cohorts according to EF value (\u0026gt;\u0026thinsp;40% vs\u0026thinsp;\u0026le;\u0026thinsp;40%). Statistical analyses were performed with STATA 18 (StataCorp, College Station, TX). A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eBaseline characteristics.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eOut of 230 patients enrolled, 108 were hospitalized in IM and 122 in CA ward. Baseline clinical characteristics are reported in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The median age was 78 years old (71.7\u0026ndash;82.6 years), with men accounting for 54.8% of the study population.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline characteristics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInternal medicine\u003c/p\u003e\u003cp\u003e108\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCardiology\u003c/p\u003e\u003cp\u003e122\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003cp\u003e230\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e53 (49.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e73 (59.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e126 (54.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.102\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e80.9 (74.6\u0026ndash;84.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e75.6 (66.7\u0026ndash;81.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e78.0 (71.7\u0026ndash;82.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoke history\u003c/p\u003e\u003cp\u003eNo\u003c/p\u003e\u003cp\u003eYes\u003c/p\u003e\u003cp\u003ePrevious\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026nbsp;\u003c/p\u003e\u003cp\u003e51 (47.2%)\u003c/p\u003e\u003cp\u003e16 (14.8%)\u003c/p\u003e\u003cp\u003e41 (38.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026nbsp;\u003c/p\u003e\u003cp\u003e64 (52.5%)\u003c/p\u003e\u003cp\u003e23 (18.9%)\u003c/p\u003e\u003cp\u003e35 (28.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026nbsp;\u003c/p\u003e\u003cp\u003e115 (50.0%)\u003c/p\u003e\u003cp\u003e39 (17.0%)\u003c/p\u003e\u003cp\u003e76 (33.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026nbsp;\u003c/p\u003e\u003cp\u003e0.308\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e98 (90.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e100 (81.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e198 (86.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.055\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDyslipidemia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e72 (66.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e73 (60.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e145 (63.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.321\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e53 (49.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e41 (33.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e94 (40.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.017\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePAD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e48 (44.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e40 (32.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e88 (38.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.069\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrior stroke\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17 (15.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13 (10.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30 (13.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.263\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrior AF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e53 (49.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e67 (54.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e120 (52.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.376\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eType_AF\u003c/p\u003e\u003cp\u003eParoxysmal\u003c/p\u003e\u003cp\u003ePersistent\u003c/p\u003e\u003cp\u003ePermanent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026nbsp;\u003c/p\u003e\u003cp\u003e32 (55.2%)\u003c/p\u003e\u003cp\u003e3 (5.2%)\u003c/p\u003e\u003cp\u003e23 (39.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026nbsp;\u003c/p\u003e\u003cp\u003e56 (73.7%)\u003c/p\u003e\u003cp\u003e5 (6.6%)\u003c/p\u003e\u003cp\u003e15 (19.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026nbsp;\u003c/p\u003e\u003cp\u003e88 (65.7%)\u003c/p\u003e\u003cp\u003e8 (6.0%)\u003c/p\u003e\u003cp\u003e38 (28.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026nbsp;\u003c/p\u003e\u003cp\u003e\u003cb\u003e0.040\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrior AF ablation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4 (3.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8 (6.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12 (5.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.341\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrior MI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19 (17.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26 (21.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e45 (19.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.478\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrior CAD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33 (30.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e41 (33.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e74 (32.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.621\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrior PCI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25 (23.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e35 (28.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e60 (26.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.361\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrior CABG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8 (7.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10 (8.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18 (7.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.824\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrior surgical valve intervention\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11 (10.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11 (9.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22 (9.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.764\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrior percutaneous valve intervention\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (0.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4 (3.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5 (2.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.222\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrior myocarditis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1 (0.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (0.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.346\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIschemic etiology\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33 (30.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e48 (39.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e81 (35.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.164\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17 (15.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14 (11.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31 (13.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.344\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eICD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4 (3.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6 (4.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10 (4.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.652\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCRT-D\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 (4.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12 (9.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17 (7.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.132\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCRT-P\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2 (1.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (0.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.180\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCOPD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34 (31.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e23 (18.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e57 (24.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.027\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCKD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e61 (56.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e52 (42.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e113 (49.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.036\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDialysis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (0.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0 (0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (0.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.287\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25 (23.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e25 (20.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e50 (21.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.626\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEF (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e48 (35\u0026ndash;55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e35.5 (30\u0026ndash;52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e42 (32\u0026ndash;55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEF\u0026thinsp;\u0026le;\u0026thinsp;40% (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e38 (35.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e71 (58.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e109 (47.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirst HF hospitalization\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e62 (57.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e76 (62.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e138 (60%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.774\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEHMRG Score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.2 (0.038-3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.5 (0.027-2.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9 (0.03\u0026ndash;2.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.059\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eAbbreviations.\u003c/em\u003e PAD (peripheral artery disease); AF (atrial fibrillation); MI (myocardial infarction); CAD (coronary artery disease); PCI (percutaneous coronary intervention); CABG (coronary artery bypass graft); PM (pacemaker); ICD (implantable cardioverter defibrillator); CRT-D (cardiac resynchronization therapy with defibrillator); CRT-P (cardiac resynchronization therapy with pacemaker); COPD (chronic obstructive pulmonary disease); CKD (chronic kidney disease); EF (ejection fraction).\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eCV risk factors and comorbidities were extremely prevalent among the global cohort. Overall, 86% of patients had hypertension, 63% had dyslipidemia, and 40% had diabetes (49.1% of IM group vs 33.6% of CA group; p\u0026thinsp;=\u0026thinsp;0.017). Ischemic cardiomyopathy was the main etiology of HF in 30% of the IM group and in 39% of the CA group (p\u0026thinsp;=\u0026thinsp;0.164). Approximately 60% of patients were experiencing their first hospitalization for AHF, with no significant differences between the two groups. IM patients were older (80.9 vs 75.6 years, p\u0026thinsp;=\u0026thinsp;0.001) and more frequently affected by non-cardiac comorbidities, such as chronic kidney disease (56.5% vs 42.6%; p\u0026thinsp;=\u0026thinsp;0.036) and chronic obstructive pulmonary disease (31.5% vs 18.9%; p\u0026thinsp;=\u0026thinsp;0.027), as compared to CA patients. IM patients had a higher mean EF value [48% (35\u0026ndash;55) vs 35.5% (30\u0026ndash;52), p\u0026thinsp;=\u0026thinsp;0.001] and lower HFrEF prevalence [38 (35.2%) vs 71 (58.1%), p\u0026thinsp;=\u0026thinsp;0.001].\u003c/p\u003e\u003cp\u003eCharacteristics at discharge, including blood test analysis, GDMT introduction and uptitration, and diuretic therapy are described in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. At discharge, no difference in beta-blockers (88% in IM vs 89.3% in CA group, p\u0026thinsp;=\u0026thinsp;0.758), renin-angiotensin system inhibitors (49.1% in IM vs 54.9% in CA group, p\u0026thinsp;=\u0026thinsp;0.376) and loop diuretics (97.2% in IM vs 92.6% in CA group, p\u0026thinsp;=\u0026thinsp;0.117) assumption was found between the two groups. On the contrary, IM inpatients were less frequently treated with mineralocorticoid receptor antagonists (MRA) [75.9% (82) vs 89.3% (109); p\u0026thinsp;=\u0026thinsp;0.007] and sodium-glucose transporter-2 inhibitors (SGLT2i) [13.9% (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) vs 27% (33); p\u0026thinsp;=\u0026thinsp;0.014] compared to CA group. Moreover, IM patients less frequently achieved\u0026thinsp;\u0026ge;\u0026thinsp;50% of the recommended target dose of beta-blockers [50% (25-72.5) vs 72.5% (37.5\u0026ndash;100); p\u0026thinsp;=\u0026thinsp;0.002].\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eLaboratory and therapeutical features of patients at discharge\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInternal Medicine\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCardiology\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTotal population\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e108 (47%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e122 (53%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e230 (100%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBNP\u003c/p\u003e\u003cp\u003e(pg/ml)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e301 (160\u0026ndash;540)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e383 (229\u0026ndash;610)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e342 (172.5\u0026ndash;602)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.874\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNa+\u003c/p\u003e\u003cp\u003emmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e140 (137\u0026ndash;142)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e138 (136\u0026ndash;140)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e139 (137\u0026ndash;141)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCreatinin\u003c/p\u003e\u003cp\u003emg/dL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.37 (0.99\u0026ndash;1.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.3 (1-1.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.35 (1-1.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.926\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrea\u003c/p\u003e\u003cp\u003emg/dL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e69 (53\u0026ndash;95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e69 (52\u0026ndash;99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e69 (53\u0026ndash;99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.872\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHemoglobin\u003c/p\u003e\u003cp\u003eg/dL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11 (9.95\u0026ndash;12.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (10.4\u0026ndash;13.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.6 (10.2\u0026ndash;13.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhite blood cell\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.98 (5.92\u0026ndash;8.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.56 (6.34\u0026ndash;9.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.37 (6.14\u0026ndash;9.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.331\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlatelets\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e213.5 (166\u0026ndash;278)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e212.5 (181\u0026ndash;270)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e212.5 (172\u0026ndash;273)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.670\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23 (19\u0026ndash;36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27 (21\u0026ndash;50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26 (20\u0026ndash;44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.183\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20 (14\u0026ndash;30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27.5 (17\u0026ndash;59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22 (15\u0026ndash;49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.038\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBeta-blocker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e95 (88.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e108 (89.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e203 (88.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.758\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBeta-blocker dosage\u003c/p\u003e\u003cp\u003e(% of target dose)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50 (25-72.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e72.5 (37.5\u0026ndash;100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e50 (25\u0026ndash;100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRASi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e53 (49.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e67 (54.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e120 (52.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.376\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRASi dosage\u003c/p\u003e\u003cp\u003e(% of target dose)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50 (33\u0026ndash;100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50 (33\u0026ndash;50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e50 (33\u0026ndash;66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.307\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMRA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e82 (75.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e109 (89.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e191 (83.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMRA dosage\u003c/p\u003e\u003cp\u003e(% of target dose)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50 (50\u0026ndash;100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50 (50\u0026ndash;100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e50 (50\u0026ndash;100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.672\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSGLT2 inhibitors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15 (13.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33 (27.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48 (20.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLoop diuretics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e105 (97.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e113 (92.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e218 (94.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.117\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFurosemide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18 (16.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (9.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30 (13.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.125\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTorasemide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e88 (81.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e106 (86.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e194 (84.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.290\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDaily furosemide dosage\u003c/p\u003e\u003cp\u003e(mg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50 (25\u0026ndash;100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50 (25\u0026ndash;100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e50 (25\u0026ndash;100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.715\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDaily torasemide dosage\u003c/p\u003e\u003cp\u003e(mg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15 (\u003cspan additionalcitationids=\"CR11 CR12 CR13 CR14 CR15 CR16 CR17 CR18 CR19\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 (\u003cspan additionalcitationids=\"CR6 CR7 CR8 CR9 CR10 CR11 CR12 CR13 CR14 CR15 CR16 CR17 CR18 CR19\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10 (\u003cspan additionalcitationids=\"CR11 CR12 CR13 CR14 CR15 CR16 CR17 CR18 CR19\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.135\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThiazidic diuretics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (1.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (0.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3 (1.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.491\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eAbbreviations.\u003c/em\u003e BNP (brain natriuretic peptide); ARNI (angiotensin receptor-neprilysin inhibitors); ACEi (angiotensin-converting enzyme inhibitors); ARB (angiotensin II receptor blockers); MRA (mineralocorticoid receptor antatagonists).\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eClinical outcomes.\u003c/b\u003e At 180 days, the primary endpoint occurred in 18 patients in the IM group and 16 in the CA group (16.6% vs. 13.1%; log-rank p\u0026thinsp;=\u0026thinsp;0.425; IR 37.5 per 100 p/y, 95% CI 23.7\u0026ndash;59.6 vs. IR 28.5 per 100 p/y 95% CI 17.4\u0026ndash;46.5; p\u0026thinsp;=\u0026thinsp;0.523) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e) (\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eClinical outcomes of patients according to ward of hospitalization\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eInternal Medicine\u003c/p\u003e\u003cp\u003e(108)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eCardiology\u003c/p\u003e\u003cp\u003e(122)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u003cp\u003eTotal population\u003c/p\u003e\u003cp\u003e(230)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN of events\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIR (p/y)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eN of events\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eIR (p/y)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eN of events\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eIR (p/y)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary endpoint\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18 (16.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23.7\u0026ndash;59.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16 (13.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e28.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e17.4\u0026ndash;46.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e34 (14.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e32.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e23.3\u0026ndash;45.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.523\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHF hospitalization\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13 (12%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.7\u0026ndash;46.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10 (8.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e17.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9.6\u0026ndash;33.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e23 (10%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e22.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e14.7\u0026ndash;33.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.423\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCV death\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9 (8.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.2\u0026ndash;34.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10 (8.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e17.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9.2\u0026ndash;31.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e19 (8.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e17.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e11.1\u0026ndash;27.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAll-cause death\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14 (12.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16.3\u0026ndash;46.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10 (8.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e17.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9.2\u0026ndash;31.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e24 (10.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e21.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e14.7\u0026ndash;32.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.339\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHF hospitalization\u0026thinsp;+\u0026thinsp;all-cause death\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23 (21.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e47.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31.9\u0026ndash;72.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17 (13.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e30.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e18.8\u0026ndash;48.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e40 (17.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e38.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e28.2\u0026ndash;52.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.195\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"11\"\u003e\u003cem\u003eAbbreviations\u003c/em\u003e. HF (heart failure); CV (cardiovascular).\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eNo significant difference in terms of CV death (8.3% vs. 8.2% log-rank p\u0026thinsp;=\u0026thinsp;0.937; IR 17.7 per 100 p/y, 95% CI 9.2\u0026ndash;34 vs. 17.1 per 100 p/y, 95% 9.2\u0026ndash;31.8; p\u0026thinsp;=\u0026thinsp;1.00) or first HF hospitalization (12% vs. 8.2% log-rank p\u0026thinsp;=\u0026thinsp;0.323; IR 27.1 per 100 p/y, 95% CI 15.7\u0026ndash;46.5 vs. 17.8 per 100 p/y, 95% CI 9.6\u0026ndash;33.1 p\u0026thinsp;=\u0026thinsp;0.423) was found between the two groups. All-cause death was reported in 12.9% of the IM group and 8.2% of the CA group (log-rank p\u0026thinsp;=\u0026thinsp;0.247; IR 27.5 per 100 p/y, 95% CI 16.3\u0026ndash;46.5 vs 17.1 per 100 p/y, 95% CI 9.2\u0026ndash;31.8; p\u0026thinsp;=\u0026thinsp;0.339).\u003c/p\u003e\u003cp\u003eThe composite secondary endpoint, including HF hospitalization and all-cause death, occurred in 21.3% of patients hospitalized in IM vs 13.9% in CA (log-rank p\u0026thinsp;=\u0026thinsp;0.149; IR 48.0 per 100 p/y, 95% CI 31.9\u0026ndash;72.2 vs IR 30.2 per 100 p/y, CI 18.8\u0026ndash;48.7; p\u0026thinsp;=\u0026thinsp;0.195) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e) (\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eSubgroup analysis.\u003c/b\u003e After stratifying the two cohorts of patients according to EF value (\u0026gt;\u0026thinsp;40% vs. \u0026le;40%), no difference in the primary endpoint was observed at 180 days [17.1% (IR 39.5 per 100 p/y, 95% CI 22.4\u0026ndash;69.5) in IM with EF\u0026thinsp;\u0026gt;\u0026thinsp;40%, 15.8% (IR 34.2 per 100 p/y 95%, CI 15.4\u0026ndash;76.1) in IM with EF\u0026thinsp;\u0026le;\u0026thinsp;40%, 17.7% (IR 39.3 per 100 p/y, 95% CI 20.5\u0026ndash;75.5) in CA with EF\u0026thinsp;\u0026gt;\u0026thinsp;40%, and 9.9% (IR 21.0 per 100 p/y, 95% CI 10.0-44.1) in CA with EF\u0026thinsp;\u0026le;\u0026thinsp;40%; p\u0026thinsp;=\u0026thinsp;0.551] \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e \u003cb\u003eand\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eClinical outcomes of patients according to ward of hospitalizations and reduced ejection fraction\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"14\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eIM and EF\u0026thinsp;\u0026gt;\u0026thinsp;40%\u003c/p\u003e\u003cp\u003e(70)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eIM and EF\u0026thinsp;\u0026lt;\u0026thinsp;40%\u003c/p\u003e\u003cp\u003e(38)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u003cp\u003eCA and EF\u0026thinsp;\u0026gt;\u0026thinsp;40%\u003c/p\u003e\u003cp\u003e(51)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u003cp\u003eCA and EF\u0026thinsp;\u0026lt;\u0026thinsp;40%\u003c/p\u003e\u003cp\u003e(71)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c14\"\u003e\u003cp\u003elogrank\u003c/p\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eN of events\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eIR (p/y)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eN of events\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eIR (p/y)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003eN of events\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003eIR (p/y)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003eN of events\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u003cb\u003eIR (p/y)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary endpoint\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12 (17.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22.4\u0026ndash;69.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6 (15.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e34.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e15.4\u0026ndash;76.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e9 (17.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e39.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e20.5\u0026ndash;75.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e7 (9.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e21.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e10.0-44.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e0.551\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"14\"\u003e\u003cem\u003eAbbreviations\u003c/em\u003e. IM (internal medicine); CA (cardiology); EF (ejection fraction); HF (heart failure); CV (cardiovascular).\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this retrospective cohort study of 230 patients discharged after hospitalization for AHF and enrolled in a structured, cardiological follow-up program, no significant difference in mid-term clinical outcomes was found between patients admitted to CA versus IM wards. Despite clinically relevant differences in baseline characteristics, treatment implementation, and titration of GDMT, the incidence of HF rehospitalization, CV mortality, and all-cause mortality at 6 months was comparable between the two groups.\u003c/p\u003e\n\u003ch3\u003eWard of hospitalization and patient characteristics\u003c/h3\u003e\n\u003cp\u003eAHF is a major health concern, associated with high morbidity and mortality driven by both the severity of the condition and the extreme frailty of patients generally affected (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDisparities in the baseline characteristics of the patients with AHF appear to significantly influence decisions regarding hospital admission setting, and CA and IM wards appear to be treating distinct phenotypes of AHF (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn our study, patients hospitalized in IM wards were significantly older and had a greater burden of non-cardiac comorbidities, including chronic kidney disease and chronic obstructive pulmonary disease. These features are known predictors of worse outcomes in HF and typically reflect higher clinical complexity (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Conversely, patients in CA wards were more likely to have HFrEF, with higher prevalence of ischemic heart disease as the underlying etiology, which has been frequently associated with worse clinical outcomes (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThese findings do not solely apply to our institution: our data align with previous evidence reporting that older patients with more comorbidities and higher EF values are more frequently admitted to non-cardiological departments, while younger patients with HFrEF are commonly managed in CA units (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Female gender and atrial fibrillation have also been associated to hospitalizations in IM rather than CA wards, but our study found no significant difference between the two groups in this regard (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAlthough early initiation and uptitration of GDMT have been shown to reduce mortality and prevent rehospitalizations, its implementation is frequently delayed, and only a minority of patients achieve the recommended target doses within one year\u0026mdash;primarily due to therapeutic inertia (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Moreover, heterogeneity of GDMT prescription was observed across hospitalization units, with patients admitted to IM wards for AHF less frequently discharged on GDMT compared to patients managed in CA units (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). In agreement with previous evidence, our study demonstrated that IM patients were less likely to be discharged on key GDMT classes, particularly MRA and SGLT2i and had lower rates of beta-blocker uptitration. These findings reflect real-world challenges in optimizing GDMT in older and more comorbid populations, although differences in HF phenotypes might partially explain lower use of GDMT (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eImplementation of an AHF follow-up program and clinical outcomes\u003c/h3\u003e\n\u003cp\u003eDespite these differences, the primary outcome of time to first HF hospitalization or CV death at 180 days did not differ significantly between the groups. Similarly, no differences were found in the individual components of the primary endpoint, including HF readmission and CV mortality, or in the broader composite endpoint including all-cause death.\u003c/p\u003e\u003cp\u003eOur data appear to be in contrast with previous evidence, despite similar patient characteristics.\u003c/p\u003e\u003cp\u003eA prospective study including 302 consecutive patients admitted for ADHF \u0026minus;\u0026thinsp;45% of whom were in IM departments \u0026ndash; identified IM hospitalization as an independent predictor of in-hospital mortality and 1-month hospital readmission (OR 3.74, CI 95% 1.72\u0026ndash;8.12, p\u0026thinsp;=\u0026thinsp;0.001) Moreover, patients discharged from IM departments had increased risk of HF rehospitalizations at six months compared to patients discharged from CA units (log-rank p\u0026thinsp;=\u0026thinsp;0.001) (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSimilarly, in a retrospective multicenter study, Selim \u003cem\u003eet al.\u003c/em\u003e found lower rates of both all-cause and HF readmissions at 30 days in CA patients; while a secondary analysis of the EAHFE (Epidemiology of Acute Heart Failure in Emergency Department) registry observed that HFpEF patients recovering from an AHF episode treated in IM wards had increased 30-days mortality risk compared to those hospitalized in CA (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eA possible explanation to these discrepancies may be found in the implementation of a CA-driven AHF follow-up program, that was not applied in any of the aforementioned studies.\u003c/p\u003e\u003cp\u003e In our study, all patients discharged following an AHF episode received structured follow-up care managed by a CA team, which included regular telephone contact by a dedicated nursing staff and in-person evaluations by HF specialists. This comprehensive approach not only may explain the differences in clinical outcomes observed between our findings and those reported in earlier studies; but also underlies the potential efficacy of a dedicated post-AHF follow-up across different hospitalization settings.\u003c/p\u003e\u003cp\u003eAs recently demonstrated by the STRONG-HF trial, a structured follow-up \u0026mdash;including frequent CA visits and rapid up-titration of GDMT\u0026mdash;significantly reduces mid-term all-cause mortality and AHF-related readmissions in patients hospitalized for AHF, with such effect possibly explained by the intensity of follow-up rather than GDMT uptitration alone (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAdditionally, an HF-specific patient education by dedicated nurses is effective in reducing HF-related hospitalizations (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHowever, the intensive follow-up regimen applied in randomized clinical trial is frequently difficult to implement in a real-world setting, contributing to the peculiar vulnerability of the post-discharge period. To address this gap, our post-AHF follow-up program was designed to reduce the disparities associated with the heterogeneous clinical management of AHF observed in clinical practice, particularly across different departments. By implementing intensive care after follow-up, as well as nurse-led patient education, it demonstrated the ability to mitigate such differences, with an effect that remained consistent across the spectrum of EF, confirming previous evidence.(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e)\u003c/p\u003e\u003cp\u003e\u003cb\u003eLimitations\u003c/b\u003e. This study has limitations that should be acknowledged. First, it reflects a single-center experience with a relatively small sample size, which limits the generalizability of the findings. Additionally, the retrospective design inherently carries methodological constraints, allowing only hypothesis generation. Moreover, to assess the effectiveness of the follow-up program, patients who died during the index hospitalization for AHF were excluded from the analysis, so that study population does not reflect the entirety of the HF population hospitalized at our center. Moreover, although we used incidence rates and Kaplan\u0026ndash;Meier estimates, the relatively low number of events may reduce the power of subgroup analyses and a longer follow up may be needed to assess the long-term impact of in-hospital differences and follow up program.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn a real-world cohort of patients discharged after an episode of AHF, clinical outcomes at 6 months were comparable between patients admitted to CA versus IM wards, despite significant differences in baseline characteristics and GDMT implementation. These findings emphasize the potential of intensive follow-up programs to harmonize care and improve outcomes for AHF patients across hospital settings and advocates for future prospective evidence to validate these results.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAll authors disclose all relationships or interests that could have direct or potential influence or impart bias on the work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKimmoun A, Takagi K, Gall E, Ishihara S, Hammoum P, El B\u0026egrave;ze N, et al. Temporal trends in mortality and readmission after acute heart failure: a systematic review and meta-regression in the past four decades. Eur J Heart Fail. 2021 Mar 1;23(3):420\u0026ndash;31. \u003c/li\u003e\n\u003cli\u003eButt JH, Fosb\u0026oslash;l EL, Gerds TA, Andersson C, McMurray JJV, Petrie MC, et al. Readmission and death in patients admitted with new-onset versus worsening of chronic heart failure: insights from a nationwide cohort. Eur J Heart Fail. 2020;22(10). \u003c/li\u003e\n\u003cli\u003eMebazaa A, Davison B, Chioncel O, Cohen-Solal A, Diaz R, Filippatos G, et al. Safety, tolerability and efficacy of up-titration of guideline-directed medical therapies for acute heart failure (STRONG-HF): a multinational, open-label, randomised, trial. The Lancet. 2022 Dec 3;400(10367):1938\u0026ndash;52. \u003c/li\u003e\n\u003cli\u003eMcDonagh TA, Metra M, Adamo M, Baumbach A, B\u0026ouml;hm M, Burri H, et al. 2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure. Vol. 42, European Heart Journal. Oxford University Press; 2021. p. 3599\u0026ndash;726. \u003c/li\u003e\n\u003cli\u003eMcDonagh TA, Metra M, Adamo M, Gardner RS, Baumbach A, B\u0026ouml;hm M, et al. 2023 Focused Update of the 2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure: Developed by the task force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC) With the special contribution of the Heart Failure Association (HFA) of the ESC. Eur J Heart Fail. 2024;26(1). \u003c/li\u003e\n\u003cli\u003eLim YMF, Molnar M, Vaartjes I, Savarese G, Eijkemans MJC, Uijl A, et al. Generalizability of randomized controlled trials in heart failure with reduced ejection fraction. Eur Heart J Qual Care Clin Outcomes. 2022;8(7). \u003c/li\u003e\n\u003cli\u003eBazmpani MA, Papanastasiou CA, Giampatzis V, Kamperidis V, Zegkos T, Zebekakis P, et al. Differences in Demographics, in-Hospital Management and Short-Term Prognosis in Admissions for Acutely Decompensated Heart Failure to Cardiology vs. Internal Medicine Departments: A Prospective Study. J Cardiovasc Dev Dis. 2023 Aug 1;10(8). \u003c/li\u003e\n\u003cli\u003eKapelios CJ, Canepa M, Benson L, Hage C, Thorvaldsen T, Dahlstr\u0026ouml;m U, et al. Non-cardiology vs. cardiology care of patients with heart failure and reduced ejection fraction is associated with lower use of guideline-based care and higher mortality: Observations from The Swedish Heart Failure Registry. Int J Cardiol. 2021 Nov 15;343:63\u0026ndash;72. \u003c/li\u003e\n\u003cli\u003eMaymon SL, Moravsky G, Marcus G, Shuvy M, Pereg D, Epstein D, et al. Disparities in the characteristics and outcomes of patients hospitalized with acute decompensated heart failure admitted to internal medicine and cardiology departments: a single-centre, retrospective cohort study. ESC Heart Fail. 2021 Feb 1;8(1):390\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003eRicciardi E, La Malfa G, Guglielmi G, Cenni E, Micali M, Corsello LM, et al. Characteristics of current heart failure patients admitted to internal medicine vs. cardiology hospital units: the VASCO study. Intern Emerg Med. 2020 Oct 1;15(7):1219\u0026ndash;29. \u003c/li\u003e\n\u003cli\u003eAtienza F, Anguita M, Martinez-Alzamora N, Osca J, Ojeda S, Almenar L, et al. Multicenter randomized trial of a comprehensive hospital discharge and outpatient heart failure management program. Eur J Heart Fail. 2004 Aug;6(5):643\u0026ndash;52. \u003c/li\u003e\n\u003cli\u003eCyrille NB, Patel SR. Late In-Hospital Management of Patients Hospitalized with Acute Heart Failure. Vol. 60, Progress in Cardiovascular Diseases. W.B. Saunders; 2017. p. 198\u0026ndash;204. \u003c/li\u003e\n\u003cli\u003eDonaho EK, Hall AC, Gass JA, Elayda MA, Lee VV, Paire S, et al. Protocol-driven allied health post-discharge transition clinic to reduce hospital readmissions in heart failure. J Am Heart Assoc. 2015 Dec 1;4(12). \u003c/li\u003e\n\u003cli\u003eBecher PM, Lindberg F, Benson L, Hage C, Dahlstr\u0026ouml;m U, Rosenkranz S, et al. Phenotyping patients with chronic obstructive pulmonary disease and heart failure. ESC Heart Fail. 2025 Apr 1;12(2):900\u0026ndash;11. \u003c/li\u003e\n\u003cli\u003eVillaschi A, Pagnesi M, Stolfo D, Baldetti L, Lombardi CM, Adamo M, et al. Ischemic Etiology in Advanced Heart Failure: Insight from the HELP-HF Registry. American Journal of Cardiology. 2023 Oct 1;204:268\u0026ndash;75. \u003c/li\u003e\n\u003cli\u003eVedin O, Lam CSP, Koh AS, Benson L, Teng THK, Tay WT, et al. Significance of Ischemic Heart Disease in Patients with Heart Failure and Preserved, Midrange, and Reduced Ejection Fraction: A Nationwide Cohort Study. Circ Heart Fail. 2017 Jun 1;10(6). \u003c/li\u003e\n\u003cli\u003eSelim AM, Mazurek JA, Iqbal M, Wang D, Negassa A, Zolty R. Mortality and Readmission Rates in Patients Hospitalized for Acute Decompensated Heart Failure: A Comparison between Cardiology and General-Medicine Service Outcomes in an Underserved Population. Clin Cardiol. 2015 Mar 1;38(3):131\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003eMir\u0026oacute; \u0026Ograve;, Gil V \u0026iacute;ctor, Mart\u0026iacute;n-S\u0026aacute;nchez FJ, Jacob J, Herrero P, Alqu\u0026eacute;zar A, et al. Short-term outcomes of heart failure patients with reduced and preserved ejection fraction after acute decompensation according to the final destination after emergency department care. Clinical Research in Cardiology. 2018 Aug 1;107(8):698\u0026ndash;710. \u003c/li\u003e\n\u003cli\u003eSavarese G, Kishi T, Vardeny O, Adamsson Eryd S, Bodeg\u0026aring;rd J, Lund LH, et al. Heart Failure Drug Treatment\u0026mdash;Inertia, Titration, and Discontinuation: A Multinational Observational Study (EVOLUTION HF). JACC Heart Fail. 2023 Jan 1;11(1):1\u0026ndash;14. \u003c/li\u003e\n\u003cli\u003eBarry AR, Grewal M, Blain L. Use of Guideline-Directed Medical Therapy in Patients Aged 80 Years or Older With Heart Failure With Reduced Ejection Fraction. CJC Open. 2023 Apr 1;5(4):303\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eVillaschi A, Chiarito M, Pagnesi M, Stolfo D, Baldetti L, Lombardi CM, et al. Frailty according to the 2019 HFA-ESC definition in patients at risk for advanced heart failure: Insights from the HELP-HF registry. Eur J Heart Fail. 2024 Jun 1;26(6):1399\u0026ndash;407. \u003c/li\u003e\n\u003cli\u003eReza N. High-Intensity Care Versus GDMT Titration: Which Rapidly Improves Health Status in Patients with Heart Failure? Vol. 17, Circulation: Heart Failure. Lippincott Williams and Wilkins; 2024. p. E011627. \u003c/li\u003e\n\u003cli\u003eMathew S, Thukha H. Pilot testing of the effectiveness of nurse-guided, patient-centered heart failure education for older adults. Geriatr Nurs (Minneap). 2018 Jul 1;39(4):376\u0026ndash;81. \u003c/li\u003e\n\u003cli\u003ePagnesi M, Metra M, Cohen-Solal A, Edwards C, Adamo M, Tomasoni D, et al. Uptitrating Treatment After Heart Failure Hospitalization Across the Spectrum of Left Ventricular Ejection Fraction. J Am Coll Cardiol. 2023 Jun 6;81(22):2131\u0026ndash;44. \u003c/li\u003e\n\u003c/ol\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":"internal-and-emergency-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"iaem","sideBox":"Learn more about [Internal and Emergency Medicine](http://link.springer.com/journal/11739)","snPcode":"11739","submissionUrl":"https://www.editorialmanager.com/iaem/default.aspx","title":"Internal and Emergency Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"acute heart failure, follow-up, heart failure hospitalization, guidelines-directed medical therapy, acute decompensated heart failure, heart failure team","lastPublishedDoi":"10.21203/rs.3.rs-7620058/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7620058/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study evaluates the efficacy of a post-discharge follow-up program in patients recovering from acute heart failure (AHF) hospitalized in internal medicine (IM) and in cardiology (CA) wards.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients hospitalized for AHF between June 2020 and November 2022 at a third-level center were retrospectively analyzed according to their hospitalization ward in CA vs IM. The primary endpoint was a composite of time to first HF hospitalization or cardiovascular (CV) death at 6 months, while secondary endpoints were its individual components, all-cause death and a composite of time to first HF hospitalization or all-cause mortality at 6 months.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOut of 230 patients, 122 were hospitalized in CA and 108 in IM wards. Patients hospitalized in CA were younger and less frequently affected by extra-cardiac comorbidities compared to patients managed in IM.\u003c/p\u003e\n\u003cp\u003eAt 6 months, no difference in the primary endpoint was registered in the two groups (IM 16.6% vs CA 13.1%, log-rank p = 0.425; IR 37.5 per 100 p/y CI 23.7-59.6 vs 28.4 per 100 p/y CI 17.4-46.5; p = 0.523). Moreover, the cohorts did not differ for any of the secondary endpoints. A secondary analysis according both to ward of hospitalization and ejection fraction (\u0026gt; 40% vs ≤ 40%) did not show any significant difference in the primary composite outcome between the subgroups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo difference in the risk of major adverse CV events were found among patients hospitalized in CA and IM wards during mid-term follow-up after the inclusion in a post-AHF follow up program.\u003c/p\u003e","manuscriptTitle":"Impact of an intensive follow up program on the outcome of acute heart failure patients hospitalized in internal medicine versus cardiology units","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-12 13:58:13","doi":"10.21203/rs.3.rs-7620058/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-09-28T22:29:14+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-28T20:08:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-17T13:53:07+00:00","index":"","fulltext":""},{"type":"submitted","content":"Internal and Emergency Medicine","date":"2025-09-16T14:51:04+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"internal-and-emergency-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"iaem","sideBox":"Learn more about [Internal and Emergency Medicine](http://link.springer.com/journal/11739)","snPcode":"11739","submissionUrl":"https://www.editorialmanager.com/iaem/default.aspx","title":"Internal and Emergency Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"3ed0f456-f362-4348-b5e1-9c16d3c3ac6e","owner":[],"postedDate":"October 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-06T09:29:06+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-12 13:58:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7620058","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7620058","identity":"rs-7620058","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}