Prognostic Factors Related to All-Cause Mortality in Very Long-term Follow-up of Patients with Heart Failure: The REMADHE Trial Extended Analysis

Prognostic Factors Related to All-Cause Mortality in Very Long-term Follow-up of Patients with Heart Failure: The REMADHE Trial Extended Analysis | 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 Prognostic Factors Related to All-Cause Mortality in Very Long-term Follow-up of Patients with Heart Failure: The REMADHE Trial Extended Analysis Edimar Alcides Bocchi, Guilherme Veiga Guimaraes, Cristhian Espinoza Romero, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8484135/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background Disease management programs (DMP) have reduced hospitalizations and improved quality of life in heart failure (HF). However, prognostic factors and survival in very long-term follow-up (> 20 years) have not been reported. Aims To identify prognostic predictors of all-cause mortality in patients with HF followed for 23.6 years. Methods The REMADHE trial (NCT00505050, 2007-07-20) was a prospective, single-center, randomized trial (n = 412) comparing DMP versus usual care (C) with initial follow-up of 2.47 years. This extended analysis followed patients for 23.6 years to identify prognostic predictors of all-cause mortality. Results The all-cause mortality rate was 88.3%. HF was the first cause of death followed by sudden death. Mortality was higher in the first 6-year follow-up. The predictive variables in multivariate analysis associated with mortality were age > 52 years (P = 0.015), Chagas etiology (P = 0.010), LVEF < 45% (P = 0.008), digoxin use (P = 0.002), NYHA IV (P = 0.01), blood urea nitrogen (BUN) (P = 0.03), and lymphopenia (P = 0.005). In very long-term follow-up, DMP did not affect mortality in patients under guideline-directed medical therapy (GDMT). HF as a cause of death was more frequent in the C group. Conclusions DMP was not effective in reducing very long-term mortality; however, causes of death differed between groups, with more HF-related deaths in controls. Our findings that age, LVEF, Chagas' disease, NYHA, renal function, lymphocytes, and digoxin use were associated with poor prognosis could influence future strategies to improve HF management. Heart failure very long-term follow-up Chagas disease prognosis renal Figures Figure 1 Figure 2 Figure 3 Figure 4 Highlights • This is the first study reporting prognostic factors in heart failure with follow-up exceeding 20 years • Independent mortality predictors included age > 52 years, Chagas disease, LVEF < 45%, digoxin use, NYHA IV, elevated BUN, and lymphopenia • Disease management programs did not affect very long-term mortality when both groups received specialized heart failure clinic care • Distribution of causes of death differed between groups, with more heart failure deaths in controls and more sudden deaths in the intervention group • Identified prognostic factors can guide long-term risk stratification and management strategies in heart failure patients Introduction Heart failure (HF) has an estimated prevalence of 1 to 4% of the global population 1 and remains associated with poor quality of life, high mortality, hospitalizations, and a substantial burden on the healthcare system. HF may have heterogeneous causes and pathways. However, HF trials and observational survival studies conducted worldwide have relatively limited short follow-up periods. 2–4 Few studies have assessed the long-term survival impact of HF beyond a 10-year period. 5–11 Thus, data regarding very long-term survival (> 20 years) and respective prognostic factors in HF are lacking (therefore, prognostic and survival indicators over 20 years are scarce or practically non-existent.) Educational and disease management programs (DMP) targeted at patients with HF have reported improvement in quality of life, and reduction in hospitalization and healthcare utilization. 11–13 However, doubt has been cast on the efficacy of these interventions in the medium and long term based on several published neutral studies. 14 , In fact, very long-term efficacy of DMP in HF is unknown. Extended pivotal trials on treatment of HF have been shown heterogeneous concordance of the results in comparison with the first published trial. 15–17 Accordingly, also extended DMP trial development should be warranted. The REMADHE trial was conducted initially during a mean follow-up period of 2.47 ± 1.75 years. The study demonstrated improvements in quality of life, reductions in hospitalizations and emergency visits among the DMP group, without statistical differences in mortality rates between the groups. 13 The REMADHE trial had a smaller number of recruited patients in comparison with multicenter trials with consequent limited number of events mainly for mortality. A trial with a limited number of participants carries a considerable risk of failing to demonstrate a treatment difference when one is really present. Neutral trials may become positive with enhanced precision afford by the greater number of events over time. Accordingly, we studied the extended very long-term follow-up of the REMADHE trial to test if this DMP is effective in the scenario of more death events provided by the extended follow-up. Understanding prognostic factors in patients with HF who survive very long periods is crucial for several reasons: (1) it provides realistic expectations for patient and family counseling; (2) it identifies modifiable risk factors that could be targeted for intervention; (3) it helps stratify risk for resource allocation in HF clinics; and (4) it generates hypotheses about mechanisms of long-term survival that can inform future therapeutic strategies. While both groups in our study received specialized HF care—an important design feature that allowed evaluation of prognostic factors under optimal management conditions—we initially hypothesized that DMP effects might persist through sustained improvements in self-care behaviors and patient empowerment over the extended follow-up period. Therefore, the objective of our current study was to extend the follow-up period of patients initially included in the REMADHE trial up to 23.6 years. Also, we aimed to identify prognostic predictors of all-cause mortality in a population with HF who initially underwent education and telephone monitoring in a specialized and multidisciplinary HF unit. Methods REMADHE was a prospective, single center, open trial with randomization 2:1, as previously detailed. 13 The REMADHE study compared the DMP group versus the Control group (C), in patients treated in a clinic specializing in HF with a multidisciplinary team. Patients in the DMP group underwent an educational program and continuous repetitive monitoring. Patients received reinforcement of education during the 2.47 ± 1.75-year follow-up at 6-month intervals. Education and monitoring were not repeated with frequent reinforcement throughout the very late follow-up. In this current extended study, we analyzed on June 2023 data of patients included in the period from October 1999 to January 18, 2005, with follow-up until 23.6 years. Data about death were obtained from reports collected during medical visits, telephone calls, review of medical records, information from family members on data contained in the death certificate, research at the SEADE Foundation (State Data Analysis System), and the central deaths registry in Brazil (Ministry of Health). Death was classified as secondary to worsening HF or sudden death. Deaths that occurred in the hospital were classified as secondary to worsening HF based on review of available medical records, death certificates, and the clinical context of hospitalization. We acknowledge that this represents a pragmatic classification approach necessitated by the very long-term nature of the follow-up. While hospital deaths in HF patients are predominantly due to decompensated HF or HF-related complications, we recognize this classification method has limitations and potential for some misclassification. However, this approach: (1) has been used in other long-term HF registries; (2) is more reliable than classifying home deaths, which could represent either sudden death or unrecognized HF decompensation; and (3) represents the best available methodology for a 23.6-year retrospective follow-up. In clinical practice dying at home in patients with HF is not necessarily synonymous of with sudden death because of the limitations of family and patients to recognize worsening HF. Also, the concept of sudden death in HF had been the subject of recent criticism. 18 The study protocol was submitted to the Heart Institute Ethical Committee receiving the number 827/99. The local ethical committees approved the study that was performed in accordance with relevant guidelines/regulations. All patients or their legal guardians gave informed consent for participation in the study. The local ethical committees approved the extended long-term follow-up study. The REMADHE study was registered at http://clinicaltrials.gov (Identifier NCT 00505050). Study population The patients included in the very long-term follow-up of the REMADHE trial were initially recruited from a tertiary cardiology referral center who were undergoing outpatient follow-up with cardiologists specialized in heart failure (HF) at the Heart Failure Clinics. All patients were under guideline-directed medical therapy (GDMT). The eligibility criteria and exclusion criteria have been described previously. 13 Statistical analysis Descriptive statistics of quantitative variables were performed using mean (M), standard deviation (SD), and number of cases (N). Relative variations (Δ%) were also calculated and, if this was not possible, absolute variations (Δ) were evaluated between the results of the sequential follow-up of each variable. The distribution of quantitative variables was evaluated using the Kolmogorov-Smirnov test. Categorical variables were described with absolute and relative frequencies. Normality was determined by the Shapiro-Wilk test. The Student's t-test was used to compare the baseline characteristics of groups C and DMP, and the Fisher exact test was used for unpaired values. In the analysis of mortality, the date of randomization up to the data obtained by telephone, by medical records or by death certificate was considered. Survival and event-free curves were calculated using the Kaplan-Meier method, and the log-rank test (Mantel-Cox) was used for comparison. P < 0.05 was considered statistically significant. The uncertainty measures of the statistical models were presented in the results, including 95% confidence intervals (CIs) for the hazard ratios (HRs), which directly reflect the uncertainties associated with the estimates. The procedures followed the software’s default methods, including well-established algorithms for proportional hazards analysis (Cox model) and stepwise variable selection in the multivariate model. A univariate and multivariate proportional hazards model was adjusted to assess prognostic factors associated with mortality outcome. The following variables were tested initially on a univariate model: sex, age < or ≥ 52 years, ethnicity (white, black, mulatto), etiology (ischemic, hypertensive, alcoholic, chagasic, valvular, and others), diabetes type II, diabetes insulin-dependent, left ventricular ejection fraction (LVEF) ≥ or < 45%, left bundle-branch block, implanted pacemaker, digoxin use, New York Heart Association (NYHA), education level, marital status, quality of life (Minnesota Questionnaire), blood plasma levels of sodium, potassium, BUN, creatinine, glycemia, hemoglobin, leucocytes, thyroid hormones (T3/T4), thyroid stimulating hormone, and uric acid. Variables with P < 0.10 values were used to compose the multivariate model with a stepwise variable selection process. P values < 0.05 were considered significant. A baseline characteristic analysis was conducted to investigate potential confounding factors among the positive predictor variables examined in the multivariable analysis. Statistical analysis was performed with SPSS v 21 (SPSS Inc, Chicago, IL). To address the potential overparameterization due to the limited number of events relative to the number of covariates, we employed the Stepwise selection method based on the Akaike Information Criterion (AIC). Additionally, we ensured that the event-to-variable ratio adhered, as much as possible, to the recommended threshold of at least 10 events per included variable to maintain the stability of the estimates. The categorization of continuous variables was based on both clinical and statistical criteria, taking into account the nature of the data and the context of the research. Continuous variables were categorized when clinically meaningful cut-offs were identified (e.g., widely accepted reference values in clinical practice) or when non-linear relationships between the variable and the outcome were observed. Furthermore, we performed checks for the proportional hazards assumption to ensure the validity of the fitted Cox model. Results Groups DMP and C had similar demographic baseline characteristics, with a total of 412 included patients as previously published. 15 The time between the first randomization and outcome analysis was 23.6 years. The baseline characteristics of the patients were previously published in the initial study. 13 During the trial follow-up extended period the use of guideline-recommended medications and devices for the treatment of HF was strongly emphasized for all patients. In the last evaluation 70% of patients were receiving spironolactone, 84% beta-blocker, 67% renin-angiotensin-aldosterone system inhibitors, 26.8% angiotensin II receptor blockers, 1% angiotensin receptor-neprilysin inhibitor, 37% thiazides, 74% furosemide, 2.5% ivabradine, 35% hydralazine, and 32% nitrate. Also, in the last evaluation the percentage of patients with implanted pacemaker was 6.2% and for implanted cardioverter defibrillators were 3%. No statistical differences were observed between the groups. Mortality data were analyzed from October 1999 to June 2023, showing all-cause mortality rate of 88.3% (Fig. 1A). HF was the cause of death in 35.9% (n = 132) of patients who died; 25.5% (n = 105) died at home, other causes of death were observed in 19.3% (n = 79), and in 11.2% (n = 46) the cause was unknown. The inclination of the survival curve is higher in the first 6-year follow-up in comparison with after 6-year follow-up (Fig. 1A). The survival curves according to DMP and C groups are shown in Fig. 1B. At 23.6-year follow-up, univariate analysis revealed that several variables were associated with lower survival rates (Table 1), including age ≥ 52 years (Fig. 2A), LVEF < 45% (Fig. 2B), chagasic etiology (Fig. 2C), digoxin users (Fig. 2D), BUN (Fig. 3A), lymphocytes (Fig. 3B), and NYHA IV (Fig. 3C), male sex ( Fig. 4A ) , and atrial fibrillation (AF) ( Fig. 4B ) . On the multivariate analysis, the predictive variables for mortality were age ≥ 52 years (HR 1.315; 95% Confidence Interval [CI], 1.055 to 1.640; P = 0.015); Chagas etiology (HR 1.672; 95% CI, 1.252 to 2.232; P < 0.001); LVEF < 45% (HR 0.582; 95% CI, 0.389 to 0.870; P = 0.008); use of digoxin (HR 1.425; 95% CI, 1.138 to 1.785; P = 0.002); NYHA IV (HR 1.604; 95% CI, 1.122 to -2.295; P = 0.010); elevation of BUN (HR 1.008; 95% CI, 1.003 to 1.014; P = 0.038); and lymphopenia (HR 0.772; 95% CI, 0.641 to 0.929; P = 0.005). Reference categories were now stated explicitly. Where a hazard ratio was < 1, it reflected the lower-risk category relative to the higher-risk reference. Interpretations were aligned accordingly, and the typographic error in the NYHA IV confidence interval was corrected. Table 1 – Univariable Analysis of Predictors Associated With Any Mortality at 23.7-year follow-up. Variable Total Death, n (%) HR (95% CI) P No Yes N=412 (%) 81 (20) 331 (80) Group DMPs 276 (67) 44 (68.8) 232 (66.7) C 136(33.0) 20 (31.3) 116 (33.3) 1.074 (0.859 to 1.343) 0.529 Transplantation 30 (7.3) 8 (12.5) 22 (6.3) 0.681 (0.442 to 1.050) 0.082 Sex (men) 282 (68.4) 36 (56.3) 246 (70.7) 1.395 (1.106 to 1.758) 0.005 Age ( > 52y) 189 (45.9) 22 (34.4) 167 (48) 1.928 (1.051­ to 1.603) 0,015 Race 0,756 White 225 (54.6) 38 (59.4) 187 (53.7) Mulatto 105 (25.5) 14 (21.9) 91 (26.1) Black 82 (19.9) 12 (18.8) 70 (20.1) Race (Nonwhite) 186 (45.1) 37 (45.7) 149 (45) 1.019 (0.825 to 1.259) 0.859 Etiology 0,011 Ischemic 116 (28.2) 17 (26.6) 99 (28.5) Hypertensive 65 (15.8) 14 (21.9) 51 (14.7) 0.723 (0.516 to 1.015) 0.061 Alcoholic 18 (4.4) 2 (3.1) 16 (4.6) 1.540 (0.907 to 2.614) 0.110 Idiopathic 100 (24.3) 14 (21.9) 86 (24.8) 1.088 (0.814 to 1.453) 0.570 Chagasic 73 (17.8) 8 (12.5) 65 (18.7) 1.469 (1.073 to 2.010) 0.016 Valvular 13 (3.2) 2 (3.1) 11 (3.2) 0.826 (0.443 - 1.541) 0.549 Congenital 3 (0.7) 1 (1.6) 2 (0.6) 0.804 (0.198 to 3.260) 0.760 Postpartum 4 (1.0) 2 (3.1) 2 (0.6) 0.362 (0.089 to 1.467) 0.155 Others 15 (3.7) 3 (3.1) 12 (3.2) 0.861 (0.462 to 1.607) 0.639 Hypertrophic 3 (0.7) 2 (3.1) 1 (0.3) 0.250 (0.035 to 1.794) 0.168 DM non insulin dependent 85 (20.6) 13 (20.3) 72 (20.7) 0.930 (0.717 to 1.205) 0.582 DM insulin dependent 19 (4.6) 4 (6.3) 15 (4.3) 0.786 (0.467 to 1.322) 0.364 LVEF ( > 45%) 43 (10.6) 15 (24.2) 28 (8.2) 0.485 (0.329 to 0.714) <0.001 LBBB 80 (19.9) 9 (14.3) 71 (20.9) 1.151 (0.886 to 1.496) 0.291 AF 81 (20.1) 9 (14.3) 72 (21.2) 1.365 (1.052 to 1.773) 0.019 PM 20 (5.0) 2 (3.2) 18 (5.3) 1.444 (0.897 to 2.323) 0.130 Digoxin use 23 (56.4) 24 (38.1) 206 (59.7) 1.44 (1.161 to 1.787) 0.001 NYHA NYHA , n (%) 0.002 I 61 (14.8) 16 (19.8) 45 (13.6) II 200 (48.5) 41 (50.6) 159 (48) 1.200 (0.868 to 1.658) 0.270 III 110 (26.7) 21 (25.9) 89 (26.9) 1.288 (0.906 to 1.830) 0.158 IV 41 (10) 3 (3.7 38 (11.5) 2.196 (1.438 to 3.353) <0.001 Sodium mmol/l, n (range) 139 (137-141) 139 (137-140) 139 (136-141) 0.997 (0.987 to 1.008) 0.577 Potassium, mmol/l 4.5 (4.2-4.9) 4.5 (4.2-4.78) 4.5 (4.1-4.9) 0.998 (0.956 to 1.042) 0.934 BUN, mg/dl 47 (36-63) 39 (32-54) 48 (37-65) 1.010 (1.005 to 1.016) 55 mg/dl (32%) 5 (23.8) 80 (34.2) 1.308 (0.997 to 1.715) 0.052 Creatinine mg/dl 1.1 (1-1.4) 1.0 (0.9-1.3) 1.2 (1-1.4) 1.044 (1.007 to 1.081) 0.019 Glucose, mg/dl 100 (91-116) 102 (94-113) 100 (91-116) 0.998 (0.996 to 1.001) 0.160 Hemoglobin g/dl 14 (13-15) 14 (13-15) 14 (13-15) 0.960 (0.910 to 1.013) 0.137 Hematocrit, % 42 (38-45) 42 (38-46) 42 (38-45) 0.991 (0.973 to 1.009) 0.304 Leukocytes x10 3 7.30 (6.10-8.80) 7.05 (5.83-8.18) 7.40 (6.20-8.90) 1.033 (0.989 to 1.079) 0.141 Lymphocytes x10 3 1.76 (1.33-2.26) 2.04 (1.55-2.45) 1.72 (1.30-2.24) 0.677 (0.575 to 0.796) 25% (50.8%) 36 (75.0) 172 (47.6) 0.611 (0.496 to 0.753) <0.001 T4, ng/dl 1.4 (1.2-1.5) 1.3 (1.1-1.6) 1.4 (1.2-1.5) 0.953 (0.586 to 1.550) 0.846 TSH, µmol/L 2.0 (1.2-3.3) 1.7 (1.1-2.5) 2.1 (1.2-3.4) 1.012 (0.996 to 1.028) 0.145 Uric Acid, mg/dl 7.9 (6.03-9.48) 6.35 (4.7-8.1) 8.2 (6.4-9.63) 1.001 (0.998 to 1.003) 0.632 DMPs, disease management program; C, usual care; DM, diabetes mellitus type 2; LVEF, left ventricular ejection fraction; LBBB, left bundle-branch block; AF, atrial fibrillation; PM, pacemaker; NYHA: New York Heart Association. Chagas’ disease tended to differ in causes of death compared with other etiologies (P > 0.07). In death from Chagas’ disease, HF was the cause in 43.2%, sudden death was observed in 17.6%, other causes in 33.8%, and unknown in 5.4%. In non-Chagas’ disease deaths, HF was the cause in 37%, sudden death was observed in 34.6, other causes in 17.1, and unknown in 11.3%. Causes of death were different according to baseline LVEF < 45% and ≥ 45% (P < 0.04). In LVEF < 45% HF as a cause of death, sudden death, other causes, and unknown causes were 40, 29.7%, 21.7%, and 8.6%, respectively. In LVEF ≥ 45%, HF cause of death, sudden death, other causes, and unknown causes were 16.7%, 29.7%, 21.7%, and unknown in 8.6% respectively. Causes of death were different according to baseline BUN > 55 mg/dl and BUN ≤ 55 mg/dl (P 55mg/dl, HF as the cause of death, sudden death, other causes, and unknown causes were 46%, 24.2%, 18.5%, and 11.3%, respectively. In BUN ≤ 55 mg/dl, HF as the cause of death, sudden death, other causes, and unknown causes were 34%, 35%, 21.8%, and 9.2%, respectively. Other independent variables related to mortality in multivariate analysis did not influence the causes of death. The mean survival was 6.2 ± 0.52 years in C versus 6.6 ± 0.37 years in DMP (P = 0.656) up to 23.6-year follow-up (Fig. 1B). HF as a cause of death and sudden death were different between groups DMP and C (P < 0.02). HF during hospitalization was the cause of death in 33.3% and 41% in DMP and C groups, respectively; and sudden death was observed in 28.4% and 20.4% of deaths in DMP and C groups, respectively. Other causes of death or unknown causes were observed in 34.7%, and 34.2% of the deaths in DMP and C groups, respectively (P = ns). Discussion One of the notable strengths of this study is the very long-term follow-up of patients with HF, which, to the best of our knowledge, represents the first DMP analysis of a follow-up period exceeding 20 years. The main findings can be summarized as follows: (1) the survival of HF patients analyzed over a 20-year period showed during the first 6 years an inclination of the survival curve suggesting initial high risk even in patients under ambulatory care; (2) age ( ≥ 52 years), Chagas disease, LVEF < 45%, digoxin use, NYHA IV, elevated BUN, and lymphopenia were independent predictors of mortality; (3) DMP and C groups had similar survival. However, HF as cause of death was more frequent in C; (4) HF was the first cause of death followed by sudden death; (5) Some independent variables on multivariate analysis were associated with different modes of death, including Chagas’ disease; baseline LVEF and renal function. While extended follow-up provided valuable insights, interpretations were tempered to reflect limitations of categorization, stepwise selection, potential misclassification of modes of death, and exposure dilution. This study is novel in the analysis of very long-term mortality (exceeding 20 years) in HF patients and who underwent DMP. Our results showed better HF survival in comparison with recently reported HF data up to 10-year follow-up. 5 One reason for this would be that our patients were followed up by HF specialists in a Heart Failure Clinic. Also, our findings add new data on modes of death in very long-term follow-up on HF. Mechanisms related to higher mortality for approximately the first 6 years are unknown. The higher mortality up to 5 years was also reported recently after HF hospitalization. Those who responded poorly or not at all to triple therapy including those who did not maintain the initial response could have died in the first years instead of those who responded to therapy and had longer follow-up. Patient characteristics under optimized therapy as observed in our results could influence the response to treatment. As main implications of our results independent modifiable markers with a strong pathophysiological rationale could be priority targets for treating or planning research on HF in very long-term follow-up. Renal function and LVEF seem to have these characteristics. We found in the literature only one publication that reported the etiology, prevalence and mortality from HF in the European part of Russia over a very long period of 20 years. The authors found that the median survival time was 8.4 years in patients with NYHA I–II and 3.8 years in patients with NYHA III–IV 19 . One important finding requiring careful interpretation is that REMADHE DMP did not affect very long-term mortality. Several factors likely explain this result: (1) Both DMP and control groups received continuous specialized care in a multidisciplinary Heart Failure Clinic with GDMT optimization throughout the 23.6-year follow-up—this is a unique and important design feature of our study; (2) The intensive structured education and monitoring reinforcement that characterized the DMP intervention was maintained primarily during the initial trial period (2.47 years) and not systematically continued with the same frequency and structure during the extended follow-up, leading to dilution of the intervention effect over time; (3) The finding that both groups had similar survival likely reflects the high-quality specialized care both groups received rather than inefficacy of DMP per se. However, the observation that the control group had more HF-related deaths while the DMP group had more sudden deaths suggests the initial DMP intervention may have had some lasting effect on disease trajectory, potentially by improving HF progression outcomes but not preventing sudden death. This differential effect on modes of death has important clinical implications for long-term HF management strategies. The prognostic factors we identified (age, Chagas disease, LVEF, renal function, lymphocytes, digoxin use) are particularly valuable because they were predictive even in a cohort receiving optimal specialized care. This suggests these factors represent fundamental disease severity markers or non-modifiable characteristics that remain important regardless of quality of care. Identifying these factors has important implications for long-term risk stratification, resource allocation, and patient counseling. Unfortunately, systematic quality of life and hospitalization data were not collected during the extended follow-up period beyond the original 2.47-year trial. This represents an important limitation of our study and an area that warrants investigation in future prospective very long-term HF studies. Understanding whether the early benefits of DMP on quality of life and hospitalizations persist, attenuate, or resolve over very long-term follow-up would provide valuable information for designing sustained intervention programs. Worse prognosis of chagasic HF on shorter follow-up was also observed in the extended long-term follow-up. 13 Despite already being first described in 1994 by Bocchi et al, the mechanisms related to worse prognosis in chagasic HF still is an unresolved issue. 20 The complex pathogenesis and physiopathology comprising persistent myocarditis with fibrosis, parasite persistence with inflammatory response, autoimmunity, damage to the parasympathetic system causing sympathetic over activity, microvascular abnormalities, conduction system abnormalities, brady- and tachyarrhythmias, biventricular dilated cardiomyopathy, apical aneurysm, thromboembolism, or remodeled ventricles may be related to worse prognosis. 21 – 22 Also, only 35.8% of patients with Chagas disease were receiving baseline beta-blocker therapy. However, the lack of knowledge about whether GDMT is effective for chagasic HF may have influenced the smaller proportion of beta-blocker therapy compared with other etiologies. Medical treatment has been extrapolated from trials that included other etiologies or studies with limited design. 22 However, a subanalysis of the REMADHE trial showed that the survival of patients with Chagas disease undergoing beta-blocker therapy was similar to that of other etiologies. 24 Our results on multivariate analysis concerning age align with findings of studies that reported a negative impact of aging on survival 5 However, in our cohort, patients were relatively younger (mean age of 51 years), and an age already ≥ 52 years was associated with lower survival. The presence of a younger population can be attributed to earlier manifestation of etiologies such as Chagas’ disease, valvar abnormalities, and limited access to prevention in a population despite risk factors of developed and undeveloped countries 25 Our findings on very long-term follow-up are in agreement with prior studies showing that reduced LVEF is a well-established predictor of HF mortality particularly with an average follow-up of up to 5 years. 26 – 28 Studies have not explored very longer follow-up periods. Otherwise, heart failure with LVEF > 45% (HFpEF) was also associated with increased mortality mainly in NYHA IV. 29,30 However, prognosis of HFpEF is controversial depending of characteristics of included patient in studies. Overall, it is expected patients with recovered LVEF in HFpEF group. Better prognosis was reported in HF with improved LVEF in comparison with persistent HFpEF, declined EF and persistent heart failure with reduced ejection fraction. 26 Additionally, the worsening of functional class is known to be associated with worse outcomes in HF, which was consistent with our findings in very long-term follow-up. NYHA IV was also associated with reduced survival, similar to observations from other studies with follow-up periods of up to 10 years. 29 , 30 Concerning the digoxin association with worse prognosis reported in our results, it is crucial to highlight that studies had reported contradictory associations of digoxin with mortality in HF. 31–33 However, most studies have the caveat of absence of serum digoxin levels assessment, which might have affected outcomes. Subanalysis of the Digitalis Investigation Group trial showed a linear dose–response relationship linking serum concentration to mortality. 34 Also, the reason for digoxin prescription may be a confounder because in clinical practice digoxin could had been prescribed for more seriously ill patients. The findings emphasize cautious prescribing of digoxin for patients with HF in very long follow-up, because its association with increased mortality was suggested in previous research and by our results. Also, the evidence of benefits of digoxin may be limited in patients undergoing contemporaneous HF treatment. Our data in which the baseline mean urea values > 55 mg/dl were associated with reduced survival confirms previous publications, however, adding new very long-term data. Numerous studies, particularly in the context of decompensated HF, have examined the prognostic value of elevated BUN (> 55–80 mg/dl) as a predictor of morbidity and mortality, albeit with short-term follow-up 35,36 . Report of the Swedish Heart Failure Kidney Registry showed that renal dysfunction is common and strongly associated with short-term and long-term outcomes up to 10-year follow-up in patients with HF. 36 Systematic review and meta-analysis reported that worsening renal function predicts substantially higher rates of mortality and hospitalization in patients with HF. 37 Baseline lymphopenia as a biomarker for prognosis in HF has been reported, but it has not yet been demonstrated in long-term follow-up as in our results. 38 – 40 Lymphopenia is also a marker for worse prognosis in other systemic diseases including COVID-19. 41 The mechanisms responsible for the increment in the relative reduction in lymphocytes in HF are not fully understood. An increase in neutrophil because of systemic inflammation, and lymphopenia caused by elevated cytokines, splanchnic congestion, apoptosis, increased endogenous cortisol and sympathetic tone may play a role. 42 HF can trigger a significant increase in systemic cortisol production. 43 The findings from this very long-term follow-up study have several important clinical implications: First, risk stratification - the identified prognostic factors (age > 52 years, Chagas disease, LVEF < 45%, elevated BUN, lymphopenia, NYHA IV, and digoxin use) can be used to develop comprehensive long-term risk stratification tools for HF patients. These factors remained predictive even in patients receiving optimal specialized care, suggesting they represent fundamental markers of disease severity; Second, modifiable risk factors - among the identified predictors, renal function represents a potentially modifiable risk factor that should be aggressively monitored and managed in HF patients. Strategies to preserve renal function, avoid nephrotoxic agents, and optimize GDMT despite mild renal impairment may improve very long-term outcomes; Third, the Chagas cardiomyopathy - patients with Chagas disease require special attention given their consistently worse prognosis. Fourth, the relevance of specialized HF clinic care - the relatively good survival observed in both groups underscores the importance of long-term specialized multidisciplinary HF clinic care. Even without intensive DMP, continued specialized care with GDMT optimization appears crucial for long-term outcomes; Finally, these data provide realistic survival expectations for counseling HF patients and their families. Some HF patients, particularly those under specialized care with favorable prognostic profiles, can survive > 20 years, which is important information for shared decision-making regarding advanced therapies, palliative care discussions, and life planning. Our study has several important limitations that should be considered when interpreting the results: First, being conducted at a single specialized tertiary HF center limits generalizability to other settings, particularly community-based practices or centers without specialized multidisciplinary HF programs. Second, t his extended follow-up was not pre-specified in the original trial design, which limits causal inferences and introduces potential biases inherent to retrospective analyses. Third, the very long-term nature of follow-up necessitated pragmatic approaches to death classification. Hospital deaths were classified as HF-related, which may have resulted in some misclassification. Cause of death determination became increasingly challenging over the 23.6-year period. Fourth, quality of life, hospitalization rates, medication adherence, and other clinical parameters were not systematically collected during the extended follow-up period beyond the initial 2.47 years. This limits our ability to understand trajectory of these important outcomes. Fifth, despite comprehensive baseline assessment, unmeasured confounders accumulated over 23.6 years (e.g., changes in social support, socioeconomic status, access to care, development of comorbidities) may have influenced outcomes. Finally, by definition, very long-term follow-up studies the characteristics of survivors. The cohort alive at later time points represents a selected population that may not be representative of all HF patients and temporal changes in HF management (survival bias). Conclusion In conclusion, DMP was not effective in reducing very long-term mortality; however, causes of death differed between groups, with more HF-related deaths in controls. Our findings that age, LVEF, Chagas' disease, NYHA, renal function, lymphocytes, and digoxin use were associated with poor prognosis and could influence future strategies to improve HF management. This study would be highly valuable for patients, doctors, and healthcare professionals seeking a comprehensive understanding and strategies for management of HF in very long-term follow-up. Abbreviations HF Heart Failure DMP Educational and disease management programs REMADHE trial Long-Term Prospective,Randomized,Controlled Study Using Repetitive Education at Six-Month Intervals and Monitoring for Adherence in Heart Failure Outpatients C Control group LVEF left ventricular ejection fraction GDMT guideline-directed medical therapy BUN blood urea nitrogen NYHA New York Heart Association CI confidence interval HR hazard ratio AF atrial fibrillation. Declarations Acknowledgements: Dr Nelson Cavas Junior for the statistical review. Conflict of Interest The authors declare no conflicts of interest related to this study Consent for publication Not applicable Funding Statement This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Artificial Intelligence (AI) Use Statement No artificial intelligence technologies were used in the conduct of this research or in the writing of this manuscript Data Availability Statement Data are available upon reasonable request. If someone wants to request the data from this study, the contact will be Prof Dr Edimar Bocchi [email protected] Ethics Statements Consent obtained directly from patient(s). The study protocol was submitted to the Heart Institute Ethical Committee in 1999 and received the number 827/99. The local ethical committees approved the study. All patients gave informed consent for participation in the study. The study was registered at http://clinicaltrials.gov (Identifier NCT 00505050). Participants gave informed consent to participate in the study before taking part. Contributors: EAB takes responsibility for the manuscript, including the data and analysis. GVG, CER and ARD made substantial contributions to drafting the work. SMAF, BB, PRC, RTM, FDC made substantial contributions to data acquisition. JTF,FDC made substantial contributions to statistical analyses. All authors made substantial contributions to the acquisition, analysis or interpretation of data for the work; revising the work critically for important intellectual content; final approval of the version to be published and agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are investigated and resolved. Competing interests: None declared Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors. 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Hashemi-Shahri SH, Aghajanloo A, Ghavami V, et al. Digoxin and outcomes in patients with heart failure and preserved ejection fraction: a systematic review and meta-analysis. Curr Drug Targets. 2023;24:191–200. Adams KF Jr, Butler J, Patterson JH, et al. Dose response characterization of the association of serum digoxin concentration with mortality outcomes in the Digitalis Investigation Group trial. Eur J Heart Fail. 2016;18:1072–81. Liu EQ, Zeng CL. Blood urea nitrogen and in-hospital mortality in critically ill patients with cardiogenic shock: analysis of the MIMIC-III Database. Biomed Res Int. 2021;2021:5948636. Löfman I, Szummer K, Hagerman I, Dahlström U, Lund LH, Jernberg T. Prevalence and prognostic impact of kidney disease on heart failure patients. Open Heart. 2016;3:e000324. Damman K, Navis G, Voors AA, et al. Worsening renal function and prognosis in heart failure: systematic review and meta-analysis. J Card Fail. 2007;13:599–608. Wu CC, Wu CH, Lee CH, Cheng CI. Association between neutrophil percentage-to-albumin ratio, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio and long-term mortality in community-dwelling adults with heart failure. BMC Cardiovasc Disord. 2023;23:312. Tamaki S, Nagai Y, Shutta R, et al. OCVC-Heart Failure Investigators. Combination of NLR and PLR as a predictor of cardiac death in acute decompensated HFpEF. J Am Heart Assoc. 2023;12:e026326. Ye GL, Chen Q, Chen X, et al. The prognostic role of platelet-to-lymphocyte ratio in patients with acute heart failure: a cohort study. Sci Rep. 2019;9:10639. Yan S, Wu G. Is lymphopenia different between SARS and COVID-19 patients? FASEB J. 2021;35:e21245. Vakhshoori M, Nemati S, Sabouhi S, et al. Prognostic impact of monocyte-to-lymphocyte ratio in coronary heart disease: systematic review and meta-analysis. J Int Med Res. 2023;51:3000605231204469. Acanfora D, Gheorghiade M, Trojano L, et al. Relative lymphocyte count as a mortality indicator in elderly CHF patients. Am Heart J. 2001;142:167–73. Tzanis G, Dimopoulos S, Agapitou V, Nanas S. Exercise intolerance in chronic heart failure: cortisol and catabolic state. Curr Heart Fail Rep. 2014;11:70–9. Graphical Abstract Graphical Abstract is not available with this version. Additional Declarations No competing interests reported. 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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-8484135","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":587916564,"identity":"57051c20-f802-4d9c-be1f-8c2cc0e12a60","order_by":0,"name":"Edimar Alcides 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Dores","lastName":"Cruz","suffix":""}],"badges":[],"createdAt":"2025-12-30 20:38:02","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8484135/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8484135/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102517111,"identity":"c4995437-3832-494f-a0d4-261a67b31eaf","added_by":"auto","created_at":"2026-02-12 13:56:12","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":42355,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier Survival Curve in the Total Population (Figure 1A). The Survival Curves According to Intervention and Usual Care Groups (Figure 1B).\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8484135/v1/544640bf9223f9d7abe269bd.jpg"},{"id":102517425,"identity":"680108e8-edcb-4c84-a13e-86ab93554909","added_by":"auto","created_at":"2026-02-12 13:57:02","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":121956,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier Survival Curve in Subgroup Analysis; 2A, According to Age \u0026lt;52 and \u003cu\u003e\u003cem\u003e\u0026gt;\u003c/em\u003e\u003c/u\u003e52 Years; 2B, According to Left Ventricular Ejection Fraction \u0026lt;45% and \u003cu\u003e\u0026gt;\u003c/u\u003e45%; 2C, According to Chagas’ and non-Chagas’ Etiology; 2D, According to Digoxin Use.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8484135/v1/68effe97afe62d8eb523ccc6.jpg"},{"id":102516610,"identity":"02535218-2cca-4e12-96dc-7a6a116c545d","added_by":"auto","created_at":"2026-02-12 13:54:16","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":111309,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier Survival According to Subgroup Analysis: 3A, According to BUN \u003cu\u003e\u0026lt;\u003c/u\u003e55 mg/dl and \u0026gt;55 mg/dl; 3B, According percentage of Lymphocytes \u0026gt;25% versus \u003cu\u003e\u0026lt;\u003c/u\u003e25%; 3C, According New York Association NYHA\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8484135/v1/f800e2a7ceb224e2de1199fa.jpg"},{"id":102516609,"identity":"211d38d1-d8d1-4e8e-aee3-890a871b4fa6","added_by":"auto","created_at":"2026-02-12 13:54:16","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":78794,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier Survival Curve:4A According Sex Men and Woman, 4B and Kaplan-Meier Survival Curve According Patients With Atrial Fibrillation\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8484135/v1/96dd351e14215747bf6b381e.png"},{"id":102517472,"identity":"d9115655-573e-4cc9-ab31-0dba283c3116","added_by":"auto","created_at":"2026-02-12 13:57:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1619007,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8484135/v1/e5bd9ad9-c68c-4ee6-8a8a-a3ffbe87ff46.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prognostic Factors Related to All-Cause Mortality in Very Long-term Follow-up of Patients with Heart Failure: The REMADHE Trial Extended Analysis","fulltext":[{"header":"Highlights","content":"\u003cp\u003e\u0026bull; This is the first study reporting prognostic factors in heart failure with follow-up exceeding 20 years\u003c/p\u003e\u003cp\u003e\u0026bull; Independent mortality predictors included age\u0026thinsp;\u0026gt;\u0026thinsp;52 years, Chagas disease, LVEF\u0026thinsp;\u0026lt;\u0026thinsp;45%, digoxin use, NYHA IV, elevated BUN, and lymphopenia\u003c/p\u003e\u003cp\u003e\u0026bull; Disease management programs did not affect very long-term mortality when both groups received specialized heart failure clinic care\u003c/p\u003e\u003cp\u003e\u0026bull; Distribution of causes of death differed between groups, with more heart failure deaths in controls and more sudden deaths in the intervention group\u003c/p\u003e\u003cp\u003e\u0026bull; Identified prognostic factors can guide long-term risk stratification and management strategies in heart failure patients\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eHeart failure (HF) has an estimated prevalence of 1 to 4% of the global population\u003c/span\u003e \u003csup\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e \u003c/span\u003e \u003c/sup\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eand remains associated with poor quality of life, high mortality, hospitalizations, and a substantial burden on the healthcare system. HF may have heterogeneous causes and pathways. However, HF trials and observational survival studies conducted worldwide have relatively limited short follow-up periods.\u003c/span\u003e \u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e2\u0026ndash;4\u003c/span\u003e\u003c/sup\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eFew studies have assessed the long-term survival impact of HF beyond a 10-year period.\u003c/span\u003e \u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e5\u0026ndash;11\u003c/span\u003e\u003c/sup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eThus, data regarding very long-term survival (\u0026gt;\u0026thinsp;20 years) and respective prognostic factors in HF are lacking (therefore, prognostic and survival indicators over 20 years are scarce or practically non-existent.)\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eEducational and disease management programs (DMP) targeted at patients with HF have reported improvement in quality of life, and reduction in hospitalization and healthcare utilization.\u003c/span\u003e \u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e11\u0026ndash;13\u003c/span\u003e\u003c/sup\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eHowever, doubt has been cast on the efficacy of these interventions in the medium and long term based on several published neutral studies.\u003c/span\u003e\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/span\u003e,\u003c/sup\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eIn fact, very long-term efficacy of DMP in HF is unknown. Extended pivotal trials on treatment of HF have been shown heterogeneous concordance of the results in comparison with the first published trial.\u003c/span\u003e \u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e15\u0026ndash;17\u003c/span\u003e\u003c/sup\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eAccordingly, also extended DMP trial development should be warranted.\u003c/span\u003e\u003c/p\u003e \u003cp\u003eThe REMADHE trial was conducted initially during a mean follow-up period of 2.47\u0026thinsp;\u0026plusmn;\u0026thinsp;1.75 years. The study demonstrated improvements in quality of life, reductions in hospitalizations and emergency visits among the DMP group, without statistical differences in mortality rates between the groups.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e The REMADHE trial had a smaller number of recruited patients in comparison with multicenter trials with consequent limited number of events mainly for mortality. A trial with a limited number of participants carries a considerable risk of failing to demonstrate a treatment difference when one is really present. Neutral trials may become positive with enhanced precision afford by the greater number of events over time. Accordingly, we studied the extended very long-term follow-up of the REMADHE trial to test if this DMP is effective in the scenario of more death events provided by the extended follow-up.\u003c/p\u003e \u003cp\u003eUnderstanding prognostic factors in patients with HF who survive very long periods is crucial for several reasons: (1) it provides realistic expectations for patient and family counseling; (2) it identifies modifiable risk factors that could be targeted for intervention; (3) it helps stratify risk for resource allocation in HF clinics; and (4) it generates hypotheses about mechanisms of long-term survival that can inform future therapeutic strategies. While both groups in our study received specialized HF care\u0026mdash;an important design feature that allowed evaluation of prognostic factors under optimal management conditions\u0026mdash;we initially hypothesized that DMP effects might persist through sustained improvements in self-care behaviors and patient empowerment over the extended follow-up period.\u003c/p\u003e \u003cp\u003eTherefore, the objective of our current study was to extend the follow-up period of patients initially included in the REMADHE trial up to 23.6 years. Also, we aimed to identify prognostic predictors of all-cause mortality in a population with HF who initially underwent education and telephone monitoring in a specialized and multidisciplinary HF unit.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eREMADHE was a prospective, single center, open trial with randomization 2:1, as previously detailed.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e The REMADHE study compared the DMP group versus the Control group (C), in patients treated in a clinic specializing in HF with a multidisciplinary team. Patients in the DMP group underwent an educational program and continuous repetitive monitoring. Patients received reinforcement of education during the 2.47\u0026thinsp;\u0026plusmn;\u0026thinsp;1.75-year follow-up at 6-month intervals. Education and monitoring were not repeated with frequent reinforcement throughout the very late follow-up. In this current extended study, we analyzed on June 2023 data of patients included in the period from October 1999 to January 18, 2005, with follow-up until 23.6 years.\u003c/p\u003e \u003cp\u003eData about death were obtained from reports collected during medical visits, telephone calls, review of medical records, information from family members on data contained in the death certificate, research at the SEADE Foundation (State Data Analysis System), and the central deaths registry in Brazil (Ministry of Health). Death was classified as secondary to worsening HF or sudden death. Deaths that occurred in the hospital were classified as secondary to worsening HF based on review of available medical records, death certificates, and the clinical context of hospitalization. We acknowledge that this represents a pragmatic classification approach necessitated by the very long-term nature of the follow-up. While hospital deaths in HF patients are predominantly due to decompensated HF or HF-related complications, we recognize this classification method has limitations and potential for some misclassification. However, this approach: (1) has been used in other long-term HF registries; (2) is more reliable than classifying home deaths, which could represent either sudden death or unrecognized HF decompensation; and (3) represents the best available methodology for a 23.6-year retrospective follow-up. In clinical practice dying at home in patients with HF is not necessarily synonymous of with sudden death because of the limitations of family and patients to recognize worsening HF. Also, the concept of sudden death in HF had been the subject of recent criticism.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe study protocol was submitted to the Heart Institute Ethical Committee receiving the number 827/99. The local ethical committees approved the study that was performed in accordance with relevant guidelines/regulations. All patients or their legal guardians gave informed consent for participation in the study. The local ethical committees approved the extended long-term follow-up study. The REMADHE study was registered at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://clinicaltrials.gov\u003c/span\u003e\u003cspan address=\"http://clinicaltrials.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (Identifier NCT 00505050).\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eThe patients included in the very long-term follow-up of the REMADHE trial were initially recruited from a tertiary cardiology referral center who were undergoing outpatient follow-up with cardiologists specialized in heart failure (HF) at the Heart Failure Clinics. All patients were under guideline-directed medical therapy (GDMT). The eligibility criteria and exclusion criteria have been described previously.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics of quantitative variables were performed using mean (M), standard deviation (SD), and number of cases (N). Relative variations (Δ%) were also calculated and, if this was not possible, absolute variations (Δ) were evaluated between the results of the sequential follow-up of each variable. The distribution of quantitative variables was evaluated using the Kolmogorov-Smirnov test. Categorical variables were described with absolute and relative frequencies. Normality was determined by the Shapiro-Wilk test. The Student's t-test was used to compare the baseline characteristics of groups C and DMP, and the Fisher exact test was used for unpaired values. In the analysis of mortality, the date of randomization up to the data obtained by telephone, by medical records or by death certificate was considered. Survival and event-free curves were calculated using the Kaplan-Meier method, and the log-rank test (Mantel-Cox) was used for comparison. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. The uncertainty measures of the statistical models were presented in the results, including 95% confidence intervals (CIs) for the hazard ratios (HRs), which directly reflect the uncertainties associated with the estimates. The procedures followed the software\u0026rsquo;s default methods, including well-established algorithms for proportional hazards analysis (Cox model) and stepwise variable selection in the multivariate model.\u003c/p\u003e \u003cp\u003eA univariate and multivariate proportional hazards model was adjusted to assess prognostic factors associated with mortality outcome. The following variables were tested initially on a univariate model: sex, age\u0026thinsp;\u0026lt;\u0026thinsp;or \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;52 years, ethnicity (white, black, mulatto), etiology (ischemic, hypertensive, alcoholic, chagasic, valvular, and others), diabetes type II, diabetes insulin-dependent, left ventricular ejection fraction (LVEF)\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;or \u0026lt;\u0026thinsp;45%, left bundle-branch block, implanted pacemaker, digoxin use, New York Heart Association (NYHA), education level, marital status, quality of life (Minnesota Questionnaire), blood plasma levels of sodium, potassium, BUN, creatinine, glycemia, hemoglobin, leucocytes, thyroid hormones (T3/T4), thyroid stimulating hormone, and uric acid. Variables with P\u0026thinsp;\u0026lt;\u0026thinsp;0.10 values were used to compose the multivariate model with a stepwise variable selection process. P values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered significant. A baseline characteristic analysis was conducted to investigate potential confounding factors among the positive predictor variables examined in the multivariable analysis. Statistical analysis was performed with SPSS v 21 (SPSS Inc, Chicago, IL).\u003c/p\u003e \u003cp\u003eTo address the potential overparameterization due to the limited number of events relative to the number of covariates, we employed the Stepwise selection method based on the Akaike Information Criterion (AIC). Additionally, we ensured that the event-to-variable ratio adhered, as much as possible, to the recommended threshold of at least 10 events per included variable to maintain the stability of the estimates. The categorization of continuous variables was based on both clinical and statistical criteria, taking into account the nature of the data and the context of the research. Continuous variables were categorized when clinically meaningful cut-offs were identified (e.g., widely accepted reference values in clinical practice) or when non-linear relationships between the variable and the outcome were observed. Furthermore, we performed checks for the proportional hazards assumption to ensure the validity of the fitted Cox model.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eGroups DMP and C had similar demographic baseline characteristics, with a total of 412 included patients as previously published.\u003csup\u003e15\u003c/sup\u003e The time between the first randomization and outcome analysis was 23.6 years. The baseline characteristics of the patients were previously published in the initial study.\u003csup\u003e13\u003c/sup\u003eDuring the trial follow-up extended period the use of guideline-recommended medications and devices for the treatment of HF was strongly emphasized for all patients. In the last evaluation 70% of patients were receiving spironolactone, 84% beta-blocker, 67% renin-angiotensin-aldosterone system inhibitors, 26.8% angiotensin II receptor blockers, 1% angiotensin receptor-neprilysin inhibitor, 37% thiazides, 74% furosemide, 2.5% ivabradine, 35% hydralazine, and 32% nitrate. Also, in the last evaluation the percentage of patients with implanted pacemaker was 6.2% and for implanted cardioverter defibrillators were 3%. No statistical differences were observed between the groups.\u003c/p\u003e\n\u003cp\u003eMortality data were analyzed from October 1999 to June 2023, showing all-cause mortality rate of 88.3% (Fig.\u0026nbsp;1A). HF was the cause of death in 35.9% (n\u0026thinsp;=\u0026thinsp;132) of patients who died; 25.5% (n\u0026thinsp;=\u0026thinsp;105) died at home, other causes of death were observed in 19.3% (n\u0026thinsp;=\u0026thinsp;79), and in 11.2% (n\u0026thinsp;=\u0026thinsp;46) the cause was unknown. The inclination of the survival curve is higher in the first 6-year follow-up in comparison with after 6-year follow-up (Fig.\u0026nbsp;1A). The survival curves according to DMP and C groups are shown in Fig.\u0026nbsp;1B. At 23.6-year follow-up, univariate analysis revealed that several variables were associated with lower survival rates (Table\u0026nbsp;1), including age\u0026thinsp;\u0026ge;\u0026thinsp;52 years (Fig.\u0026nbsp;2A), LVEF\u0026thinsp;\u0026lt;\u0026thinsp;45% (Fig.\u0026nbsp;2B), chagasic etiology (Fig.\u0026nbsp;2C), digoxin users (Fig.\u0026nbsp;2D), BUN (Fig.\u0026nbsp;3A), lymphocytes (Fig.\u0026nbsp;3B), and NYHA IV (Fig.\u0026nbsp;3C), male sex \u003cstrong\u003e(\u003c/strong\u003eFig.\u0026nbsp;4A\u003cstrong\u003e)\u003c/strong\u003e, and atrial fibrillation (AF) \u003cstrong\u003e(\u003c/strong\u003eFig.\u0026nbsp;4B\u003cstrong\u003e)\u003c/strong\u003e. On the multivariate analysis, the predictive variables for mortality were age\u0026thinsp;\u0026ge;\u0026thinsp;52 years (HR 1.315; 95% Confidence Interval [CI], 1.055 to 1.640; P\u0026thinsp;=\u0026thinsp;0.015); Chagas etiology (HR 1.672; 95% CI, 1.252 to 2.232; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001); LVEF\u0026thinsp;\u0026lt;\u0026thinsp;45% (HR 0.582; 95% CI, 0.389 to 0.870; P\u0026thinsp;=\u0026thinsp;0.008); use of digoxin (HR 1.425; 95% CI, 1.138 to 1.785; P\u0026thinsp;=\u0026thinsp;0.002); NYHA IV (HR 1.604; 95% CI, 1.122 to -2.295; P\u0026thinsp;=\u0026thinsp;0.010); elevation of BUN (HR 1.008; 95% CI, 1.003 to 1.014; P\u0026thinsp;=\u0026thinsp;0.038); and lymphopenia (HR 0.772; 95% CI, 0.641 to 0.929; P\u0026thinsp;=\u0026thinsp;0.005). Reference categories were now stated explicitly. Where a hazard ratio was \u0026lt;\u0026thinsp;1, it reflected the lower-risk category relative to the higher-risk reference. Interpretations were aligned accordingly, and the typographic error in the NYHA IV confidence interval was corrected.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1 \u0026ndash; Univariable Analysis of Predictors Associated With Any Mortality at 23.7-year follow-up.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"679\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 252px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDeath, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eN=412 (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e81 (20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e331 (80)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGroup\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDMPs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e276 (67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e44 (68.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e232 (66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e136(33.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e20 (31.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e116 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.074 (0.859 to 1.343)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.529\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTransplantation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e30 (7.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e8 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e22 (6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.681 (0.442 to 1.050)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.082\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex (men)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e282 (68.4)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e36 (56.3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e246 (70.7)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.395 (1.106 to 1.758)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (\u003cu\u003e\u0026gt;\u003c/u\u003e52y)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e189 (45.9)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e22 (34.4)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e167 (48)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.928 (1.051\u0026shy; to 1.603)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,015\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0,756\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWhite\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e225 (54.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e38 (59.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e187 (53.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMulatto\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e105 (25.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e14 (21.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e91 (26.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBlack\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e82 (19.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e12 (18.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e70 (20.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace (Nonwhite)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e186 (45.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e37 (45.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e149 (45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.019 (0.825 \u0026nbsp; \u0026nbsp; to 1.259)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.859\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEtiology\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,011\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Ischemic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e116 (28.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e17 (26.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e99 (28.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Hypertensive\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e65 (15.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e14 (21.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e51 (14.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.723 (0.516 to \u0026nbsp;1.015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Alcoholic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e18 (4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e2 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e16 (4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.540 (0.907 to 2.614)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Idiopathic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e100 (24.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e14 (21.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e86 (24.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.088 (0.814 to \u0026nbsp;1.453)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.570\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Chagasic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e73 (17.8)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e8 (12.5)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e65 (18.7)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.469 (1.073 to \u0026nbsp; \u0026nbsp; 2.010)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.016\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Valvular\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e13 (3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e2 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e11 (3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.826 (0.443 - 1.541)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.549\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCongenital\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e3 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e1 (1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e2 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.804 (0.198 to 3.260)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.760\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePostpartum\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e4 (1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e2 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e2 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.362 (0.089 to 1.467)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.155\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Others\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e15 (3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e3 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e12 (3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.861 (0.462 to 1.607)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.639\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypertrophic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e3 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e2 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e1 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.250 (0.035 to 1.794)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.168\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDM non insulin dependent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e85 (20.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e13 (20.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e72 (20.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.930 (0.717 to 1.205)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.582\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDM insulin dependent\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e19 (4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e4 (6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e15 (4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.786 (0.467\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eto 1.322)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.364\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLVEF (\u003cu\u003e\u0026gt;\u003c/u\u003e45%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e43 (10.6)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e15 (24.2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e28 (8.2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.485 (0.329 to \u0026nbsp; \u0026nbsp; 0.714)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLBBB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e80 (19.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e9 (14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e71 (20.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.151 (0.886 to 1.496)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.291\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e81 (20.1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e9 (14.3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e72 (21.2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.365 (1.052 to 1.773)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.019\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e20 (5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e2 (3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e18 (5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.444 (0.897 to 2.323)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.130\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDigoxin use\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e23 (56.4)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e24 (38.1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e206 (59.7)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.44 (1.161 to 1.787)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNYHA NYHA , n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e61 (14.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e16 (19.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e45 (13.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eII\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e200 (48.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e41 (50.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e159 (48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.200 (0.868 to 1.658)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.270\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIII\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e110 (26.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e21 (25.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e89 (26.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.288 (0.906 to 1.830)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.158\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e41 (10)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3 (3.7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e38 (11.5)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.196 (1.438 to 3.353)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSodium\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003emmol/l, n (range)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e139 (137-141)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e139 (137-140)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e139 (136-141)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.997 (0.987 to 1.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.577\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePotassium,\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003emmol/l\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e4.5 (4.2-4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e4.5 (4.2-4.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e4.5 (4.1-4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.998 (0.956 to 1.042)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.934\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBUN, mg/dl\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e47 (36-63)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e39 (32-54)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e48 (37-65)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.010 (1.005 to 1.016)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eBUN \u0026gt; 55 mg/dl (32%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e5 (23.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e80 (34.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.308 (0.997 to 1.715)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCreatinine\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003emg/dl\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.1 (1-1.4)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.0 (0.9-1.3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.2 (1-1.4)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.044 (1.007 to 1.081)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.019\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGlucose, mg/dl\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e100 (91-116)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e102 (94-113)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e100 (91-116)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.998 (0.996 to 1.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.160\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHemoglobin\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eg/dl\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e14 (13-15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e14 (13-15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e14 (13-15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.960 (0.910 to 1.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.137\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHematocrit, %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e42 (38-45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e42 (38-46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e42 (38-45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.991 (0.973 to 1.009)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.304\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLeukocytes\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ex10\u003csup\u003e3\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e7.30 (6.10-8.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e7.05 (5.83-8.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e7.40 (6.20-8.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.033 (0.989 to 1.079)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.141\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLymphocytes\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ex10\u003csup\u003e3\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.76 (1.33-2.26)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.04 (1.55-2.45)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.72 (1.30-2.24)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.677 (0.575 to 0.796)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLymphocytes\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u0026gt;\u003c/strong\u003e\u003cstrong\u003e25%\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;(50.8%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e36 (75.0)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e172 (47.6)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.611 (0.496 to 0.753)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT4, ng/dl\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.4 (1.2-1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e1.3 (1.1-1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e1.4 (1.2-1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e0.953 (0.586 to 1.550)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.846\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTSH, \u0026micro;mol/L\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e2.0 (1.2-3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e1.7 (1.1-2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e2.1 (1.2-3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.012 (0.996 to 1.028)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUric Acid, mg/dl\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e7.9 (6.03-9.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e6.35 (4.7-8.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e8.2 (6.4-9.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.001 (0.998 to 1.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.632\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eDMPs, disease management program; C, usual care; DM, diabetes mellitus type 2; LVEF, left ventricular ejection fraction; LBBB, left bundle-branch block; AF, atrial fibrillation; PM, pacemaker; NYHA: New York Heart Association. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eChagas\u0026rsquo; disease tended to differ in causes of death compared with other etiologies (P\u0026thinsp;\u0026gt;\u0026thinsp;0.07). In death from Chagas\u0026rsquo; disease, HF was the cause in 43.2%, sudden death was observed in 17.6%, other causes in 33.8%, and unknown in 5.4%. In non-Chagas\u0026rsquo; disease deaths, HF was the cause in 37%, sudden death was observed in 34.6, other causes in 17.1, and unknown in 11.3%. Causes of death were different according to baseline LVEF\u0026thinsp;\u0026lt;\u0026thinsp;45% and \u0026ge;\u0026thinsp;45% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.04). In LVEF\u0026thinsp;\u0026lt;\u0026thinsp;45% HF as a cause of death, sudden death, other causes, and unknown causes were 40, 29.7%, 21.7%, and 8.6%, respectively. In LVEF\u0026thinsp;\u0026ge;\u0026thinsp;45%, HF cause of death, sudden death, other causes, and unknown causes were 16.7%, 29.7%, 21.7%, and unknown in 8.6% respectively. Causes of death were different according to baseline BUN\u0026thinsp;\u0026gt;\u0026thinsp;55 mg/dl and BUN\u0026thinsp;\u0026le;\u0026thinsp;55 mg/dl (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). In BUN\u0026thinsp;\u0026gt;\u0026thinsp;55mg/dl, HF as the cause of death, sudden death, other causes, and unknown causes were 46%, 24.2%, 18.5%, and 11.3%, respectively. In BUN\u0026thinsp;\u0026le;\u0026thinsp;55 mg/dl, HF as the cause of death, sudden death, other causes, and unknown causes were 34%, 35%, 21.8%, and 9.2%, respectively. Other independent variables related to mortality in multivariate analysis did not influence the causes of death.\u003c/p\u003e\n\u003cp\u003eThe mean survival was 6.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52 years in C versus 6.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37 years in DMP (P\u0026thinsp;=\u0026thinsp;0.656) up to 23.6-year follow-up (Fig.\u0026nbsp;1B). HF as a cause of death and sudden death were different between groups DMP and C (P\u0026thinsp;\u0026lt;\u0026thinsp;0.02). HF during hospitalization was the cause of death in 33.3% and 41% in DMP and C groups, respectively; and sudden death was observed in 28.4% and 20.4% of deaths in DMP and C groups, respectively. Other causes of death or unknown causes were observed in 34.7%, and 34.2% of the deaths in DMP and C groups, respectively (P\u0026thinsp;=\u0026thinsp;ns).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOne of the notable strengths of this study is the very long-term follow-up of patients with HF, which, to the best of our knowledge, represents the first DMP analysis of a follow-up period exceeding 20 years. The main findings can be summarized as follows: (1) the survival of HF patients analyzed over a 20-year period showed during the first 6 years an inclination of the survival curve suggesting initial high risk even in patients under ambulatory care; (2) age (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;52 years), Chagas disease, LVEF\u0026thinsp;\u0026lt;\u0026thinsp;45%, digoxin use, NYHA IV, elevated BUN, and lymphopenia were independent predictors of mortality; (3) DMP and C groups had similar survival. However, HF as cause of death was more frequent in C; (4) HF was the first cause of death followed by sudden death; (5) Some independent variables on multivariate analysis were associated with different modes of death, including Chagas\u0026rsquo; disease; baseline LVEF and renal function. While extended follow-up provided valuable insights, interpretations were tempered to reflect limitations of categorization, stepwise selection, potential misclassification of modes of death, and exposure dilution.\u003c/p\u003e \u003cp\u003eThis study is novel in the analysis of very long-term mortality (exceeding 20 years) in HF patients and who underwent DMP. Our results showed better HF survival in comparison with recently reported HF data up to 10-year follow-up.\u003csup\u003e5\u003c/sup\u003e One reason for this would be that our patients were followed up by HF specialists in a Heart Failure Clinic. Also, our findings add new data on modes of death in very long-term follow-up on HF. Mechanisms related to higher mortality for approximately the first 6 years are unknown. The higher mortality up to 5 years was also reported recently after HF hospitalization. Those who responded poorly or not at all to triple therapy including those who did not maintain the initial response could have died in the first years instead of those who responded to therapy and had longer follow-up. Patient characteristics under optimized therapy as observed in our results could influence the response to treatment. As main implications of our results independent modifiable markers with a strong pathophysiological rationale could be priority targets for treating or planning research on HF in very long-term follow-up. Renal function and LVEF seem to have these characteristics.\u003c/p\u003e \u003cp\u003eWe found in the literature only one publication that reported the etiology, prevalence and mortality from HF in the European part of Russia over a very long period of 20 years. The authors found that the median survival time was 8.4 years in patients with NYHA I\u0026ndash;II and 3.8 years in patients with NYHA III\u0026ndash;IV\u003csup\u003e19\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOne important finding requiring careful interpretation is that REMADHE DMP did not affect very long-term mortality. Several factors likely explain this result: (1) Both DMP and control groups received continuous specialized care in a multidisciplinary Heart Failure Clinic with GDMT optimization throughout the 23.6-year follow-up\u0026mdash;this is a unique and important design feature of our study; (2) The intensive structured education and monitoring reinforcement that characterized the DMP intervention was maintained primarily during the initial trial period (2.47 years) and not systematically continued with the same frequency and structure during the extended follow-up, leading to dilution of the intervention effect over time; (3) The finding that both groups had similar survival likely reflects the high-quality specialized care both groups received rather than inefficacy of DMP per se. However, the observation that the control group had more HF-related deaths while the DMP group had more sudden deaths suggests the initial DMP intervention may have had some lasting effect on disease trajectory, potentially by improving HF progression outcomes but not preventing sudden death. This differential effect on modes of death has important clinical implications for long-term HF management strategies.\u003c/p\u003e \u003cp\u003eThe prognostic factors we identified (age, Chagas disease, LVEF, renal function, lymphocytes, digoxin use) are particularly valuable because they were predictive even in a cohort receiving optimal specialized care. This suggests these factors represent fundamental disease severity markers or non-modifiable characteristics that remain important regardless of quality of care. Identifying these factors has important implications for long-term risk stratification, resource allocation, and patient counseling.\u003c/p\u003e \u003cp\u003eUnfortunately, systematic quality of life and hospitalization data were not collected during the extended follow-up period beyond the original 2.47-year trial. This represents an important limitation of our study and an area that warrants investigation in future prospective very long-term HF studies. Understanding whether the early benefits of DMP on quality of life and hospitalizations persist, attenuate, or resolve over very long-term follow-up would provide valuable information for designing sustained intervention programs.\u003c/p\u003e \u003cp\u003eWorse prognosis of chagasic HF on shorter follow-up was also observed in the extended long-term follow-up.\u003csup\u003e13\u003c/sup\u003e Despite already being first described in 1994 by Bocchi et al, the mechanisms related to worse prognosis in chagasic HF still is an unresolved issue.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e The complex pathogenesis and physiopathology comprising persistent myocarditis with fibrosis, parasite persistence with inflammatory response, autoimmunity, damage to the parasympathetic system causing sympathetic over activity, microvascular abnormalities, conduction system abnormalities, brady- and tachyarrhythmias, biventricular dilated cardiomyopathy, apical aneurysm, thromboembolism, or remodeled ventricles may be related to worse prognosis.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e Also, only 35.8% of patients with Chagas disease were receiving baseline beta-blocker therapy. However, the lack of knowledge about whether GDMT is effective for chagasic HF may have influenced the smaller proportion of beta-blocker therapy compared with other etiologies. Medical treatment has been extrapolated from trials that included other etiologies or studies with limited design.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003eHowever, a subanalysis of the REMADHE trial showed that the survival of patients with Chagas disease undergoing beta-blocker therapy was similar to that of other etiologies.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eOur results on multivariate analysis concerning age align with findings of studies that reported a negative impact of aging on survival \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e However, in our cohort, patients were relatively younger (mean age of 51 years), and an age already\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;52 years was associated with lower survival. The presence of a younger population can be attributed to earlier manifestation of etiologies such as Chagas\u0026rsquo; disease, valvar abnormalities, and limited access to prevention in a population despite risk factors of developed and undeveloped countries \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eOur findings on very long-term follow-up are in agreement with prior studies showing that reduced LVEF is a well-established predictor of HF mortality particularly with an average follow-up of up to 5 years.\u003csup\u003e\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e Studies have not explored very longer follow-up periods. Otherwise, heart failure with LVEF\u0026thinsp;\u0026gt;\u0026thinsp;45% (HFpEF) was also associated with increased mortality mainly in NYHA IV.\u003csup\u003e29,30\u003c/sup\u003e However, prognosis of HFpEF is controversial depending of characteristics of included patient in studies. Overall, it is expected patients with recovered LVEF in HFpEF group. Better prognosis was reported in HF with improved LVEF in comparison with persistent HFpEF, declined EF and persistent heart failure with reduced ejection fraction.\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e Additionally, the worsening of functional class is known to be associated with worse outcomes in HF, which was consistent with our findings in very long-term follow-up. NYHA IV was also associated with reduced survival, similar to observations from other studies with follow-up periods of up to 10 years.\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eConcerning the digoxin association with worse prognosis reported in our results, it is crucial to highlight that studies had reported contradictory associations of digoxin with mortality in HF.\u003csup\u003e31\u0026ndash;33\u003c/sup\u003e However, most studies have the caveat of absence of serum digoxin levels assessment, which might have affected outcomes. Subanalysis of the Digitalis Investigation Group trial showed a linear dose\u0026ndash;response relationship linking serum concentration to mortality.\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e Also, the reason for digoxin prescription may be a confounder because in clinical practice digoxin could had been prescribed for more seriously ill patients. The findings emphasize cautious prescribing of digoxin for patients with HF in very long follow-up, because its association with increased mortality was suggested in previous research and by our results. Also, the evidence of benefits of digoxin may be limited in patients undergoing contemporaneous HF treatment.\u003c/p\u003e \u003cp\u003eOur data in which the baseline mean urea values\u0026thinsp;\u0026gt;\u0026thinsp;55 mg/dl were associated with reduced survival confirms previous publications, however, adding new very long-term data. Numerous studies, particularly in the context of decompensated HF, have examined the prognostic value of elevated BUN (\u0026gt;\u0026thinsp;55\u0026ndash;80 mg/dl) as a predictor of morbidity and mortality, albeit with short-term follow-up\u003csup\u003e35,36\u003c/sup\u003e. Report of the Swedish Heart Failure Kidney Registry showed that renal dysfunction is common and strongly associated with short-term and long-term outcomes up to 10-year follow-up in patients with HF.\u003csup\u003e36\u003c/sup\u003e Systematic review and meta-analysis reported that worsening renal function predicts substantially higher rates of mortality and hospitalization in patients with HF.\u003csup\u003e37\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eBaseline lymphopenia as a biomarker for prognosis in HF has been reported, but it has not yet been demonstrated in long-term follow-up as in our results.\u003csup\u003e\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e Lymphopenia is also a marker for worse prognosis in other systemic diseases including COVID-19.\u003csup\u003e41\u003c/sup\u003e The mechanisms responsible for the increment in the relative reduction in lymphocytes in HF are not fully understood. An increase in neutrophil because of systemic inflammation, and lymphopenia caused by elevated cytokines, splanchnic congestion, apoptosis, increased endogenous cortisol and sympathetic tone may play a role.\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e HF can trigger a significant increase in systemic cortisol production.\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe findings from this very long-term follow-up study have several important clinical implications: First, risk stratification - the identified prognostic factors (age\u0026thinsp;\u0026gt;\u0026thinsp;52 years, Chagas disease, LVEF\u0026thinsp;\u0026lt;\u0026thinsp;45%, elevated BUN, lymphopenia, NYHA IV, and digoxin use) can be used to develop comprehensive long-term risk stratification tools for HF patients. These factors remained predictive even in patients receiving optimal specialized care, suggesting they represent fundamental markers of disease severity; Second, modifiable risk factors - among the identified predictors, renal function represents a potentially modifiable risk factor that should be aggressively monitored and managed in HF patients. Strategies to preserve renal function, avoid nephrotoxic agents, and optimize GDMT despite mild renal impairment may improve very long-term outcomes; Third, the Chagas cardiomyopathy - patients with Chagas disease require special attention given their consistently worse prognosis. Fourth, the relevance of specialized HF clinic care - the relatively good survival observed in both groups underscores the importance of long-term specialized multidisciplinary HF clinic care. Even without intensive DMP, continued specialized care with GDMT optimization appears crucial for long-term outcomes; Finally, these data provide realistic survival expectations for counseling HF patients and their families. Some HF patients, particularly those under specialized care with favorable prognostic profiles, can survive\u0026thinsp;\u0026gt;\u0026thinsp;20 years, which is important information for shared decision-making regarding advanced therapies, palliative care discussions, and life planning.\u003c/p\u003e \u003cp\u003eOur study has several important limitations that should be considered when interpreting the results: First, being conducted at a single specialized tertiary HF center limits generalizability to other settings, particularly community-based practices or centers without specialized multidisciplinary HF programs. Second, \u003cb\u003et\u003c/b\u003ehis extended follow-up was not pre-specified in the original trial design, which limits causal inferences and introduces potential biases inherent to retrospective analyses. Third, the very long-term nature of follow-up necessitated pragmatic approaches to death classification. Hospital deaths were classified as HF-related, which may have resulted in some misclassification. Cause of death determination became increasingly challenging over the 23.6-year period. Fourth, quality of life, hospitalization rates, medication adherence, and other clinical parameters were not systematically collected during the extended follow-up period beyond the initial 2.47 years. This limits our ability to understand trajectory of these important outcomes. Fifth, despite comprehensive baseline assessment, unmeasured confounders accumulated over 23.6 years (e.g., changes in social support, socioeconomic status, access to care, development of comorbidities) may have influenced outcomes. Finally, by definition, very long-term follow-up studies the characteristics of survivors. The cohort alive at later time points represents a selected population that may not be representative of all HF patients and temporal changes in HF management (survival bias).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, DMP was not effective in reducing very long-term mortality; however, causes of death differed between groups, with more HF-related deaths in controls. Our findings that age, LVEF, Chagas' disease, NYHA, renal function, lymphocytes, and digoxin use were associated with poor prognosis and could influence future strategies to improve HF management. This study would be highly valuable for patients, doctors, and healthcare professionals seeking a comprehensive understanding and strategies for management of HF in very long-term follow-up.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHeart Failure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDMP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEducational and disease management programs\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eREMADHE trial\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLong-Term Prospective,Randomized,Controlled Study Using Repetitive Education at Six-Month Intervals and Monitoring for Adherence in Heart Failure Outpatients\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eControl group\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLVEF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eleft ventricular ejection fraction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGDMT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eguideline-directed medical therapy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBUN\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eblood urea nitrogen\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNYHA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNew York Heart Association\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003econfidence interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehazard ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eatrial fibrillation.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDr Nelson Cavas Junior for the statistical review.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest related to this study\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eArtificial Intelligence (AI) Use Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo artificial intelligence technologies were used in the conduct of this research or in the writing of this manuscript\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData are available upon reasonable request. If someone wants to request the data from this study, the contact will be Prof Dr Edimar Bocchi [email protected]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Statements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsent obtained directly from patient(s). The study protocol was submitted to the Heart Institute Ethical Committee in 1999 and received the number 827/99. The local ethical committees approved the study. All patients gave informed consent for participation in the study. The study was registered at http://clinicaltrials.gov (Identifier NCT 00505050). Participants gave informed consent to participate in the study before taking part.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributors:\u003c/strong\u003e EAB takes responsibility for the manuscript, including the data and analysis. GVG, CER and ARD made substantial contributions to drafting the work. SMAF, BB, PRC, RTM, FDC made substantial contributions to data acquisition. JTF,FDC \u0026nbsp; \u0026nbsp;made substantial contributions to statistical analyses. All authors made substantial contributions to the acquisition, analysis or interpretation of data for the work; revising the work critically for important intellectual content; final approval of the version to be published and agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are investigated and resolved.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eNone declared\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eShahim B, Kapelios CJ, Savarese G, Lund LH. 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Beta-blocker therapy and mortality of patients with Chagas cardiomyopathy: a subanalysis of the REMADHE prospective trial. Circ Heart Fail. 2010;3:82\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBocchi EA, Arias A, Verdejo H, Diez M, G\u0026oacute;mez E, Castro P. Interamerican Society of Cardiology. The reality of heart failure in Latin America. J Am Coll Cardiol. 2013;62:949\u0026ndash;58.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBreathett K, Allen LA, Udelson J, Davis G, Bristow M. Changes in left ventricular ejection fraction predict survival and hospitalization in heart failure with reduced ejection fraction. Circ Heart Fail. 2016;9:e002962.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePark JJ, Mebazaa A, Hwang IC, Park JB, Park JH, Cho GY. Phenotyping heart failure according to longitudinal ejection fraction change: myocardial strain, predictors, and outcomes. J Am Heart Assoc. 2020;9:e015009.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSolomon SD, Anavekar N, Skali H, et al. CHARM Investigators. Influence of ejection fraction on cardiovascular outcomes in a broad spectrum of heart failure patients. Circulation. 2005;112:3738\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmed A, Aronow WS, Fleg JL. Higher New York Heart Association classes and increased mortality and hospitalization in patients with heart failure and preserved left ventricular function. Am Heart J. 2006;151:444\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKajimoto K, Sato N. Investigators of the ATTEND Registry. Sex differences in New York Heart Association classification and survival in acute heart failure patients with preserved or reduced ejection fraction. Can J Cardiol. 2020;36:30\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDigitalis Investigation Group. The effect of digoxin on mortality and morbidity in patients with heart failure. N Engl J Med. 1997;336:525\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eElayi CS, Shohoudi A, Moodie E, et al. AF-CHF Investigators. Digoxin, mortality, and cardiac hospitalizations in patients with atrial fibrillation and heart failure with reduced ejection fraction. Int J Cardiol. 2020;313:48\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHashemi-Shahri SH, Aghajanloo A, Ghavami V, et al. Digoxin and outcomes in patients with heart failure and preserved ejection fraction: a systematic review and meta-analysis. Curr Drug Targets. 2023;24:191\u0026ndash;200.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdams KF Jr, Butler J, Patterson JH, et al. Dose response characterization of the association of serum digoxin concentration with mortality outcomes in the Digitalis Investigation Group trial. 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Association between neutrophil percentage-to-albumin ratio, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio and long-term mortality in community-dwelling adults with heart failure. BMC Cardiovasc Disord. 2023;23:312.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTamaki S, Nagai Y, Shutta R, et al. OCVC-Heart Failure Investigators. Combination of NLR and PLR as a predictor of cardiac death in acute decompensated HFpEF. J Am Heart Assoc. 2023;12:e026326.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYe GL, Chen Q, Chen X, et al. The prognostic role of platelet-to-lymphocyte ratio in patients with acute heart failure: a cohort study. Sci Rep. 2019;9:10639.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYan S, Wu G. Is lymphopenia different between SARS and COVID-19 patients? FASEB J. 2021;35:e21245.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVakhshoori M, Nemati S, Sabouhi S, et al. Prognostic impact of monocyte-to-lymphocyte ratio in coronary heart disease: systematic review and meta-analysis. J Int Med Res. 2023;51:3000605231204469.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAcanfora D, Gheorghiade M, Trojano L, et al. Relative lymphocyte count as a mortality indicator in elderly CHF patients. Am Heart J. 2001;142:167\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTzanis G, Dimopoulos S, Agapitou V, Nanas S. Exercise intolerance in chronic heart failure: cortisol and catabolic state. Curr Heart Fail Rep. 2014;11:70\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Graphical Abstract","content":"\u003cp\u003eGraphical Abstract is not available with this version.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Heart failure, very long-term follow-up, Chagas disease, prognosis, renal","lastPublishedDoi":"10.21203/rs.3.rs-8484135/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8484135/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eDisease management programs (DMP) have reduced hospitalizations and improved quality of life in heart failure (HF). However, prognostic factors and survival in very long-term follow-up (\u0026gt;\u0026thinsp;20 years) have not been reported.\u003c/p\u003e\u003ch2\u003eAims\u003c/h2\u003e \u003cp\u003eTo identify prognostic predictors of all-cause mortality in patients with HF followed for 23.6 years.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe REMADHE trial (NCT00505050, 2007-07-20) was a prospective, single-center, randomized trial (n\u0026thinsp;=\u0026thinsp;412) comparing DMP versus usual care (C) with initial follow-up of 2.47 years. This extended analysis followed patients for 23.6 years to identify prognostic predictors of all-cause mortality.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe all-cause mortality rate was 88.3%. HF was the first cause of death followed by sudden death. Mortality was higher in the first 6-year follow-up. The predictive variables in multivariate analysis associated with mortality were age\u0026thinsp;\u0026gt;\u0026thinsp;52 years (P\u0026thinsp;=\u0026thinsp;0.015), Chagas etiology (P\u0026thinsp;=\u0026thinsp;0.010), LVEF\u0026thinsp;\u0026lt;\u0026thinsp;45% (P\u0026thinsp;=\u0026thinsp;0.008), digoxin use (P\u0026thinsp;=\u0026thinsp;0.002), NYHA IV (P\u0026thinsp;=\u0026thinsp;0.01), blood urea nitrogen (BUN) (P\u0026thinsp;=\u0026thinsp;0.03), and lymphopenia (P\u0026thinsp;=\u0026thinsp;0.005). In very long-term follow-up, DMP did not affect mortality in patients under guideline-directed medical therapy (GDMT). HF as a cause of death was more frequent in the C group.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eDMP was not effective in reducing very long-term mortality; however, causes of death differed between groups, with more HF-related deaths in controls. Our findings that age, LVEF, Chagas' disease, NYHA, renal function, lymphocytes, and digoxin use were associated with poor prognosis could influence future strategies to improve HF management.\u003c/p\u003e","manuscriptTitle":"Prognostic Factors Related to All-Cause Mortality in Very Long-term Follow-up of Patients with Heart Failure: The REMADHE Trial Extended Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-12 13:53:02","doi":"10.21203/rs.3.rs-8484135/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-16T07:28:53+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-12T10:51:09+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-09T02:51:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"235077551704399073419480936951688087093","date":"2026-01-29T14:04:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"223357299851897523956696286218727835629","date":"2026-01-29T09:11:37+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-29T04:54:05+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-29T04:48:38+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-12T10:34:33+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-11T23:59:41+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2026-01-11T23:53:33+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"61b7f85d-548b-4bf0-95c5-feafd6adf715","owner":[],"postedDate":"February 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-26T07:10:42+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-12 13:53:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8484135","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8484135","identity":"rs-8484135","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}