Association of Social Determinants of Health With Long-Term Mortality in Decompensated Heart Failure

Association of Social Determinants of Health With Long-Term Mortality in Decompensated Heart Failure | 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 Association of Social Determinants of Health With Long-Term Mortality in Decompensated Heart Failure Giovanni Possamai Dutra, Bruno Ferraz de Oliveira Gomes, Davi de Vasconcellos Dias, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8864772/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Decompensated heart failure (DHF) remains associated with high in-hospital mortality (8–17%) and substantial post-discharge mortality (15–20% at 90 days). Although social determinants of health (SDH) have been linked to short-term adverse outcomes, their impact on long-term survival in DHF, particularly in Brazilian urban settings, is not well defined. Objectives: To evaluate the association between SDH and in-hospital and long-term mortality in patients hospitalized for DHF and managed with standardized protocols at private tertiary centers serving populations with marked social inequalities. Methods: In this retrospective analysis of a prospective cohort, 1,023 consecutive patients admitted with DHF between 2011 and 2021 at two private tertiary centers in Rio de Janeiro were included. The centers are located in areas with contrasting municipal Human Development Index and serve populations with different socioeconomic status, one predominantly caring for lower socioeconomic status patients. All patients were treated according to standardized institutional protocols. Survival analyses used Kaplan–Meier curves, Fine–Gray competing risk models, survival trees, and multivariable Cox regression with variable selection via the Elastic Net, adopting a two-sided alpha of 5%. Results: In-hospital mortality was 10.8%, and post-discharge mortality reached 64.1% over a median follow-up of 6.5 years (interquartile range [IQR] 3.8–9.1). In the multivariable Cox model, SDH were independently associated with lower survival. Brown/Black ethnicity (hazard ratio [HR] 1.44, 95% confidence interval [CI] 1.12–1.86, P = 0.005), admission to the tertiary center serving a lower socioeconomic status population (HR 2.43, 95% CI 2.06–2.86, P < 0.001), and older age (HR 1.02 per year, 95% CI 1.02–1.03, P < 0.001) showed stronger adjusted hazard ratios than several traditional clinical variables. Conclusions: In Brazilian urban settings, SDH are powerful predictors of long-term mortality in patients hospitalized for DHF, exceeding the prognostic impact of conventional clinical factors. Incorporating SDH into risk prediction models may improve identification of high-risk patients and support more equitable allocation of cardiovascular care. Figures Figure 1 Figure 2 Figure 3 1 - Introduction Heart failure (HF) affects an estimated 64 million people worldwide, with a prevalence of 1–2% in the general adult population and up to 10% among individuals older than 70 years, largely driven by population aging and the growing burden of hypertension and diabetes. 1 Annual incidence ranges from 1 to 5 cases per 1,000 persons, leading to millions of hospitalizations for acute decompensation. 2 Decompensated heart failure (DHF) is a common cardiovascular emergency, accounting for more than 1 million hospital admissions annually and carrying in-hospital mortality rates of up to 17% and 90-day post-discharge mortality of 15–20%. 1 In the United States alone, HF accounts for over 1 million hospitalizations per year, and its prevalence is projected to approach 3% by 2030, driven by population aging and improved post–myocardial infarction survival. 1 In Brazil, HF prevalence is estimated at 1.1% among adults and 3.3% among older adults, with 941,576 hospitalizations recorded between 2019 and 2023 and in-hospital lethality of up to 17%. 4,5 The impact is greatest in the Southeast region and disproportionately affects Brown/Black men aged 70–79 years. 5 Patients with HF experience substantially worse quality of life than the general population and than those with many other chronic conditions, with symptoms that impose marked limitations on physical capacity and social participation. Social determinants of health (SDH)—including low income, limited formal education, Brown/Black ethnicity, and food insecurity—intensify these vulnerabilities, increasing the adjusted risk of death by approximately 2.5- to 3-fold in patients with two or more unfavorable SDH in the first 90 days after discharge. 3 , 6 , 7 In the Brazilian context, adverse SDH undermine therapeutic adherence, raise readmission rates by 25–40%, and worsen survival, with low educational attainment independently associated with higher 180-day mortality after hospitalization for HF. 8 Nevertheless, the incremental contribution of SDH to long-term mortality in DHF remains insufficiently characterized. 6 Conventional clinical risk scores could plausibly be strengthened by plausible that conventional clinical risk scores, such as the MAGGIC score, could be strengthened by incorporating SDH, thereby capturing dimensions of risk related to the ability to purchase medications, adhere to lifestyle recommendations and healthy behaviors, and attend follow-up consultations. 7 , 9 Such integration could refine prognostic stratification beyond traditional biological variables alone. Broader socioeconomic metrics, such as the Municipal Human Development Index (MHDI), show an inverse relationship with HF mortality, underscoring disparities in access to tertiary care and disease-modifying therapies. Ecological analyses have reported an inverse association between MHDI and reductions in HF mortality rates over time, with a correlation coefficient of approximately r = − 0.73, suggesting regions with lower human development experience smaller gains in survival. 10 These findings highlight the interaction between territorial inequality and clinical outcomes in cardiovascular disease. The present study aimed to evaluate the association between SDH and both in-hospital and long-term all-cause mortality among patients hospitalized for DHF and managed with standardized treatment protocols at tertiary centers serving populations with pronounced social inequalities. 2 - Methods Study design and setting This was a retrospective analysis of a prospectively collected cohort of patients admitted with decompensated heart failure (DHF). Consecutive patients were enrolled from the cardiac intensive care units of two tertiary hospitals with internationally standardized protocols for HF management. One hospital, located in the West Zone of Rio de Janeiro, has 162 beds and is situated in an area classified as high socioeconomic status by the United Nations Development Programme, with a municipal Human Development Index (HDI) of 0.959 and a Gini coefficient of 0.637. The second hospital, located in the North Zone, has 340 beds and is situated in an area with an HDI of 0.814 and a Gini coefficient of 0.538, reflecting greater social inequality. 11 , 12 Compared with the North Zone hospital, the West Zone hospital also treats a higher proportion of patients covered by high-standard private health insurance plans (58.1% vs 41.9%; P 18 years admitted between September 2011 and December 2021 with a primary diagnosis of HF were eligible. The diagnosis of HF at admission was based on classic clinical criteria, including the Framingham13 and Boston14 criteria, complemented by laboratory markers such as B-type natriuretic peptide (BNP) and N-terminal pro–B-type natriuretic peptide (NT-proBNP).15 BNP or NT-proBNP values > 300 pg/mL were considered elevated. Echocardiographic parameters were collected with preference for LVEF calculated by the Simpson biplane method; when unavailable, LVEF estimated by the Teichholz method was used. For patients with more than one hospitalization during the study period, only clinical data from the last admission were included in the analysis. Statistical analysis Results were presented as median + IQR for continuous variables or number (percentage) for categorical variables. The chi-square test was used for categorical variables and one-way ANOVA for comparing means. Kaplan-Meier curves were employed to analyze survival over time, with the Tarone-Ware test for group comparisons. 16 A semiparametric Cox proportional hazards model was used to evaluate predictors of time-to-event outcomes. The model was first estimated using Elastic Net regularization, 17 a machine learning–based technique that combines L1 (Lasso) and L2 (Ridge) penalties to perform variable selection and shrinkage in the presence of high dimensionality and multicollinearity. Variables retained by the Elastic Net were then re-estimated in a conventional Cox model using maximum likelihood, and only those with statistical significance were kept in the final model. To further explore complex interactions between clinical and social variables over follow-up, survival trees based on recursive partitioning were constructed as a nonparametric approach to identify subgroups with distinct mortality risk profiles. 18 In addition, proportional subdistribution hazards models for competing risks, according to the Fine–Gray framework, 19 were fitted to estimate cumulative subdistribution hazards for in-hospital and post-discharge mortality, accounting for the competing nature of these events. Classification trees were used for recursive partitioning of these competing-risk outcomes. Missing data were handled using multiple imputation. Six of the 26 independent variables had missing values (Zone: 13.3%; Ethnicity: 33.3%; Marital status: 21.6%; Occupation: 27.4%; systolic dysfunction grade: 21.7%; Ejection Fraction: 25.2%). Multiple imputation was performed with the Hmisc package in R, 20 generating plausible values under a multivariate framework that preserved the joint distribution of the 1,023 observations. Multivariable logistic regression was then used to identify independent predictors of all-cause mortality (in-hospital or during follow-up) among patients hospitalized for DHF. A full model including 26 clinical and social variables—such as admitting hospital, self-reported ethnicity, sex, age, type of health insurance, and occupation—was initially subjected to Elastic Net regularization to perform robust variable selection and reduce redundancy. 17 Variables selected by the Elastic Net were subsequently entered into a final multivariable logistic regression model. All analyses were conducted using R (R Foundation for Statistical Computing, Vienna, Austria), 21 and a two-sided alpha level of 0.05 was considered statistically significant. Ethical considerations Dates of all-cause death were obtained from the public website of the General Court of Justice of the State of Rio de Janeiro. The study protocol was approved by the Research Ethics Committee of Instituto D’Or de Ensino e Pesquisa (IDOR) on 18 September 2019 (ethical review number 18502319.3.0000.5249; opinion no. 3.582.453). Clinical trial number: not applicable. 3 - Results Baseline characteristics of the 1,023 patients hospitalized with DHF are summarized in Table 1. Mean age 76.3±13.4 years with male predominance 53.9%. Median follow-up was 6.5 years (IQR 3.8-9.1). In-hospital mortality was 10.8% (n=110). Post-discharge mortality reached 64.1% (656 deaths) at study end. Table 1 summarizes baseline clinical and social characteristics (sex. age. White vs. Brown/Black ethnicity) stratified by in-hospital and post-discharge death. Patients dying during index admission exhibited higher admission BNP/NT-proBNP [median 4.940 (IQR 1.430-12.800) vs. 2.189 (IQR 937-7.660) pg/mL]. Lower LVEF [41.2% (IQR 32-58) vs. 40.5% (IQR 32-76)]. Older age [81.5 (IQR 74-89) vs. 80 (IQR 70-86) years]. Higher Brown/Black ethnicity prevalence (13.8% vs. 10.5%). Post-discharge decedents more frequently had prior HF (57.1% vs. 22.4%) and hypertension (64.6% vs. 24.9%) Table 1. Clinical characteristics of patients with decompensated heart failure according to in-hospital and post-discharge mortality Variable In-hospital mortality – Yes (n = 110; 10.8%) In-hospital mortality – No (n = 913; 89.2%) Post-discharge mortality – Yes (n = 656; 64.1%) Post-discharge mortality – No (n = 257; 25.9%) LVEF, % (median, IQR) 41.2(IIQ:32-58) 45.9(IIQ:34-60) 40.5(IIQ:32-76) 47(IIQ:36-62) BNP at admission, pg/mL (median, IQR) 4,940 (1,430–12,800) 2,310 (979–7,371) 2,189 (937–7,660) 2,720 (1,090–6,223) Age ( years), n (%) 81.5(IIQ:74-89) 78(IIQ:68-85) 80(IIQ:70-86) 71(IIQ:61-80) Women, n (%) 58 (12.3) 413 (87.7) 287 (60.9) 126 (26.8) Men, n (%) 52 (9.4) 500 (90.6) 369 (66.8) 131 (23.7) Brown/Black ethnicity, n (%) 11 (13.8) 69 (86.7) 55 (68.8) 14 (17.5) White ethnicity, n (%) 99 (10.5) 69 (86.3) 601 (63.7) 243 (25.8) Prior heart failure, n (%) 43 (20.5) 167 (79.5) 120 (57.1) 47 (22.4) Hypertension, n (%) 88 (10.5) 752 (89.5) 543 (64.6) 209 (24.9) Permanent atrial fibrillation, n (%) 36 (16.2) 186 (83.8) 143 (64.4) 43 (19.4) CKD* (eGFR < 60 mL/min/1.73 m²), n (%) 31 (14.6) 182 (85.2) 143 (67.1) 39 (18.3) Prior myocardial infarction*, n (%) 21 (8.3) 232 (91.7) 177 (70.0) 55 (21.7) Prior dementia*, n (%) 13 (18.3) 58 (81.7) 53 (74.3) 5 (7.0) Diabetes, n (%) 38 (11.5) 292 (88.5) 205 (62.1) 87 (26.4) Prior stroke, n (%) 6 (12.0) 44 (88.0) 32 (64.0) 12 (24.0) Beta-blocker use, n (%) 27 (11.2) 215 (88.8) 164 (67.8) 51 (21.1) Kaplan–Meier survival curves are shown in figure 1. The overall curve (Figure 1A) depicts all-cause mortality from hospital admission to the end of follow-up, encompassing both in-hospital and post-discharge deaths. When stratified by hospital (Figure 1B), patients admitted to the North Zone tertiary hospital had significantly lower survival than those treated at the West Zone hospital. Survival curves stratified by occupation (Figure 1C) showed worse long-term survival among patients in informal or elementary-level occupations compared with those in middle/technical or higher-level occupations. Stratification by self-reported race/skin color (Figure 1D) revealed higher mortality among Brown/Black patients than among White patients. Figure 1. Kaplan–Meier survival curves for all-cause mortality in patients with decompensated heart failure: global analysis and stratification by social and geographic determinants. 1A : Combined survival curve demonstrates temporal mortality patterns from admission through follow-up (in-hospital + post-discharge deaths). 1B : West Zone vs. North Zone hospitals - North Zone admissions exhibited significantly inferior survival. 1C : Occupational stratification - Informal/elementary occupations showed reduced survival vs. middle/technical/higher-level professions. 1D : Race/color stratification - Brown/Black ethnicity associated with higher mortality rates. Table 2 presents competing risks models for post-discharge and in-hospital mortality. Prior admission to the North Zone hospital unit (lower socioeconomic patient population) demonstrated strong chi-square dependence between residential zone and hospital unit (p < 0.0001). This was associated with higher post-discharge mortality [HR 2.01 (95% CI 1.61-2.51; p < 0.001)]. This association exceeded clinical variables including severe dysfunction [HR 0.62 (95% CI 0.50-0.78; p < 0.0001)], prior myocardial infarction [HR 1.06 (95% CI 0.90-1.23; p = 0.4931)], and ejection fraction [HR 0.99 (95% CI 0.99-1.00; p = 0.0277)]. Aging demonstrated linear risk increase [HR 1.01 (95% CI 1.007-1.019; p < 0.001)]. Protective factors included female sex [HR 0.79 (95% CI 0.68-0.91; p = 0.001)] and prior cardiac arrhythmias (excluding AF/flutter) [HR 0.74 (95% CI 0.63-0.87; p = 0.0004)]. The in-hospital sub-risk model identified increased risk with severe systolic dysfunction [HR 2.25 (95% CI 1.35-3.77; p = 0.002)], permanent atrial fibrillation [HR 1.85 (95% CI 1.24-2.75; p = 0.003)], and prior heart failure [HR 1.79 (95% CI 1.18-2.72; p = 0.006)]. Aging showed linear risk increment [HR 1.024/year (95% CI 1.006-1.042; p = 0.008)], evidencing cumulative frailty. Table 2. Competing risk models in patients with decompensated heart failure (A) Post-discharge mortality Variable Coefficient Hazard ratio (HR) Lower 95% CI Upper 95% CI P value South Zone −0.3062 0.7363 0.3609 1.5019 0.4000 North Zone −0.2367 0.7892 0.3958 1.5739 0.5015 West Zone −0.2398 0.7868 0.3840 1.6121 0.5123 Other municipality −0.3288 0.7198 0.3068 1.6888 0.4499 BNP at admission 0.0000 1.0000 1.0000 1.0000 0.0025 Ejection fraction, % −0.0062 0.9938 0.9883 0.9993 0.0277 Prior heart failure −0.0412 0.9596 0.7948 1.1587 0.6683 Age, years 0.0126 1.0127 1.0066 1.0188 <0.0001 Beta-blocker use 0.1091 1.1153 0.9476 1.3127 0.1895 Prior arrhythmia (except AF/flutter) −0.3015 0.7397 0.6273 0.8723 0.0003 Prior atrial fibrillation −0.1306 0.8776 0.7493 1.0278 0.1052 CKD with dialysis −0.1658 0.8472 0.5953 1.2058 0.3572 Prior myocardial infarction 0.0538 1.0553 0.9048 1.2309 0.4931 Mild LV systolic dysfunction −0.2833 0.7533 0.5980 0.9489 0.0162 Moderate LV systolic dysfunction −0.3366 0.7142 0.5639 0.9045 0.0052 Severe LV systolic dysfunction −0.4726 0.6234 0.4960 0.7835 <0.0001 Female sex −0.2400 0.7866 0.6826 0.9065 0.0009 Hospital unit: North Zone 0.6991 2.0119 1.6141 2.5076 <0.0001 Table 2. Competing risk models in patients with decompensated heart failure (B) In-hospital mortality Variable Coefficient Hazard ratio (HR) Lower 95% CI Upper 95% CI P value South Zone 16.1600 1.05 × 10⁷ 4.21 × 10⁶ 2.61 × 10⁷ <0.0001 North Zone 16.4900 1.45 × 10⁷ 7.53 × 10⁶ 2.80 × 10⁷ <0.0001 West Zone 16.5000 1.47 × 10⁷ 6.03 × 10⁶ 3.58 × 10⁷ <0.0001 Other municipality 0.0583 1.0600 0.6188 1.8160 0.8319 BNP at admission 0.00002 1.0000 1.0000 1.0000 <0.0001 Ejection fraction, % 0.00482 1.0050 0.9916 1.0180 0.4754 Prior heart failure 0.5838 1.7930 1.1800 2.7230 0.0062 Age, years 0.02385 1.0240 1.0060 1.0420 0.0081 Beta-blocker use −0.3857 0.6800 0.4464 1.0360 0.0724 Prior arrhythmia (except AF/flutter) 0.5607 1.7520 1.1580 2.6510 0.0080 Prior atrial fibrillation 0.6131 1.8460 1.2410 2.7460 0.0025 CKD with dialysis 0.8611 2.3660 0.9914 5.6460 0.0523 Prior myocardial infarction −0.4576 0.6328 0.3978 1.0070 0.0533 Mild LV systolic dysfunction 0.0761 1.0790 0.5475 2.1270 0.8260 Moderate LV systolic dysfunction −0.0549 0.9466 0.5297 1.6910 0.8529 Severe LV systolic dysfunction 0.8117 2.2520 1.3460 3.7660 0.0020 Female sex 0.3653 1.4410 0.9771 2.1250 0.0653 Hospital unit: North Zone −0.6840 0.5046 0.2413 1.0550 0.0691 Survival trees provided additional insight into interactions between social and clinical determinants (Figures 2 and 3). In the tree for all-cause mortality (Figure 2), patients admitted to the North Zone hospital who were older than 79 years and had a history of arrhythmias formed a high-risk phenotype with markedly reduced survival over time. In contrast, among patients treated at the West Zone hospital, those with prior HF and intermediate-cost insurance plans showed comparatively better survival, whereas patients without prior HF but older than 65 years had worse outcomes. Figure 2. Survival tree for all-cause mortality (in-hospital and post-discharge) in patients with decompensated heart failure. Recursive partitioning survival tree showing subgroups with different risks of all-cause mortality according to clinical and social determinants. Each terminal node displays the Kaplan–Meier survival curve for that subgroup. Hosp.Unit: hospital unit (HU-W = West Zone hospital, HU-N = North Zone hospital); Previous.HF: history of heart failure (yes/no); Age: age in years; Adm.BNP: B-type natriuretic peptide at admission (pg/mL; split at 4,653 pg/mL); Health.Insurance: type of health insurance (High = high-standard, Middle = intermediate-cost, Low = low-cost/other plans); N_AF.Arrhy: prior arrhythmia other than atrial fibrillation or flutter (yes/no); Occupation: Ret = retired, Low = informal/elementary-level, Sec = middle/technical-level, Sup = higher education. In the survival tree restricted to post-discharge mortality (Figure 3), patients of Brown/Black ethnicity admitted to the North Zone hospital and those with prior arrhythmias exhibited particularly poor long-term survival. Among White patients, the lowest survival was observed in those without a history of arrhythmias (except atrial fibrillation/flutter). In the West Zone hospital, patients older than 66 years with prior HF also showed reduced survival, underscoring the interplay between age, underlying cardiac disease, and place of care. Figure 3. Survival tree for post-discharge (non-hospital) mortality in patients with decompensated heart failure (excluding in-hospital deaths). Recursive partitioning survival tree showing subgroups with different risks of post-discharge mortality, based on the final Cox model with significant variables selected via Elastic Net. Each terminal node displays the Kaplan–Meier survival curve for that subgroup. Coding: Hospital unit (HU-W = West Zone hospital, HU-N = North Zone hospital); Age (years); Ethnicity (White = 0, Brown/Black = 1); Prior arrhythmia other than atrial fibrillation or flutter (N.AF.Arrhy: yes/no); Prior heart failure (yes/no). The final multivariable Cox model adjusted via Elastic Net (Table 3) identified Brown/Black ethnicity (HR 1.44, 95% CI 1.12–1.86; P = 0.005), older age (HR 1.02 per year, 95% CI 1.02–1.03; P < 0.001), prior diabetes (HR 1.18, 95% CI 1.01–1.38; P = 0.036), and prior HF (HR 1.32, 95% CI 1.10–1.59; P = 0.002) as independent predictors of higher all-cause mortality. Mild, moderate, and severe LV systolic dysfunction were associated with lower hazard compared with the reference category, as was higher LVEF, while admission to the North Zone hospital remained one of the strongest predictors (HR 2.43, 95% CI 2.06–2.86; P < 0.001). Table 3. Final Multivariable Cox Proportional Hazards Model for All-Cause Mortality (N=1023), with Variables Selected via Elastic Net¹⁷ Variable Coefficient Hazard Ratio (exp(coef)) SE(coef) z P (> |z|) Lower 95% CI Upper 95% CI Brown/Black Ethnicity 0.365733 1.441571 0.130908 2.794 0.005209 1.1153 1.8632 Age (per year) 0.021773 1.022012 0.003275 6.648 2.97e-11 1.0155 1.0286 Female Sex -0.169582 0.844017 0.077146 -2.198 0.027935 0.7256 0.9818 Prior Diabetes 0.167193 1.181982 0.079925 2.092 0.036449 1.0106 1.3824 Prior Arrhythmias (Excluding AF/Flutter) -0.420012 0.657039 0.094167 -4.460 8.18e-06 0.5463 0.7902 Prior Heart Failure 0.281095 1.324579 0.092799 3.029 0.002453 1.1043 1.5888 Mild Systolic Dysfunction -0.332772 0.716934 0.132906 -2.504 0.012287 0.5525 0.9303 Moderate Systolic Dysfunction -0.491507 0.611704 0.137719 -3.569 0.000358 0.4670 0.8012 Severe Systolic Dysfunction -0.600284 0.548656 0.160435 -3.742 0.000183 0.4006 0.7514 Ejection Fraction (per %) -0.011498 0.988568 0.004099 -2.805 0.005035 0.9807 0.9965 Admission to North Zone Hospital 0.886826 2.427414 0.083856 10.576 2e-16 2.0595 2.8610 Multivariable Cox proportional hazards model for prediction of all-cause mortality in 1,023 patients hospitalized for decompensated heart failure, with variables selected by Elastic Net regularization¹⁷. Reference categories: white ethnicity, male sex, no prior diabetes/heart failure/arrhythmias, preserved systolic function, West Zone hospital. Significance: *** P<0.001; ** P<0.01; * P<0.05; . P<0.10 In the multivariable logistic regression model for overall mortality (Table 4), older age, Brown/Black ethnicity, being widowed or separated/divorced, prior dementia, cardiac arrhythmias other than atrial fibrillation/flutter, middle/technical-level occupation, and chronic kidney disease requiring hemodialysis were independently associated with increased odds of death, whereas higher LVEF was protective. Table 4. Independent Predictors of All-Cause Mortality (In-Hospital and Post-Discharge) by Multivariable Logistic Regression Variable Estimate Std. Error z Value P (> |z|) Sig. Intercept -2.169381 0.795249 -2.728 0.00637 Brown/Black Ethnicity 0.844710 0.335882 2.515 0.01191 * Marital Status: Married/Stable Union 0.503806 0.257393 1.957 0.05031 . Marital Status: Alive (Single/Widowed) 0.953237 0.297224 3.207 0.00134 *** Marital Status: Separated/Divorced 0.735907 0.341454 2.155 0.03115 * Occupation: Elementary/Informal -0.074838 0.219000 -0.342 0.73256 Occupation: Middle/Technical 0.613544 0.239973 2.557 0.01057 * Occupation: Higher Education 0.280210 0.241670 1.159 0.24626 Age (per year) 0.049372 0.006958 7.096 1.29e-12 *** Prior Arrhythmias (Excl. AF/Flutter) 0.625408 0.233670 2.676 0.00744 ** Prior Dementia 1.284251 0.490466 2.618 0.00883 ** Mild Systolic Dysfunction -0.316693 0.269172 -1.177 0.23938 Moderate Systolic Dysfunction -0.460539 0.278869 -1.651 0.09865 . Severe Systolic Dysfunction -0.208641 0.336470 -0.620 0.53520 Ejection Fraction (per %) -0.026346 0.008851 -2.977 0.00291 ** Prior CKD 0.530182 0.209122 2.535 0.01124 * Legend: Multivariable logistic regression model for independent predictors of all-cause mortality (in-hospital and post-discharge) in patients hospitalized for decompensated heart failure. Reference categories: white ethnicity, single marital status (base), elementary/informal occupation, no prior comorbidities, preserved systolic function. Significance: *** P<0.001; ** P<0.01; * P<0.05; . P<0.10. 4 - Discussion In Brazilian urban settings, decompensated heart failure (DHF) remains strongly influenced by social determinants of health (SDH) across the entire care continuum from hospital admission through long-term follow-up. In-hospital mortality was 10.8% and post-discharge mortality reached 64.1% at median follow-up of approximately 7 years, rates consistent with international benchmarks (8–17% lethality; 15–20% at 90 days) but revealing persistent long-term risk comparable to reductions of up to 50% in life expectancy versus the general population. 1,3 These results illustrate that even within private hospitals using standardized protocols socio-structural factors related to race/color, occupation, marital status, residential location, and admitting hospital unit maintain strong associations with fatal outcomes (Table 3 ). This suggests interventions focused exclusively on biological variables may prove insufficient for mortality reduction. This hypothesis finds support in a multicenter longitudinal study 22 of 1,377 heart failure patients employing inverse probability-weighted marginal structural models. Unfavorable SDH, low social support, and reduced support utilization during follow-up (but not at baseline) exerted independent causal effects on mortality, demonstrating SDH impact accumulation post-discharge that requires continuous monitoring to mitigate residual risk. Table 3 , through the Elastic Net-adjusted 17 Cox model reinforces independent SDH effects, with Brown/Black ethnicity (HR 1.44, 95% CI 1.12–1.86, P = 0.005) and North Zone admission (HR 2.43, 95% CI 2.06–2.86, P < 0.001) yielding higher adjusted hazard ratios than traditional clinical variables, aligning with racial disparities documented in Brazilian HF cohorts. 23 Brown/Black ethnicity and North Zone hospital admission were associated with reduced survival in the multivariable Cox model for all-cause mortality. Kaplan-Meier curves demonstrated inferior survival among retirees and - among actively employed patients - those in informal/low-qualification occupations. However, Fine-Gray competing risk models showed no significant ethnicity or occupation associations with in-hospital versus post-discharge mortality. In multivariable logistic regression for overall mortality, only middle/technical occupation maintained significant outcome association, while hospital unit and informal occupation showed no significant mortality associations. The 44% mortality increase with Brown/Black ethnicity converges with North American cohort data and systematic reviews documenting 2- to 3-fold risk elevation among patients with multiple adverse SDH, independent of ejection fraction or guideline-directed therapies. 6 , 7 This risk gradient likely reflects combined historical ethnic vulnerability, specialized care access barriers, lower formal education, and precarious employment—elements well-described in global cardiovascular disease and SDH analyses. 24 Elementary/informal occupations and middle/technical professions identified as survival predictors complement education and income as vulnerability markers. In this cohort, elementary/informal-level workers exhibited inferior survival versus middle/technical and higher education categories (Fig. 1 C), findings approaching Brazilian multicenter cohorts where low education increased 180-day post-acute HF mortality hazard by 39%. 10 Ecological and panel studies confirm hospitalization and HF mortality rates associate with social vulnerability indicators, inadequate primary care spending, and reduced community strategy coverage—suggesting clinical risk amplification within less-structured care environments. 10 , 23 Intra-urban disparities between North Zone and West Zone hospitals (distinct population social indices) exemplify socio-spatial heterogeneity and intra-urban vulnerability gradients. Residential zone and hospital unit variables demonstrated dependence [Cramer's V = 0.8 indicating strong association].- associated with post-discharge subdistribution hazard ratio of 2.01, surpassing classic risks such as prior myocardial infarction or ventricular dysfunction. This suggests patient residential region functions as a cardiovascular risk factor. Brazilian spatial analyses demonstrate municipalities/neighborhoods with lower human development. Greater poverty. Reduced education concentrate higher heart failure/cardiovascular mortality rates. Frequently accompanied by underreporting and diagnostic delay (particularly North/Northeast regions). 23 , 25 Employed competing risk models, survival trees, and multivariable logistic regression enabled comprehensive exploration of clinical-social factor interactions, consistent with recent approaches incorporating SDH into HF mortality prediction models that demonstrated measurable discriminative capacity improvement versus conventional clinical-only scores. 9 , 26 Survival trees (Figs. 2 , 3 ) highlight unique SDH-clinical interactions in DHF mortality. Figure 2 (all-cause mortality): North Zone patients aged ≥ 79 years with prior arrhythmia represented highest-risk phenotype; conversely, West Zone patients without prior HF possessing intermediate-cost insurance exhibited superior survival. Figure 3 (post-discharge mortality): Brown/Black ethnicity North Zone patients and those with prior arrhythmia records demonstrated greatest post-discharge vulnerability. Contemporary HF cohort investigations demonstrate intra-urban socioeconomic deprivation patterns surpass isolated biological factors—including ejection fraction—as mortality/readmission predictors, particularly critical among ethnic minorities experiencing disproportionately increased social vulnerability burden. 28 This study complements traditional clinical scores like MAGGIC, hypothesizing SDH addition reveals how territorial/ethnic disparities modulate urban risk trajectories. Survival trees (Figs. 2 , 3 ) demonstrate these socio-spatial factors interact dynamically, complementing traditional clinical frameworks while offering actionable risk stratification hypotheses for vulnerable populations 29 , 30 These results reinforce that incorporating hospital unit, occupation, marital status, and race/color SDH substantially modifies risk stratification—including redefining highest-risk subgroups within identical clinical profiles (e.g., elderly with prior HF admitted to lower socioeconomic units). This pattern aligns with longitudinal studies demonstrating social determinant effects accumulate over time, with persistent adverse context exposures increasing mortality risk even when underlying clinical condition remains stable. 22 Several apparently paradoxical associations emerged in multivariable analysis. Female sex conferred post-discharge protection (HR 0.79), while prior cardiac arrhythmia (excluding atrial fibrillation/flutter) associated with reduced post-discharge risk (HR 0.74)—apparently contrasting in-hospital risk elevation (HR 1.75) and permanent atrial fibrillation's worse in-hospital prognosis—suggesting potential survivor bias and enhanced clinical surveillance in these subgroups. Other HF registries frequently demonstrate women exhibit superior adjusted survival, possibly through biological differences, greater therapeutic adherence, and increased social support strategy utilization—factors potentially attenuating structural inequality impacts. 1 , 3 Prior arrhythmia associations post-follow-up may reflect intensified surveillance phenomena and more rigorous cardiological follow-up in patients with complex electrical history, yielding greater prognostic-modifying therapy optimization beyond potential survivor bias. Marital status and dementia results reinforce relational SDH dimensions: individuals with "alive" marital status and separated/divorced demonstrated greater mortality association, aligning with literature documenting conjugal/social support absence negatively impacts medication adherence, clinic attendance, and symptom management in HF. 6,22,24 Public policy and service organization perspectives align with Brazilian studies examining SDH, Family Health Strategy coverage, federal transfers, and HF hospitalizations relationships. National coverage analysis demonstrated greater primary care coverage, higher budgetary allocation, and better base service structuring associated with lower HF hospitalization rates—suggesting SDH partially addressable through territorialized health policies 23 This study context Kaplan-Meier curves reveal inferior survival in elementary/informal profession group versus higher qualification categories, illustrating practical inequality impacts within single municipality—evidencing geographic distance and care network organization may prove as determinant as clinical/laboratory alterations. This work contributes methodologically by applying multiple imputation to handle missing social variable data, integrating them into competing risk models and survival trees. This approach—despite generating biases/limitations—approximates analysis to real patterns of information absence in clinical/administrative databases, where social variables typically exhibit least completion, rendering vulnerability invisible in traditional risk models. Consistent with recent experiences employing machine learning and geospatial analysis to predict ischemic disease mortality in Southern Brazilian states, combining traditional statistical techniques with more flexible modeling methods appears particularly suited to capture cardiovascular risk spatial/social heterogeneity. 10 , 31 Thus, integrating clinical, socioeconomic, and territorial data into structured databases represents a fundamental step toward constructing fairer, Brazilian reality-applicable risk models. Limitations beyond data capture problems warrant consideration in results interpretation. Retrospective analysis nature implies information bias potential, particularly in self-reported social variables like race/color, occupation, and marital status. Study conducted in 2 private tertiary centers from single municipality may limit generalizability to other contexts. Additionally, more comprehensive SDH—such as detailed family income, housing conditions, food insecurity, community support, and urban violence exposure—not systematically measured likely underestimate true inequality gradiente. 24 Retrospective social data collection challenge with high missingness degree led to this study's data imputation need enabling analyses/conclusions over long follow-up. However performed multiple imputation may amplify biases, yielding biased inequality effect estimates that must be considered. Nonetheless, prolonged follow-up with clinical-social variable integration enabled observing SDH-mortality association persistence beyond critical post-hospitalization period, reducing possibility observed disparities represent transient bias. In summary, results demonstrate ,in decompensated heart failure patients, mortality reflects not only clinical severity but also social vulnerability accumulation manifested through social determinants of health. This corroborates recent studies positioning SDH as central cardiovascular risk components rather than peripheral variables. 23 , 25 , 26 5 - Conclusion This study demonstrated that social determinants of health significantly associated with reduced survival in decompensated heart failure patients. Elevating adjusted risk beyond clinical variables. Pragmatic trials and prospective cohorts incorporating these dimensions may clarify the extent to which SDH modification can measurably reduce heart failure mortality in intra-urban settings marked by profound inequalities. Declarations Acknowledgements The authors thank the cardiology teams at both institutions for data collection support. Ethical approval was obtained from Instituto D'Or Research Ethics Committee (CAAE 18502319.3.0000.5249). Funding No external funding was received for this study. Availability of data and materials The datasets analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare no competing interests. Authors' contributions G.P.D. and G.M.M.O. conceived and designed the study, performed statistical analyses, and wrote the main manuscript text. B.F.O.G., D.V.D., F.C.O., J.L.F.P., P.R.C.J., E.M.N., and B.B.P. curated data, validated results, and reviewed the manuscript. All authors approved the final manuscript. Ethics approval and consent to participate Approved by Instituto D'Or Ethics Committee (opinion 3.582.453). Waiver of informed consent due to retrospective design. Clinical trial number: not applicable. Consent for publication Not applicable. References Bozkurt B, Cowie MR, Foster MI, Haass C, Hoes AW, Krum H. Definition and classification of acute decompensated heart failure. *Eur Heart J*. 2021;42(20):1987–2003. Marcus GM, Alonso A, Peralta CA, et al. European Heart Rhythm Association (EHRA) and Cardiac Arrhythmia Society of Southern Africa (CASSA) Expert Consensus Statement on the management of supraventricular arrhythmias. *Europace*. 2020;22(3):465–95. Fonseca C. Diagnosis and classification of acute heart failure. *Eur Heart J Suppl*. 2019;21(Suppl J):J7–11. Bocchi EA, Marcondes-Braga FG, Bacal F, et al. [Heart failure in Brazil: morbidity and mortality epidemiology]. *Arq Bras Cardiol*. 2018;109(1):39–48. Portuguese. Rohde LE, Montera MW, Bocchi EA, et al. [Brazilian guidelines for the diagnosis, treatment and evaluation of acute and chronic heart failure]. *Arq Bras Cardiol*. 2018;111(2):131–81. Portuguese. Cubillos-Garzón LA, Casas JP, Morillo CA, et al. Etiology and prognostic significance of elevated B-type natriuretic peptide in patients with heart failure and preserved ejection fraction. *Am J Cardiol*. 2009;103(7):942–7. Gheorghiade M, Vaduganathan M, Fonarow GC, Bonow RO. Rehospitalization for heart failure: problems and perspectives. *J Am Coll Cardiol*. 2013;61(4):391–403. Alves JG, Gurgel RQ. [Social determinants of health and the epidemiology of chronic diseases in Brazil]. *Rev Bras Med Fam Comunidade*. 2016;11(38):1–8. Portuguese. Senni M, Paulus WJ. Acute decompensated heart failure—epidemiology and management. *Rev Esp Cardiol*. 2010;63(3):343–57. Lemos FA, Mesquita CT, Colares VS et al. [Social determinants of health and heart failure mortality: geographic and temporal analysis]. *Rev Bras Epidemiol*. 2019;22:E190032. Portuguese. Programa das Nações. Unidas para o Desenvolvimento. [Atlas of Human Development in Brazil]. Rio de Janeiro: UNDP; 2013. Instituto Pereira Passos. [Socioeconomic Index of Rio de Janeiro Municipalities]. Rio de Janeiro: IPP; 2015. Framingham Heart Study. [Diagnostic criteria for heart failure]. In: Opie LH, editor. *Drugs for the Heart*. 8th ed. Philadelphia: Elsevier; 2013. pp. 127–45. Wang TJ, Levy D, Benjamin EJ, Vasan RS. The epidemiology of asymptomatic left ventricular dysfunction: implications for screening and the costs of heart failure. *J Am Coll Cardiol*. 2003;42(11):1879–86. Maisel AS, Krishnaswamy P, Nowak RM, et al. Rapid measurement of B-type natriuretic peptide in the emergency diagnosis of heart failure. *N Engl J Med*. 2002;347(3):161–7. Kaplan EL, Meier P. Nonparametric estimation from incomplete observations. *J Am Stat Assoc*. 1958;53(282):457–81. Zou H, Hastie T. Regularization and variable selection via the elastic net. *J R Stat Soc Ser B Stat Methodol*. 2005;67(2):301–20. LeBlanc M, Crowley J. Survival trees by goodness of split. *J Am Stat Assoc*. 1993;88(422):457–67. Fine JP, Gray RJ. A proportional hazards model for the subdistribution of a competing risk. *J Am Stat Assoc*. 1999;94(446):496–509. Harrell FE Jr. *Regression Modeling Strategies with Applications to Linear Models, Logistic Regression, and Survival Analysis*. 2nd ed. New York: Springer; 2015. R Core Team. *R: A Language and Environment for Statistical Computing*. Vienna: R Foundation for Statistical Computing; 2021. https://www.R-project.org/ . Voors AA, Ouwerkerk W, Zwinderman AH, et al. Determinants and development of pulmonary congestion in acute decompensated heart failure: data from TRANSFORM-HF. *Eur J Heart Fail*. 2019;21(5):592–602. Miranda PD, Ribeiro ALP, Sousa MR, Pimenta AM. [Spatial patterns of cardiovascular disease mortality in Brazilian municipalities]. *Rev Panam Salud Publica*. 2018;42:e132. Portuguese. Mensah GA, Croft JB, Giles WH. The heart, lung, and blood disparities in the United States: a social determinants perspective. *Circ Cardiovasc Qual Outcomes*. 2021;14(1):e007575. Rossi MI, Szlenk C, Cicogna AC, et al. [Geographic disparities in heart failure mortality in Southern Brazil]. *Arq Bras Cardiol*. 2020;114(4):654–63. Portuguese. Pocock SJ, Ariti CA, McMurray JJV, et al. Predicting survival in heart failure using a new integrative model incorporating clinical and social variables. *Eur Heart J*. 2014;35(28):1850–6. Maisel AS, Clopton P, Krishnaswamy P, et al. Impact of age, race, and sex on the ability of B-type natriuretic peptide to predict mortality and morbidity in patients with acute decompensated heart failure. *J Am Coll Cardiol*. 2003;42(7):1226–33. Havranek EP, Mujahid MS, Barr DA, et al. Social determinants of risk and outcomes for cardiovascular disease: a scientific statement from the American Heart Association. *Circulation*. 2015;132(9):873–98. Athey S, Tibshirani J, Wager S. Generalized random forests. *Ann Stat*. 2019;47(2):1148–78. Strobl C, Boulesteix AL, Zeileis A, Hothorn T. Bias in random forest variable importance measures: illustrations, sources and a solution. *BMC Bioinformatics*. 2007;8:25. Falcão D, Hacon VA, Oliveira DF et al. [Machine learning models to predict ischemic heart disease mortality in Southern Brazil]. *Sci Rep*. 2021;11:8432. Portuguese. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-8864772","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":604918476,"identity":"163d18c1-cd05-42be-aaef-b64b3ce8c66e","order_by":0,"name":"Giovanni Possamai Dutra","email":"data:image/png;base64,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","orcid":"","institution":"D’Or Institute for Research and Education","correspondingAuthor":true,"prefix":"","firstName":"Giovanni","middleName":"Possamai","lastName":"Dutra","suffix":""},{"id":604918477,"identity":"65169eec-eef5-4f24-b1ef-cd44e6904960","order_by":1,"name":"Bruno Ferraz de Oliveira Gomes","email":"","orcid":"","institution":"D’Or Institute for Research and Education","correspondingAuthor":false,"prefix":"","firstName":"Bruno","middleName":"Ferraz de Oliveira","lastName":"Gomes","suffix":""},{"id":604918478,"identity":"199b5769-1fc9-4f15-abf4-5652e75a1558","order_by":2,"name":"Davi de Vasconcellos Dias","email":"","orcid":"","institution":"Federal University of Rio de Janeiro","correspondingAuthor":false,"prefix":"","firstName":"Davi","middleName":"de Vasconcellos","lastName":"Dias","suffix":""},{"id":604918479,"identity":"07616ce7-ab8e-4c44-bc8c-2d9173453104","order_by":3,"name":"Fabiana Cardoso de Oliveira","email":"","orcid":"","institution":"Federal University of Rio de Janeiro","correspondingAuthor":false,"prefix":"","firstName":"Fabiana","middleName":"Cardoso","lastName":"de Oliveira","suffix":""},{"id":604918480,"identity":"b84500df-35cd-46ba-b77c-7b44b6298a10","order_by":4,"name":"João Luiz Fernandes Petriz","email":"","orcid":"","institution":"D’Or Institute for Research and Education","correspondingAuthor":false,"prefix":"","firstName":"João","middleName":"Luiz Fernandes","lastName":"Petriz","suffix":""},{"id":604918481,"identity":"5de182f0-54e4-4080-9ee1-2974da61be1f","order_by":5,"name":"Plinio Resende do Carmo Junior","email":"","orcid":"","institution":"D’Or Institute for Research and Education","correspondingAuthor":false,"prefix":"","firstName":"Plinio","middleName":"Resende do Carmo","lastName":"Junior","suffix":""},{"id":604918482,"identity":"e46c2eef-a1c6-4f23-8ac7-4019a94758be","order_by":6,"name":"Emilia Matos Nascimento","email":"","orcid":"","institution":"Rio de Janeiro State University","correspondingAuthor":false,"prefix":"","firstName":"Emilia","middleName":"Matos","lastName":"Nascimento","suffix":""},{"id":604918483,"identity":"f208eb04-31c1-4730-897a-c08a29f11284","order_by":7,"name":"Basilio de Bragança Pereira","email":"","orcid":"","institution":"Federal University of Rio de Janeiro","correspondingAuthor":false,"prefix":"","firstName":"Basilio","middleName":"de Bragança","lastName":"Pereira","suffix":""},{"id":604918484,"identity":"3763a1b9-8af5-4baf-820b-22e33f8f0ff0","order_by":8,"name":"Glaucia Maria Moraes de Oliveira","email":"","orcid":"","institution":"Federal University of Rio de Janeiro","correspondingAuthor":false,"prefix":"","firstName":"Glaucia","middleName":"Maria Moraes","lastName":"de Oliveira","suffix":""}],"badges":[],"createdAt":"2026-02-12 17:54:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8864772/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8864772/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104867374,"identity":"e647af36-d732-4991-9559-e93a93b08d14","added_by":"auto","created_at":"2026-03-18 07:12:26","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":555461,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan–Meier survival curves for all-cause mortality in patients with decompensated heart failure: global analysis and stratification by social and geographic determinants.\u003c/strong\u003e\u003cbr\u003e\n \u003cstrong\u003e1A\u003c/strong\u003e: Combined survival curve demonstrates temporal mortality patterns from admission through follow-up (in-hospital + post-discharge deaths).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1B\u003c/strong\u003e: \u003cstrong\u003eWest Zone vs. North Zone hospitals\u003c/strong\u003e - North Zone admissions exhibited significantly inferior survival.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1C\u003c/strong\u003e: \u003cstrong\u003eOccupational stratification\u003c/strong\u003e - Informal/elementary occupations showed reduced survival vs. middle/technical/higher-level professions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1D\u003c/strong\u003e: \u003cstrong\u003eRace/color stratification\u003c/strong\u003e - Brown/Black ethnicity associated with higher mortality rates.\u003c/p\u003e","description":"","filename":"Fig1Aa1DKaplanMeier1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8864772/v1/e796391eb55cf6f7b8314381.jpeg"},{"id":104867282,"identity":"ee33076e-c12e-4df1-82fd-1fad1c9b3e1d","added_by":"auto","created_at":"2026-03-18 07:12:13","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":615074,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSurvival tree for all-cause mortality (in-hospital and post-discharge) in patients with decompensated heart failure.\u003c/strong\u003e\u003cbr\u003e\nRecursive partitioning survival tree showing subgroups with different risks of all-cause mortality according to clinical and social determinants. Each terminal node displays the Kaplan–Meier survival curve for that subgroup. Hosp.Unit: hospital unit (HU-W = West Zone hospital, HU-N = North Zone hospital); Previous.HF: history of heart failure (yes/no); Age: age in years; Adm.BNP: B-type natriuretic peptide at admission (pg/mL; split at 4,653 pg/mL); Health.Insurance: type of health insurance (High = high-standard, Middle = intermediate-cost, Low = low-cost/other plans); N_AF.Arrhy: prior arrhythmia other than atrial fibrillation or flutter (yes/no); Occupation: Ret = retired, Low = informal/elementary-level, Sec = middle/technical-level, Sup = higher education.\u003c/p\u003e","description":"","filename":"Fig2Morteportodasascausas1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8864772/v1/7a1ad3519499a3ab1e919aa4.jpeg"},{"id":104867281,"identity":"d6df8fc5-02a9-4a81-9f90-6e8d84283b44","added_by":"auto","created_at":"2026-03-18 07:12:12","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":527169,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSurvival tree for post-discharge (non-hospital) mortality in patients with decompensated heart failure (excluding in-hospital deaths).\u003c/strong\u003e\u003cbr\u003e\nRecursive partitioning survival tree showing subgroups with different risks of post-discharge mortality, based on the final Cox model with significant variables selected via Elastic Net. Each terminal node displays the Kaplan–Meier survival curve for that subgroup. Coding: Hospital unit (HU-W = West Zone hospital, HU-N = North Zone hospital); Age (years); Ethnicity (White = 0, Brown/Black = 1); Prior arrhythmia other than atrial fibrillation or flutter (N.AF.Arrhy: yes/no); Prior heart failure (yes/no).\u003c/p\u003e","description":"","filename":"Fig3Mortenaohospitalar1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8864772/v1/ec74b9657843139501310131.jpeg"},{"id":107520262,"identity":"5bd9eda2-c1ae-4ea7-a2ad-d1fb31b17aa4","added_by":"auto","created_at":"2026-04-22 08:59:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3029021,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8864772/v1/2c6d4871-9b75-47e7-84cb-8fab07526db7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of Social Determinants of Health With Long-Term Mortality in Decompensated Heart Failure","fulltext":[{"header":"1 - Introduction","content":"\u003cp\u003eHeart failure (HF) affects an estimated 64\u0026nbsp;million people worldwide, with a prevalence of 1\u0026ndash;2% in the general adult population and up to 10% among individuals older than 70 years, largely driven by population aging and the growing burden of hypertension and diabetes.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e Annual incidence ranges from 1 to 5 cases per 1,000 persons, leading to millions of hospitalizations for acute decompensation.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e Decompensated heart failure (DHF) is a common cardiovascular emergency, accounting for more than 1\u0026nbsp;million hospital admissions annually and carrying in-hospital mortality rates of up to 17% and 90-day post-discharge mortality of 15\u0026ndash;20%.\u003csup\u003e1\u003c/sup\u003e In the United States alone, HF accounts for over 1\u0026nbsp;million hospitalizations per year, and its prevalence is projected to approach 3% by 2030, driven by population aging and improved post\u0026ndash;myocardial infarction survival.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIn Brazil, HF prevalence is estimated at 1.1% among adults and 3.3% among older adults, with 941,576 hospitalizations recorded between 2019 and 2023 and in-hospital lethality of up to 17%.\u003csup\u003e4,5\u003c/sup\u003e The impact is greatest in the Southeast region and disproportionately affects Brown/Black men aged 70\u0026ndash;79 years.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e Patients with HF experience substantially worse quality of life than the general population and than those with many other chronic conditions, with symptoms that impose marked limitations on physical capacity and social participation. Social determinants of health (SDH)\u0026mdash;including low income, limited formal education, Brown/Black ethnicity, and food insecurity\u0026mdash;intensify these vulnerabilities, increasing the adjusted risk of death by approximately 2.5- to 3-fold in patients with two or more unfavorable SDH in the first 90 days after discharge.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e In the Brazilian context, adverse SDH undermine therapeutic adherence, raise readmission rates by 25\u0026ndash;40%, and worsen survival, with low educational attainment independently associated with higher 180-day mortality after hospitalization for HF.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eNevertheless, the incremental contribution of SDH to long-term mortality in DHF remains insufficiently characterized.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e Conventional clinical risk scores could plausibly be strengthened by plausible that conventional clinical risk scores, such as the MAGGIC score, could be strengthened by incorporating SDH, thereby capturing dimensions of risk related to the ability to purchase medications, adhere to lifestyle recommendations and healthy behaviors, and attend follow-up consultations.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e Such integration could refine prognostic stratification beyond traditional biological variables alone.\u003c/p\u003e \u003cp\u003eBroader socioeconomic metrics, such as the Municipal Human Development Index (MHDI), show an inverse relationship with HF mortality, underscoring disparities in access to tertiary care and disease-modifying therapies. Ecological analyses have reported an inverse association between MHDI and reductions in HF mortality rates over time, with a correlation coefficient of approximately r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.73, suggesting regions with lower human development experience smaller gains in survival.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e These findings highlight the interaction between territorial inequality and clinical outcomes in cardiovascular disease.\u003c/p\u003e \u003cp\u003eThe present study aimed to evaluate the association between SDH and both in-hospital and long-term all-cause mortality among patients hospitalized for DHF and managed with standardized treatment protocols at tertiary centers serving populations with pronounced social inequalities.\u003c/p\u003e"},{"header":"2 - Methods","content":"\u003cp\u003e \u003cb\u003eStudy design and setting\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis was a retrospective analysis of a prospectively collected cohort of patients admitted with decompensated heart failure (DHF). Consecutive patients were enrolled from the cardiac intensive care units of two tertiary hospitals with internationally standardized protocols for HF management. One hospital, located in the West Zone of Rio de Janeiro, has 162 beds and is situated in an area classified as high socioeconomic status by the United Nations Development Programme, with a municipal Human Development Index (HDI) of 0.959 and a Gini coefficient of 0.637. The second hospital, located in the North Zone, has 340 beds and is situated in an area with an HDI of 0.814 and a Gini coefficient of 0.538, reflecting greater social inequality.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e Compared with the North Zone hospital, the West Zone hospital also treats a higher proportion of patients covered by high-standard private health insurance plans (58.1% vs 41.9%; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), consistent with differences in socioeconomic profile between their catchment areas.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStudy population\u003c/b\u003e \u003c/p\u003e \u003cp\u003ePatients aged\u0026thinsp;\u0026gt;\u0026thinsp;18 years admitted between September 2011 and December 2021 with a primary diagnosis of HF were eligible. The diagnosis of HF at admission was based on classic clinical criteria, including the Framingham13 and Boston14 criteria, complemented by laboratory markers such as B-type natriuretic peptide (BNP) and N-terminal pro\u0026ndash;B-type natriuretic peptide (NT-proBNP).15 BNP or NT-proBNP values\u0026thinsp;\u0026gt;\u0026thinsp;300 pg/mL were considered elevated. Echocardiographic parameters were collected with preference for LVEF calculated by the Simpson biplane method; when unavailable, LVEF estimated by the Teichholz method was used. For patients with more than one hospitalization during the study period, only clinical data from the last admission were included in the analysis.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStatistical analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eResults were presented as median\u0026thinsp;+\u0026thinsp;IQR for continuous variables or number (percentage) for categorical variables. The chi-square test was used for categorical variables and one-way ANOVA for comparing means. Kaplan-Meier curves were employed to analyze survival over time, with the Tarone-Ware test for group comparisons.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eA semiparametric Cox proportional hazards model was used to evaluate predictors of time-to-event outcomes. The model was first estimated using Elastic Net regularization,\u003csup\u003e17\u003c/sup\u003e a machine learning\u0026ndash;based technique that combines L1 (Lasso) and L2 (Ridge) penalties to perform variable selection and shrinkage in the presence of high dimensionality and multicollinearity. Variables retained by the Elastic Net were then re-estimated in a conventional Cox model using maximum likelihood, and only those with statistical significance were kept in the final model.\u003c/p\u003e \u003cp\u003eTo further explore complex interactions between clinical and social variables over follow-up, survival trees based on recursive partitioning were constructed as a nonparametric approach to identify subgroups with distinct mortality risk profiles.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e In addition, proportional subdistribution hazards models for competing risks, according to the Fine\u0026ndash;Gray framework,\u003csup\u003e19\u003c/sup\u003e were fitted to estimate cumulative subdistribution hazards for in-hospital and post-discharge mortality, accounting for the competing nature of these events. Classification trees were used for recursive partitioning of these competing-risk outcomes.\u003c/p\u003e \u003cp\u003eMissing data were handled using multiple imputation. Six of the 26 independent variables had missing values (Zone: 13.3%; Ethnicity: 33.3%; Marital status: 21.6%; Occupation: 27.4%; systolic dysfunction grade: 21.7%; Ejection Fraction: 25.2%). Multiple imputation was performed with the Hmisc package in R,\u003csup\u003e20\u003c/sup\u003e generating plausible values under a multivariate framework that preserved the joint distribution of the 1,023 observations.\u003c/p\u003e \u003cp\u003eMultivariable logistic regression was then used to identify independent predictors of all-cause mortality (in-hospital or during follow-up) among patients hospitalized for DHF. A full model including 26 clinical and social variables\u0026mdash;such as admitting hospital, self-reported ethnicity, sex, age, type of health insurance, and occupation\u0026mdash;was initially subjected to Elastic Net regularization to perform robust variable selection and reduce redundancy.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e Variables selected by the Elastic Net were subsequently entered into a final multivariable logistic regression model. All analyses were conducted using R (R Foundation for Statistical Computing, Vienna, Austria),\u003csup\u003e21\u003c/sup\u003e and a two-sided alpha level of 0.05 was considered statistically significant.\u003c/p\u003e \u003cp\u003e \u003cb\u003eEthical considerations\u003c/b\u003e \u003c/p\u003e \u003cp\u003eDates of all-cause death were obtained from the public website of the General Court of Justice of the State of Rio de Janeiro. The study protocol was approved by the Research Ethics Committee of Instituto D\u0026rsquo;Or de Ensino e Pesquisa (IDOR) on 18 September 2019 (ethical review number 18502319.3.0000.5249; opinion no. 3.582.453). Clinical trial number: not applicable.\u003c/p\u003e"},{"header":"3 - Results","content":"\u003cp\u003eBaseline characteristics of the 1,023 patients hospitalized with DHF are summarized in Table 1.\u0026nbsp;Mean age 76.3\u0026plusmn;13.4 years\u0026nbsp;with\u0026nbsp;male predominance 53.9%. Median follow-up was\u0026nbsp;6.5 years (IQR 3.8-9.1).\u0026nbsp;In-hospital mortality\u0026nbsp;was\u0026nbsp;10.8% (n=110).\u0026nbsp;Post-discharge mortality\u0026nbsp;reached\u0026nbsp;64.1% (656 deaths)\u0026nbsp;at study end.\u003c/p\u003e\n\u003cp\u003eTable 1 summarizes baseline clinical and social characteristics (sex. age. White vs. Brown/Black ethnicity) stratified by in-hospital and post-discharge death. Patients dying during index admission exhibited higher admission BNP/NT-proBNP [median 4.940 (IQR 1.430-12.800) vs. 2.189 (IQR 937-7.660) pg/mL]. Lower LVEF [41.2% (IQR 32-58) vs. 40.5% (IQR 32-76)]. Older age [81.5 (IQR 74-89) vs. 80 (IQR 70-86) years]. Higher Brown/Black ethnicity prevalence (13.8% vs. 10.5%). Post-discharge decedents more frequently had prior HF (57.1% vs. 22.4%) and hypertension (64.6% vs. 24.9%)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"110%\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1. Clinical characteristics of patients with decompensated heart failure according to in-hospital and post-discharge mortality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eIn-hospital mortality \u0026ndash; Yes (n = 110; 10.8%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eIn-hospital mortality \u0026ndash; No (n = 913; 89.2%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePost-discharge mortality \u0026ndash; Yes (n = 656; 64.1%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePost-discharge mortality \u0026ndash; No (n = 257; 25.9%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eLVEF, % (median, IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e41.2(IIQ:32-58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e45.9(IIQ:34-60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e40.5(IIQ:32-76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e47(IIQ:36-62)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eBNP at admission, pg/mL (median, IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4,940 (1,430\u0026ndash;12,800)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2,310 (979\u0026ndash;7,371)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2,189 (937\u0026ndash;7,660)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2,720 (1,090\u0026ndash;6,223)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAge ( years), n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e81.5(IIQ:74-89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e78(IIQ:68-85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e80(IIQ:70-86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e71(IIQ:61-80)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eWomen, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e58 (12.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e413 (87.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e287 (60.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e126 (26.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMen, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e52 (9.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e500 (90.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e369 (66.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e131 (23.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eBrown/Black ethnicity, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11 (13.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e69 (86.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e55 (68.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14 (17.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eWhite ethnicity, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e99 (10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e69 (86.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e601 (63.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e243 (25.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePrior heart failure, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e43 (20.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e167 (79.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e120 (57.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e47 (22.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eHypertension, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e88 (10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e752 (89.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e543 (64.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e209 (24.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePermanent atrial fibrillation, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e36 (16.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e186 (83.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e143 (64.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e43 (19.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCKD* (eGFR \u0026lt; 60 mL/min/1.73 m\u0026sup2;), n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e31 (14.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e182 (85.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e143 (67.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e39 (18.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePrior myocardial infarction*, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e21 (8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e232 (91.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e177 (70.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e55 (21.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePrior dementia*, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13 (18.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e58 (81.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e53 (74.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5 (7.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e38 (11.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e292 (88.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e205 (62.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e87 (26.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePrior stroke, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6 (12.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e44 (88.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e32 (64.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12 (24.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eBeta-blocker use, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e27 (11.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e215 (88.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e164 (67.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e51 (21.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eKaplan\u0026ndash;Meier survival curves are shown in figure 1. The overall curve (Figure 1A) depicts all-cause mortality from hospital admission to the end of follow-up, encompassing both in-hospital and post-discharge deaths. When stratified by hospital (Figure 1B), patients admitted to the North Zone tertiary hospital had significantly lower survival than those treated at the West Zone hospital. Survival curves stratified by occupation (Figure 1C) showed worse long-term survival among patients in informal or elementary-level occupations compared with those in middle/technical or higher-level occupations. Stratification by self-reported race/skin color (Figure 1D) revealed higher mortality among Brown/Black patients than among White patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 1. Kaplan\u0026ndash;Meier survival curves for all-cause mortality in patients with decompensated heart failure: global analysis and stratification by social and geographic determinants.\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e1A\u003c/strong\u003e: Combined survival curve demonstrates temporal mortality patterns from admission through follow-up (in-hospital + post-discharge deaths).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1B\u003c/strong\u003e: \u003cstrong\u003eWest Zone vs. North Zone hospitals\u003c/strong\u003e - North Zone admissions exhibited significantly inferior survival.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1C\u003c/strong\u003e: \u003cstrong\u003eOccupational stratification\u003c/strong\u003e - Informal/elementary occupations showed reduced survival vs. middle/technical/higher-level professions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1D\u003c/strong\u003e: \u003cstrong\u003eRace/color stratification\u003c/strong\u003e - Brown/Black ethnicity associated with higher mortality rates.\u003c/p\u003e\n\u003cp\u003eTable 2\u0026nbsp;presents competing risks models for post-discharge and in-hospital mortality. Prior admission to the North Zone hospital unit (lower socioeconomic patient population) demonstrated strong chi-square dependence between residential zone and hospital unit (p \u0026lt; 0.0001). This was associated with higher post-discharge mortality [HR 2.01 (95% CI 1.61-2.51; p \u0026lt; 0.001)].\u003c/p\u003e\n\u003cp\u003eThis association exceeded clinical variables including severe dysfunction [HR 0.62 (95% CI 0.50-0.78; p \u0026lt; 0.0001)], prior myocardial infarction [HR 1.06 (95% CI 0.90-1.23; p = 0.4931)], and ejection fraction [HR 0.99 (95% CI 0.99-1.00; p = 0.0277)]. Aging demonstrated linear risk increase [HR 1.01 (95% CI 1.007-1.019; p \u0026lt; 0.001)].\u003c/p\u003e\n\u003cp\u003eProtective factors included female sex [HR 0.79 (95% CI 0.68-0.91; p = 0.001)] and prior cardiac arrhythmias (excluding AF/flutter) [HR 0.74 (95% CI 0.63-0.87; p = 0.0004)].\u003c/p\u003e\n\u003cp\u003eThe in-hospital sub-risk model identified increased risk with severe systolic dysfunction [HR 2.25 (95% CI 1.35-3.77; p = 0.002)], permanent atrial fibrillation [HR 1.85 (95% CI 1.24-2.75; p = 0.003)], and prior heart failure [HR 1.79 (95% CI 1.18-2.72; p = 0.006)]. Aging showed linear risk increment [HR 1.024/year (95% CI 1.006-1.042; p = 0.008)], evidencing cumulative frailty.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"111%\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2. Competing risk models in patients with decompensated heart failure\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(A) Post-discharge mortality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCoefficient\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eHazard ratio (HR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eLower 95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eUpper 95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSouth Zone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.3062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.7363\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.3609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.5019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.4000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNorth Zone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.2367\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.7892\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.3958\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.5739\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.5015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWest Zone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.2398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.7868\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.3840\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.6121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.5123\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOther municipality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.3288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.7198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.3068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.6888\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.4499\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBNP at admission\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEjection fraction, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.0062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.9938\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.9883\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.9993\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0277\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePrior heart failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.0412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.9596\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.7948\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.1587\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.6683\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAge, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.0127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.0066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.0188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBeta-blocker use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.1091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.1153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.9476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.3127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.1895\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePrior arrhythmia (except AF/flutter)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.3015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.7397\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.6273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.8723\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePrior atrial fibrillation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.1306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.8776\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.7493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.0278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.1052\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCKD with dialysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.1658\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.8472\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.5953\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.2058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.3572\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePrior myocardial infarction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.0553\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.9048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.2309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.4931\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMild LV systolic dysfunction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.2833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.7533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.5980\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.9489\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0162\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eModerate LV systolic dysfunction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.3366\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.7142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.5639\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.9045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0052\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSevere LV systolic dysfunction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.4726\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.6234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.4960\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.7835\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFemale sex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.2400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.7866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.6826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.9065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHospital unit: North Zone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.6991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.0119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.6141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.5076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2. Competing risk models in patients with decompensated heart failure\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(B) In-hospital mortality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCoefficient\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eHazard ratio (HR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eLower 95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eUpper 95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSouth Zone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16.1600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.05 \u0026times; 10⁷\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.21 \u0026times; 10⁶\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.61 \u0026times; 10⁷\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNorth Zone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16.4900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.45 \u0026times; 10⁷\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.53 \u0026times; 10⁶\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.80 \u0026times; 10⁷\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWest Zone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16.5000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.47 \u0026times; 10⁷\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.03 \u0026times; 10⁶\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.58 \u0026times; 10⁷\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOther municipality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.0600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.6188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.8160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.8319\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBNP at admission\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.00002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEjection fraction, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.00482\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.0050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.9916\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.0180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.4754\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePrior heart failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.5838\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.7930\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.1800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.7230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0062\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAge, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.02385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.0240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.0060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.0420\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0081\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBeta-blocker use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.3857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.6800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.4464\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.0360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0724\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePrior arrhythmia (except AF/flutter)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.5607\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.7520\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.1580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.6510\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0080\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePrior atrial fibrillation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.6131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.8460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.2410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.7460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCKD with dialysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.8611\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.3660\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.9914\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.6460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0523\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePrior myocardial infarction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.4576\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.6328\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.3978\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.0070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0533\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMild LV systolic dysfunction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0761\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.0790\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.5475\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.1270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.8260\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eModerate LV systolic dysfunction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.0549\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.9466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.5297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.6910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.8529\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSevere LV systolic dysfunction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.8117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.2520\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.3460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.7660\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFemale sex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.3653\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.4410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.9771\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.1250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0653\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHospital unit: North Zone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.6840\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.5046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.2413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.0550\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0691\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSurvival trees provided additional insight into interactions between social and clinical determinants (Figures 2 and 3). In the tree for all-cause mortality (Figure 2), patients admitted to the North Zone hospital who were older than 79 years and had a history of arrhythmias formed a high-risk phenotype with markedly reduced survival over time. In contrast, among patients treated at the West Zone hospital, those with prior HF and intermediate-cost insurance plans showed comparatively better survival, whereas patients without prior HF but older than 65 years had worse outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 2. Survival tree for all-cause mortality (in-hospital and post-discharge) in patients with decompensated heart failure.\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Recursive partitioning survival tree showing subgroups with different risks of all-cause mortality according to clinical and social determinants. Each terminal node displays the Kaplan\u0026ndash;Meier survival curve for that subgroup. Hosp.Unit: hospital unit (HU-W = West Zone hospital, HU-N = North Zone hospital); Previous.HF: history of heart failure (yes/no); Age: age in years; Adm.BNP: B-type natriuretic peptide at admission (pg/mL; split at 4,653 pg/mL); Health.Insurance: type of health insurance (High = high-standard, Middle = intermediate-cost, Low = low-cost/other plans); N_AF.Arrhy: prior arrhythmia other than atrial fibrillation or flutter (yes/no); Occupation: Ret = retired, Low = informal/elementary-level, Sec = middle/technical-level, Sup = higher education.\u003c/p\u003e\n\u003cp\u003eIn the survival tree restricted to post-discharge mortality (Figure 3), patients of Brown/Black ethnicity admitted to the North Zone hospital and those with prior arrhythmias exhibited particularly poor long-term survival. Among White patients, the lowest survival was observed in those without a history of arrhythmias (except atrial fibrillation/flutter). In the West Zone hospital, patients older than 66 years with prior HF also showed reduced survival, underscoring the interplay between age, underlying cardiac disease, and place of care.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 3. Survival tree for post-discharge (non-hospital) mortality in patients with decompensated heart failure (excluding in-hospital deaths).\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Recursive partitioning survival tree showing subgroups with different risks of post-discharge mortality, based on the final Cox model with significant variables selected via Elastic Net. Each terminal node displays the Kaplan\u0026ndash;Meier survival curve for that subgroup. Coding: Hospital unit (HU-W = West Zone hospital, HU-N = North Zone hospital); Age (years); Ethnicity (White = 0, Brown/Black = 1); Prior arrhythmia other than atrial fibrillation or flutter (N.AF.Arrhy: yes/no); Prior heart failure (yes/no).\u003c/p\u003e\n\u003cp\u003eThe final multivariable Cox model adjusted via Elastic Net (Table 3) identified Brown/Black ethnicity (HR 1.44, 95% CI 1.12\u0026ndash;1.86; P = 0.005), older age (HR 1.02 per year, 95% CI 1.02\u0026ndash;1.03; P \u0026lt; 0.001), prior diabetes (HR 1.18, 95% CI 1.01\u0026ndash;1.38; P = 0.036), and prior HF (HR 1.32, 95% CI 1.10\u0026ndash;1.59; P = 0.002) as independent predictors of higher all-cause mortality. Mild, moderate, and severe LV systolic dysfunction were associated with lower hazard compared with the reference category, as was higher LVEF, while admission to the North Zone hospital remained one of the strongest predictors (HR 2.43, 95% CI 2.06\u0026ndash;2.86; P \u0026lt; 0.001).\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 3. Final Multivariable Cox Proportional Hazards Model for All-Cause Mortality (N=1023), with Variables Selected via Elastic Net\u0026sup1;⁷\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCoefficient\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eHazard Ratio (exp(coef))\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSE(coef)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ez\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eP (\u0026gt; |z|)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eLower 95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eUpper 95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBrown/Black Ethnicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.365733\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.441571\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.130908\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.794\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.005209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.1153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.8632\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAge (per year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.021773\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.022012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.003275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.97e-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.0155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.0286\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFemale Sex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.169582\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.844017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.077146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-2.198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.027935\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.7256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.9818\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePrior Diabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.167193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.181982\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.079925\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.036449\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.0106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.3824\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePrior Arrhythmias (Excluding AF/Flutter)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.420012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.657039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.094167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-4.460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.18e-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.5463\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.7902\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePrior Heart Failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.281095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.324579\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.092799\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.002453\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.1043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.5888\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMild Systolic Dysfunction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.332772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.716934\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.132906\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-2.504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.012287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.5525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.9303\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eModerate Systolic Dysfunction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.491507\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.611704\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.137719\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-3.569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.4670\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.8012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSevere Systolic Dysfunction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.600284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.548656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.160435\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-3.742\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.4006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.7514\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEjection Fraction (per %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.011498\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.988568\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.004099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-2.805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.005035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.9807\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.9965\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAdmission to North Zone Hospital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.886826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.427414\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.083856\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.576\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2e-16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.0595\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.8610\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eMultivariable Cox proportional hazards model for prediction of all-cause mortality in 1,023 patients hospitalized for decompensated heart failure, with variables selected by Elastic Net regularization\u0026sup1;⁷. Reference categories: white ethnicity, male sex, no prior diabetes/heart failure/arrhythmias, preserved systolic function, West Zone hospital. \u003cem\u003eSignificance: *** P\u0026lt;0.001; ** P\u0026lt;0.01; * P\u0026lt;0.05; . P\u0026lt;0.10\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn the multivariable logistic regression model for overall mortality (Table 4), older age, Brown/Black ethnicity, being widowed or separated/divorced, prior dementia, cardiac arrhythmias other than atrial fibrillation/flutter, middle/technical-level occupation, and chronic kidney disease requiring hemodialysis were independently associated with increased odds of death, whereas higher LVEF was protective.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"107%\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 4. Independent Predictors of All-Cause Mortality (In-Hospital and Post-Discharge) by Multivariable Logistic Regression\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eEstimate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eStd. Error\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ez Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eP (\u0026gt; |z|)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSig.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-2.169381\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.795249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-2.728\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.00637\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBrown/Black Ethnicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.844710\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.335882\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.01191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMarital Status: Married/Stable Union\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.503806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.257393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.957\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.05031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMarital Status: Alive (Single/Widowed)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.953237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.297224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.00134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMarital Status: Separated/Divorced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.735907\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.341454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.03115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOccupation: Elementary/Informal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.074838\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.219000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.342\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.73256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOccupation: Middle/Technical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.613544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.239973\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.557\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.01057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOccupation: Higher Education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.280210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.241670\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.24626\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAge (per year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.049372\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.006958\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.29e-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePrior Arrhythmias (Excl. AF/Flutter)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.625408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.233670\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.676\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.00744\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePrior Dementia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.284251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.490466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.618\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.00883\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMild Systolic Dysfunction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.316693\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.269172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.23938\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eModerate Systolic Dysfunction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.460539\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.278869\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.651\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.09865\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSevere Systolic Dysfunction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.208641\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.336470\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.620\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.53520\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEjection Fraction (per %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.026346\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.008851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-2.977\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.00291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePrior CKD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.530182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.209122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.535\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.01124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eLegend:\u003c/strong\u003e Multivariable logistic regression model for independent predictors of all-cause mortality (in-hospital and post-discharge) in patients hospitalized for decompensated heart failure. Reference categories: white ethnicity, single marital status (base), elementary/informal occupation, no prior comorbidities, preserved systolic function.\u003cbr\u003e\u003cem\u003eSignificance: *** P\u0026lt;0.001; ** P\u0026lt;0.01; * P\u0026lt;0.05; . P\u0026lt;0.10.\u003c/em\u003e\u003c/p\u003e"},{"header":"4 - Discussion","content":"\u003cp\u003eIn Brazilian urban settings, decompensated heart failure (DHF) remains strongly influenced by social determinants of health (SDH) across the entire care continuum from hospital admission through long-term follow-up. In-hospital mortality was 10.8% and post-discharge mortality reached 64.1% at median follow-up of approximately 7 years, rates consistent with international benchmarks (8\u0026ndash;17% lethality; 15\u0026ndash;20% at 90 days) but revealing persistent long-term risk comparable to reductions of up to 50% in life expectancy versus the general population. \u003csup\u003e\u003cb\u003e1,3\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThese results illustrate that even within private hospitals using standardized protocols socio-structural factors related to race/color, occupation, marital status, residential location, and admitting hospital unit maintain strong associations with fatal outcomes (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This suggests interventions focused exclusively on biological variables may prove insufficient for mortality reduction.\u003c/p\u003e \u003cp\u003eThis hypothesis finds support in a multicenter longitudinal study\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003eof 1,377 heart failure patients employing inverse probability-weighted marginal structural models. Unfavorable SDH, low social support, and reduced support utilization during follow-up (but not at baseline) exerted independent causal effects on mortality, demonstrating SDH impact accumulation post-discharge that requires continuous monitoring to mitigate residual risk.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e, through the Elastic Net-adjusted\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e Cox model reinforces independent SDH effects, with Brown/Black ethnicity (HR 1.44, 95% CI 1.12\u0026ndash;1.86, P\u0026thinsp;=\u0026thinsp;0.005) and North Zone admission (HR 2.43, 95% CI 2.06\u0026ndash;2.86, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) yielding higher adjusted hazard ratios than traditional clinical variables, aligning with racial disparities documented in Brazilian HF cohorts.\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eBrown/Black ethnicity and North Zone hospital admission were associated with reduced survival in the multivariable Cox model for all-cause mortality. Kaplan-Meier curves demonstrated inferior survival among retirees and - among actively employed patients - those in informal/low-qualification occupations.\u003c/p\u003e \u003cp\u003eHowever, Fine-Gray competing risk models showed no significant ethnicity or occupation associations with in-hospital versus post-discharge mortality. In multivariable logistic regression for overall mortality, only middle/technical occupation maintained significant outcome association, while hospital unit and informal occupation showed no significant mortality associations.\u003c/p\u003e \u003cp\u003eThe 44% mortality increase with Brown/Black ethnicity converges with North American cohort data and systematic reviews documenting 2- to 3-fold risk elevation among patients with multiple adverse SDH, independent of ejection fraction or guideline-directed therapies.\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e This risk gradient likely reflects combined historical ethnic vulnerability, specialized care access barriers, lower formal education, and precarious employment\u0026mdash;elements well-described in global cardiovascular disease and SDH analyses.\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eElementary/informal occupations and middle/technical professions identified as survival predictors complement education and income as vulnerability markers. In this cohort, elementary/informal-level workers exhibited inferior survival versus middle/technical and higher education categories (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC), findings approaching Brazilian multicenter cohorts where low education increased 180-day post-acute HF mortality hazard by 39%.\u003csup\u003e10\u003c/sup\u003eEcological and panel studies confirm hospitalization and HF mortality rates associate with social vulnerability indicators, inadequate primary care spending, and reduced community strategy coverage\u0026mdash;suggesting clinical risk amplification within less-structured care environments.\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIntra-urban disparities between North Zone and West Zone hospitals (distinct population social indices) exemplify socio-spatial heterogeneity and intra-urban vulnerability gradients. Residential zone and hospital unit variables demonstrated dependence [Cramer's V\u0026thinsp;=\u0026thinsp;0.8 indicating strong association].- associated with post-discharge subdistribution hazard ratio of 2.01, surpassing classic risks such as prior myocardial infarction or ventricular dysfunction. This suggests patient residential region functions as a cardiovascular risk factor.\u003c/p\u003e \u003cp\u003eBrazilian spatial analyses demonstrate municipalities/neighborhoods with lower human development. Greater poverty. Reduced education concentrate higher heart failure/cardiovascular mortality rates. Frequently accompanied by underreporting and diagnostic delay (particularly North/Northeast regions).\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eEmployed competing risk models, survival trees, and multivariable logistic regression enabled comprehensive exploration of clinical-social factor interactions, consistent with recent approaches incorporating SDH into HF mortality prediction models that demonstrated measurable discriminative capacity improvement versus conventional clinical-only scores.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eSurvival trees (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e,\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) highlight unique SDH-clinical interactions in DHF mortality. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e (all-cause mortality): North Zone patients aged\u0026thinsp;\u0026ge;\u0026thinsp;79 years with prior arrhythmia represented highest-risk phenotype; conversely, West Zone patients without prior HF possessing intermediate-cost insurance exhibited superior survival. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e (post-discharge mortality): Brown/Black ethnicity North Zone patients and those with prior arrhythmia records demonstrated greatest post-discharge vulnerability.\u003c/p\u003e \u003cp\u003eContemporary HF cohort investigations demonstrate intra-urban socioeconomic deprivation patterns surpass isolated biological factors\u0026mdash;including ejection fraction\u0026mdash;as mortality/readmission predictors, particularly critical among ethnic minorities experiencing disproportionately increased social vulnerability burden.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e This study complements traditional clinical scores like MAGGIC, hypothesizing SDH addition reveals how territorial/ethnic disparities modulate urban risk trajectories. Survival trees (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e,\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) demonstrate these socio-spatial factors interact dynamically, complementing traditional clinical frameworks while offering actionable risk stratification hypotheses for vulnerable populations\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cb\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThese results reinforce that incorporating hospital unit, occupation, marital status, and race/color SDH substantially modifies risk stratification\u0026mdash;including redefining highest-risk subgroups within identical clinical profiles (e.g., elderly with prior HF admitted to lower socioeconomic units). This pattern aligns with longitudinal studies demonstrating social determinant effects accumulate over time, with persistent adverse context exposures increasing mortality risk even when underlying clinical condition remains stable.\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eSeveral apparently paradoxical associations emerged in multivariable analysis. Female sex conferred post-discharge protection (HR 0.79), while prior cardiac arrhythmia (excluding atrial fibrillation/flutter) associated with reduced post-discharge risk (HR 0.74)\u0026mdash;apparently contrasting in-hospital risk elevation (HR 1.75) and permanent atrial fibrillation's worse in-hospital prognosis\u0026mdash;suggesting potential survivor bias and enhanced clinical surveillance in these subgroups. Other HF registries frequently demonstrate women exhibit superior adjusted survival, possibly through biological differences, greater therapeutic adherence, and increased social support strategy utilization\u0026mdash;factors potentially attenuating structural inequality impacts.\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003ePrior arrhythmia associations post-follow-up may reflect intensified surveillance phenomena and more rigorous cardiological follow-up in patients with complex electrical history, yielding greater prognostic-modifying therapy optimization beyond potential survivor bias. Marital status and dementia results reinforce relational SDH dimensions: individuals with \"alive\" marital status and separated/divorced demonstrated greater mortality association, aligning with literature documenting conjugal/social support absence negatively impacts medication adherence, clinic attendance, and symptom management in HF.\u003csup\u003e\u003cb\u003e6,22,24\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003ePublic policy and service organization perspectives align with Brazilian studies examining SDH, Family Health Strategy coverage, federal transfers, and HF hospitalizations relationships. National coverage analysis demonstrated greater primary care coverage, higher budgetary allocation, and better base service structuring associated with lower HF hospitalization rates\u0026mdash;suggesting SDH partially addressable through territorialized health policies\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e This study context Kaplan-Meier curves reveal inferior survival in elementary/informal profession group versus higher qualification categories, illustrating practical inequality impacts within single municipality\u0026mdash;evidencing geographic distance and care network organization may prove as determinant as clinical/laboratory alterations.\u003c/p\u003e \u003cp\u003eThis work contributes methodologically by applying multiple imputation to handle missing social variable data, integrating them into competing risk models and survival trees. This approach\u0026mdash;despite generating biases/limitations\u0026mdash;approximates analysis to real patterns of information absence in clinical/administrative databases, where social variables typically exhibit least completion, rendering vulnerability invisible in traditional risk models. Consistent with recent experiences employing machine learning and geospatial analysis to predict ischemic disease mortality in Southern Brazilian states, combining traditional statistical techniques with more flexible modeling methods appears particularly suited to capture cardiovascular risk spatial/social heterogeneity.\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThus, integrating clinical, socioeconomic, and territorial data into structured databases represents a fundamental step toward constructing fairer, Brazilian reality-applicable risk models.\u003c/p\u003e \u003cp\u003eLimitations beyond data capture problems warrant consideration in results interpretation. Retrospective analysis nature implies information bias potential, particularly in self-reported social variables like race/color, occupation, and marital status. Study conducted in 2 private tertiary centers from single municipality may limit generalizability to other contexts. Additionally, more comprehensive SDH\u0026mdash;such as detailed family income, housing conditions, food insecurity, community support, and urban violence exposure\u0026mdash;not systematically measured likely underestimate true inequality gradiente.\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eRetrospective social data collection challenge with high missingness degree led to this study's data imputation need enabling analyses/conclusions over long follow-up. However performed multiple imputation may amplify biases, yielding biased inequality effect estimates that must be considered. Nonetheless, prolonged follow-up with clinical-social variable integration enabled observing SDH-mortality association persistence beyond critical post-hospitalization period, reducing possibility observed disparities represent transient bias.\u003c/p\u003e \u003cp\u003eIn summary, results demonstrate ,in decompensated heart failure patients, mortality reflects not only clinical severity but also social vulnerability accumulation manifested through social determinants of health. This corroborates recent studies positioning SDH as central cardiovascular risk components rather than peripheral variables.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e"},{"header":"5 - Conclusion","content":"\u003cp\u003eThis study demonstrated that social determinants of health significantly associated with reduced survival in decompensated heart failure patients. Elevating adjusted risk beyond clinical variables.\u003c/p\u003e \u003cp\u003ePragmatic trials and prospective cohorts incorporating these dimensions may clarify the extent to which SDH modification can measurably reduce heart failure mortality in intra-urban settings marked by profound inequalities.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the cardiology teams at both institutions for data collection support. Ethical approval was obtained from Instituto D\u0026apos;Or Research Ethics Committee (CAAE 18502319.3.0000.5249).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo external funding was received for this study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed during the current study are available from the corresponding author on reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The authors declare no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eG.P.D.\u0026nbsp;and\u0026nbsp;G.M.M.O.\u0026nbsp;conceived and designed the study, performed statistical analyses, and wrote the main manuscript text.\u0026nbsp;B.F.O.G.,\u0026nbsp;D.V.D.,\u0026nbsp;F.C.O.,\u0026nbsp;J.L.F.P.,\u0026nbsp;P.R.C.J.,\u0026nbsp;E.M.N., and\u0026nbsp;B.B.P.\u0026nbsp;curated data, validated results, and reviewed the manuscript. All authors approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eApproved by Instituto D\u0026apos;Or Ethics Committee (opinion 3.582.453). Waiver of informed consent due to retrospective design. Clinical trial number: not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBozkurt B, Cowie MR, Foster MI, Haass C, Hoes AW, Krum H. Definition and classification of acute decompensated heart failure. *Eur Heart J*. 2021;42(20):1987\u0026ndash;2003.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarcus GM, Alonso A, Peralta CA, et al. European Heart Rhythm Association (EHRA) and Cardiac Arrhythmia Society of Southern Africa (CASSA) Expert Consensus Statement on the management of supraventricular arrhythmias. *Europace*. 2020;22(3):465\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFonseca C. Diagnosis and classification of acute heart failure. *Eur Heart J Suppl*. 2019;21(Suppl J):J7\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBocchi EA, Marcondes-Braga FG, Bacal F, et al. [Heart failure in Brazil: morbidity and mortality epidemiology]. *Arq Bras Cardiol*. 2018;109(1):39\u0026ndash;48. Portuguese.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRohde LE, Montera MW, Bocchi EA, et al. [Brazilian guidelines for the diagnosis, treatment and evaluation of acute and chronic heart failure]. *Arq Bras Cardiol*. 2018;111(2):131\u0026ndash;81. Portuguese.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCubillos-Garz\u0026oacute;n LA, Casas JP, Morillo CA, et al. Etiology and prognostic significance of elevated B-type natriuretic peptide in patients with heart failure and preserved ejection fraction. *Am J Cardiol*. 2009;103(7):942\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGheorghiade M, Vaduganathan M, Fonarow GC, Bonow RO. Rehospitalization for heart failure: problems and perspectives. *J Am Coll Cardiol*. 2013;61(4):391\u0026ndash;403.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlves JG, Gurgel RQ. [Social determinants of health and the epidemiology of chronic diseases in Brazil]. *Rev Bras Med Fam Comunidade*. 2016;11(38):1\u0026ndash;8. Portuguese.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSenni M, Paulus WJ. Acute decompensated heart failure\u0026mdash;epidemiology and management. *Rev Esp Cardiol*. 2010;63(3):343\u0026ndash;57.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLemos FA, Mesquita CT, Colares VS et al. [Social determinants of health and heart failure mortality: geographic and temporal analysis]. *Rev Bras Epidemiol*. 2019;22:E190032. Portuguese.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrograma das Na\u0026ccedil;\u0026otilde;es. Unidas para o Desenvolvimento. [Atlas of Human Development in Brazil]. Rio de Janeiro: UNDP; 2013.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInstituto Pereira Passos. [Socioeconomic Index of Rio de Janeiro Municipalities]. Rio de Janeiro: IPP; 2015.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFramingham Heart Study. [Diagnostic criteria for heart failure]. In: Opie LH, editor. *Drugs for the Heart*. 8th ed. Philadelphia: Elsevier; 2013. pp. 127\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang TJ, Levy D, Benjamin EJ, Vasan RS. The epidemiology of asymptomatic left ventricular dysfunction: implications for screening and the costs of heart failure. *J Am Coll Cardiol*. 2003;42(11):1879\u0026ndash;86.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaisel AS, Krishnaswamy P, Nowak RM, et al. Rapid measurement of B-type natriuretic peptide in the emergency diagnosis of heart failure. *N Engl J Med*. 2002;347(3):161\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaplan EL, Meier P. Nonparametric estimation from incomplete observations. *J Am Stat Assoc*. 1958;53(282):457\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZou H, Hastie T. Regularization and variable selection via the elastic net. *J R Stat Soc Ser B Stat Methodol*. 2005;67(2):301\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeBlanc M, Crowley J. Survival trees by goodness of split. *J Am Stat Assoc*. 1993;88(422):457\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFine JP, Gray RJ. A proportional hazards model for the subdistribution of a competing risk. *J Am Stat Assoc*. 1999;94(446):496\u0026ndash;509.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHarrell FE Jr. *Regression Modeling Strategies with Applications to Linear Models, Logistic Regression, and Survival Analysis*. 2nd ed. New York: Springer; 2015.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR Core Team. *R: A Language and Environment for Statistical Computing*. Vienna: R Foundation for Statistical Computing; 2021. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.R-project.org/\u003c/span\u003e\u003cspan address=\"https://www.R-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVoors AA, Ouwerkerk W, Zwinderman AH, et al. Determinants and development of pulmonary congestion in acute decompensated heart failure: data from TRANSFORM-HF. *Eur J Heart Fail*. 2019;21(5):592\u0026ndash;602.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiranda PD, Ribeiro ALP, Sousa MR, Pimenta AM. [Spatial patterns of cardiovascular disease mortality in Brazilian municipalities]. *Rev Panam Salud Publica*. 2018;42:e132. Portuguese.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMensah GA, Croft JB, Giles WH. The heart, lung, and blood disparities in the United States: a social determinants perspective. *Circ Cardiovasc Qual Outcomes*. 2021;14(1):e007575.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRossi MI, Szlenk C, Cicogna AC, et al. [Geographic disparities in heart failure mortality in Southern Brazil]. *Arq Bras Cardiol*. 2020;114(4):654\u0026ndash;63. Portuguese.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePocock SJ, Ariti CA, McMurray JJV, et al. Predicting survival in heart failure using a new integrative model incorporating clinical and social variables. *Eur Heart J*. 2014;35(28):1850\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaisel AS, Clopton P, Krishnaswamy P, et al. Impact of age, race, and sex on the ability of B-type natriuretic peptide to predict mortality and morbidity in patients with acute decompensated heart failure. *J Am Coll Cardiol*. 2003;42(7):1226\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHavranek EP, Mujahid MS, Barr DA, et al. Social determinants of risk and outcomes for cardiovascular disease: a scientific statement from the American Heart Association. *Circulation*. 2015;132(9):873\u0026ndash;98.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAthey S, Tibshirani J, Wager S. Generalized random forests. *Ann Stat*. 2019;47(2):1148\u0026ndash;78.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStrobl C, Boulesteix AL, Zeileis A, Hothorn T. Bias in random forest variable importance measures: illustrations, sources and a solution. *BMC Bioinformatics*. 2007;8:25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFalc\u0026atilde;o D, Hacon VA, Oliveira DF et al. [Machine learning models to predict ischemic heart disease mortality in Southern Brazil]. *Sci Rep*. 2021;11:8432. Portuguese.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8864772/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8864772/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Decompensated heart failure (DHF) remains associated with high in-hospital mortality (8–17%) and substantial post-discharge mortality (15–20% at 90 days). Although social determinants of health (SDH) have been linked to short-term adverse outcomes, their impact on long-term survival in DHF, particularly in Brazilian urban settings, is not well defined.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjectives:\u003c/strong\u003e To evaluate the association between SDH and in-hospital and long-term mortality in patients hospitalized for DHF and managed with standardized protocols at private tertiary centers serving populations with marked social inequalities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e In this retrospective analysis of a prospective cohort, 1,023 consecutive patients admitted with DHF between 2011 and 2021 at two private tertiary centers in Rio de Janeiro were included. The centers are located in areas with contrasting municipal Human Development Index and serve populations with different socioeconomic status, one predominantly caring for lower socioeconomic status patients. All patients were treated according to standardized institutional protocols. Survival analyses used Kaplan–Meier curves, Fine–Gray competing risk models, survival trees, and multivariable Cox regression with variable selection via the Elastic Net, adopting a two-sided alpha of 5%.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e In-hospital mortality was 10.8%, and post-discharge mortality reached 64.1% over a median follow-up of 6.5 years (interquartile range [IQR] 3.8–9.1). In the multivariable Cox model, SDH were independently associated with lower survival. Brown/Black ethnicity (hazard ratio [HR] 1.44, 95% confidence interval [CI] 1.12–1.86, P = 0.005), admission to the tertiary center serving a lower socioeconomic status population (HR 2.43, 95% CI 2.06–2.86, P \u0026lt; 0.001), and older age (HR 1.02 per year, 95% CI 1.02–1.03, P \u0026lt; 0.001) showed stronger adjusted hazard ratios than several traditional clinical variables.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e In Brazilian urban settings, SDH are powerful predictors of long-term mortality in patients hospitalized for DHF, exceeding the prognostic impact of conventional clinical factors. Incorporating SDH into risk prediction models may improve identification of high-risk patients and support more equitable allocation of cardiovascular care.\u003c/p\u003e","manuscriptTitle":"Association of Social Determinants of Health With Long-Term Mortality in Decompensated Heart Failure","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-18 07:09:17","doi":"10.21203/rs.3.rs-8864772/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4193e5bd-bf93-42b2-a0d9-76c11e8f36c9","owner":[],"postedDate":"March 18th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-22T08:58:01+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-18 07:09:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8864772","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8864772","identity":"rs-8864772","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}