Heart failure (HF) is a growing public health challenge worldwide, with high rates of morbidity, frequent hospitalizations, and significant healthcare costs. Acute decompensated heart failure (ADHF), defined as either new-onset HF or acute exacerbation of chronic HF, accounts for a substantial proportion of cardiovascular emergency admissions [1]. Globally, more than 26 million people are estimated to live with HF, and its burden continues to rise due to improved survival after myocardial infarction and population aging [2]. In high-income countries, large registries such as ADHERE (Acute Decompensated Heart Failure National Registry) and OPTIMIZE-HF have provided critical insights into the clinical characteristics, management patterns, and outcomes of ADHF [3–5]. These initiatives have also demonstrated that quality improvement programs can reduce in-hospital mortality and improve adherence to guideline-directed medical therapy [4,6]. In contrast, India lacks large-scale surveillance programs for heart failure. Available data on HF in the Indian population are sparse, regionally fragmented, and often derived from small observational studies. A recent systematic estimate suggests that the prevalence of HF in India ranges between 1.3 and 4.6 million, with an annual incidence between 0.5 to 1.8 million cases, largely driven by coronary artery disease, hypertension, diabetes, obesity, and rheumatic heart disease [2]. Notably, Indian patients tend to present with HF at a younger age compared to Western cohorts, often with multiple coexisting risk factors and limited access to advanced therapies. Furthermore, in-hospital mortality due to cardiovascular disease, including ADHF, is disproportionately higher in India than in high-income countries [2,7]. Several systemic factors contribute to this discrepancy, including late presentation, delayed diagnosis, underutilization of evidence-based therapies, and the lack of uniform care protocols in smaller hospitals [8]. The economic burden of prolonged hospitalizations and readmissions is also significant, especially in resource-limited settings, where access to medications like ARNIs, SGLT2 inhibitors, and device therapies remains restricted. International guidelines recommend early assessment with biomarkers (e.g., NT-proBNP), optimization of neurohormonal blockade, and individualized risk stratification to improve short-term outcomes [1,3,9]. However, real-world implementation in India remains inconsistent, and local studies are essential to inform evidence-based practices tailored to the Indian population. This study was conducted to assess the clinical profile, etiology, and in-hospital outcomes of ADHF in an Indian tertiary care setting, with comparative analysis between patients with heart failure with reduced ejection fraction (HFrEF) and preserved ejection fraction (HFpEF). By identifying predictors of poor outcomes, this study aims to inform risk stratification and treatment optimization in Indian patients hospitalized with ADHF. Objectives This study was undertaken to comprehensively assess the clinical characteristics and in-hospital outcomes of patients presenting with acute decompensated heart failure (ADHF) at a tertiary care centre in Eastern India. Specific objectives included: 1. To describe the demographic and clinical profiles of patients admitted with ADHF, including presenting symptoms and comorbid conditions. 2. To determine the etiological factors contributing to ADHF, with stratification based on left ventricular ejection fraction: o Heart Failure with Reduced Ejection Fraction (HFrEF; LVEF <40%) o Heart Failure with Preserved Ejection Fraction (HFpEF; LVEF >50%) 3. To evaluate in-hospital outcomes, specifically: o Duration of hospital stay o In-hospital mortality 4. To identify clinical and laboratory predictors (e.g., NT-proBNP, Delta Neutrophil Index, comorbidities) associated with increased mortality or complications in ADHF.

Study Design and Setting This was a cross-sectional, observational study conducted over 16 months (September 3, 2019 to January 31, 2021) in the Departments of General Medicine and Cardiology at Medica Superspecialty Hospital, Kolkata — a tertiary care teaching hospital in Eastern India. Participants Patients aged 18 years and above admitted with a diagnosis of acute decompensated heart failure (ADHF) were screened for eligibility. ADHF was defined as rapid onset or worsening of symptoms and signs of heart failure requiring hospitalization. All participants provided informed consent prior to enrolment. To ensure clear stratification, only patients with either reduced ejection fraction (<40%) or preserved ejection fraction (>50%) were included. Those with mid-range ejection fraction (40–50%) were excluded. Inclusion Criteria • Adults (≥18 years) with ADHF (either de novo or acute on chronic) • Diagnosis based on Framingham criteria and echocardiographic confirmation • Availability of NT-proBNP and LVEF measurement • Written informed consent obtained Exclusion Criteria • Patients with LVEF 40–50% (mid-range EF) • Age <18 years • Refusal to participate • Concurrent enrollment in any clinical trial Data Collection and Definitions Data were collected using a structured case record form. Baseline demographics, clinical history, comorbidities, and presenting symptoms were documented. Physical examination findings and investigation results were systematically recorded. Laboratory evaluations included complete blood count, renal and liver function tests, serum electrolytes, NT-proBNP, and Delta Neutrophil Index (DNI). Echocardiography was used to assess LVEF and structural abnormalities. Key operational definitions: • Anemia: Hemoglobin <13 g/dL in males, <12 g/dL in females • Iron Deficiency Anemia (IDA): Defined using iron studies in anemic patients • Chronic Kidney Disease (CKD): As per KDIGO guidelines • Delta Neutrophil Index (DNI): Calculated via automated haematology analyzer; values >1.8% were considered indicative of concurrent infection • NT-proBNP: Interpreted in line with age-adjusted cut-offs for ADHF diagnosis Outcome Measures • Primary Outcome: In-hospital mortality • Secondary Outcomes: o Duration of hospital stay o Association of clinical/laboratory parameters with LVEF subtype (HFrEF vs HFpEF) o Correlation between etiological factors and in-hospital outcomes Statistical Analysis All statistical analyses were performed using SPSS version 23.0 (IBM Corp., Armonk, NY, USA). Continuous variables were tested for normality using the Kolmogorov–Smirnov test and expressed as mean ± standard deviation. Group comparisons were done using the independent t-test for continuous variables and the Chi-square test or Fisher’s exact test for categorical variables. A two-tailed p-value < 0.05 was considered statistically significant. Ethical Approval The study was approved by the Institutional Ethics Committee of Medica Superspecialty Hospital, Kolkata. Written informed consent was obtained from all patients prior to data collection. Ethical principles outlined in the Declaration of Helsinki were followed throughout the study.

1. Baseline Characteristics Of the 79 patients included in the study, 63.3% had HFrEF and 36.7% had HFpEF. The two groups were comparable in terms of gender distribution, NYHA functional class, and presenting symptoms. Patients with HFrEF were significantly older than those with HFpEF (p = 0.03). All patients presented with shortness of breath, while other common symptoms such as fatigue, cough, fever, and pedal oedema occurred with similar frequency across both groups, with no statistically significant differences. Most patients in both cohorts were in NYHA Class IV at admission, reflecting advanced functional impairment, though the distribution between Class III and IV did not differ significantly by LVEF category. These findings are detailed in Table 1. Table 1. Baseline Characteristics of the Study Population Variable HFrEF (n = 50) HFpEF (n = 29) Total (n = 79) Mean Age (years) 69.1 ± 6.12 64.0 ± 5.76 67.2 ± 6.40 Male (%) 62.0 58.6 60.75 Female (%) 38.0 41.4 39.25 NYHA Class III (%) 14.0 17.2 15.18 NYHA Class IV (%) 86.0 82.7 84.81 Shortness of Breath (%) 100.0 100.0 100.0 Fatigue (%) 50.0 44.8 48.1 Cough (%) 30.0 31.0 30.37 Fever (%) 28.0 27.5 27.84 Pedal Oedema (%) 14.0 13.7 13.92 Data are presented as mean ± standard deviation or percentage (%). p-values derived from t-test or Chi-square test as appropriate. 2. Aetiology and Comorbidities The prevalence of underlying aetiological factors and comorbid conditions varied significantly between patients with HFrEF and those with HFpEF. Coronary artery disease (CAD) was the most prevalent underlying condition overall, affecting 72% of HFrEF patients compared to 37.9% in the HFpEF group (p = 0.012). Dilated cardiomyopathy (DCM) was exclusively observed in the HFrEF group (28%; p = 0.003), as was a significantly higher prevalence of chronic kidney disease (CKD) (30% vs 10.3%; p = 0.020). Other notable associations with HFrEF included iron deficiency anaemia (IDA) (38% vs 10.3%; p = 0.045) and pneumonia (34% vs 6.8%; p = 0.002). Additionally, nearly half of the HFrEF patients had a history of chronic heart failure (48%) compared to only 6.8% in the HFpEF group (p = 0.001), suggesting a more advanced disease trajectory. Conditions such as hypertension, diabetes mellitus, COPD, and atrial fibrillation were common across both groups, but the differences were not statistically significant (all p > 0.05). These findings are detailed in Table 2, and key statistically significant comorbidities are illustrated visually in Figure 1. Table 2. Aetiological and Comorbid Conditions in HFrEF vs HFpEF Condition HFrEF (%) Hypertension (HTN) HFpEF (%) 46.0 44.8 p-value Coronary artery disease (CAD) 0.910 72.0 37.9 Dilated cardiomyopathy (DCM) 0.012 28.0 0.0 Chronic kidney disease (CKD) 0.003 30.0 10.3 Chronic obstructive pulmonary disease 0.020 24.0 27.5 Type 2 diabetes mellitus (T2DM) 0.790 42.0 37.9 Iron deficiency anaemia (IDA) 0.710 38.0 10.3 Pneumonia 0.045 34.0 6.8 Atrial fibrillation (AF) 0.002 12.0 10.3 History of chronic heart failure 0.810 48.0 6.8 0.001 Data are expressed as percentages. p-values were calculated using the Chi-square test or Fisher’s exact test where appropriate. Bar chart comparing major comorbid conditions in patients with HFrEF and HFpEF. CAD, DCM, CKD, IDA, pneumonia, and prior heart failure were significantly more frequent in the HFrEF group (p < 0.05). 3. In-hospital Outcomes Overall, in-hospital mortality among the study cohort was 17.7%. Mortality was significantly higher in patients with HFrEF (26%) compared to those with HFpEF (3.45%, p = 0.02) (figure 2). The mean duration of hospital stay was also longer for non-survivors compared to survivors across both groups. In the total population, survivors had an average stay of 7.18 ± 1.13 days, while non-survivors stayed significantly longer (11.28 ± 1.68 days, p = 0.04). These findings, summarised in Table 3, highlight the greater disease burden and adverse outcomes in the HFrEF group. Table 3. In-hospital Mortality and Hospital Stay in HFrEF vs HFpEF Outcome HFrEF (n = 50) HFpEF (n = 29) Total (n = 79) In-hospital mortality (%) 26.0 3.45 p-value 17.7 Mean hospital stay (days) 7.5 ± 1.4 (survivors), 11.6 ± 1.8 (non-survivors) 6.8 ± 0.02 1.1 (survivors), 10.9 ± 1.5 (non-survivors) 7.18 ± 1.13 (survivors), 11.28 ± 1.68 (non-survivors) Data expressed as mean ± standard deviation (SD) or percentages as appropriate. 4. Mortality Risk Factors Several factors were associated with increased in hospital mortality. Patients aged ≥70 years had a significantly higher risk of death compared to younger patients (78.6% vs 48.0%, p = 0.013). Dilated cardiomyopathy (DCM) emerged as the strongest predictor, present in 71.4% of non-survivors compared to only 10% of survivors (p < 0.001). Additionally reduced LVEF (<40%) was significantly associated with mortality (92.9% vs 58.0%, p = 0.030). Other comorbidities, including CKD, IDA, pneumonia, hypertension, diabetes mellitus, atrial fibrillation, history of chronic heart failure, and NYHA class IV status, did not show statistically significant associations with mortality. These results are summarised in Table 4. Table 4. Mortality by Key Risk Factors Variable Survivors (%) Non-survivors (%) p-value Age ≥ 70 years 48.0 78.6 0.013 Dilated cardiomyopathy (DCM) 10.0 71.4 <0.001 Reduced LVEF (<40%) 58.0 92.9 0.030 Chronic kidney disease (CKD) 26.0 35.7 0.25 Iron deficiency anaemia (IDA) 30.0 28.6 0.80 Pneumonia 24.0 28.6 0.70 Hypertension (HTN) 44.0 50.0 0.60 Type 2 diabetes mellitus (T2DM) 38.0 42.9 0.75 Atrial fibrillation (AF) 12.0 14.3 0.90 History of chronic HF 35.0 42.9 0.40 NYHA Class IV 82.0 92.9 0.60 Data are expressed as percentages of survivors and non-survivors. p-values calculated using Chi-square or Fisher’s exact test, as appropriate. 5. Biomarkers and Functional Status Among laboratory parameters, NT-proBNP values were higher in non-survivors compared to survivors (4510 ± 1620 vs 3890 ± 1400 pg/ml), but this difference did not reach statistical significance (p = 0.13). By contrast, the Delta Neutrophil Index (DNI) was markedly elevated in non-survivors (2.6 ± 0.7%) compared to survivors (1.2 ± 0.5%), with a highly significant association (p < 0.001). This suggests that DNI may serve as a valuable marker of poor prognosis, particularly in ADHF patients with superimposed pneumonia. These findings are summarised in Table 5. Table 5. Biomarkers in Survivors vs Non-survivors Variable Survivors (n = 65) Non-survivors (n = 14) p-value NT-proBNP (pg/ml) 3890 ± 1400 4510 ± 1620 0.13 Delta Neutrophil Index (DNI, %) 1.2 ± 0.5 2.6 ± 0.7 <0.001 Data are expressed as mean ± standard deviation (SD). p-values derived using independent t-test.

In this observational study of 79 patients with acute decompensated heart failure (ADHF), we found that 63.3% had HFrEF and 36.7% had HFpEF. Mortality was significantly higher in the HFrEF group (26.0%) compared to the HFpEF group (3.45%, p = 0.02). Predictors of in-hospital mortality included age ≥70 years (78.6% vs 48.0% in survivors, p = 0.013), dilated cardiomyopathy (71.4% vs 10.0%, p < 0.001), and reduced LVEF (<40%, 92.9% vs 58.0%, p = 0.030). Non survivors had a significantly longer hospital stay compared with survivors (11.28 ± 1.68 vs 7.18 ± 1.13 days, p = 0.04). Our findings align with global registries such as ADHERE and OPTIMIZE-HF, where in-hospital mortality rates were reported at approximately 4.0%–4.5% in Western cohorts [10,11]. However, our observed mortality (17.7% overall) is notably higher, reflecting both disease severity at presentation and limitations in resource availability. This discrepancy echoes earlier data from India and Africa, where mortality rates in ADHF have ranged from 12%–20% [12,13]. By contrast, the Framingham Heart Study reported mortality rates closer to 8% in community-based populations [14], underscoring regional differences in risk factors, healthcare delivery, and treatment adherence. The role of age as a predictor of mortality is consistent with prior studies. The Clinical Quality Improvement Network found age ≥70 to nearly double the risk of in hospital death [15], while our data demonstrate a similar trend (78.6% of non-survivors were ≥70). Similarly, DCM was a powerful predictor in our cohort (71.4% of non-survivors), mirroring findings from OPTIMIZE-HF, where non-ischemic cardiomyopathy carried an adjusted odds ratio of 2.1 for mortality [11]. Interestingly, iron deficiency anaemia (IDA) showed a strong association with HFrEF in our study (38% vs 10.3% in HFpEF, p = 0.045), though not directly with mortality. This parallels findings from Nigeria, where Akintunde et al. reported 45% prevalence of anaemia in HF patients and demonstrated poorer outcomes in those affected [12]. Similarly, data from Turkey suggested anaemia worsened prognosis in ADHF by increasing both rehospitalisation and mortality [16]. By contrast, some Western registries found anaemia to be associated more with rehospitalisation risk rather than in-hospital death [11], suggesting population-level differences in nutritional status and comorbidity burden. With regard to biomarkers, our study demonstrated that NT-proBNP was elevated (mean 4510 ± 1620 pg/ml in non-survivors vs 3890 ± 1400 in survivors) but not predictive of mortality (p = 0.13). This contrasts with large cohorts where NT-proBNP was a strong independent predictor of short-term outcomes [17,18]. One possible explanation may be the late presentation and advanced NYHA class in our patients, limiting discriminatory capacity. On the other hand, the Delta Neutrophil Index (DNI) was significantly higher in non survivors (2.6 ± 0.7% vs 1.2 ± 0.5%, p < 0.001), echoing its established role in sepsis severity [19] and suggesting a novel prognostic application in ADHF, particularly when associated with pneumonia. To our knowledge, this represents one of the first Indian datasets highlighting DNI in HF. Contrastingly, NYHA functional class, while historically linked to outcomes [14], did not predict mortality in our cohort (p = 0.60), likely reflecting a ceiling effect with nearly all patients presenting in NYHA Class IV (92.9% of non-survivors, 82% of survivors). Similarly, comorbidities such as hypertension, diabetes, COPD, and atrial fibrillation showed no significant mortality associations, despite their established impact in Western cohorts [20–22]. This divergence may again point to differences in relative burden and interplay of risk factors in Indian patients. From a clinical standpoint, these results reinforce the urgent need for early risk stratification and adaptation of guideline-directed therapies. The 2021 ACC Expert Consensus emphasises optimisation of HF therapies, while disease-specific guidelines for hypertension (JNC 8), diabetes, and CKD all highlight aggressive comorbidity control. However, real-world application in India is often constrained by cost, availability, and delayed presentation [23]. Our study has several strengths, including the comprehensive evaluation of both clinical and laboratory predictors and the novel incorporation of DNI. Limitations include its single-centre design, relatively small sample size, and short-term focus on in hospital outcomes. Future directions should include larger multicentre registries across India to validate these predictors and to test whether interventions such as anaemia correction or DNI-guided infection management can improve outcomes.

In this single-centre study of patients admitted with acute decompensated heart failure, we observed that HFrEF was the predominant phenotype (63.3%) and was associated with significantly higher in-hospital mortality compared with HFpEF (26.0% vs 3.45%). Increasing age, dilated cardiomyopathy, and reduced left ventricular ejection fraction emerged as independent predictors of death, while the Delta Neutrophil Index proved to be a novel and powerful prognostic marker. By contrast, NT-proBNP, although elevated, did not predict short-term outcomes. These findings highlight the distinct clinical profile and outcome determinants of Indian patients, which differ in important ways from Western cohorts. Recognition of age, cardiomyopathy, iron deficiency anaemia, and infection-related inflammatory markers as critical risk factors should inform both bedside care and the development of locally relevant management strategies. Larger multicentre studies are warranted to validate these predictors and to evaluate the role of anaemia correction and biomarker-guided therapy in improving survival.

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