The Adjusted Ferritin Inflammation Index: A Novel Metric for Predicting Mortality in Heart Failure

The Adjusted Ferritin Inflammation Index: A Novel Metric for Predicting Mortality in 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 Article The Adjusted Ferritin Inflammation Index: A Novel Metric for Predicting Mortality in Heart Failure Çetin ALAK, Şükrü Çiriş, Furkan Fatih Yurdalan, Fazil Çağrı Hunutlu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5942346/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 Iron deficiency (ID) is common in heart failure (HF) patients and associated with poorer outcomes. However, traditional markers like ferritin and transferrin saturation (TSAT) may fail to accurately assess ID due to the confounding effects of inflammation. In this study, we introduce the Adjusted Ferritin Inflammation Index (AFII), a composite score combining ferritin/CRP ratio and albumin levels, designed to improve the precision of ID assessment in HF patients. A total of 322 HF patients with reduced ejection fraction were included in the analysis, following the application of specific inclusion and exclusion criteria. Multivariate analysis identified AFII as an independent predictor of mortality (HR: 2.155, 95% CI: 1.361–3.412, p = 0.001), demonstrating strong discriminatory power (AUC: 0.713). Survival analysis showed that patients with AFII ≥ 2.1 had significantly lower survival rates across all subgroups. These findings suggest that AFII could be a valuable tool for risk stratification in HF, offering a more comprehensive measure of mortality risk compared to traditional iron markers. Further validation in multi-center studies is needed to confirm its clinical utility. Health sciences/Cardiology Health sciences/Biomarkers/Prognostic markers Heart Failure Ferritin Inflammation Mortality Biomarkers AFII (Adjusted Ferritin Inflammation Index) Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Iron deficiency (ID) is a pervasive and clinically significant condition in individuals with heart failure (HF), affecting approximately 50% of subjects with chronic HF. This comorbidity has been consistently linked to poorer functional ability, degraded quality of life, and elevated hospitalization rates [1]. Current international guidelines recommend intravenous iron therapy (IV) for symptomatic HF subjects with reduced ejection fraction (HFrEF) and ID, described by serum ferritin < 100 ng/mL or 100–299 ng/mL with transferrin saturation (TSAT) < 20%. These recommendations are supported by robust evidence from randomized controlled trials demonstrating improved exercise capacity and reduced HF-related hospitalizations. However, a consistent reduction in mortality has not been observed, highlighting the limitations of current ID diagnostic criteria and therapeutic strategies [2,3,4]. One major challenge in accurately assessing ID in HF Patients is inflammation's confounding role. Ferritin, an acute-phase reactant, is elevated during systemic inflammation, potentially obscuring the detection of true iron deficiency. This is particularly relevant in HF, a chronic condition characterized by low-grade systemic inflammation. The reliance on ferritin and TSAT as markers of iron status may fail to differentiate between inflammation-driven elevations in ferritin and actual iron stores, potentially leading to misclassification and suboptimal treatment strategies. Recent studies underscore the need for innovative approaches to disentangle ferritin’s dual role as a marker of but iron storage and inflammation [5,6]. To overcome these limitations, we propose an inventive composite index, the Adjusted Ferritin Inflammation Index (AFII), incorporating the ferritin/CRP ratio and albumin levels. This index aims to account for the confounding effects of inflammation on ferritin levels, providing a more precise assessment of iron status in HF Patients. By integrating inflammation and nutritional status biomarkers, AFII offers a comprehensive approach to evaluating the interplay between iron deficiency, inflammation, and clinical outcomes in HF. This study proposes a novel approach to assess ID by introducing the AFII, a composite score that incorporates the ferritin/CRP ratio and albumin levels. This index aims to correct for the confounding effects of inflammation on ferritin measurements, thereby providing a more dependable assessment of iron status in HF Patients. Using data from a well-characterized cohort, we tested the prognostic worth of AFII in predicting mortality and compared its performance to existing diagnostic parameters. By addressing the limitations of current methodologies, AFII has the potential to enhance patient classification and navigate targeted therapeutic strategies in this high-risk cohort. Methods Study Design and Population This retrospective cohort study intended to measure the predictive worth of AFII in HF cases. The data were sourced from 1,968 patients who presented with HF symptoms and an EF below 50% and were evaluated at a single tertiary care centre among January 1, 2017, and September 1, 2023. Patients were screened based on the inclusion and exclusion standards of the CONFIRM-HF trial, which assesses iron deficiency and HF outcomes [7]. The exclusion criteria included: Absence of detailed iron parameter assessments at admission (n = 922) Malignancy (n = 217) Hemoglobin levels > 15 g/dL (n = 179) Dialysis or end-stage renal disease (n = 170) Presence of metallic heart valves (n = 79) Active infection (n = 42) Recent major surgery or valve replacement (n = 36) Coronary angiography within the last 3 months (n = 1) A total of 1,646 patients were eliminated, leaving 322 patients for ultimate assessment. The last follow-up date for survival outcomes was December 1, 2024 (Fig. 1 ). Data Collection and AFII Score Development Baseline demographics, clinical characteristics, and laboratory parameters, including ferritin, CRP(C-reactive protein), albumin, hemoglobin, transferrin saturation, total cholesterol, HDL, LDL, and lymphocyte count, were collected. To derive the AFII score, univariate Cox regression initially analyzed inflammation-related biochemical markers, including Ferritin/CRP ratio, CRP/albumin ratio and the CONUT score. Subsequently, binary logistic regression with the forward likelihood ratio (LR) method was used to evaluate significant parameters. The final model included ferritin/CRP ratio and albumin, which were identified as statistically significant. Including HDL slightly improved the model's performance (AUC: 0.724) but did not substantially enhance predictive power. Thus, the final model was adopted, incorporating only ferritin/CRP ratio and albumin (AUC: 0.713). The AFII score was calculated using the following equation derived from logistic regression coefficients: AFII = (Albumin×−0.168) + (Ferritin/CRP×−0.012) + 6.958 The score was log-transformed (base 2), and an optimal cutoff point of 2.1 was determined via ROC curve analysis using the Youden index. Statistical Analysis Data were analyzed using SPSS version 28.0 (IBM, NY, USA). Continuous variables were represented as the average ± standard deviation or median with interquartile range (IQR), depending on the distribution, and categorical variables as counts and percentages. Normality was evaluated using the Kolmogorov-Smirnov test. For comparisons between groups, Student’s t-test was employed for normally distributed continuous variables, while Mann-Whitney U test was used for non-normally distributed variables. The ability of AFII to distinguish mortality risk was assessed through ROC curve analysis, and the area under the curve (AUC) with 95% confidence intervals (CI) was determined. Survival analysis was conducted using the Kaplan-Meier method, and log-rank tests were employed to compare survival distributions. To assess independent risk predictors, Cox proportional hazards regression analysis was conducted in two steps: Univariate analysis identified potential risk factors. Multivariate analysis included variables with p < 0.2 in univariate analysis to determine independent predictors of mortality. Validation of the Score The AFII score was validated by incorporating it into a Cox regression model, where it demonstrated a hazard ratio (HR) of 2.155 (95% CI: 1.361–3.412, p = 0.001), confirming its significance as an independent predictor of mortality in HF patients. The score exhibited strong discriminatory power (AUC: 0.713) for risk stratification. Results Baseline Characteristics and Univariate Analysis A total of 322 cases were participated in the trial, with a median follow-up period of 41 months (IQR: 27–55 months). 106 patients (32.9%) died during follow-up, while 216 (67.1%) survived. Baseline characteristics were compared between the deceased and surviving individuals and are presented in Table-1. Patients in the demised group had significantly higher NYHA (New York Heart Association) Class (p <0.001), CRP levels (5.35 mg/dL [IQR: 1.5–8.45] vs. 3.65 mg/dL [IQR: 1–6.2], p = 0.002), CONUT scores (2 [IQR: 1–4] vs. 2 [IQR: 1–3], p < 0.001), CRP/albumin ratio (0.094 [IQR: 0.087–0.1] vs. 0.141 [IQR: 0.132–0.15], p < 0.001), and AFII (2.24 [IQR: 2.08–2.34] vs. 2 [IQR: 1.85–2.16], p < 0.001). Conversely, no notable disparities were detected between the deceased and survived groups regarding age (67.5 years [IQR: 59.5–75.5] vs. 67 years [IQR: 59–75], p = 0.122), ferritin levels (52.5 ng/mL [IQR: 30–108.25] vs. 59 ng/mL [IQR: 30–100.5], p = 0.733), triglyceride levels (112.5 mg/dL [IQR: 72.5–146.75] vs. 112 mg/dL [IQR: 80–160.75], p = 0.591), and TSAT groups (p = 0.284), indicating that neither ferritin nor TSAT levels were significant determinants of survival in univariate analysis (Table 1). Baseline Characteristics and Univariate Analysis According to AFII In patients stratified by the AFII, those with AFII ≥ 2.1 had significantly higher age (69 years vs. 65 years, p = 0.005), and notably, a worse Left Ventricular Ejection Fraction (LVEF) (32% vs. 38%, p = 0.005) compared to those with AFII < 2.1. Furthermore, subjects in the higher AFII group exhibited a greater prevalence of coronary artery disease (74.2% vs. 61.6%, p = 0.015) and CRP scales were significantly increased in subjects with AFII ≥ 2.1 (5.5 mg/dL vs. 3.07 mg/dL, p < 0.001) (Table 2). These findings underline the association of a higher AFII with poorer clinical characteristics, including more severe coronary artery disease and worse inflammation and nutritional status, which are crucial factors influencing heart failure prognosis. Cox Regression Analysis Univariate Cox regression analysis identified various key death predictors as strong independent mortality predictors (Table 3). Multivariate Cox regression analysis confirmed the significance of AFII (HR: 2.155, 95% CI: 1.361–3.412, p = 0.001), NYHA class (HR: 1.095, 95% CI: 1.038–1.156, p < 0.001), sodium levels (HR: 0.905, 95% CI: 0.862–0.949, p < 0.001), BNP levels (HR: 1.000, 95% CI: 1.000–1.001, p < 0.001), and smoking (HR: 1.944, 95% CI: 1.303–2.900, p = 0.001) as independent predictors of mortality. Survival Analysis Kaplan-Meier survival analysis was carried out to examine the survival probabilities across different AFII groups. At various time points, significant dissimilarities in survival rates were noted across patients with low and high AFII. Overall cohort : At 1-year follow-through the survival expectancy for cases with low AFII was 89.9%, whereas those with high AFII had a survival ratio of 76.1%. At 3 years, the survival rates diverged significantly, with the low AFII group demonstrating a survival rate of 84.6%, compared to 54.9% in the high AFII group (Figure 2). NYHA class 1-2 : The 1-year survival expectancy was 97.2% for the low AFII group and 93% for the high AFII group. At 3 years, the survival ratio for the low AFII group remained high at 95.2%, while the high AFII group declined to 79.9% (Figure 3). NYHA class 3-4 : At 1-year, the survival expectancy was 75% for the low AFII group and 63% for the high AFII group. At 3 years, the survival rat for the low AFII group was 63.1%, compared to 35.8% for the high AFII group (Figure 4). Outpatient group : The 1-year survival expectancy was 92% for the low AFII group and 83.5% for the high AFII group. At 3 years, the low AFII group maintained a survival rate of 89.1%, while the high AFII group had a survival ratio of 63.7% (Figure 5). Inpatient group : The 1-year survival expectancy for the low AFII group was 85.1% and 63.3% for the high AFII group. At 3 years, the low AFII group had a survival rate of 74.1%, while the high AFII group had a survival ratio of 40%(Figure 6). These findings underscore the prognostic significance of AFII in predicting long-term survival. Patients in the high AFII group consistently exhibited worse outcomes across all subgroups, emphasizing the need for tailored clinical management based on AFII levels. Discussion This study intended to ascertain the marker of fatality in HF patients with an LVEF below 50%, who were evaluated for detailed iron parameters in the context of iron therapy needs. Low serum ferritin levels and TSAT are reliable parameters for diagnosing ID in healthy individuals. Despite that, ferritin is an acute-phase reactant protein secreted in response to cytokines during pro-inflammatory processes [8,9]. The association between HF and inflammation and proinflammatory cytokines (e.g., tumor necrosis factor-alpha (TNF- \(\:\alpha\:)\) , interleukin-1 (IL-1), and interleukin-6 (IL-6)) has long been recognized. This relationship complicates functional iron deficiency diagnosis in HF cases [10]. In our study, the AFII score, formulated by the ratio of inflammation parameters, was identified as the most significant predictor of mortality in heart failure patients with an ejection fraction (EF) below 50% who were evaluated for detailed iron parameters as determined by multivariate analysis (HR: 2.155, 95% CI: 1.361–3.412, p = 0.001). Similarly, patients with an AFII ≥ 2.1 demonstrated lower survival rates over a 3-year follow-up period (84.6% vs. 54.9%). Currently, in clinical practice, the criteria for defining ID in HF patients (serum ferritin levels < 100 ng/mL or ferritin levels between 100–299 ng/mL with TSAT < 20%) were first introduced in the FAIR-HF study in 2008 [11]. These criteria have since been widely accepted in subsequent studies. However, it is important to note that the FAIR-HF criteria are based on parameters used in the literature for diagnosing ID in subjects with chronic kidney disease. Subsequently, in a study involving 42 HF subjects who received coronary artery bypass grafting (CABG), these parameters were compared with bone marrow iron staining results, which are considered the gold standard for diagnosing iron deficiency. The findings revealed that the FAIR-HF criteria demonstrated a sensitivity of 82.4%, specificity of 72.0%, a positive predictive value of 66.7%, and a negative predictive value of 85.7%. According to the FAIR-HF criteria, one-third of patients diagnosed with iron deficiency were found to have normal bone marrow iron stores. [12]. Furthermore, the FAIR-HF criteria highlighted that ferritin levels were unrelated to mortality [13]. In a meta-analysis published in February 2024, which included nine randomized controlled trials, IV iron therapy in HF patients was shown to significantly reduce the composite risk of hospitalization for heart failure (HFH) or cardiovascular death by 16%. Additionally, it diminished the composite risk of hospitalization for any reason or all-cause mortality by 8%. However, these outcomes were primarily guided by reductions in HFH and all-cause hospitalizations [14]. Consequently, uncertainties remain regarding the impact of IV iron therapy on mortality outcomes in this patient population. In the patient population included in our study, mortality predictors were also evaluated. Among these, BNP emerged as a statistically significant predictor of mortality. BNP is released from the heart in reaction to heightened wall stress, heightened sympathetic tone, and vasoconstriction, serving as a compensatory regulatory mechanism. Elevated circulating levels of natriuretic peptides can integrate cardiovascular and hemodynamic stress from multiple sources, making BNP a crucial marker for predicting mortality in this patient group [15]. In the multivariate Cox regression analysis, BNP was revealed to be a statistically significant indicator of mortality (HR: 1.000, 95% CI: 1.000–1.001, p < 0.001). However, the hazard ratio was equal to 1, indicating that while BNP remains significant, its clinical effect on mortality is minimal. This suggests that BNP, although a key marker in heart failure prognosis, may have a limited independent impact on predicting mortality when considering other covariates. The NYHA functional classification, a well-established parameter, has long been used to categorize symptoms in HF patients. A 2019 study by Briongos-Figuero et al. involving HF patients with implantable cardioverter defibrillators (ICDs) reported lethality rates of 6.9% in NYHA I patients, 11% in NYHA II patients (HR: 2.2, 95% CI: 1.1–4.9), and 23.9% in NYHA III patients (HR: 5.5, 95% CI: 2.4–12.7). Similarly, in our study, an increase in NYHA class was discovered to correlate with a higher risk of mortality (HR: 1.095, 95% CI: 1.038–1.156, p ) [16]. Sodium levels, identified as another predictor of mortality, may exert their effects through various mechanisms. Hyponatremia can lead to volume overload by triggering inappropriate secretion of antidiuretic hormone, which is affiliated with end-organ dysfunction such as renal and hepatic failure and/or heart failure. Mechanisms as seen in the renin-angiotensin-aldosterone system, excessive sympathetic nervous system stimulation, and the over release of antidiuretic hormone contribute to water retention and an increase in overall blood volume [17]. Consequently, the resulting fluid imbalance in patients with limited cardiac reserve may help explain the increased mortality observed (HR: 0.905, 95% CI: 0.862–0.949, p < 0.001). Smoking was identified as an independent risk factor for mortality in HF patients, with a HR of 1.944 (95% CI: 1.303–2.900, p = 0.001). This finding is consistent with previous studies, such as the Cardiovascular Health Study (Gottdiener et al., 2021), which reported that ongoing smoking is associated with poor prognosis and increased mortality in HF patients, as it contributes to elevated inflammation and myocardial injury [18]. Similarly, our results demonstrate that smoking plays a significant role in exacerbating HF outcomes. The chemical constituents of cigarette smoke can enhance atherosclerosis through endothelial damage mediated by their potent oxidant and inflammatory effects. Moreover, studies have shown that nicotine, through carbon monoxide and oxidative stress, can trigger cardiac fibrosis, leading to structural remodeling and cardiac arrhythmias. Through these mechanisms, smoking may cause systolic and diastolic dysfunction, resulting in worsening symptoms and increased mortality in patients with HF [19]. The insights of our study highlight the importance of addressing smoking cessation as part of HF management strategies to improve patient outcomes. However, when comparing baseline characteristics by AFII levels, no significant difference in smoking status was observed between the high and low AFII groups (p = 0.825). This discrepancy suggests that while smoking remains an independent predictor of mortality, its effect does not appear to be directly influenced by the AFII score. AFII, a composite score adjusting for inflammation, offers a broader and more comprehensive measure of mortality risk in HF patients, potentially reflecting multiple systemic factors beyond smoking. These findings further support the potential utility of AFII as a prognostic tool in HF management, providing more accurate risk stratification independent of specific risk factors such as smoking. We also observed significant differences in survival rates based on AFII levels. Kaplan-Meier analyses revealed lower survival rates in patients with higher AFII levels across multiple subgroups, including NYHA class, outpatient/inpatient status, and overall. Notably, significant survival differences were found between patients with low and high AFII in both the NYHA class 1–2 and NYHA class 3–4 groups. In the NYHA 1–2 group, higher AFII levels were associated with a more pronounced decline in survival. In the NYHA 3–4 group, the survival rates for patients with high AFII were considerably lower compared to those with low AFII. Even in the NYHA class 1–2 patient group, where survival is expected to be relatively better, AFII levels could serve as an important marker in predicting the disease progression of these patients. Conclusion This study highlights AFII, NYHA class, sodium levels, BNP, and smoking as independent markers of fatality in subjects with HF. In contrast, neither ferritin levels nor TSAT were significant determinants of survival. These findings underscore the potential role of inflammation-adjusted iron indices in risk stratification, while traditional iron markers may not accurately reflect prognosis in this population. The present study proposes that AFII could be an esteemed tool for guiding treatment decisions and identifying high-risk patients, offering clinicians a more precise method for predicting mortality and ultimately enabling more personalized management strategies. However, further confirmation of this score in broader, diverse cohorts is essential to establish its utility and robustness as a prognostic marker. Limitations Numerous limitations should be taken into account when interpreting this study's findings. First, the study's retrospective design restricts the ability to establish causal relationships and may introduce selection bias, as it relies on existing clinical data. Second, the trial's single-centre design may limit the generalizability of our findings to broader patient populations, as the results are based on data from a single hospital setting. A multi-center study could provide more robust evidence and improve the external validity of our results. Another limitation is the relatively narrow cohort size, which may have reduced statistical power, especially when analyzing subgroups of patients. Larger studies with a more diverse patient population would enhance the ability to detect meaningful differences between groups. Additionally, the limited follow-up period of 41 months may not capture long-term mortality outcomes, and longer-term follow-up could provide further insights into the impact of AFII on survival in HF patients. While we focused on inflammation-adjusted iron indices, particularly the AFII score, a limitation of this study is that we did not assess other established inflammatory biomarkers such as IL-6, IL-1, TNF-α, or high-sensitivity CRP (hs-CRP), which are commonly applied in research settings but not routinely measured in clinical practice. This limited our ability to evaluate the inflammatory processes involved in HF comprehensively. However, a key strength of this study is that we utilized routinely measured laboratory parameters, such as ferritin, albumin, and CRP, which are commonly available in clinical practice, making our findings more applicable to daily clinical settings. Finally, although we noted significant relationships between certain biomarkers and mortality, including the AFII score, additional prospective future research is essential to substantiate these results. and resolve the practical utility of the AFII score as a prognostic tool in HF management. Future Directions In this study, we observed that the current criteria for selecting patients for IV iron therapy, primarily based on ferritin levels, may not adequately predict the mortality benefit of treatment. This is because ferritin, as an acute-phase reactant, is influenced by inflammation, leading to potential misclassification of patients. In contrast, we propose that using an inflammation-adjusted score, such as the AFII score, could improve patient selection for IV iron therapy by identifying those who truly suffer from iron deficiency and are at higher mortality risk. The AFII score has demonstrated strong predictive power as a distinct risk factor for mortality, particularly in cases with HF. By assessing baseline characteristics and iron indices such as ferritin and TSAT, his score can more accurately recognize subjects who would profit from iron supplementation and those who are at a elevated risk of mortality. Furthermore, future studies could explore the potential of this score in identifying patients who might derive mortality benefit from IV iron therapy. These studies aim to establish the clinical importance of the AFII score as a tool for determining true iron deficiency and predicting outcomes in HF patients. Additionally, research could be designed to compare the AFII score with the gold standard of bone marrow biopsy for assessing iron stores, further validating the utility of this score in clinical practice and advancing our understanding of iron deficiency management in HF. Abbreviations AFII: Adjusted Ferritin Inflammation Index AHF : Acute Heart Failure AUC : Area Under the Curve BNP : Brain Natriuretic Peptide CABG : Coronary Artery By-pass Grafting CI : Confidence Interval CHF : Chronic Heart Failure CRP : C-Reactive Protein CRT : Cardiac Resynchronization Therapy ED : Emergency Department ECG : Electrocardiogram GFR: Glomerular Filtration Rate Hb : Hemoglobin HF: Heart Failure HFH: Hospitalization for Heart Failure HfmrEF : Heart Failure with Mildly Reduced Ejection Fraction HfrEF :Heart Failure with Reduced Ejection Fraction hs-CRP: High-Sensitivity CRP ICDs: Implantable Cardioverter Defibrillators ID : Iron Deficiency IL-1 : Interleukin-1 IL -6 : Interleukin-6 IV : Intravenous LVEF : Left Ventricular Ejection Fraction NYHA: New York Heart Association RBCs : Red Blood Cells ROC : Receiver-Operating Characteristic TNF- : Tumour Necrosis Factor-Alpha TSAT : Transferrin Saturation TTE : Transthoracic Echocardiography Declarations Acknowledgements Not applicable. Authors’ Contribution ÇA, ŞÇ and FFY, contributed to the design, data collecting, data analysis, writing, and translation of the manuscript. ÇA, ZK and FÇH: Contributed to data analysis, visualization and writing of the manuscript. ÇA, and TŞ: Contributed to writing and supervision of the manuscript. All the authors have approved the final version of the manuscript to be published. Availability of Data and Materials The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. The authors assure that this paper has not been published before nor has been submitted for publication to another scientific journal. Competing interests The authors declare no competing interests. Ethics This study was conducted in accordance with the principles outlined in the Declaration of Helsinki and approved by the Bursa Uludağ University Faculty of Medicine, Research Ethics Committee (Approval No: 2023-26/9, Date: December 7, 2023). Due to the study's retrospective nature, the requirement for written informed consent was waived. Patient confidentiality and data privacy were strictly maintained, with all patient data anonymized prior to analysis. References Theresa A. McDonagh, Marco Metra, Marianna Adamo et al. 2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure. European Heart Journal (2021) 42, 35993726 doi:10.1093/eurheartj/ehab368 Theresa A. McDonagh, Marco Metra, Marianna Adamo et al. 2023 Focused Update of the 2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure. European Heart Journal (2023) 44, 3627–3639. doi.org/10.1093/eurheartj/ehad195 Ponikowski P, Kirwan BA, Anker SD, McDonagh T et al. AFFIRMAHF Investigators. Ferric carboxymaltose for iron deficiency at discharge after acute heart failure: a multicentre, double-blind, randomised, controlled trial. Lancet 2020;396:18951904. Kalra PR, Cleland JGF, Petrie MC et al. Intravenous ferric derisomaltose in patients with heart failure and iron deficiency in the UK (IRONMAN): an investigator-initiated, prospective, randomised, open-label, blinded-endpoint trial. Lancet 2022;400:2199–209. https://doi.org/10.1016/S0140-6736(22)02083-9 Oriana Marques, Günter Weiss, Martina U. Muckenthaler et al. The role of iron in chronic inflammatory diseases: from mechanisms to treatment options in anemia of inflammation. American Society of Hematolog-Blood, Volume 140, Issue 19, 10 November 2022, Pages 2011–2023. doi.org/10.1182/blood.2021013472 Dignass A, Farrag Karima, Stein J. Limitations of Serum Ferritin in Diagnosing Iron Deficiency in Inflammatory Conditions. International Journal of Chronic Diseases, March 2018.doi.org/10.1155/2018/9394060 Ponikowski P, van Veldhuisen DJ, Comin-Colet J, Ertl G, Komajda M, Mareev V, et al. Beneficial effects of long-term intravenous iron therapy with ferric carboxymaltose in patients with symptomatic heart failure and iron deficiency. Eur Heart J 2015;36:657–68. Kate F Kernan and Joseph A Carcillo. Hyperferritinemia and inflammation. Int Immunol. 2017 Nov; 29(9): 401–409. doi: 10.1093/intimm/dxx031 Wei Wang, Mary Ann Knovich and Lan G. Coffman et al. Serum Ferritin: Past, Present and Future. Biochim Biophys Acta. 2010 Aug; 1800(8): 760–769. 2010 Mar 19. doi: 10.1016/j.bbagen.2010.03.011 Murphy MB, BAO B, Kakkar R et al. Inflammation in Heart Failure: JACC State-of-the-Art Review. JACC, Volume 75, Issue 11, 24 March 2020, Pages 1324–1340. doi.org/10.1016/j.jacc.2020.01.014. Stefan D. Anker, Josep Comin Colet, Gerasimos Filippatos et al. Ferric Carboxymaltose in Patients with Heart Failure and Iron Deficiency. N Engl J Med 2009; 361:2436–2448. December 17, 2009. doi: 10.1056/NEJMoa0908355 Grote Beverborg N, Klip IT, Meijers WC, et al.De nition of iron de ciency based on the gold standard of bone marrow iron staining in heart failure patients. Circ Heart Fail. 2018;11:e004519 Graham FJ, Pellicori P, Kalra PR, Ford I, Bruzzese D, Cleland JGF. Intravenous iron in patients with heart failure and iron deficiency: an updated meta-analysis. Eur J Heart Fail 2023;25:528–37. https://doi.org/10.1002/ejhf.2810 45. Bhatia K, Sabharwal B, Gupta K et al. Clinical outcomes of intravenous iron therapy in patients with heart failure and iron deficiency: Meta-analysis and trial sequential analysis of randomized clinical trials. Journal of Cardiology, Volume 83, Issue 2, February 2024, Pages 105–112. doi.org/10.1016/j.jjcc.2023.06.012 York M, Gupta K, Reynolds C et al. B-Type Natriuretic Peptide Levels and Mortality in Patients With and Without Heart Failure. JACC, Volume 71, Issue 19, 15 May 2018, Pages 2079–2088. doi.org/10.1016/j.jacc.2018.02.071 Briongos-Figuero S, Estévez A, Pérez M.L. et al. Prognostic role of NYHA class in heart failure patients undergoing primary prevention ICD therapy. ESC Heart Failure, December 2019, Volume 7, Issue 1, Pages 280–284. doi.org/10.1002/ehf2.12548 Peng S, Peng J, Yang L et al. Relationship between serum sodium levels and all-cause mortality in congestive heart failure patients: A retrospective cohort study based on the Mimic-III database. Front Cardiovasc Med, 2023 Jan 13;9:1082845. doi: 10.3389/fcvm.2022.1082845 Kamel, H., Bartz, T.M., Longstreth, W.T. et al. Cardiac mechanics and incident ischemic stroke: the Cardiovascular Health Study. Sci Rep 11 , 17358 (2021). doi.org/10.1038/s41598-021-96702-z Son, Youn-Jung et al. "Association between persistent smoking after a diagnosis of heart failure and adverse health outcomes: A systematic review and meta-analysis." Tobacco Induced Diseases, vol. 18, no. January, 2020, 5. doi:10.18332/tid/116411. Tables Table 1: Baseline Characteristics of Deceased and Surviving Heart Failure Patients Deceased (n=106) Survived (n=216) p value Age,years (median, IQR 1-3) 67.5(59.5-75.5) 67(59-75) 0.122 Gender 0.229 Female(n,%) 33(31.1) 82(38) Male(n,%) 73(68.9) 134(62) Hypertension (n,%) 85(80.2) 165(76.4) 0.442 Diabetes Mellitus (n, %) 61(57.5) 101(46.8) 0,069 Coronary Artery Heart Disease (n, %) 84(79.2) 135(62,5) 0.007 Active Smoking (n, %) 63(59.4) 97(44.9) 0.014 Ejection Fraction (median, IQR) 30(22-35) 38(30-45) <0,001 Intravenous Iron Therapy (n, %) 14(13,2) 28(13) 0,951 ACE Inhibitors/ARBs/ARNi (n, %) 76(71.7) 174(80,6) 0,073 Beta-Blockers (n, %) 97(91,5) 190(88) 0,337 Mineralocorticoid Receptor Antagonists (n, %) 56(52,8) 111(51.4) 0,808 NYHA Classification (n, %) <0,001 1 1(0,9) 64(29,6) 2 22(20,8) 90(41,7) 3 51(48,1) 50(23,1) 4 31(29,2) 12(5,6) Sodium, mmol/L (median, IQR 1-3) 137(134-140) 138(136-140) <0,001 Potassium, mmol/L (median, IQR 1-3) 4,4±0,6 4,3±0,5 0.258 Chloride, mmol/L(median, IQR 1-3) 102,5(99-105) 104(101-106) 0,007 Hemoglobin (g/dL) (median, IQR 1-3) 11.7 (10.5- 13) 12.4 (11.1 - 13.2) 0,021 White Blood Cells, 10⁹ per L (median, IQR 1-3) 8,0±2.0 8.2±1.9 0,453 Lymphocytes, 10⁹ per L 1,500(1.045 - 1.975) 1,755(1.285 - 2.265) <0,001 Iron, mcg/dL 40(30,25-70,25) 49(34-83) 0,003 Ferritin (ng/dL) 52,5(30-108,25) 59(30-100,5) 0,733 Transferrin saturastion group Group 1 < %20 Group 2 ≥ %20 Ferritin Group (ng/dL) (n,%) 0,790 Group 1 <100 76(71,7) 163(75,5) Group 2 Transferrin Saturation %20 and 100-299 10(9,4) 15(6,9) Group 4 ≥300 5(4,7) 12(5,6) Total Cholesterol, mg/dL 151(128-194) 167,5(14,5-205,5) 0,089 LDL, mg/dL 91,5(77-123,5) 99,5(73-125.75) 0,341 HDL, mg/dL 36(29,25-45,5) 42(33,75-50,5) <0,001 Triglyceride, mg/dL 112,5(72,5-146,75) 112(80-160.75) 0,591 Creatinine, mg/dL 1,2(1,05-1,67) 1,03(0,88-1,3) <0,001 eGFR, mL/min/1.73m2 59(42,5-80) 70(52,5-87,5) 0,002 CRP,mg/dL 5.35(1.5-8.45) 3.65(1-6.2) 0,002 Albumin, g/L 38(36-40) 41(39-44) <0,001 CONUT Score 2(1-4) 2(1-3) <0,001 CRP/Albumin Ratio 0,094(0,087-0.1) 0,141(0,132-0,15) <0,001 Ferritin/CRP Ratio 11,1(7,8-14.1) 13,5(8-14,5) 0,071 Adjusted Ferritin Inflamation Index 2,24(2,08-2,34) 2(1,85-2,16) <0,001 First Admission <0,001 Outpatient (n,%) 52(49,1) 55(25,5) Inpatient (n,%) 54(50,9) 161(74,5) Table 2: Baseline Characteristics and Univariate Analysis According to Adjusted Ferritin Inflammation Index (AFII) AFII < 2,1 (n=159) AFII ≥ 2.1 (n=163) p value Age,years (median, IQR 1-3) 65(56-71) 69(61-76) 0.005 Gender 0.678 Female(n,%) 55(47.8) 60(52.2) Male(n,%) 104(50.2) 103(49.8) Hypertension (n,%) 122(76.7) 128(78.5) 0.699 Diabetes Mellitus (n, %) 77(48.4) 85(52.1) 0.505 Coronary Artery Heart Disease (n, %) 98(61.6) 121(74.2) 0.015 Active Smoking (n, %) 80(50.3) 80(49.1) 0.825 Ejection Fraction (median, IQR) 38(30-45) 32(25-40) 0.005 ACE Inhibitors/ARBs/ARNi (n, %) 127(79.9) 123(75.5) 0.342 Beta-Blockers (n, %) 134(84.3) 153(93.9) 0.006 Mineralocorticoid Receptor Antagonists (n, %) 78(49.1) 89(54.6) 0.319 NYHA Classification (n, %) <0.001 1 49(30.8) 16(9.8) 2 58(36.5) 55(33.7) 3 40(25.1) 61(37.4) 4 12(7.5) 31(19) Sodium, mmol/L (median, IQR 1-3) 138(136-140) 137(135-140) 0.096 Potassium, mmol/L (median, IQR 1-3) 4.3(4-4.67) 4.4(4-4.72) 0.190 Chloride, mmol/L(median, IQR 1-3) 104(100-108) 103(99-108) 0.213 Hemoglobin (g/dL) (median, IQR 1-3) 12.8(11.9-13.9) 11.7(10.6-12.8) <0.001 White Blood Cells, 10⁹ per L (median, IQR 1-3) 8.23 ± 1.90 8.08 ± 2.02 0.501 Lymphocytes, 10⁹ per L 1.96 (1.39 - 2.42) 1.48 (1.00 - 2.01) <0.001 Iron, mcg/dL 59(28- 90) 38(23-50) <0.001 Ferritin (ng/dL) 60(20-100) 47(19-80) 0.076 Transferrin saturastion group 0.015 Group 1 < %20 99(62.3) 122(74.8) Group 2 ≥ %20 60(37.7) 41(25.2) Ferritin Group (ng/dL) (n,%) 0.140 Group 1 <100 114(71.7) 125(76.7) Group 2 Transferrin Saturation %20 and 100-299 13(8.2) 12(7.4) Group 4 ≥300 13(8.2) 4(2.5) Total Cholesterol, mg/dL 170(104-210) 154(98-195) 0.012 LDL, mg/dL 102(76-129) 94(72-117) 0.333 HDL, mg/dL 43(33-53) 39(30-46) <0.001 Triglyceride, mg/dL 125(106-197) 105(79-148) 0.004 Creatinine, mg/dL 1.03(0.86-1.26) 1.15(0.95-1.53) 0.002 eGFR, mL/min/1.73m2 72(53-87) 61(45-81) <0.001 CRP,mg/dL 3.07(1.9-6.1) 5.5(2.9-9.9) <0.001 Albumin, g/L 43(41-44) 37(34-38) <0.001 First Admission 0.167 Outpatient (n,%) 112(70.4) 103(63.2) Inpatient (n,%) 47(29.6) 60(36.8) Table 3: Univariate and Multivariate Cox Regression Analysis of Clinical and Laboratory Parameters Factor Univariate Analysis Multivariate Analysis HR %95 CI HR %95 CI Lower Upper p value Lower Upper p value Sodium 0.910 0.871 0.952 <0.001 0.905 0.862 0.949 <0.001 Age 1.015 0.999 1.032 0.071 1.015 0.999 1.032 0.073 Smoking 1.693 1.149 2.496 0.008 1.944 1.303 2.900 0.001 NYHA 2.893 2.304 3.631 <0.001 1.095 1.038 1.156 <0.001 BNP 1.000 1.000 1.001 <0.001 1.000 1.000 1.001 <0.001 AFII 2.201 1.698 2.852 <0.001 2.155 1.361 3.412 0.001 HT 1.201 0.744 1.936 0.453 DM 1.423 0.968 2.092 0.073 CAD 1.996 1.248 3.193 0.004 EF 0.956 0.936 0.976 <0.001 Potassium 1.216 0.859 1.722 0.269 Chloride 0.944 0.910 0.980 0.002 Hemoglobin 0.848 0.748 0.961 0.010 WBC 0.970 0.879 1.070 0.542 Lymphocyte 0.588 0.438 0.788 <0.001 Iron 0.988 0.980 0.997 0.008 Ferritin 1.000 0.998 1.002 0.866 Total Cholesterol 0.995 0.991 1.000 0.031 HDL 0.971 0.956 0.987 <0.001 LDL 0.996 0.991 1.001 0.141 Triglycerides 0.999 0.996 1.002 0.368 Creatinine 2.263 1.465 3.495 <0.001 eGFR 0.987 0.979 0.996 0.004 CRP 1.034 1.008 1.061 0.010 Albumin 0.874 0.833 0.916 <0.001 CONUT Score 1.285 1.156 1.428 <0.001 Abbreviations: AFII: Adjusted Ferritin Inflammation Index, BNP: Brain natriuretic peptide , CAD: Coronary artery disease , CONUT Score: Controlling nutritional status score , CRP: C-reactive protein , DM: Diabetes mellitus , EF: Ejection fraction , eGFR: Estimated glomerular filtration rate , HDL: High-density lipoprotein , HT : Hypertension , LDL: Low-density lipoprotein , Lymphocyte: Lymphocyte count , NYHA: New York Heart Association classification , WBC: White blood cells . Additional Declarations No competing interests reported. Supplementary Files CentralFigure.jpeg Central Figure CentralFigure.jpeg Central Figure 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5942346","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":411216949,"identity":"cd5ddee7-d8b0-4b7f-96be-70a1efdc555b","order_by":0,"name":"Çetin ALAK","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIiWNgGAWjYJCCAwwMFgwSzIyND4AcHj7CGphBWiQYJNiZDxuAtLARo4UBrIWfLU0CxCSohb+9/+DhghoJeclmHrPKrzl2MmwMzA8f3cCjReLMYYbDM45JGM5m5jG7LbstGegwNmPjHDxaDCSSGQ7zsEkwzgNpkdzGDNTCwyaNV4v8Y6CWfxL2IC3FktvqidAiwcxwmLdNInE2M1sa48dthwlrkTiTbHCYt08ieWYz82Fpxm3HediYCfiFv/3g488832xsZ5w/2Pjx57Zqe3725oeP8WlBAcw8YJJY5SDA+IMU1aNgFIyCUTBiAABnfD627wM+UQAAAABJRU5ErkJggg==","orcid":"","institution":"Bursa Uludag University","correspondingAuthor":true,"prefix":"","firstName":"Çetin","middleName":"","lastName":"ALAK","suffix":""},{"id":411216951,"identity":"b17d64ea-0b1c-4317-b0f4-61f090275da5","order_by":1,"name":"Şükrü Çiriş","email":"","orcid":"","institution":"Gaziantep State Hospital","correspondingAuthor":false,"prefix":"","firstName":"Şükrü","middleName":"","lastName":"Çiriş","suffix":""},{"id":411216952,"identity":"aafeb72b-4327-4265-9e3c-e43baa9b22a3","order_by":2,"name":"Furkan Fatih Yurdalan","email":"","orcid":"","institution":"Bursa Uludag University","correspondingAuthor":false,"prefix":"","firstName":"Furkan","middleName":"Fatih","lastName":"Yurdalan","suffix":""},{"id":411216953,"identity":"44d15fb2-5e17-498f-a6ec-985de06dcbba","order_by":3,"name":"Fazil Çağrı Hunutlu","email":"","orcid":"","institution":"Bursa Uludag University","correspondingAuthor":false,"prefix":"","firstName":"Fazil","middleName":"Çağrı","lastName":"Hunutlu","suffix":""},{"id":411216954,"identity":"9e51aba6-dcec-4c46-b864-84e8f3af7ac2","order_by":4,"name":"Zeynep Kumral","email":"","orcid":"","institution":"Unye State Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zeynep","middleName":"","lastName":"Kumral","suffix":""},{"id":411216955,"identity":"b11efbe3-5090-4441-82c4-2d2dd3d9c88a","order_by":5,"name":"Tunay Şentürk","email":"","orcid":"","institution":"Bursa Uludag University","correspondingAuthor":false,"prefix":"","firstName":"Tunay","middleName":"","lastName":"Şentürk","suffix":""}],"badges":[],"createdAt":"2025-02-01 14:53:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5942346/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5942346/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":75518948,"identity":"f67eb18c-a828-495e-a211-7efda8e22b6b","added_by":"auto","created_at":"2025-02-05 11:53:28","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":172620,"visible":true,"origin":"","legend":"\u003cp\u003eStudy Design and Patient Selection for the Assessment of the Adjusted Ferritin Inflammation Index (AFII) as a Mortality Risk Metric in Heart Failure\u003c/p\u003e","description":"","filename":"Figure1.StudyDesignandPatientSelectionfortheAssessmentoftheAdjustedFerritinInflammationIndexAFIIasaMortalityRiskMetricinHeartFailure.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5942346/v1/c3fa48eb03ff0ad8eb981adf.jpg"},{"id":75518102,"identity":"b769f500-c113-42e1-857d-f9cae7f36890","added_by":"auto","created_at":"2025-02-05 11:45:28","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":32927,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier survival analysis for the overall cohort stratified by AFII levels (\u0026lt;2.1 vs. ≥2.1)\u003c/p\u003e","description":"","filename":"Figure2.KaplanMeiersurvivalanalysisfortheoverallcohortstratifiedbyAFIIlevels2.1vs.2.1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5942346/v1/b0c62364feaace966fbdfa70.jpg"},{"id":75518946,"identity":"6544026c-f2b7-4a41-a419-c7c9615236c0","added_by":"auto","created_at":"2025-02-05 11:53:28","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":30204,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier survival analysis for the NYHA class 1-2 patient group stratified by AFII levels (\u0026lt;2.1 vs. ≥2.1)\u003c/p\u003e","description":"","filename":"Figure3.KaplanMeiersurvivalanalysisfortheNYHAclass12patientgroupstratifiedbyAFIIlevels2.1vs.2.1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5942346/v1/e8ede9bf6f9a8c2373f402ed.jpg"},{"id":75518945,"identity":"24bf51ac-fd41-4a53-9cf7-8af4fad6d6c5","added_by":"auto","created_at":"2025-02-05 11:53:28","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":30515,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier survival analysis for the NYHA class 3-4 patient group stratified by AFII levels (\u0026lt;2.1 vs. ≥2.1)\u003c/p\u003e","description":"","filename":"Figure4.KaplanMeiersurvivalanalysisfortheNYHAclass34patientgroupstratifiedbyAFIIlevels2.1vs.2.1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5942346/v1/f097f431c4076ea4e9f81c06.jpg"},{"id":75520229,"identity":"1d0107a7-baec-4913-aa36-b56f9f11d276","added_by":"auto","created_at":"2025-02-05 12:01:28","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":28508,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier survival analysis for the inpatient patient group stratified by AFII levels (\u0026lt;2.1 vs. ≥2.1)\u003c/p\u003e","description":"","filename":"Figure5.KaplanMeiersurvivalanalysisfortheinpatientpatientgroupstratifiedbyAFIIlevels2.1vs.2.1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5942346/v1/265fd280ad7478a78739bfdb.jpg"},{"id":75518950,"identity":"368ace29-61c9-4a0b-9b6c-6779dc3e7d65","added_by":"auto","created_at":"2025-02-05 11:53:28","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":31514,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier survival analysis for the outpatient patient group stratified by AFII levels (\u0026lt;2.1 vs. ≥2.1)\u003c/p\u003e","description":"","filename":"Figure6.KaplanMeiersurvivalanalysisfortheoutpatientpatientgroupstratifiedbyAFIIlevels2.1vs.2.1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5942346/v1/e3a21a0726f4a7d926f4b387.jpg"},{"id":78811766,"identity":"d6896e7d-0657-4b37-bf05-62a6ab71e460","added_by":"auto","created_at":"2025-03-19 09:17:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1618049,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5942346/v1/89b1a838-3450-49ab-b03b-23b025aab895.pdf"},{"id":75518106,"identity":"2fad6a6a-75af-43e7-ac46-367e2bb4bfed","added_by":"auto","created_at":"2025-02-05 11:45:28","extension":"jpeg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":260611,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCentral Figure\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"CentralFigure.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5942346/v1/c96f3614b55f824f7108d4cb.jpeg"},{"id":75518126,"identity":"25a5e56c-d503-4f48-b9f9-96272a746e9e","added_by":"auto","created_at":"2025-02-05 11:45:29","extension":"jpeg","order_by":20,"title":"","display":"","copyAsset":false,"role":"supplement","size":260611,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCentral Figure\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"CentralFigure.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5942346/v1/0b76062bfe414b66a65b2ee1.jpeg"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eThe Adjusted Ferritin Inflammation Index: A Novel Metric for Predicting Mortality in Heart Failure\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIron deficiency (ID) is a pervasive and clinically significant condition in individuals with heart failure (HF), affecting approximately 50% of subjects with chronic HF. This comorbidity has been consistently linked to poorer functional ability, degraded quality of life, and elevated hospitalization rates [1]. Current international guidelines recommend intravenous iron therapy (IV) for symptomatic HF subjects with reduced ejection fraction (HFrEF) and ID, described by serum ferritin\u0026thinsp;\u0026lt;\u0026thinsp;100 ng/mL or 100\u0026ndash;299 ng/mL with transferrin saturation (TSAT)\u0026thinsp;\u0026lt;\u0026thinsp;20%. These recommendations are supported by robust evidence from randomized controlled trials demonstrating improved exercise capacity and reduced HF-related hospitalizations. However, a consistent reduction in mortality has not been observed, highlighting the limitations of current ID diagnostic criteria and therapeutic strategies [2,3,4].\u003c/p\u003e \u003cp\u003eOne major challenge in accurately assessing ID in HF Patients is inflammation's confounding role. Ferritin, an acute-phase reactant, is elevated during systemic inflammation, potentially obscuring the detection of true iron deficiency. This is particularly relevant in HF, a chronic condition characterized by low-grade systemic inflammation. The reliance on ferritin and TSAT as markers of iron status may fail to differentiate between inflammation-driven elevations in ferritin and actual iron stores, potentially leading to misclassification and suboptimal treatment strategies. Recent studies underscore the need for innovative approaches to disentangle ferritin\u0026rsquo;s dual role as a marker of but iron storage and inflammation [5,6].\u003c/p\u003e \u003cp\u003eTo overcome these limitations, we propose an inventive composite index, the Adjusted Ferritin Inflammation Index (AFII), incorporating the ferritin/CRP ratio and albumin levels. This index aims to account for the confounding effects of inflammation on ferritin levels, providing a more precise assessment of iron status in HF Patients. By integrating inflammation and nutritional status biomarkers, AFII offers a comprehensive approach to evaluating the interplay between iron deficiency, inflammation, and clinical outcomes in HF.\u003c/p\u003e \u003cp\u003eThis study proposes a novel approach to assess ID by introducing the AFII, a composite score that incorporates the ferritin/CRP ratio and albumin levels. This index aims to correct for the confounding effects of inflammation on ferritin measurements, thereby providing a more dependable assessment of iron status in HF Patients. Using data from a well-characterized cohort, we tested the prognostic worth of AFII in predicting mortality and compared its performance to existing diagnostic parameters. By addressing the limitations of current methodologies, AFII has the potential to enhance patient classification and navigate targeted therapeutic strategies in this high-risk cohort.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Population\u003c/h2\u003e \u003cp\u003eThis retrospective cohort study intended to measure the predictive worth of AFII in HF cases. The data were sourced from 1,968 patients who presented with HF symptoms and an EF below 50% and were evaluated at a single tertiary care centre among January 1, 2017, and September 1, 2023.\u003c/p\u003e \u003cp\u003ePatients were screened based on the inclusion and exclusion standards of the CONFIRM-HF trial, which assesses iron deficiency and HF outcomes [7]. The exclusion criteria included:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eAbsence of detailed iron parameter assessments at admission (n\u0026thinsp;=\u0026thinsp;922)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eMalignancy (n\u0026thinsp;=\u0026thinsp;217)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eHemoglobin levels\u0026thinsp;\u0026gt;\u0026thinsp;15 g/dL (n\u0026thinsp;=\u0026thinsp;179)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDialysis or end-stage renal disease (n\u0026thinsp;=\u0026thinsp;170)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePresence of metallic heart valves (n\u0026thinsp;=\u0026thinsp;79)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eActive infection (n\u0026thinsp;=\u0026thinsp;42)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eRecent major surgery or valve replacement (n\u0026thinsp;=\u0026thinsp;36)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eCoronary angiography within the last 3 months (n\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eA total of 1,646 patients were eliminated, leaving 322 patients for ultimate assessment. The last follow-up date for survival outcomes was December 1, 2024 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData Collection and AFII Score Development\u003c/h3\u003e\n\u003cp\u003eBaseline demographics, clinical characteristics, and laboratory parameters, including ferritin, CRP(C-reactive protein), albumin, hemoglobin, transferrin saturation, total cholesterol, HDL, LDL, and lymphocyte count, were collected. To derive the AFII score, univariate Cox regression initially analyzed inflammation-related biochemical markers, including Ferritin/CRP ratio, CRP/albumin ratio and the CONUT score. Subsequently, binary logistic regression with the forward likelihood ratio (LR) method was used to evaluate significant parameters. The final model included ferritin/CRP ratio and albumin, which were identified as statistically significant. Including HDL slightly improved the model's performance (AUC: 0.724) but did not substantially enhance predictive power. Thus, the final model was adopted, incorporating only ferritin/CRP ratio and albumin (AUC: 0.713). The AFII score was calculated using the following equation derived from logistic regression coefficients:\u003c/p\u003e\n\u003ch3\u003eAFII = (Albumin×−0.168) + (Ferritin/CRP×−0.012) + 6.958\u003c/h3\u003e\n\u003cp\u003eThe score was log-transformed (base 2), and an optimal cutoff point of 2.1 was determined via ROC curve analysis using the Youden index.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eData were analyzed using SPSS version 28.0 (IBM, NY, USA). Continuous variables were represented as the average\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or median with interquartile range (IQR), depending on the distribution, and categorical variables as counts and percentages. Normality was evaluated using the Kolmogorov-Smirnov test. For comparisons between groups, Student\u0026rsquo;s t-test was employed for normally distributed continuous variables, while Mann-Whitney U test was used for non-normally distributed variables. The ability of AFII to distinguish mortality risk was assessed through ROC curve analysis, and the area under the curve (AUC) with 95% confidence intervals (CI) was determined. Survival analysis was conducted using the Kaplan-Meier method, and log-rank tests were employed to compare survival distributions. To assess independent risk predictors, Cox proportional hazards regression analysis was conducted in two steps:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eUnivariate analysis identified potential risk factors.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eMultivariate analysis included variables with p\u0026thinsp;\u0026lt;\u0026thinsp;0.2 in univariate analysis to determine independent predictors of mortality.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eValidation of the Score\u003c/h3\u003e\n\u003cp\u003eThe AFII score was validated by incorporating it into a Cox regression model, where it demonstrated a hazard ratio (HR) of 2.155 (95% CI: 1.361\u0026ndash;3.412, p\u0026thinsp;=\u0026thinsp;0.001), confirming its significance as an independent predictor of mortality in HF patients. The score exhibited strong discriminatory power (AUC: 0.713) for risk stratification.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eBaseline Characteristics and Univariate Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 322 cases were\u0026nbsp;participated\u0026nbsp;in the trial, with a median follow-up period of 41 months (IQR: 27\u0026ndash;55 months). 106 patients (32.9%) died during follow-up, while 216 (67.1%) survived.\u003c/p\u003e\n\u003cp\u003eBaseline characteristics were compared between the deceased and surviving individuals and are presented in Table-1. Patients in the demised group had significantly higher NYHA (New York Heart Association) Class (p \u0026lt;0.001), CRP levels (5.35 mg/dL [IQR: 1.5\u0026ndash;8.45] vs. 3.65 mg/dL [IQR: 1\u0026ndash;6.2], p = 0.002), CONUT scores (2 [IQR: 1\u0026ndash;4] vs. 2 [IQR: 1\u0026ndash;3], p \u0026lt; 0.001), CRP/albumin ratio (0.094 [IQR: 0.087\u0026ndash;0.1] vs. 0.141 [IQR: 0.132\u0026ndash;0.15], p \u0026lt; 0.001), and AFII (2.24 [IQR: 2.08\u0026ndash;2.34] vs. 2 [IQR: 1.85\u0026ndash;2.16], p \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003eConversely, no notable disparities were detected\u0026nbsp;between the deceased and survived groups regarding age (67.5 years [IQR: 59.5\u0026ndash;75.5] vs. 67 years [IQR: 59\u0026ndash;75], p = 0.122), ferritin levels (52.5 ng/mL [IQR: 30\u0026ndash;108.25] vs. 59 ng/mL [IQR: 30\u0026ndash;100.5], p = 0.733), triglyceride levels (112.5 mg/dL [IQR: 72.5\u0026ndash;146.75] vs. 112 mg/dL [IQR: 80\u0026ndash;160.75], p = 0.591), and TSAT groups (p = 0.284), indicating that neither ferritin nor TSAT levels were significant determinants of survival in univariate analysis (Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBaseline Characteristics and Univariate Analysis According to AFII\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn patients stratified by the AFII, those with AFII \u0026ge; 2.1 had significantly higher age (69 years vs. 65 years, p = 0.005), and notably, a worse Left Ventricular Ejection Fraction (LVEF) (32% vs. 38%, p = 0.005) compared to those with AFII \u0026lt; 2.1. Furthermore, subjects in the higher AFII group exhibited a greater prevalence of coronary artery disease (74.2% vs. 61.6%, p = 0.015) and CRP scales were significantly increased in subjects with AFII \u0026ge; 2.1 (5.5 mg/dL vs. 3.07 mg/dL, p \u0026lt; 0.001) (Table 2).\u003c/p\u003e\n\u003cp\u003eThese findings underline the association of a higher AFII with poorer clinical characteristics, including more severe coronary artery disease and worse inflammation and nutritional status, which are crucial factors influencing heart failure prognosis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCox Regression Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUnivariate Cox regression analysis identified\u0026nbsp;various key death predictors as strong independent mortality predictors (Table 3).\u003c/p\u003e\n\u003cp\u003eMultivariate Cox regression analysis confirmed the significance of AFII (HR: 2.155, 95% CI: 1.361\u0026ndash;3.412, p = 0.001), NYHA class (HR: 1.095, 95% CI: 1.038\u0026ndash;1.156, p \u0026lt; 0.001), sodium levels (HR: 0.905, 95% CI: 0.862\u0026ndash;0.949, p \u0026lt; 0.001), BNP levels (HR: 1.000, 95% CI: 1.000\u0026ndash;1.001, p \u0026lt; 0.001), and smoking (HR: 1.944, 95% CI: 1.303\u0026ndash;2.900, p = 0.001) as independent predictors of mortality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSurvival Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKaplan-Meier survival analysis was carried out to examine the survival probabilities across different AFII groups. At various time points, significant dissimilarities in survival rates were noted across patients with low and high AFII.\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eOverall cohort\u003c/strong\u003e: At 1-year follow-through the survival expectancy for cases with low AFII was 89.9%, whereas those with high AFII had a survival ratio of 76.1%. At 3 years, the survival rates diverged significantly, with the low AFII group demonstrating a survival rate of 84.6%, compared to 54.9% in the high AFII group (Figure 2).\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eNYHA class 1-2\u003c/strong\u003e: The 1-year survival expectancy was 97.2% for the low AFII group and 93% for the high AFII group. At 3 years, the survival ratio for the low AFII group remained high at 95.2%, while the high AFII group declined to 79.9% (Figure 3).\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eNYHA class 3-4\u003c/strong\u003e: At 1-year, the survival expectancy was 75% for the low AFII group and 63% for the high AFII group. At 3 years, the survival rat for the low AFII group was 63.1%, compared to 35.8% for the high AFII group (Figure 4).\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eOutpatient group\u003c/strong\u003e: The 1-year survival expectancy was 92% for the low AFII group and 83.5% for the high AFII group. At 3 years, the low AFII group maintained a survival rate of 89.1%, while the high AFII group had a survival ratio of 63.7% (Figure 5).\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eInpatient group\u003c/strong\u003e: The 1-year survival expectancy for the low AFII group was 85.1% and 63.3% for the high AFII group. At 3 years, the low AFII group had a survival rate of 74.1%, while the high AFII group had a survival ratio of 40%(Figure 6).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThese findings underscore the prognostic significance of AFII in predicting long-term survival. Patients in the high AFII group consistently exhibited worse outcomes across all subgroups, emphasizing the need for tailored clinical management based on AFII levels.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study intended to ascertain the marker of fatality in HF patients with an LVEF below 50%, who were evaluated for detailed iron parameters in the context of iron therapy needs. Low serum ferritin levels and TSAT are reliable parameters for diagnosing ID in healthy individuals. Despite that, ferritin is an acute-phase reactant protein secreted in response to cytokines during pro-inflammatory processes [8,9]. The association between HF and inflammation and proinflammatory cytokines (e.g., tumor necrosis factor-alpha (TNF-\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\alpha\\:)\\)\u003c/span\u003e\u003c/span\u003e, interleukin-1 (IL-1), and interleukin-6 (IL-6)) has long been recognized. This relationship complicates functional iron deficiency diagnosis in HF cases [10].\u003c/p\u003e \u003cp\u003eIn our study, the AFII score, formulated by the ratio of inflammation parameters, was identified as the most significant predictor of mortality in heart failure patients with an ejection fraction (EF) below 50% who were evaluated for detailed iron parameters as determined by multivariate analysis (HR: 2.155, 95% CI: 1.361\u0026ndash;3.412, p\u0026thinsp;=\u0026thinsp;0.001). Similarly, patients with an AFII\u0026thinsp;\u0026ge;\u0026thinsp;2.1 demonstrated lower survival rates over a 3-year follow-up period (84.6% vs. 54.9%).\u003c/p\u003e \u003cp\u003eCurrently, in clinical practice, the criteria for defining ID in HF patients (serum ferritin levels\u0026thinsp;\u0026lt;\u0026thinsp;100 ng/mL or ferritin levels between 100\u0026ndash;299 ng/mL with TSAT\u0026thinsp;\u0026lt;\u0026thinsp;20%) were first introduced in the FAIR-HF study in 2008 [11]. These criteria have since been widely accepted in subsequent studies. However, it is important to note that the FAIR-HF criteria are based on parameters used in the literature for diagnosing ID in subjects with chronic kidney disease. Subsequently, in a study involving 42 HF subjects who received coronary artery bypass grafting (CABG), these parameters were compared with bone marrow iron staining results, which are considered the gold standard for diagnosing iron deficiency. The findings revealed that the FAIR-HF criteria demonstrated a sensitivity of 82.4%, specificity of 72.0%, a positive predictive value of 66.7%, and a negative predictive value of 85.7%. According to the FAIR-HF criteria, one-third of patients diagnosed with iron deficiency were found to have normal bone marrow iron stores. [12]. Furthermore, the FAIR-HF criteria highlighted that ferritin levels were unrelated to mortality [13].\u003c/p\u003e \u003cp\u003eIn a meta-analysis published in February 2024, which included nine randomized controlled trials, IV iron therapy in HF patients was shown to significantly reduce the composite risk of hospitalization for heart failure (HFH) or cardiovascular death by 16%. Additionally, it diminished the composite risk of hospitalization for any reason or all-cause mortality by 8%. However, these outcomes were primarily guided by reductions in HFH and all-cause hospitalizations [14]. Consequently, uncertainties remain regarding the impact of IV iron therapy on mortality outcomes in this patient population.\u003c/p\u003e \u003cp\u003eIn the patient population included in our study, mortality predictors were also evaluated. Among these, BNP emerged as a statistically significant predictor of mortality. BNP is released from the heart in reaction to heightened wall stress, heightened sympathetic tone, and vasoconstriction, serving as a compensatory regulatory mechanism. Elevated circulating levels of natriuretic peptides can integrate cardiovascular and hemodynamic stress from multiple sources, making BNP a crucial marker for predicting mortality in this patient group [15]. In the multivariate Cox regression analysis, BNP was revealed to be a statistically significant indicator of mortality (HR: 1.000, 95% CI: 1.000\u0026ndash;1.001, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, the hazard ratio was equal to 1, indicating that while BNP remains significant, its clinical effect on mortality is minimal. This suggests that BNP, although a key marker in heart failure prognosis, may have a limited independent impact on predicting mortality when considering other covariates.\u003c/p\u003e \u003cp\u003eThe NYHA functional classification, a well-established parameter, has long been used to categorize symptoms in HF patients. A 2019 study by Briongos-Figuero et al. involving HF patients with implantable cardioverter defibrillators (ICDs) reported lethality rates of 6.9% in NYHA I patients, 11% in NYHA II patients (HR: 2.2, 95% CI: 1.1\u0026ndash;4.9), and 23.9% in NYHA III patients (HR: 5.5, 95% CI: 2.4\u0026ndash;12.7). Similarly, in our study, an increase in NYHA class was discovered to correlate with a higher risk of mortality (HR: 1.095, 95% CI: 1.038\u0026ndash;1.156, \u003cem\u003ep\u003c/em\u003e ) [16].\u003c/p\u003e \u003cp\u003eSodium levels, identified as another predictor of mortality, may exert their effects through various mechanisms. Hyponatremia can lead to volume overload by triggering inappropriate secretion of antidiuretic hormone, which is affiliated with end-organ dysfunction such as renal and hepatic failure and/or heart failure. Mechanisms as seen in the renin-angiotensin-aldosterone system, excessive sympathetic nervous system stimulation, and the over release of antidiuretic hormone contribute to water retention and an increase in overall blood volume [17]. Consequently, the resulting fluid imbalance in patients with limited cardiac reserve may help explain the increased mortality observed (HR: 0.905, 95% CI: 0.862\u0026ndash;0.949, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eSmoking was identified as an independent risk factor for mortality in HF patients, with a HR of 1.944 (95% CI: 1.303\u0026ndash;2.900, p\u0026thinsp;=\u0026thinsp;0.001). This finding is consistent with previous studies, such as the Cardiovascular Health Study (Gottdiener et al., 2021), which reported that ongoing smoking is associated with poor prognosis and increased mortality in HF patients, as it contributes to elevated inflammation and myocardial injury [18]. Similarly, our results demonstrate that smoking plays a significant role in exacerbating HF outcomes. The chemical constituents of cigarette smoke can enhance atherosclerosis through endothelial damage mediated by their potent oxidant and inflammatory effects. Moreover, studies have shown that nicotine, through carbon monoxide and oxidative stress, can trigger cardiac fibrosis, leading to structural remodeling and cardiac arrhythmias. Through these mechanisms, smoking may cause systolic and diastolic dysfunction, resulting in worsening symptoms and increased mortality in patients with HF [19]. The insights of our study highlight the importance of addressing smoking cessation as part of HF management strategies to improve patient outcomes.\u003c/p\u003e \u003cp\u003eHowever, when comparing baseline characteristics by AFII levels, no significant difference in smoking status was observed between the high and low AFII groups (p\u0026thinsp;=\u0026thinsp;0.825). This discrepancy suggests that while smoking remains an independent predictor of mortality, its effect does not appear to be directly influenced by the AFII score. AFII, a composite score adjusting for inflammation, offers a broader and more comprehensive measure of mortality risk in HF patients, potentially reflecting multiple systemic factors beyond smoking. These findings further support the potential utility of AFII as a prognostic tool in HF management, providing more accurate risk stratification independent of specific risk factors such as smoking.\u003c/p\u003e \u003cp\u003eWe also observed significant differences in survival rates based on AFII levels. Kaplan-Meier analyses revealed lower survival rates in patients with higher AFII levels across multiple subgroups, including NYHA class, outpatient/inpatient status, and overall. Notably, significant survival differences were found between patients with low and high AFII in both the NYHA class 1\u0026ndash;2 and NYHA class 3\u0026ndash;4 groups. In the NYHA 1\u0026ndash;2 group, higher AFII levels were associated with a more pronounced decline in survival. In the NYHA 3\u0026ndash;4 group, the survival rates for patients with high AFII were considerably lower compared to those with low AFII. Even in the NYHA class 1\u0026ndash;2 patient group, where survival is expected to be relatively better, AFII levels could serve as an important marker in predicting the disease progression of these patients.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study highlights AFII, NYHA class, sodium levels, BNP, and smoking as independent markers of fatality in subjects with HF. In contrast, neither ferritin levels nor TSAT were significant determinants of survival. These findings underscore the potential role of inflammation-adjusted iron indices in risk stratification, while traditional iron markers may not accurately reflect prognosis in this population. The present study proposes that AFII could be an esteemed tool for guiding treatment decisions and identifying high-risk patients, offering clinicians a more precise method for predicting mortality and ultimately enabling more personalized management strategies. However, further confirmation of this score in broader, diverse cohorts is essential to establish its utility and robustness as a prognostic marker.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eNumerous limitations should be taken into account when interpreting this study's findings. First, the study's retrospective design restricts the ability to establish causal relationships and may introduce selection bias, as it relies on existing clinical data. Second, the trial's single-centre design may limit the generalizability of our findings to broader patient populations, as the results are based on data from a single hospital setting. A multi-center study could provide more robust evidence and improve the external validity of our results.\u003c/p\u003e \u003cp\u003eAnother limitation is the relatively narrow cohort size, which may have reduced statistical power, especially when analyzing subgroups of patients. Larger studies with a more diverse patient population would enhance the ability to detect meaningful differences between groups. Additionally, the limited follow-up period of 41 months may not capture long-term mortality outcomes, and longer-term follow-up could provide further insights into the impact of AFII on survival in HF patients.\u003c/p\u003e \u003cp\u003eWhile we focused on inflammation-adjusted iron indices, particularly the AFII score, a limitation of this study is that we did not assess other established inflammatory biomarkers such as IL-6, IL-1, TNF-α, or high-sensitivity CRP (hs-CRP), which are commonly applied in research settings but not routinely measured in clinical practice. This limited our ability to evaluate the inflammatory processes involved in HF comprehensively. However, a key strength of this study is that we utilized routinely measured laboratory parameters, such as ferritin, albumin, and CRP, which are commonly available in clinical practice, making our findings more applicable to daily clinical settings.\u003c/p\u003e \u003cp\u003eFinally, although we noted significant relationships between certain biomarkers and mortality, including the AFII score, additional prospective future research is essential to substantiate these results. and resolve the practical utility of the AFII score as a prognostic tool in HF management.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eFuture Directions\u003c/h2\u003e \u003cp\u003eIn this study, we observed that the current criteria for selecting patients for IV iron therapy, primarily based on ferritin levels, may not adequately predict the mortality benefit of treatment. This is because ferritin, as an acute-phase reactant, is influenced by inflammation, leading to potential misclassification of patients. In contrast, we propose that using an inflammation-adjusted score, such as the AFII score, could improve patient selection for IV iron therapy by identifying those who truly suffer from iron deficiency and are at higher mortality risk. The AFII score has demonstrated strong predictive power as a distinct risk factor for mortality, particularly in cases with HF. By assessing baseline characteristics and iron indices such as ferritin and TSAT, his score can more accurately recognize subjects who would profit from iron supplementation and those who are at a elevated risk of mortality.\u003c/p\u003e \u003cp\u003eFurthermore, future studies could explore the potential of this score in identifying patients who might derive mortality benefit from IV iron therapy. These studies aim to establish the clinical importance of the AFII score as a tool for determining true iron deficiency and predicting outcomes in HF patients. Additionally, research could be designed to compare the AFII score with the gold standard of bone marrow biopsy for assessing iron stores, further validating the utility of this score in clinical practice and advancing our understanding of iron deficiency management in HF.\u003c/p\u003e \u003c/div\u003e "},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eAFII:\u003c/strong\u003e Adjusted Ferritin Inflammation Index\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAHF\u003c/strong\u003e: Acute Heart Failure\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUC\u003c/strong\u003e: Area Under the Curve\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBNP\u003c/strong\u003e: Brain Natriuretic Peptide\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCABG\u003c/strong\u003e: Coronary Artery By-pass Grafting\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCI\u003c/strong\u003e: Confidence Interval\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCHF\u003c/strong\u003e: Chronic Heart Failure\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRP\u003c/strong\u003e: C-Reactive Protein\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRT\u003c/strong\u003e: Cardiac Resynchronization Therapy\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eED\u003c/strong\u003e: Emergency Department\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eECG\u003c/strong\u003e: Electrocardiogram\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGFR:\u003c/strong\u003e Glomerular Filtration Rate\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHb\u003c/strong\u003e: Hemoglobin\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHF:\u003c/strong\u003e Heart Failure\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHFH:\u003c/strong\u003e Hospitalization for Heart Failure\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHfmrEF\u003c/strong\u003e: Heart Failure with Mildly Reduced Ejection Fraction\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHfrEF\u003c/strong\u003e:Heart Failure with Reduced Ejection Fraction\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ehs-CRP:\u003c/strong\u003e High-Sensitivity CRP\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eICDs:\u003c/strong\u003e Implantable Cardioverter Defibrillators\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eID\u003c/strong\u003e: Iron Deficiency\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIL-1\u003c/strong\u003e: Interleukin-1\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIL\u003c/strong\u003e\u003cstrong\u003e-6\u003c/strong\u003e: Interleukin-6\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIV\u003c/strong\u003e: Intravenous\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLVEF\u003c/strong\u003e: Left Ventricular Ejection Fraction\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNYHA:\u003c/strong\u003e New York Heart Association\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRBCs\u003c/strong\u003e: Red Blood Cells\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eROC\u003c/strong\u003e: Receiver-Operating Characteristic\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTNF-\u003c/strong\u003e : Tumour Necrosis Factor-Alpha\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTSAT\u003c/strong\u003e: Transferrin Saturation\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTTE\u003c/strong\u003e: Transthoracic Echocardiography\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026Ccedil;A, Ş\u0026Ccedil; and FFY, contributed to the design, data collecting, data analysis, writing, and translation of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026Ccedil;A, ZK \u0026nbsp;and F\u0026Ccedil;H: Contributed to data analysis, visualization and writing of the manuscript.\u003cbr\u003e\u0026nbsp;\u0026Ccedil;A, and TŞ: Contributed to writing and supervision of the manuscript. All the authors have approved the final version of the manuscript to be published.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. The authors assure that this paper has not been published before nor has been submitted for publication to another scientific journal.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003cbr\u003eThe authors declare no competing interests.\u003c/p\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eEthics\u003c/h2\u003e \u003cp\u003e This study was conducted in accordance with the principles outlined in the Declaration of Helsinki and approved by the Bursa Uludağ University Faculty of Medicine, Research Ethics Committee (Approval No: 2023-26/9, Date: December 7, 2023). Due to the study's retrospective nature, the requirement for written informed consent was waived. Patient confidentiality and data privacy were strictly maintained, with all patient data anonymized prior to analysis.\u003c/p\u003e \u003c/div\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTheresa A. McDonagh, Marco Metra, Marianna Adamo et al. 2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure. European Heart Journal (2021) 42, 35993726 doi:10.1093/eurheartj/ehab368\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTheresa A. McDonagh, Marco Metra, Marianna Adamo et al. 2023 Focused Update of the 2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure. European Heart Journal (2023) 44, 3627\u0026ndash;3639. doi.org/10.1093/eurheartj/ehad195\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePonikowski P, Kirwan BA, Anker SD, McDonagh T et al. AFFIRMAHF Investigators. Ferric carboxymaltose for iron deficiency at discharge after acute heart failure: a multicentre, double-blind, randomised, controlled trial. Lancet 2020;396:18951904.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKalra PR, Cleland JGF, Petrie MC et al. Intravenous ferric derisomaltose in patients with heart failure and iron deficiency in the UK (IRONMAN): an investigator-initiated, prospective, randomised, open-label, blinded-endpoint trial. Lancet 2022;400:2199\u0026ndash;209. https://doi.org/10.1016/S0140-6736(22)02083-9\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOriana Marques, G\u0026uuml;nter Weiss, Martina U. Muckenthaler et al. The role of iron in chronic inflammatory diseases: from mechanisms to treatment options in anemia of inflammation. American Society of Hematolog-Blood, Volume 140, Issue 19, 10 November 2022, Pages 2011\u0026ndash;2023. doi.org/10.1182/blood.2021013472\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDignass A, Farrag Karima, Stein J. Limitations of Serum Ferritin in Diagnosing Iron Deficiency in Inflammatory Conditions. International Journal of Chronic Diseases, March 2018.doi.org/10.1155/2018/9394060\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePonikowski P, van Veldhuisen DJ, Comin-Colet J, Ertl G, Komajda M, Mareev V, et al. Beneficial effects of long-term intravenous iron therapy with ferric carboxymaltose in patients with symptomatic heart failure and iron deficiency. Eur Heart J 2015;36:657\u0026ndash;68.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKate F Kernan and Joseph A Carcillo. Hyperferritinemia and inflammation. Int Immunol. 2017 Nov; 29(9): 401\u0026ndash;409. doi: 10.1093/intimm/dxx031\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWei Wang, Mary Ann Knovich and Lan G. Coffman et al. Serum Ferritin: Past, Present and Future. Biochim Biophys Acta. 2010 Aug; 1800(8): 760\u0026ndash;769. 2010 Mar 19. doi: 10.1016/j.bbagen.2010.03.011\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMurphy MB, BAO B, Kakkar R et al. Inflammation in Heart Failure: \u003cem\u003eJACC\u003c/em\u003e State-of-the-Art Review. JACC, Volume 75, Issue 11, 24 March 2020, Pages 1324\u0026ndash;1340. doi.org/10.1016/j.jacc.2020.01.014.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStefan D. Anker, Josep Comin Colet, Gerasimos Filippatos et al. Ferric Carboxymaltose in Patients with Heart Failure and Iron Deficiency. N Engl J Med 2009; 361:2436\u0026ndash;2448. December 17, 2009. doi: 10.1056/NEJMoa0908355\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrote Beverborg N, Klip IT, Meijers WC, et al.De nition of iron de ciency based on the gold standard of bone marrow iron staining in heart failure patients. Circ Heart Fail. 2018;11:e004519\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGraham FJ, Pellicori P, Kalra PR, Ford I, Bruzzese D, Cleland JGF. Intravenous iron in patients with heart failure and iron deficiency: an updated meta-analysis. Eur J Heart Fail 2023;25:528\u0026ndash;37. https://doi.org/10.1002/ejhf.2810 45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBhatia K, Sabharwal B, Gupta K et al. Clinical outcomes of intravenous iron therapy in patients with heart failure and iron deficiency: Meta-analysis and trial sequential analysis of randomized clinical trials. Journal of Cardiology, Volume 83, Issue 2, February 2024, Pages 105\u0026ndash;112. doi.org/10.1016/j.jjcc.2023.06.012\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYork M, Gupta K, Reynolds C et al. B-Type Natriuretic Peptide Levels and Mortality in Patients With and Without Heart Failure. JACC, Volume 71, Issue 19, 15 May 2018, Pages 2079\u0026ndash;2088. doi.org/10.1016/j.jacc.2018.02.071\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBriongos-Figuero S, Est\u0026eacute;vez A, P\u0026eacute;rez M.L. et al. Prognostic role of NYHA class in heart failure patients undergoing primary prevention ICD therapy. ESC Heart Failure, December 2019, Volume 7, Issue 1, Pages 280\u0026ndash;284. doi.org/10.1002/ehf2.12548\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeng S, Peng J, Yang L et al. Relationship between serum sodium levels and all-cause mortality in congestive heart failure patients: A retrospective cohort study based on the Mimic-III database. Front Cardiovasc Med, 2023 Jan 13;9:1082845. doi: 10.3389/fcvm.2022.1082845\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKamel, H., Bartz, T.M., Longstreth, W.T. \u003cem\u003eet al.\u003c/em\u003e Cardiac mechanics and incident ischemic stroke: the Cardiovascular Health Study. Sci Rep \u003cb\u003e11\u003c/b\u003e, 17358 (2021). doi.org/10.1038/s41598-021-96702-z\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSon, Youn-Jung et al. \"Association between persistent smoking after a diagnosis of heart failure and adverse health outcomes: A systematic review and meta-analysis.\" Tobacco Induced Diseases, vol. 18, no. January, 2020, 5. doi:10.18332/tid/116411.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1:\u003c/strong\u003e Baseline Characteristics of Deceased and Surviving Heart Failure Patients\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eDeceased (n=106)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eSurvived (n=216)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eAge,years (median, IQR 1-3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e67.5(59.5-75.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e67(59-75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.122\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.229\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eFemale(n,%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e33(31.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e82(38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eMale(n,%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e73(68.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e134(62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eHypertension (n,%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e85(80.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e165(76.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.442\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eDiabetes Mellitus (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e61(57.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e101(46.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0,069\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eCoronary Artery Heart Disease (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e84(79.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e135(62,5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.007\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eActive Smoking \u0026nbsp;(n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e63(59.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e97(44.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.014\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eEjection Fraction (median, IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e30(22-35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e38(30-45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0,001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eIntravenous Iron Therapy (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e14(13,2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e28(13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0,951\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eACE Inhibitors/ARBs/ARNi (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e76(71.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e174(80,6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0,073\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eBeta-Blockers (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e97(91,5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e190(88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0,337\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eMineralocorticoid Receptor Antagonists (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e56(52,8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e111(51.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0,808\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eNYHA Classification (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0,001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1(0,9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e64(29,6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e22(20,8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e90(41,7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e51(48,1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e50(23,1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e31(29,2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e12(5,6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eSodium, mmol/L (median, IQR 1-3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e137(134-140)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e138(136-140)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0,001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003ePotassium, mmol/L (median, IQR 1-3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e4,4\u0026plusmn;0,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e4,3\u0026plusmn;0,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.258\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eChloride, mmol/L(median, IQR 1-3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e102,5(99-105)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e104(101-106)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,007\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eHemoglobin (g/dL) (median, IQR 1-3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e11.7 (10.5- 13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e12.4 (11.1 - 13.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eWhite Blood Cells, 10⁹ per L (median, IQR 1-3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e8,0\u0026plusmn;2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e8.2\u0026plusmn;1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0,453\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eLymphocytes, 10⁹ per L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1,500(1.045 - 1.975)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e1,755(1.285 - 2.265)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0,001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eIron, mcg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e40(30,25-70,25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e49(34-83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eFerritin (ng/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e52,5(30-108,25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e59(30-100,5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0,733\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eTransferrin saturastion group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eGroup 1 \u0026lt; %20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eGroup 2 \u0026ge; %20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eFerritin Group (ng/dL) (n,%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0,790\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eGroup 1 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026lt;100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e76(71,7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e163(75,5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eGroup 2\u003c/p\u003e\n \u003cp\u003eTransferrin Saturation \u0026lt;%20 and 100-299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e15(14,2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e26(12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eGroup 3\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eTransferrin Saturation \u0026gt;%20 and 100-299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e10(9,4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e15(6,9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eGroup 4 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026ge;300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e5(4,7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e12(5,6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eTotal Cholesterol, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e151(128-194)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e167,5(14,5-205,5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0,089\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eLDL, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e91,5(77-123,5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e99,5(73-125.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0,341\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eHDL, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e36(29,25-45,5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e42(33,75-50,5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0,001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eTriglyceride, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e112,5(72,5-146,75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e112(80-160.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0,591\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eCreatinine, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1,2(1,05-1,67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e1,03(0,88-1,3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0,001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eeGFR, mL/min/1.73m2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e59(42,5-80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e70(52,5-87,5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eCRP,mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e5.35(1.5-8.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e3.65(1-6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0,002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eAlbumin, g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e38(36-40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e41(39-44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0,001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eCONUT Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e2(1-4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e2(1-3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0,001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eCRP/Albumin Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0,094(0,087-0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e0,141(0,132-0,15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0,001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eFerritin/CRP Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e11,1(7,8-14.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e13,5(8-14,5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0,071\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eAdjusted Ferritin Inflamation Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e2,24(2,08-2,34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e2(1,85-2,16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0,001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eFirst Admission\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0,001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eOutpatient (n,%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e52(49,1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e55(25,5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eInpatient (n,%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e54(50,9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e161(74,5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2:\u003c/strong\u003e Baseline Characteristics and Univariate Analysis According to Adjusted Ferritin Inflammation Index (AFII)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eAFII \u0026lt; 2,1 (n=159)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eAFII\u0026nbsp;\u0026ge; 2.1\u0026nbsp;(n=163)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eAge,years (median, IQR 1-3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e65(56-71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e69(61-76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.678\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eFemale(n,%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e55(47.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e60(52.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eMale(n,%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e104(50.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e103(49.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eHypertension (n,%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e122(76.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e128(78.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.699\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eDiabetes Mellitus (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e77(48.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e85(52.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.505\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eCoronary Artery Heart Disease (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e98(61.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e121(74.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.015\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eActive Smoking \u0026nbsp; \u0026nbsp; (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e80(50.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e80(49.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.825\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eEjection Fraction (median, IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e38(30-45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e32(25-40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eACE Inhibitors/ARBs/ARNi (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e127(79.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e123(75.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.342\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eBeta-Blockers (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e134(84.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e153(93.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.006\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eMineralocorticoid Receptor Antagonists (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e78(49.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e89(54.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.319\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eNYHA Classification (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e49(30.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e16(9.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e58(36.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e55(33.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e40(25.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e61(37.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e12(7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e31(19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eSodium, mmol/L\u0026nbsp;(median, IQR 1-3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e138(136-140)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e137(135-140)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.096\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003ePotassium, mmol/L\u0026nbsp;(median, IQR 1-3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e4.3(4-4.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e4.4(4-4.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.190\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eChloride, mmol/L(median, IQR 1-3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e104(100-108)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e103(99-108)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.213\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eHemoglobin (g/dL) (median, IQR 1-3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e12.8(11.9-13.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e11.7(10.6-12.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eWhite Blood Cells, 10⁹ per L (median, IQR 1-3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e8.23 \u0026plusmn; 1.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e8.08 \u0026plusmn; 2.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.501\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eLymphocytes, 10⁹ per L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1.96 (1.39 - 2.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e1.48 (1.00 - 2.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eIron,\u0026nbsp;mcg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e59(28- 90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e38(23-50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eFerritin (ng/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e60(20-100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e47(19-80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eTransferrin saturastion group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.015\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eGroup 1 \u0026lt; %20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e99(62.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e122(74.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eGroup 2\u0026nbsp;\u0026ge;\u0026nbsp;%20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e60(37.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e41(25.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eFerritin Group (ng/dL) (n,%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.140\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eGroup 1 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026lt;100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e114(71.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e125(76.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eGroup 2\u003c/p\u003e\n \u003cp\u003eTransferrin Saturation \u0026lt;%20 and 100-299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e19(11.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e22(13.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eGroup 3\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eTransferrin Saturation \u0026gt;%20 and 100-299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e13(8.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e12(7.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eGroup 4 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026ge;300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e13(8.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e4(2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eTotal Cholesterol, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e170(104-210)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e154(98-195)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.012\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eLDL, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e102(76-129)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e94(72-117)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.333\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eHDL, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e43(33-53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e39(30-46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eTriglyceride, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e125(106-197)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e105(79-148)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eCreatinine, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1.03(0.86-1.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e1.15(0.95-1.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eeGFR,\u0026nbsp;mL/min/1.73m2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e72(53-87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e61(45-81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eCRP,mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e3.07(1.9-6.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e5.5(2.9-9.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eAlbumin, g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e43(41-44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e37(34-38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eFirst Admission\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.167\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eOutpatient (n,%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e112(70.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e103(63.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 245px;\"\u003e\n \u003cp\u003eInpatient (n,%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e47(29.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e60(36.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3:\u003c/strong\u003e Univariate and Multivariate Cox Regression Analysis of Clinical and Laboratory Parameters\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eFactor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 249px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnivariate Analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 242px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultivariate Analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%95 CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%95 CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eLower\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003eUpper\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cem\u003ep value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eLower\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003eUpper\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cem\u003ep value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSodium\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e0.905\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.862\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.949\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e1.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e1.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e1.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e1.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e1.693\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e1.149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e2.496\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.008\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e1.944\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e1.303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e2.900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNYHA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e2.893\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e2.304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e3.631\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e1.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e1.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e1.156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBNP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e1.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e1.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAFII\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e2.201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e1.698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e2.852\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e2.155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e1.361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e3.412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e1.201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.744\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e1.936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.453\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e1.423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.968\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e2.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCAD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e1.996\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e1.248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e3.193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.976\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePotassium\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e1.216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.859\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e1.722\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChloride\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.944\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.980\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHemoglobin\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.848\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.748\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.961\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.010\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWBC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.970\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.879\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e1.070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.542\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLymphocyte\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.788\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIron\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.980\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.008\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFerritin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e1.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Cholesterol\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.031\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHDL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.971\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.987\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLDL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.996\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e1.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTriglycerides\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.996\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e1.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCreatinine\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e2.263\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e1.465\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e3.495\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eeGFR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.987\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.979\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.996\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCRP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e1.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e1.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e1.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.010\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlbumin\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.874\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.916\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCONUT Score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e1.285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e1.156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e1.428\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e \u003cstrong\u003eAFII:\u003c/strong\u003e \u003cem\u003eAdjusted Ferritin Inflammation Index,\u003c/em\u003e \u003cstrong\u003eBNP:\u003c/strong\u003e \u003cem\u003eBrain natriuretic peptide\u003c/em\u003e, \u003cstrong\u003eCAD:\u003c/strong\u003e \u003cem\u003eCoronary artery disease\u003c/em\u003e, \u003cstrong\u003eCONUT Score:\u003c/strong\u003e \u003cem\u003eControlling nutritional status score\u003c/em\u003e, \u003cstrong\u003eCRP:\u003c/strong\u003e \u003cem\u003eC-reactive protein\u003c/em\u003e, \u003cstrong\u003eDM:\u003c/strong\u003e \u003cem\u003eDiabetes mellitus\u003c/em\u003e, \u003cstrong\u003eEF:\u003c/strong\u003e \u003cem\u003eEjection fraction\u003c/em\u003e, \u003cstrong\u003eeGFR:\u003c/strong\u003e \u003cem\u003eEstimated glomerular filtration rate\u003c/em\u003e, \u003cstrong\u003eHDL:\u003c/strong\u003e \u003cem\u003eHigh-density lipoprotein\u003c/em\u003e, \u003cstrong\u003eHT\u003cem\u003e:\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e\u0026nbsp;Hypertension\u003c/em\u003e, \u003cstrong\u003eLDL:\u003c/strong\u003e \u003cem\u003eLow-density lipoprotein\u003c/em\u003e, \u003cstrong\u003eLymphocyte:\u003c/strong\u003e \u003cem\u003eLymphocyte count\u003c/em\u003e, \u003cstrong\u003eNYHA:\u003c/strong\u003e \u003cem\u003eNew York Heart Association classification\u003c/em\u003e, \u003cstrong\u003eWBC:\u003c/strong\u003e \u003cem\u003eWhite blood cells\u003c/em\u003e.\u003c/p\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":"Heart Failure, Ferritin, Inflammation, Mortality, Biomarkers, AFII (Adjusted Ferritin Inflammation Index)","lastPublishedDoi":"10.21203/rs.3.rs-5942346/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5942346/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIron deficiency (ID) is common in heart failure (HF) patients and associated with poorer outcomes. However, traditional markers like ferritin and transferrin saturation (TSAT) may fail to accurately assess ID due to the confounding effects of inflammation. In this study, we introduce the Adjusted Ferritin Inflammation Index (AFII), a composite score combining ferritin/CRP ratio and albumin levels, designed to improve the precision of ID assessment in HF patients. A total of 322 HF patients with reduced ejection fraction were included in the analysis, following the application of specific inclusion and exclusion criteria. Multivariate analysis identified AFII as an independent predictor of mortality (HR: 2.155, 95% CI: 1.361\u0026ndash;3.412, p\u0026thinsp;=\u0026thinsp;0.001), demonstrating strong discriminatory power (AUC: 0.713). Survival analysis showed that patients with AFII\u0026thinsp;\u0026ge;\u0026thinsp;2.1 had significantly lower survival rates across all subgroups. These findings suggest that AFII could be a valuable tool for risk stratification in HF, offering a more comprehensive measure of mortality risk compared to traditional iron markers. Further validation in multi-center studies is needed to confirm its clinical utility.\u003c/p\u003e","manuscriptTitle":"The Adjusted Ferritin Inflammation Index: A Novel Metric for Predicting Mortality in Heart Failure","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-05 11:45:23","doi":"10.21203/rs.3.rs-5942346/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":"7473e525-abc4-479a-8ab5-ee712bfc4fe3","owner":[],"postedDate":"February 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":43859889,"name":"Health sciences/Cardiology"},{"id":43859890,"name":"Health sciences/Biomarkers/Prognostic markers"}],"tags":[],"updatedAt":"2025-03-19T09:09:01+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-05 11:45:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5942346","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5942346","identity":"rs-5942346","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}