Hrev_master [page 84] [Emergency Care Journal 2022; 18:10747] Emergency Care Journal 2022; volume 18:10747 Abstract To reveal the ability of Red cell Distribution Width (RDW) to predict short-term mortality in geriatric patients presenting to the emergency department with acute heart failure and compare the results with pro B-type Natriuretic Peptide (pro-BNP). This prospective cohort study was conducted to evaluate the data of patients admitted to the emergency department between August 15th, 2021, and November 15th, 2021. The study population enrolled volunteers aged 65 years and over, who presented with acute heart failure signs and symptoms. Demographics, vital parameters, and laboratory parameters were noted. A total of 424 patients were included in the study. The 30 day-mortality rate of the study cohort was 14.4%. Older age, active malignancy, RDW, C-reactive protein, blood urea nitrogen, and pro-BNP were early independent predictors of short-term mortality. pro-BNP was a bet- ter predictor than RDW with a greater area under the curve value (0.841 versus 0.752, p=0.045, DeLong equality test). The created multivariate regression model was able to detect the risk of short- term mortality with high accuracy (area under the curve: 0.943, accuracy: 0.936, sensitivity: 98.1, specificity: 67.2, p<0.001). Initial RDW and pro-BNP were significantly higher in the mortal- ity group among the geriatric patients with acute decompensated heart failure presenting to the emergency department, and pro- BNP was found to be a better predictor of mortality than RDW. RDW presents as a promising hematological marker that aids in the prognosticating short-term mortality in this patient population. Introduction Heart failure is a structural or functional clinical condition with typical symptoms and signs, causing low cardiac output and/or ele- vated intracardiac pressures due to cardiac abnormalities.1 Heart failure is a disease with a poor prognosis. Approximately 50% of the diagnosed patients die within five years. Older age, exercise intolerance, elevated plasma norepinephrine and natriuretic pep- tide levels, anemia, renal dysfunction, hyponatremia, increased troponin levels, and ischemic electrocardiographic findings indi- cate a poor prognosis. The recognition and early treatment of patients with heart failure and cardiac functional and structural abnormalities may be effective in reducing mortality.1,2 Red cell Distribution Width (RDW) is a measure of the range of variation of red blood cell volume reported as part of a standard complete blood count. The prognostic value of RDW has been demonstrated in many diseases and clinical conditions.3 Many studies have emphasized that RDW can be used to predict short- and long-term mortality in cardiovascular diseases, including stroke and heart failure.3-5 On the other hand, RDW is a hematolog- ical parameter affected by changes in demographic parameters, such as age and gender.6 In this study, we aimed to reveal the abil- ity of RDW to predict short-term mortality in geriatric patients pre- senting to the Emergency Department (ED) with acute heart failure and compare the results to pro B-type Natriuretic Peptide (pro- BNP), which has previously been shown to be a strong predictor of mortality in the elderly population with this condition. Correspondence: Serdar Özdemir, Department of Emergency Medicine, University of Health Sciences Ümraniye Training and Research Hospital, Istanbul, Turkey. Tel.: +90.505.2673292 E-mail: dr.serdar55@hotmail.com Key words: Aging; emergency services; geriatrics; heart failure; mortal- ity; older; red cell distribution width. Conflict of interest: The Authors declare no conflict of interest. Availability of data and materials: All data generated or analyzed during this study are included in this published article. Ethics approval and consent to participate: Ethical approval for the study was obtained from the local ethics committee with the approval number 240 and date August 5th, 2021. Informed consent was provided by the patients or their legal guardians prior to the study. All the researchers adhered to the principles of the Declaration of Helsinki throughout the study period. Informed consent: Written informed consent was obtained from a legal- ly authorized representative(s) for anonymized patient information to be published in this article. Received for publication: 18 July 2022. Revision received: 20 November 2022. Accepted for publication: 6 December 2022. This work is licensed under a Creative Commons Attribution 4.0 License (by-nc 4.0). ©Copyright: the Author(s), 2022 Licensee PAGEPress, Italy Emergency Care Journal 2022; 18:10747 doi:10.4081/ecj.2022.10747 Publisher's note: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organiza- tions, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its man- ufacturer is not guaranteed or endorsed by the publisher. RDW and pro-BNP in predicting short-term mortality in geriatric patients presenting to the emergency department with acute decompensated heart failure Serdar Özdemir, Abuzer Özkan Department of Emergency Medicine, University of Health Sciences Ümraniye Training and Research Hospital, Istanbul, Turkey Non -co mmerc ial us e o nly Material and Methods Study design This prospective, single-center, observational study was car- ried out at the ED of a 685-bed tertiary education hospital, receiv- ing 1,110 patient admissions per day (annual average). The data of geriatric volunteers who presented to ED between August 15th, 2021, and November 15th, 2021, were documented prospectively. Study population Our study population enrolled volunteers aged 65 years and over, who presented to our ED between August 15th, 2021, and November 15th, 2021, with acute heart failure signs and symp- toms. The heart failure was defined when the patients stated: a medical history of heart failure, a specific treatment for heart fail- ure (beta blockers, diuretics, ace�inhibitors, angiotensin II recep- tor blockers, and digitalis), signs of heart failure (dyspnea, rales, pretibial oedema), or medical history of clinical or radiographic findings of cardiomegaly or pulmonary oedema or ventricular dilatation and abnormalities of ventricular kinetics assessed by echocardiography. Patients with missing data or unknown mortal- ity status were excluded. Patients who were not tested for RDW or pro-BNP were also excluded. Other exclusion criteria were the presence of diseases or medical history that could affect the RDW level, such as inflammatory bowel disease, pregnancy, and chronic lung diseases, and treated for anemia with erythropoietin and/or iron preparations and/or other anemia treatment modalities. Figure 1 shows the flowchart of the study. Consent was obtained from the patients or the legal guardian if the patient did not have a sufficient level of consciousness to provide consent for participation in the study due to dementia or critical illness. Data collection Data were collected using three sources: study form, comput- er-based system of hospital, and researcher phone call notes. The study form was completed for each patient providing consent at the time of admission to ED. This form contained information on age, gender, peripheral oxygen saturation, pulse rate, systolic blood pressure, diastolic blood pressure, respiratory rate, body tempera- ture, and comorbidities. Comorbidities were noted as diabetes mel- litus, hypertension, chronic obstructive pulmonary disease, coro- nary artery disease, congestive heart failure, immunodeficiency, and malignancy. Initial laboratory parameters of all patients and the 30 day-mortality data of the inpatients were noted from the computer-based system of the hospital. Clinical outcomes within the first 24 hours were recorded as discharge, hospitalization, and intensive care unit admission. The following laboratory parameters were recorded: white blood cell count, neutrophil count, lympho- cyte count, hemoglobin, hematocrit, RDW, mean platelet volume, Blood Urea Nitrogen (BUN), C-Reactive Protein (CRP), creati- nine, sodium, potassium, troponin I, and neutrophil-to-lymphocyte ratio. The mortality data of the outpatients were obtained through the phone calls made by the researchers. Pro-BNP levels were measured using an Enzyme Linked Immunosorbent Assay (Biosite Diagnostics, San Diego, CA, USA). The cut off recommended by the manufacturer was 125 pg/mL. Complete blood count testing utilized clinical laboratory methods (Coulter LH 780 Hematology Analyzer: Beckman Coulter Inc., Brea, CA). Statistical analysis Jamovi (Version 1.6.21.0; The Jamovi Project, 2020; R Core Team, 2019) was used for statistical analyses. The Kolmogorov- Smirnov test was used for the normality analysis of continuous data. Categorical data were presented as number (%) and com- pared using the chi-squared test. Quantitative variables were pre- sented as median and interquartile range (25th-75th percentile) val- ues, and then compared using the Mann-Whitney test or Student’s t-test according to the normality of distribution for the two groups. The study population was determined as 399 with Jamovi program by taking impact size 0.6, α=0.05, power (1-β) =0.95 at a confi- dence level of 95%. To determine which parameters were the inde- pendent predictors of short-term mortality, the parameters were first examined using the univariate logistic regression analysis, and those with a p value of lower than 0.20 were further analyzed with the multivariate logistic regression analysis using the backward stepwise elimination method. The Odds Ratio (OR) and 95% Confidence Interval (CI) values were also calculated for the Article [Emergency Care Journal 2022; 18:10747] [page 85] Figure 1. Flowchart of the study. Figure 2. Receiver operating characteristic curves of red blood cell distribution width (RDW), C-reactive protein (CRP), blood urea nitrogen (BUN), and pro-BNP for predicting short-term mortality in geriatric patients with acute decompensated heart failure. Non -co mmerc ial us e o nly parameters included in the regression model. The Receiver Operating Characteristic (ROC) curves were used to determine the accuracy of RDW, BUN, CRP, and pro-BNP in predicting mortality, and the results were reported as the Area Under the Curve (AUC) values. The optimal cut-off value for the parameters with the highest sensitivity and specificity were deter- mined using Youden’s index. The ROC curve was used to deter- mine the accuracy of the regression model in predicting short-term mortality. The DeLong equality test was conducted to evaluate the differences between the AUC values.7,8 We grouped the patients according to the cut-off values found by using the best Youden’s index. We used the chi-square test to evaluate the difference between groups. P values greater than 0.05 were considered statis- tically significant. Results During the study period, a total of 726 patients presented to our ED with acute heart failure signs and symptoms. Using the exclu- sion criteria, 302 patients were excluded, and finally 424 patients were included in the study (Figure 1). The median age of the patients was 76 (71-84) years, and 235 (55.4%) were male. Sixty- one patients died within 30 days of admission, and the mortality rate of the study cohort was 14.4%. The baseline characteristics of the enrolled patients and comparison of the patient characteristics between the survivor and non-survivor groups are shown in Table 1. In the univariate analysis, older age, active malignancy, low ejection fraction, and certain initial laboratory parameters (RDW, creatinine, CRP, blood urea nitrogen, and pro-BNP) were deter- mined to be associated with mortality. The multivariate analysis revealed that of these potential risk factors, only RDW (OR: 8.29, 95% CI: 3.67-18.7), CRP (OR: 15.59, 95% CI: 2.68-11.68), BUN (OR: 22.87, 95% CI: 11.91-43.89), and pro-BNP (OR: 35.16, 95% CI: 17.47-70.75) were independent predictors of short-term mor- tality in geriatric patients with acute decompensated heart failure (Table 1). The ROC curve analysis was performed to determine the pre- Article Table 1. Baseline characteristics of the enrolled patients and comparison of the patient characteristics between the survivor and non- survivor groups. Variables Total Survivor Non-survivor Univariate analysis Multivariate analysis n = 424 n = 363 (85.6%) n = 61 (14.4%) P values Odds ratio P values (%, 25th-75th (%, 25th-75th (%, 25th-75th (95% confidence percentile) percentile) percentile) interval) Age, years 76 (71-84) 76 (71-83) 83 (75-88) <0.001 3.16 (1.81-5.5) 0.039 Gender 0.244 Male 235 (55.4) 197 (45.7) 38 (37.7) Female 189 (44.6) 166 (54.3) 23 (62.3) Emergency department outcomes <0.001 Death 3 0 3 Discharge 244 (27) 217 (30.3) 27 Hospitalization 116 (68.9) 110 (69.7) 6 (61.5) Intensive care unit admission 61 (4.1) 36 25 (38.5) Comorbidities Chronic obstructive pulmonary disease 100 (13.3) 86 (13.8) 14 (7.7) 0.900 Hypertension 190 (49.2) 166 (50.5) 24 (38.5) 0.355 Diabetes mellitus 143 (25.4) 125 (25.7) 18 (23.1) 0.453 Coronary artery disease 87 (11.5) 75 (11) 12 (15.2) 0.860 Congestive heart failure 241 (5.7) 207 (3.7) 34 (23.1) 0.851 Malignancy 20 (0.8) 10 (0.9) 10 <0.001 6.92 (2.75-17.44) 0.003 Vital parameters, median (25th-75th percentile) Systolic blood pressure (mmHg) 140 (124-165) 140 (125-170) 140 (112-154) 0.112 Diastolic blood pressure (mmHg) 80 (68-94) 80 (69-95) 74 (65-92) 0.373 Oxygen saturation (%) 90 (88-94) 90 (88-95) 90 (88-94) 0.648 Left ventricular ejection fraction (%) 45 (35-50) 45 (35-55) 40 (35-45) 0.005 0.45 (0.23-0.85) 0.063 Laboratory parameters, median (25th-75th percentile) White blood cell count (/µL) 9.65 (7.23-13.1) 9.64 (7.22-13.2) 9.76 (7.43-12.3) 0.959 Neutrophil count (/µL) 6.87 (4.95-9.17) 6.86 (4.81-9.02) 7.11 (5.22-9.76) 0.108 Lymphocyte count (/µL) 1.50 (1.03-2.12) 1.49 (1.04-2.11) 1.53 (0.95-2.54) 0.669 Red cell distribution width (%) 26.8 (23.2-28.4) 26.1 (22.9-28.1) 28.2 (27.1-30.3) <0.001 8.29 (3.67-18.7) <0.001 Hemoglobin (g/dL) 11.1 (9.80-12.7) 11.1 (9.80-12.7) 11 (9.8-12.9) 0.909 0.85 (0.49-1.48) 0.823 Hematocrit (%) 35.3 (31.2-40.0) 35.5 (31.2-40.0) 34.3 (30.8-39.9) 0.656 Mean corpuscular volume (fL) 86.6 (81.3-91.6) 86.6 (81.2-91.5) 86.6 (81.4-91.9) 0.882 Neutrophil-to-lymphocyte ratio 4.06 (2.63-6.95) 3.76 (2.44-6.75) 11.3 (4.48-13.3) 0.682 C-reactive protein (mg/L) 1.2 (0.3-4.1) 1.1 (0.3-3.4) 2 (0.5-9.9) 0.002 5.59 (2.68-11.68) 0.022 Blood urea nitrogen (mg/dL) 55.6 (38.5-83.5) 51.4 (38.5-72.8) 124 (70.6-162) <0.001 22.87 (11.91-43.89) <0.001 Creatinine (mg/dL) 1.02 (0.8-1.25) 1.05 (0.82-1.29) 0.92 (0.73-1.16) 0.040 0.50 (0.29-1.08) 0.316 Sodium (mEq/L) 138 135-140) 138 (135-140) 137 (134-140) 0.374 Potassium (mEq/L) 4.8 (4.3-5.3) 4.80 (4.3-5.27) 4.7 (4.3-5.4) 0.116 Troponin I (µg/L) 0.029 (0.014-0.07) 0.025 (0.013-0.0612) 0.061 (0.025-0.132) 0.761 Pro-BNP (pg/mL) 787 (560-1173) 739 (537-1004) 2400 (1171-3838) <0.001 35.16 (17.47-70.75) <0.001 [page 86] [Emergency Care Journal 2022; 18:10747] Non -co mmerc ial us e o nly dictive ability of RDW, CRP, BUN, and pro-BNP for short-term mortality. Table 2 and Figure 2 show the cut-off values of these parameters according to the best Youden’s index, as well as their sensitivity, specificity, AUC and 95% CI values. Significant differ- ences were observed between the AUC values of RDW and CRP (0.752 versus 0.612, p = 0.006), RDW and pro-BNP (0.752 versus 0.841, p = 0.045), BUN and CRP (0.829 versus 0.612, p < 0.001), and pro-BNP and CRP (0.841 versus 0.612, p < 0.001; DeLong equality test). However, no significant difference was observed between the AUC values of RDW and BUN (0.752 versus 0.729, p = 0.088) and BUN and pro-BNP (0.829 versus 0.841, p = 0.808; DeLong equality test). Frequency of survivor and non-survivor patients according to cut-off values found by using the best Youden’s index for RDW, CRP, BUN and Pro-BNP are presented in Table 3. There was a statistically significant difference in mor- tality between the groups based on cut-off values (all p values < 0.001, chi-square test). The created multivariate regression model predicts short-term mortality, the AUC value was calculated as 0.943 (accuracy: 0.936, sensitivity: 98.1, specificity 67.2, p < 0.001; Figure 3). Discussion In this study, we evaluated 424 geriatric cases of acute decom- pensated heart failure to investigate the ability of RDW and pro- BNP to predict short-term mortality in ED. We also found that older age, active malignancy, RDW, CRP, BUN, and pro-BNP were early independent predictors of short-term mortality. Pro- BNP was a better predictor than RDW with a greater AUC value (0.841 versus 0.752).7,8 Additionally, there was a statistically sig- nificant difference in mortality between the groups based on cut- off values of RDW, CRP, BUN, and Pro-BNP. More importantly, the created multivariate regression model was able to detect the risk of short-term mortality with high accuracy (AUC: 0.943). In analysis of study, firstly, nonparametric tests were used to determine the relationship between biomarkers and mortality. Significantly difference was observed between survivor and non- survivor groups in the terms of RDW, CRP, BUN, pro-BNP, and creatinine. Secondly, multivariant analysis were performed to determine independent predictors. Multivariant analysis deter- mined RDW, CRP, BUN, and pro-BNP were independent predic- tors. A further analysis was performed based on ROC curve to show the biomarkers’ ability to distinguish whether a patient died or survived. ROC analysis showed pro-BNP has highest AUC value, and CRP has the lowest. According to DeLong’s test results pro-BNP and BUN best predictors with no significant different AUC values. 7,8 RDW is a parameter included in the complete blood count, has Article [Emergency Care Journal 2022; 18:10747] [page 87] Figure 3. Receiver operating characteristic curve of the multivari- ate logistic regression model for predicting short-term mortality in geriatric patients with acute decompensated heart failure. Table 2. Accuracy of RDW, CRP, BUN, and pro-BNP in predicting short-term mortality in geriatric patients with acute decompensated heart failure Variables AUC 95% CI p Accuracy Cut-off value Sensitivity Specificity PPV NPV RDW 0.752 0.694-810 <0.001 0.868 26.3 9.84 99.72 85.71 84.81 CRP 0.612 0.532-691 0.003 0.854 9.9 26.23 94.49 44.44 88.4 BUN 0.829 0.763-0.810 <0.001 0.896 94.16 13.11 99.72 88.89 87.23 Pro-BNP 0.841 0.769-0.912 <0.001 0.913 1696.6 31.15 99.72 95 89.6 AUC: area under the curve, CI: confidence interval, PPV: positive predictive value, NPV: negative predictive value, RDW: Red blood cell distribution width, CRP: C-reactive protein, BUN: blood urea nitrogen. Table 3. Frequency of survivor and non-survivor patients according to cut-off values for RDW, CRP, BUN, and Pro-BNP Variables Survivor n = 363 (85.6%) (%) Non-survivor n = 61 (14.4%) (%) P RDW <26.3 % 188 (52) 7 (11) <0.001 ≥26.3 % 175 (48) 54 (89) CRP <9.9 mg/L 343 (94) 45 (74) <0.001 ≥9.9 mg/L 20 (5.5) 16 (26) BUN <94.16 mg/dL 325 (90) 18 (30) <0.001 ≥94.16 mg/dL 38 (10) 43 (70) Pro-BNP <1696.6 pg/mL 343 (94) 20 (33) <0.001 ≥1696.6 pg/mL 20 (5.5) 41 (67) RDW: Red blood cell distribution width, CRP: C-reactive protein;, BUN: blood urea nitrogen. Non -co mmerc ial us e o nly a low cost, and is used in the differential diagnosis of blood dis- eases, such as anemia and thalassemia.3 It refers to the variability in mean corpuscular volume values of circulating erythrocytes. RDW has also been evaluated as a predictor of mortality in patients with cardiovascular disease, cancer, chronic lung diseases, symp- tomatic chronic congestive heart failure, and acute heart failure. In the current literature, it has also been demonstrated that RDW is associated with poor outcomes in thromboembolic events, such as acute myocardial infarction, stroke, and heart failure.3-5 There are several plausible explanations for the role of RDW in the pathogenesis of heart failure. Oxygen delivery in the myocardium is vital in heart failure. RDW is one of the most important parameters showing the quality of circulating erythro- cytes. Elevated RDW is commonly seen in the presence of infec- tive erythrocytes in the circulation, which is mostly associated with nutritional disorders and irregular erythropoietin secretion. On the other hand, RDW is also affected in clinical conditions in which erythrocyte function is impaired, such as hemolysis and post blood transfusion.9,10 Another logical explanation for the role of RDW in heart failure is anemia of chronic disease, in which the reticuloen- dothelial block plays a role in pathogenesis. This is accompanied by impaired iron use and ineffective erythropoiesis. This whole pathological process results in high RDW.11 In 2010, van Kimmenade et al. reported a significant relation- ship between one-year mortality and RDW, and suggested RDW as a prognostic factor in acute heart failure.12 Oh et al. investigated the relationship between RDW and echocardiographic parameters in acute heart failure and revealed the relationship between left ventricular ejection fraction and RDW.13 In another study with a methodology similar to that of the current study, He et al. com- pared the ability of pro-BNP and RDW to predict 30- and 90-day mortality in acute heart failure. The authors suggested that RDW was a better predictor than pro-BNP in predicting 30-day mortality.14 The difference between our study and that of He et al. is that we evaluated a geriatric population. Nishizaki et al. retro- spectively evaluated the diagnoses of patients who died in a geri- atric health center and showed that RDW was a predictor of fatal heart failure.15 The strengths of the present study, compared to that of Nishizaki et al., include the prospective design and the data being obtained from the follow-up of geriatric patients presenting to ED with acute decompensated heart failure. In the current study, BUN values were found to be significantly higher in the mortality group. The clinical utility and clinical sig- nificance of high BUN values are still unclear.16-19 On the other hand, three hypotheses have been proposed regarding the cause of elevated BUN in patients with acute heart failure: i) increased con- centration dependent urea reabsorption in proximal tubules due to increased renin angiotensin aldosterone system activity;16,17 ii) increased flow dependent urea reabsorption in distal tubules due to systemic nervous system hyperactivity;17,18 iii) upregulation of urea transporters in inner medullary collecting duct due to increased arginine vasopressin release.19 Limitations Our study has several limitations. The observational design of the study can be considered as the most important limitation. In addition, parameters such as iron, iron-binding capacity, erythro- poietin, and ferritin, which may contribute to the explanation of the pathogenesis of acute heart failure, were not evaluated because these parameters are not routinely tested in ED. Frailty is a clinical condition in which an individual is more vulnerable to developing addiction or death when exposed to a stressor. All geriatric patients do not have the same frailty and risk of mortality due to heart fail- ure.20 In the light of this information, it should be considered that fragility is a confounding factor for current study. Thus, compre- hensive geriatric assessment and frailty identification could not be done due to the intensity of ED and the observational design of the study is another important limitation of current study. Furthermore, the single-center nature and the relatively small cohort were fac- tors that reduced the generalizability of the findings. We consider that multicenter studies should be conducted to validate our results in larger patient populations. Conclusions In conclusion, according to our results, initial RDW was sig- nificantly higher in the mortality group among the geriatric patients presenting to ED with acute decompensated heart failure, and pro-BNP was a better predictor of mortality than RDW. RDW presents as a promising hematological marker that assists in prog- nosticating short-term mortality in this patient population. References 1. Weintraub NL, Collins SP, Pang PS, et al. Acute heart failure syndromes: emergency department presentation, treatment, and disposition: current approaches and future aims a scientific statement from the American Heart Association. Circulation 2010;122:1975-96. 2. Benjamin EJ, Muntner P, Alonso A, et al. Heart disease and stroke statistics-2019 update: a report from the American Heart Association. Circulation 2019;139:e56-e528. 3. Akça HŞ, Algın A, Özdemir S, et al. Effect of baseline RDW and troponin levels on prognosis in patients with acute chest pain. Signa Vitae 2020;16:97-103. 4. Ani C, Ovbiagele B. Elevated red blood cell distribution width predicts mortality in persons with known stroke. J Neurol Sci 2009;277:103-8. 5. Dabbah S, Hammerman H, Markiewicz W, Aronson D. Relation between red cell distribution width and clinical out- comes after acute myocardial infarction. Am J Cardiol 2010;105:312-7. 6. Hoffmann JJ, Nabbe KC, van den Broek NM. Effect of age and gender on reference intervals of red blood cell distribution width (RDW) and mean red cell volume (MCV). Clin Chem Lab Med. 2015;53:2015-9. 7. DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating charac- teristic curves: a nonparametric approach. Biometrics 1988;44:837-45. 8. Özdemir S, Algın A. Interpretation of the area under the receiv- er operating characteristic curve. Exp App Med Sci 2022;3:310-1. 9. van Empel VP, Mariani J, Borlaug BA, Kaye DM. Impaired myocardial oxygen availability contributes to abnormal exer- cise hemodynamics in heart failure with preserved ejection fraction. J Am Heart Assoc 2014;3:e001293. 10. Allen LA, Felker GM, Mehra MR, et al. Validation and poten- tial mechanisms of red cell distribution width as a prognostic marker in heart failure. J Card Fail 2010;16:230-8. 11. Fitzsimons EJ, Brock JH. The anaemia of chronic disease. BMJ 2001;322:811-2. 12. van Kimmenade RR, Mohammed AA, Uthamalingam S, et al. Article [page 88] [Emergency Care Journal 2022; 18:10747] Non -co mmerc ial us e o nly Red blood cell distribution width and 1-year mortality in acute heart failure. Eur J Heart Fail 2010;12:129-36. 13. Oh J, Kang SM, Hong N, et al. Relation between red cell dis- tribution width with echocardiographic parameters in patients with acute heart failure. J Card Fail 2009;15:517-22. 14. He W, Jia J, Chen J, et al. Comparison of prognostic value of red cell distribution width and NT-proBNP for short-term clin- ical outcomes in acute heart failure patients. Int Heart J 2014;55:58-64. 15. Nishizaki Y, Yamagami S, Suzuki H, et al. Red blood cell dis- tribution width as an effective tool for detecting fatal heart fail- ure in super-elderly patients. Intern Med 2012;51:2271-6. 16. Lytvyn Y, Burns KD, Testani JM, et al. Renal hemodynamics and renin-angiotensin-aldosterone system profiles in patients with heart failure. J Card Fail 2022;28:385-393. 17. Kataoka H. Arginine vasopressin as an important mediator of fluctuations in the serum creatinine concentration under decon- gestion treatment in heart failure patients. Circ Rep 2021;3:324-32. 18. Bencivenga L, Palaia ME, Sepe I, et al. Why do we not assess sympathetic nervous system activity in heart failure manage- ment: might GRK2 serve as a new biomarker? Cells 2021;10:457. 19. Kataoka H. Arginine vasopressin as an important mediator of fluctuations in the serum creatinine concentration under decon- gestion treatment in heart failure patients. Circ Rep 2021;3:324-32. 20. Testa G, Curcio F, Liguori I, et al. Physical vs. multidimen- sional frailty in older adults with and without heart failure. ESC Heart Fail 2020;7:1371-80. Article [Emergency Care Journal 2022; 18:10747] [page 89] Non -co mmerc ial us e o nly