The prognostic impact of malnutrition in elderly patients with HFpEF, as assessed using GNRI and CONUT score

The prognostic impact of malnutrition in elderly patients with HFpEF, as assessed using GNRI and CONUT score | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The prognostic impact of malnutrition in elderly patients with HFpEF, as assessed using GNRI and CONUT score Yao Li, Hairui Shao, Ying Liu, Jingyu Wang, Yugang Yin, Chun Yang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9073716/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background and aims: This study aimed to compare the application of the Geriatric Nutritional Risk Index (GNRI) and Controlling Nutritional Status (CONUT) score in assessing malnutrition in elderly patients with heart failure with preserved ejection fraction (HFpEF), and to investigate the impact of malnutrition on their prognosis. Methods and Results: A total of 196 HFpEF patients aged over 75 years were enrolled, with an average age of 92.12±4.28 years. Patients were grouped based on their CONUT and GNRI scores. These with the higher CONUT scores or lower GNRI scores were under the worse nutritional status, had longer hospital stays, lower levels of nutrition-related indices (albumin, TC, LDL-C, SF, TSAT), lower immune indices (lymphocytes), and higher levels of NT-pro BNP and inflammatory markers (CRP, PCT, IL-6). After a follow-up of 11.62±9.49 months, 83 patients (42.3%) died. Patients with worse nutritional status had significantly higher rates of all-cause death, readmission and heart failure readmission ( P <0.05). along with a higher risk of all-cause mortality and lower survival probability. CONUT and GNRI scores showed poor consistency in elderly HFpEF patients ( P < 0.01, k= 0.235). The area under the curve (AUC) for predicting all-cause mortality was 0.86 [95%CI 0.80-0.91] for CONUT, 0.79 [95%CI 0.73-0.86] for GNRI, and 0.87[95%CI 0.82-0.92] for their combination. Conclusions: Malnutrition significantly impacts the prognosis of elderly HFpEF patients. Combined assessment using CONUT and GNRI scores provides superior efficacy in predicting adverse prognosis in this population. Malnutrition Geriatric Nutritional Risk Index CONUT score Heart failure with preserved ejection fraction Prognosis Figures Figure 1 Figure 2 Figure 3 Introduction Heart failure (HF) is the severe and terminal stage of various heart diseases. Elderly HF patients usually have poorer prognosis due to comorbidities and physiological decline [ 1 ] . Heart failure with preserved ejection fraction (HFpEF) is the most common in elderly patients, characterized by left ventricular ejection fraction ≥ 50% [ 2 ] . Now, improving their prognosis and establishing effective long-term management remain critical challenges in geriatric cardiology. Malnutrition is common in elderly HFpEF patients, linked to metabolic disorders, gastrointestinal dysfunction, and underlying diseases. Controlling Nutritional Status (CONUT) and Geriatric Nutritional Risk Index (GNRI) scores are common tools for evaluating nutritional status [ 3 ] , with the former derived from serum albumin, total cholesterol and lymphocyte count [ 4 ] , and the latter from body mass index (BMI) and serum albumin [ 5 ] . Nutritional assessment is emphasized in various diseases and has been proven to be associated with disease prognosis [ 6 , 7 ] . Current evidence also indicates that malnutrition is linked to poor prognosis in elderly HF patients [ 8 , 9 ] . However, few studies have specifically explored its impact on elderly HFpEF patients, nor have any compared the utility of CONUT and GNRI score in this population. Therefore, this study aimed to compare GNRI and CONUT scores for malnutrition assessment in elderly HFpEF patients, and investigate the prognostic role of malnutrition, to identify suitable assessment tools, guide early intervention, and improve patient outcomes. Methods Study population Patients aged ≥ 75 admitted to the geriatric ward of Jinling Hospital from November 2021 to June 2024 were included if diagnosed with HFpEF per the 2023 ESC Guidelines [ 10 ] . To ensure the reliability of research data, all patients must complete a nutritional status assessment within 24 hours of admission, with complete clinical data and follow-up information available. Exclusion criteria were applied for the following reasons: ① Malignant tumors, hematological diseases, end-stage hepatic/renal insufficiency, or severe infections (these conditions independently alter nutritional status and prognosis, potentially confounding study results); ② Incomplete clinical data or loss to follow-up (to minimize bias and maintain statistical power). For missing data management: Missing values of key variables (e.g., nutritional assessment indices, prognostic outcomes) were imputed using multiple imputation (MI) with 5 iterations. Cases were excluded if the missing rate of any single critical variable exceeded 20%, so as to preserve the validity of statistical analyses. Nutritional status assessment and clinical data collection The general information and clinical data of the patients upon admission were documented. The former encompassed gender, age, BMI, comorbidities and baseline drug. The latter included cardiac Doppler ultrasound findings and early morning fasting blood tests within 24 hours of admission such as lymphocyte count (LY), hemoglobin (HGB), creatinine (CRE), albumin (ALB), prealbumin (PA), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), NT-pro B-type natriuretic peptide (NT-pro BNP) and C-reactive protein (CRP). The nutritional status of enrolled patients was assessed by CONUT score and GNRI. Patients were categorized into four groups based on their CONUT scores which ranges from 0 to 12 points [ 4 ] : normal (0–1), mild (2–4), moderate (5–8), and severe malnutrition (9–12). GNRI [ 5 ] was calculated from BMI and ALB according to the following calculation: GNRI = 1.489×ALB (g/L) + 41.7×actual body mass (Kg)/ideal body mass (Kg). Ideal body mass was calculated based on Lorentz equation, Male: 0.75×height (cm) -62.5 while Female: 0.6×height (cm)-40. If actual body mass was greater than ideal body mass, the ratio between them was defined as 1. Patients were divided into four groups: No risk of malnutrition (GNRI > 98), low (92 ≤ GNRI ≤ 98), medium (82 ≤ GNRI < 92) and high risk (GNRI < 82). General and clinical data of patients with different nutritional status were compared. The consistency and difference of the two tools for assessing malnutrition was analyzed. Follow-up and endpoint events Patients were followed up by telephone or outpatient, and the rates of all-cause death, cardiovascular death, readmission and heart failure readmission were recorded. All-cause death was the primary outcome indicator in this study, patients who did not reach the primary endpoint were followed up until September 2024. The prognosis of the elderly patients with HFpEF under different nutritional status was compared. Ethical review The study’s protocol was approved by the hospital’s ethics committee, and patient enrolment was carried out according to the principles of the Declaration of Helsinki. Statistical analysis Continuous variables with normal distribution were presented as \(\:\:\stackrel{-}{x}\) ±s, the difference of which among groups were compared by T test analysis. Continuous variables with non-normal distribution were expressed as median and interquartile range [M (P25, P75)], and the differences of which were compared using the Mann-Whitney U test. Categorical variables were expressed as numbers (percentages) and were compared by the Fisher test. Kaplan-Meier survival analysis was used to evaluate the effect of nutritional status on the endpoint. Agreement between diagnoses of the two tools for assessing malnutrition was assessed using the κ coefficient. The diagnostic performance of each nutritional indicator for nutritional status and clinical outcomes were assessed by the receiver operating curves (ROC), and the differences in diagnostic performance were compared by the area under the curve (AUC). Statistical analyses were performed with the software SPSS 26.0. P values < 0.05 were considered statistically significant. Results Characteristics of patients A total of 196 patients over 75 years with HFpEF were enrolled, including 182 males and 14 females, an mean age of 92.12 ± 4.28 years. The patient flowchart is shown in Fig. 1 . Clinical data of different groups patients under CONUT score and GNRI score (Table ) Patients in the higher CONUT group had longer hospital stays, higher proportion of anemia and nutritional support therapy. Nutrition-related indicators (e.g. albumin, prealbumin, hemoglobin, TC, LDL-C, SF, TSAT), immune indicators (lymphocytes) were significantly lower, while inflammatory indicators (CRP, PCT, IL-6) and cardiac function indicators (NT-pro BNP) were significantly higher (all P < 0.05). No significant differences were observed in echocardiographic parameters among CONUT groups. A similar trend in clinical characteristics was observed with decreasing GNRI scores (higher nutritional risk), consistent with CONUT groups. However, distinct differences included smaller left atrial diameter (LAD) and left ventricular diastolic diameter (LVDD) in the high nutritional risk group (P < 0.05), as well as significant variations in HDL-C, interventricular septum (IVS), and left ventricular posterior wall (LVPW) across GNRI groups ( P < 0.05). Table 1 Clinical data of different groups patients under CONUT score and GNRI score Characteristic CONUT score P value Normal (0–1, n = 21) Mild malnutrition (2–4, n = 73) Moderate malnutrition (5–8, n = 90) Severe malnutrition (9–12, n = 12) General situation Hospitalization days (days) 18 (10, 33) 29 (17, 41) 31 (20, 47) * 46 (22, 55) * 0.011 Age (years) 93 (91, 95) 92 (9, 94) 92 (91 94) 94 (92, 96) 0.318 Male (%) 20 (95.2) 67 (91.8) 85 (94.4) 10 (83.3) 0.413 Heart rate (beats/min) 70 (66, 78) 72 (66, 81) 73 (65, 80) 78 (75, 89) 0.078 Systolic blood pressure (mmHg) 132 (120, 158) 132 (120, 144) 132 (120, 145) 123 (115, 145) 0.560 Diastolic blood pressure (mmHg) 71 (62, 80) 71 (61, 78) 68 (62, 76) 73 (63, 77) 0.452 BMI (kg/m 2 ) 22.0 (21.5, 24.6) 23.0 (22.5, 25.9) 22.4 (20.8, 24.2) 24.5 (22.0, 24.7) 0.289 Conut score 1 (0, 1) 3 (2.5, 4) * 6 (5, 7) *# 9 (9, 9) *#^ < 0.001 GNRI score 98.4 (96.1, 102.6) 96.5 (91.6, 105.0) * 88.0 (83.9, 91.4) *# 82.1 (76.5, 87.1) *#^ < 0.001 Laboratory data Albumin (g/L) 38.4 ± 1.8 36.2 ± 4.0 * 31.0 ± 3.0 *# 26.5 ± 2.6 *#^ < 0.001 Lymphocyte (*10^9/L) 1.8 (1.7, 2.1) 1.5 (1.2, 1.9) * 1.0 (0.7, 1.2) *# 0.7 (0.6, 1.4) *# < 0.001 Cholesterol (mmol/L) 4.6 (3.9, 5.5) 3.7 (2.9, 4.0) * 3.3 (2.9, 3.8) * 2.6 (2.5, 3.3) *#^ < 0.001 Hemoglobin (g/L) 124 (116, 131) 120 (100.5, 130) 107 (96.5, 120) *# 99 (85.3, 121) *# < 0.001 C-reactive protein (mg/L) 0.5 (0.5, 3.5) 2.2 (0.5, 15.1) 14.5 (3.7, 52.2) *# 13.6 (3.3, 50.6) *# < 0.001 Urea nitrogen (mmol/L) 8 (6.7, 12.5) 10.1 (7.6, 14) 9.2 (7.1, 13.6) 10.0 (6.7, 14) 0.419 Creatinine (umol/L) 101.6 (80.2, 124.5) 110.4 (80.8, 174.5) 102.2 (79.6, 141.1) 100.8 (63.8, 135.4) 0.535 Prealbumin (mg/L) 229.2 ± 43.6 209.3 ± 51.6 185.3 ± 58.5 *# 150.5 ± 80.1 *# < 0.001 Triglyceride (mmol/L) 1.1 (0.9, 1.3) 1.0 (0.7, 1.3) 0.9 (0.6, 1.3) 0.7 (0.6, 1.1) 0.173 LDL-C (mmol/L) 2.4 (2.0, 3.0) 1.7 (1.3, 2.1) * 1.6 (1.3, 2.0) * 1.4 (1.1, 1.7) *#^ < 0.001 HDL-C (mmol/L) 1.2 (1.0, 1.5) 1.0 (0.8, 1.2) * 0.9 (0.8, 1.2) * 0.9 (0.8, 1.1) * 0.003 PCT (ug/L) 0.05 (0.03, 0.11) 0.07 (0.04, 0.11) 0.11 (0.06, 0.22) *# 0.10 (0.06, 0.27) * < 0.001 IL-6 (ng/L) 7.5 (3.7, 12.3) 10.7 (7.5, 20.9) * 18.9 (8.9, 37.8) *# 36.6 (13.3, 56.7) *# < 0.001 NT-pro BNP (pmol/L) 230.8 (143.8, 341.5) 269.5 (195, 392.9) 329.0 (225.5, 519.3) *# 398.4 (242.8, 600.8) * 0.012 TSAT (%) 0.31 (0.24, 0.40) 0.26 (0.19, 0.34) 0.24 (0.18, 0.33) * 0.20 (0.15, 0.27) * 0.041 SF (umol/L) 13.2 (11.6, 18.8) 11.6 (8.13.8) * 9.1 (6.5, 11.5) *# 8.1 (5.4, 10.3) *# < 0.001 UIBC (umol/L) 31.8 (27.5, 38.1) 30.4 (23.5, 37.5) 25.5 (20.6, 32.1) *# 28.4 (22.5, 32.9) 0.009 TIBC (umol/L) 48.7 (42.9, 52.3) 42.9 (34.2, 51.1) * 35.7 (30.3, 41.4) *# 36.1 (30.8, 41.9) * < 0.001 Echocardiographic data LVEF (%) 63 (58.5, 69) 60 (57, 64) 61 (56, 66.3) 59.5 (55.8, 64) 0.180 AOD (mm) 34 (32, 37) 34 (32, 37.5) 35 (32.8, 38) 34.5 (33.3, 36.8) 0.797 LAD (mm) 43 (37.5, 45) 44 (38.5, 48.5) 41 (37, 45) 41 (39.3, 44.8) 0.072 LVDD (mm) 47 (42.5, 49.5) 47 (43.5, 50.5) 47 (44, 51) 45 (42.3, 46.8) 0.167 RVD (mm) 24 (21, 26) 25 (23, 26) 25 (22, 26) 24 (22.3, 26.8) 0.417 IVS (mm) 11 (10, 12) 11 (10, 12) 11 (10, 12) 11 (11, 13) 0.155 LVPW (mm) 10 (9, 11) 10 (10, 11) 10 (10, 11) 11 (10, 13) 0.284 Comorbidity Hypertension (%) 15 (71.4) 59 (80.8) 70 (77.8) 9 (75) 0.776 Diabetes (%) 5 (23.8) 32 (43.8) 40 (44.4) 5 (41.7) 0.373 Coronary heart disease (%) 20 (95.2) 70 (95.9) 85 (94.4) 12 (100) 0.940 COPD (%) 2 (9.5) 29 (39.7) * 31 (34.3) * 5 (41.7) * 0.055 Chronic renal insufficiency (%) 8 (38.1) 33 (45.2) 41 (45.6) 5 (41.7) 0.931 Anaemia (%) 2 (9.5) 28 (38.4) * 54 (60.0) *# 7 (58.3) * < 0.001 Atrial fibrillation (%) 10 (47.6) 40 (54.8) 28 (31.1) # 7 (58.3) 0.014 Medications Diuretic (%) 10 (47.6) 48 (65.8) 55 (61.1) 9 (75) 0.366 ACEI/ARB/ARNI (%) 11 (52.4) 27 (37.0) 16 (17.8) *# 3 (25) 0.003 β-Blocker (%) 11 (52.4) 45 (61.6) 37 (41.1) # 7 (58.3) 0.069 Spironolactone (%) 7 (33.3) 34 (46.6) 34 (37.8) 4 (33.3) 0.552 SGLT2i (%) 2 (9.5) 6 (8.2) 1 (1.1) 0 (0) 0.064 Cardiotonic (%) 0 (0) 6 (8.2) 6 (6.7) 4 (33.3) *#^ 0.024 Ferralia (%) 0 (0) 11 (15.1) 12 (13.3) 3 (25) 0.128 Nutrition support (%) 4 (19.0) 19 (26.0) 38 (42.2) *# 9 (75) *#^ 0.002 GNRI score Normal (> 98, n = 57) Low malnutrition risk (92 ≤ GNRI ≤ 98, n = 38) Medium malnutrition risk (82 ≤ GNRI < 92, n = 78) High malnutrition risk (< 82, n = 23) General situation Hospitalization days (days) 27 (16, 40) 28.5 (16.8, 39.3) 29 (18, 43.3) 42 (25, 56) *#^ 0.037 Age (years) 92 (90, 94) 92.5 (90.5, 94) 93 (90, 94.25) 94 (91, 97) 0.149 Male (%) 53 (93.0) 35 (92.1) 75 (96.2) 19 (82.6) 0.175 Heart rate (beats/min) 73 (68, 79.5) 70 (63.5, 83.5) 73 (65., 79.25) 77 (70, 84) 0.559 Systolic blood pressure (mmHg) 134 (119.5, 150) 138.5 (121, 150) 126 (120, 144) 135 (115, 140) 0.515 Diastolic blood pressure (mmHg) 70 (63, 79) 70 (61.75, 77) 68 (61.75, 76) 70 (63, 79) 0.36 BMI (kg/m 2 ) 25.9 (23.7, 28.1) 22.7 (21.3, 24.4) * 22 (20.6, 22.9) *# 20.3 (19.0, 22.1) *# < 0.001 Conut score 3 (2, 4) 4 (2, 5) 6 (4, 7) *# 7 (6, 9) *#^ < 0.001 GNRI score 102.6 (99.6, 106.7) 94.5 (93.1, 96.3) * 87.8 (86.2, 89.8) *# 78.7 (76, 3, 80.5) *#^ < 0.001 Laboratory data Albumin (g/L) 37.63 ± 4.34 34.92 ± 0.66 * 31.63 ± 2.49 *# 26.78 ± 2.44 *#^ < 0.001 Lymphocyte (*10^9/L) 1.51 (1.10, 1.82) 1.40 (0.905, 1.835) 1.1 (0.735, 1.54) *# 1.11 (0.66, 1.56) *# 0.001 Cholesterol (mmol/L) 3.39 (2.70, 4.00) 3.75 (3.33, 4.49) 3.25 (2.91, 3.8525) 3.63 (3.16, 4.05) 0.067 Hemoglobin (g/L) 118 (99.5 ± 135.5) 117 (106.5, 126.25) 112 (100.75, 123.25) 99 (87, 109) *#^ 0.004 C-reactive protein (mg/L) 2.6 (0.5, 14.25) 2.35 (0.5, 16.15) 12.1 (2, 44.325) *# 18.2 (7.5, 55.1) *# < 0.001 Urea nitrogen (mmol/L) 10.2 (7.65, 13.9) 9.4 (7.2, 12.425) 8.9 (6.8, 13.375) 9.5 (7.2, 14) 0.454 Creatinine (umol/L) 122.7 (81.4, 176.9) 96.9 (78.4, 136.4) 98.9 (76.7, 135.9) 102.9 (78.4, 125.8) 0.113 Prealbumin (mg/L) 221.79 ± 56.29 210.03 ± 51.45 184.5 ± 49.98 *# 149 ± 73.7 *# < 0.001 Triglyceride (mmol/L) 1.02 (0.715, 1.32) 1.03 (0.675, 1.6325) 0.89 (0.66, 1.28) 0.79 (0.61, 1.21) 0.419 LDL-C (mmol/L) 1.63 (1.26, 2.28) 1.82 (1.21, 2.28) 1.68 (1.37, 2.17) 1.81 (1.42, 2.13) 0.916 HDL-C (mmol/L) 0.96 (0.79, 1.145) 1.15 (0.93, 1.47) * 0.97 (0.81, 1.14) # 1 (0.85, 1.33) 0.023 PCT (ug/L) 0.08 (0.04, 0.12) 0.05 (0.04, 0.11) 0.10 (0.05,0.21) *# 0.16 (0.10, 0.39) *#^ < 0.001 IL-6 (ng/L) 10.65 (5.77, 20.14) 8.94 (5.62, 17.67) 17.74 (9.91, 37.84) *# 25.6 (13.0, 52.23) *# < 0.001 NT-pro BNP (pmol/L) 249 (208.5, 380.8) 278.5 (170.8, 373.4) 316.9 (209.9, 500.7) 361.4 (270.9, 724.4) *# 0.012 TSAT (%) 0.27 (0.20, 0.34) 0.26 (0.23, 0.35) 0.27 (0.18, 0.35) 0.22 (0.14, 0.26) *#^ 0.033 SF (umol/L) 11.6 (8.4, 15.8) 12.4 (8.65, 13.525) 9.6 (6.75, 12.3) * 6.9 (5.4, 9) *#^ < 0.001 UIBC (umol/L) 32.2 (25.95, 38.4) 30.35 (23.5, 37.93) 25.75 (20.18, 31.33) *# 26.9 (22.4, 32.3) * 0.001 TIBC (umol/L) 45.1 (37.2, 51.35) 40.45 (34.85, 51) 35.85 (30.28, 40.43) *# 33.8 (28.2, 42.2) *# < 0.001 Echocardiographic data LVEF (%) 59 (56, 63.5) 61 (58, 64) 61 (57.66.3) 60 (57, 66) 0.558 AOD (mm) 35 (42, 50) 35 (32, 37.25) 34 (31.75, 38) 34 (33, 36) 0.653 LAD (mm) 45 (42, 50) 43 (39.75, 45.5) * 41 (37, 44) * 39 (35, 41) *# < 0.001 LVDD (mm) 48 (45, 50) 49.5 (44.75, 53) 45 (43, 49) *# 46 (42, 49) *# 0.002 RVD (mm) 25 (23, 26) 24 (22.5, 26) 25 (22, 27) 23 (18, 26) 0.102 IVS (mm) 11 (10, 12) 10 (10, 11) * 11 (10, 12) * 11 (10, 12) 0.025 LVPW (mm) 11 (10, 12) 10 (10, 11) * 10 (9, 11) * 10 (10, 11) 0.020 Comorbidity Hypertension (%) 47 (82.5) 28 (73.7) 61 (78.2) 17 (73.9) 0.729 Diabetes (%) 27 (47.4) 16 (42.1) 32 (41.0) 7 (30.4) 0.579 Coronary heart disease (%) 55 (96.5) 37 (97.4) 75 (96.2) 20 (87.0) 0.264 COPD (%) 22 (38.6) 13 (34.2) 24 (30.8) 8 (34.8) 0.825 Chronic renal insufficiency (%) 32 (56.1) 12 (31.6) 33 (42.3) 10 (43.5) 0.119 Anaemia (%) 24 (42.1) 13 (34.2) 37 (46.4) 17 (73.9) *#^ 0.021 Atrial fibrillation (%) 34 (59.6) 14 (36.8) * 31 (39.7) * 6 (26.1) * 0.018 Medications Diuretic (%) 37 (64.9) 19 (50.0) 49 (62.8) 17 (73.9) 0.268 ACEI/ARB/ARNI (%) 26 (45.6) 9 (23.7) * 21 (26.9) 1 (4.3) *#^ 0.002 β-Blocker (%) 35 (61.4) 17 (44.7) 35 (44.9) 13 (56.5) 0.211 Spironolactone (%) 27 (47.4) 10 (26.3) 32 (41.0) 10 (43.5) 0.223 SGLT2i (%) 3 (5.3) 4 (10.5) 2 (2.6) 0 (0) 0.217 Cardiotonic (%) 6 (10.5) 1 (2.6) 5 (6.4) 4 (17.4) 0.176 Ferralia (%) 5 (8.8) 6 (15.8) 10 (12.8) 5 (21.7) 0.447 Nutrition support (%) 10 (17.5) 11 (28.9) 31 (39.7) < 0.001 18 (78.3) *#^ < 0.001 * P values < 0.05 vs normal malnutrition or no risk of malnutrition; # P values < 0.05 vs mild malnutrition or low malnutrition risk; ^ P values < 0.05 vs moderate malnutrition or medium malnutrition risk. BMI, body mass index; CONUT, Controlling Nutritional Status; GNRI, Geriatric Nutritional Risk Index; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; PCT, procalcitonin; IL-6, interleukin-6; NT-pro BNP, N-terminal probrain natriuretic peptide; TSAT, transferrin saturation; SF, serum ferritin; UIBC, unsaturated iron-binding capacity; TIBC, total iron-binding capacity; LVEF, left ventricular ejection fraction; AOD, aortic diameter; LAD, left atrial diameter; LVDD, left ventricular diastolic diameter; RVD, right ventricular diameter; IVS, interventricular septum; LVPW, left ventricular posterior wall; COPD, chronic obstructive pulmonary disease; ACEI, angiotensin-converting-enzyme inhibitor; ARB, angiotensin II receptor blocker; ARNI, angiotensin receptor enkephalase inhibitors; SGLT2i, sodium-dependent glucose transporters 2 inhibitor. Prognosis of different groups patients under CONUT score and GNRI score (Table 2 ) After a follow-up of 11.62 ± 9.49 months, 83 (42.3%) patients died. As the CONUT scores increased, the rates of all-cause death, cardiovascular death, readmission, and heart failure readmission rose significantly ( P < 0.05). Patients in the higher nutritional risk group based on the GNRI scores had notably higher rates of all-cause death, readmission, and heart failure readmission ( P < 0.05) but cardiovascular death ( P = 0.136). Table 2 Prognosis of different groups patients under CONUT and GNRI score. * P values < 0.05 vs normal malnutrition or no risk of malnutrition; # P values < 0.05 vs mild malnutrition or low malnutrition risk; ^ P values < 0.05 vs moderate malnutrition or medium malnutrition risk. CONUT, Controlling Nutritional Status; GNRI, Geriatric Nutritional Risk Index. Prognosis CONUT score P value Normal (0–1, n = 21) Mild malnutrition (2–4, n = 73) Moderate malnutrition (5–8, n = 90) Severe malnutrition (9–12, n = 12) All-cause death (%) 1 (4.8) 13 (17.8) 57 (63.3) *# 12 (100) *# < 0.001 Cardiovascular death (%) 0 (0.0) 4 (5.5) 14 (15.6) 3 (25.0) 0.019 Readmission (%) 11 (52.4) 59 (80.8) * 84 (93.3) *# 10 (83.3) * < 0.001 heart failure readmission (%) 2 (9.5) 23 (31.5) * 55 (61.1) *# 10 (83.3) *# 98, n = 57) Low malnutrition risk (92 ≤ GNRI ≤ 98, n = 38) Medium malnutrition risk (82 ≤ GNRI < 92, n = 78) High malnutrition risk (< 82, n = 23) All-cause death (%) 11 (19.3) 9 (23.7) 41 (52.6) *# 22 (95.7) *#^ < 0.001 Cardiovascular death (%) 2 (3.5) 4 (10.5) 12 (15.4) 3 (13.0) 0.136 Readmission (%) 43 (75.4) 29 (76.3) 71 (91.0) * 21 (91.3) 0.038 heart failure readmission (%) 15 (26.3) 12 (31.6) 46 (59.0) *# 17 (73.9) *# < 0.001 Survival analysis of different groups patients under CONUT score and GNRI score (Fig. 2 ) For CONUT scores, higher scores were linked to increased all-cause mortality risk and decreased survival probability, and for GNRI scores, lower scores were associated with increased all-cause mortality risk and decreased survival probability. Consistency and difference between CONUT score and GNRI score in evaluating nutritional status of elderly patients with HFpEF ( Fig. 3 ) The two scores showed poor consistency in assessing nutritional status of elderly HFpEF patients (P < 0.01, k = 0.235). Both scores effectively predicted all-cause mortality. The area under the curve (AUC) was 0.86 [95%CI 0.80–0.91] for CONUT (optimal cut-off: 5.5, maximum Youden’s index: 0.593), 0.79 [95%CI 0.73–0.86] for GNRI (optimal cut-off: 83.83, maximum Youden’s index: 0.498), and 0.87 [95%CI 0.82–0.92] for the combined score. Diagnostic performance of CONUT alone and the combined score was significantly superior to GNRI alone (P < 0.01), with no significant difference between CONUT and the combined score (P = 0.078). Discussion HFpEF is the most prevalent subtype of HF in the elderly population and a major contributor to mortality among hospitalized elderly patients [ 2 ] . Due to systemic metabolic alterations, diminished gastrointestinal function, underlying diseases and other, the HFpEF elderly often experience increased nutrient intake and absorption disorders, which can easily lead to malnutrition [ 11 ] . Malnutrition compromises myocardial energy supply, exacerbates heart failure, forming a vicious cycle that leads to poor prognosis [ 8 , 9 , 12 ] Despite this critical interplay, previous studies have largely overlooked elderly patients with HFpEF as a distinct subgroup, failing to address their unique nutritional challenges and the specific prognostic implications of malnutrition in this population—this represents a key research gap that the present study aims to fill. Currently, various clinical tools are available for nutritional risk screening [ 13 , 14 ] . The Nutritional Risk Screening 2002 (NRS 2002) is widely used for inpatients but significantly influenced by the reliability of patient medical history. Subjective Global Assessment (SGA) and Mini Nutritional Assessment (MNA) require professional medical intervention and involve subjective questions, making it challenging to obtain accurate data from patients with unclear expressions, particularly the elderly [ 15 , 16 ] . Compared with these existing tools, this study specifically selected two objective, clinically feasible scoring systems—CONUT and GNRI scores—to assess the nutritional status of elderly HFpEF patients, which addresses the limitations of subjective tools and ensures suitability for the elderly, a key advantage not fully explored in previous HF-related nutritional studies. CONUT score collectively reflects protein reserves, caloric status, and immune function without being influenced by subjective factors or fluid retention. Lower levels of these parameters result in a higher CONUT score, indicating poorer nutritional status [ 4 ] . The prevalence of coronary heart disease among elderly patients with HFpEF is notably higher, and the majority are undergoing lipid-lowering therapy, which could potentially influence the accuracy of cholesterol-based nutritional assessments [ 15 ] . While previous studies have not accounted for this confounding factor, the present study explicitly analyzed the proportion of combined CHD across different CONUT score groups. Results showed that all four CONUT groups had high CHD prevalence with no statistically significant differences, confirming that lipid-lowering treatment did not bias inter-group comparisons of cholesterol levels. This methodological rigor enhances the reliability of our findings, a strength lacking in earlier research. In this study, elderly HFpEF patients with higher CONUT scores exhibited decreased the levels of nutrition-related and immune indices, increased the levers of NT-pro BNP and inflammatory indicators, and significantly longer hospital stays. Previous studies have demonstrated that the CONUT score is a robust predictor of readmission and all-cause mortality in HF patients [ 17 – 19 ] . A meta-analysis has also indicated that malnutrition, defined by a CONUT score ≥ 2, is associated with an elevated risk of all-cause mortality, with HF patients experiencing a 1.92-fold increased risk of death from any cause [ 20 ] . However, these studies neither stratified analyses by age (i.e., focusing on the elderly) nor distinguished HF subtypes. This study investigated the association between the CONUT scores and prognosis in elderly patients with heart failure with HFpEF. The results revealed that patients with higher CONUT scores exhibit significantly increased rates of all-cause mortality, cardiovascular mortality, and heart failure readmission, leading to a poorer overall prognosis. GNRI score is a nutritional assessment tool that incorporates serum albumin levels and BMI [ 5 , 21 ] , the most frequently utilized nutritional assessment metric for elderly patients with HF. Serum albumin, a critical component of the GNRI, plays a pivotal role in cardiovascular health beyond its nutritional significance. In the context of cardiovascular disease, serum albumin reflects systemic inflammation, endothelial function, and myocardial tissue integrity [ 22 , 23 ] —all key determinants of disease progression and prognosis. In patients with acute coronary syndrome, serum albumin also overlaps with other nutritional indices to predict disease severity and outcomes, underscoring its role as a multifaceted biomarker in cardiovascular care [ 24 ] . Numerous studies have demonstrated that the GNRI score effectively predicts prognosis in elderly patients [ 25 , 26 ] . This study found that patients with low GNRI scores, indicative of high malnutrition risk, experienced prolonged hospital stays, significantly higher all-cause mortality, and increased rates of heart failure rehospitalization. Additionally, research by Liang confirmed that the GNRI score serves as an independent predictor of all-cause mortality in patients with heart failure with HFpEF [ 27 ] . Currently, the pathophysiology of malnutrition in heart failure patients remains incompletely understood. Existing theories propose that fluid retention may cause intestinal edema, nausea, anorexia, and other symptoms, thereby impacting nutrient intake and absorption [ 3 ] . This study revealed that elderly HFpEF patients with higher CONUT scores exhibited more severe iron deficiency, and lower levels of cholesterol, triglycerides, LDL-C, and other lipid metabolism indices, indirectly supporting this hypothesis. Furthermore, alterations in intestinal morphology and function in heart failure patients compromise the intestinal wall's immune barrier, triggering the release of pro-inflammatory cytokines. Chronic inflammation and neurohormonal activation lead to protein and adipose tissue degradation, resulting in body mass loss and cachexia, which contribute to poor prognosis [ 28 – 31 ] . In this study, under both nutritional assessment methods, patients with poorer nutritional status showed significantly elevated levels of inflammatory markers. In addition, this study showed that CONUT score and GNRI score in elderly HFpEF patients were poorly consistent, suggesting that the two cannot be completely replaced in clinical application. This study also evaluated the predictive value of the two nutritional status scores for all-cause death in elderly HFpEF patients through ROC curve analysis. The area under the combined ROC curve of the two scores was the largest and the highest predictive value, followed by the CONUT score and the lowest GNRI score, which may be related to the susceptibility of heart failure patients to water and sodium retention affecting BMI. As the aging population increases, greater attention must be given to elderly heart failure patients with malnutrition. Current heart failure guidelines [ 2 ] recommend that all HF patients undergo nutritional risk assessment, with nutritional intervention advised when such risks are identified. However, there remains a gap between clinical practice and these guidelines. Therefore, it is imperative to establish and refine management strategies for the elderly HFpEF patients with malnutrition. Conclusions Malnutrition is a prevalent complication among elderly patients with HFpEF, significantly impacting patient prognosis, correlating with prolonged hospital stays, increased mortality rates, and higher readmission rates for heart failure. CONUT score and GNRI score are widely utilized in clinical nutrition assessments and cannot be entirely substituted for one another, combined evaluation of malnutrition using the two demonstrates superior efficacy in predicting adverse prognoses for elderly patients with HFpEF. Limitations This study was a single-center retrospective analysis, and the findings warrant further validation through multi-center studies with larger sample sizes. Abbreviations HFpEF heart failure with preserved ejection fraction CONUT Controlling Nutritional Status GNRI Geriatric Nutritional Risk Index LDL-C low-density lipoprotein cholesterol HDL-C high-density lipoprotein cholesterol BMI body mass index PCT procalcitonin IL-6 interleukin-6 NT-pro BNP N-terminal probrain natriuretic peptide TSAT transferrin saturation SF serum ferritin UIBC unsaturated iron-binding capacity TIBC total iron-binding capacity LVEF left ventricular ejection fraction AOD aortic diameter LAD left atrial diameter LVDD left ventricular diastolic diameter RVD right ventricular diameter IVS interventricular septum LVPW left ventricular posterior wall COPD chronic obstructive pulmonary disease ACEI angiotensin-converting-enzyme inhibitor ARB angiotensin II receptor blocker ARNI angiotensin receptor enkephalase inhibitors SGLT2i sodium-dependent glucose transporters 2 inhibitor ROC Receiver Operating Characteristic AUC the area under the curve Declarations Ethics approval and consent to participate The study’s protocol was approved by ethics committee(2023DZKY-051-01) of Affiliated Jinling Hospital, Medical School of Nanjing University, and patient enrolment was carried out according to the principles of the Declaration of Helsinki. Informed consent to participate was obtained from all of the participants in the study. Consent for publication Not applicable. Availability of data and materials The datasets used and analysed during the current study are available from the corresponding author on reasonable request. Competing Interests The authors declare no competing interests. Funding This study was funded by Jiangsu Provincial Health Committee (LKZ 2023011), Clinical Diagnosis and Treatment New Technology Projects of Eastern Theater General Hospital (2023LCZLXB042) and General Project of the Basic Research Program of the General Hospital of Eastern Theater Command (22JCYYYB48). Authors' contributions Y.L. conceived and designed the study, collected and analyzed the data, drafted the original manuscript. H.R.S. assisted in data collection and statistical analysis. Y.L. contributed to data management and patient information collection. J.Y.W. helped with patient recruitment and data interpretation. Y.G.Y., C.Y., X.W.,provided clinical resources, revised the manuscript critically。 Y.Z. revised the manuscript critically, and gave intellectual input. L.L. revised the manuscript critically, and gave intellectual input, and supervised the project。 All authors read and approved the final manuscript. Acknowledgements Not applicable. References Heidenreich P, Bozkurt B, Aguilar D, et al. 2022 AHA/ACC/HFSA guideline for the management of heart failure: a report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines[J]. J Am Coll Cardiol. 2022;79(17):e263–421. Cardiovascular Group G, Branch CM, Association. Editing Group of Chinese Expert Consensus on the Diagnosis and Treatment of Chronic Heart Failure in the Elderly. Chinese experts consensus on the diagnosis and treatment of chronic heart failure in the elderly[J]. Chin J Geriatr. 2021;40(5):550–61. Driggin E, Cohen LP, Gallagher D, et al. Nutrition assessment and dietary interventions in heart failure: JACC review topic of the week[J]. J Am Coll Cardiol. 2022;79(16):1623–35. Ignacio de Ulíbarri J, González-Madroño A, de Villar NG, et al. CONUT: a tool for controlling nutritional status. First validation in a hospital population[J]. Nutr Hosp. 2005;20(1):38–45. Bouillanne O, Morineau G, Dupont C, et al. Geriatric nutritional risk index: a new index for evaluating at-risk elderly medical patients[J]. Am J Clin Nutr. 2005;82(4):777–83. Çinier G, Hayıroğlu Mİ, Pay L, et al. Prognostic nutritional index as the predictor of long-term mortality among HFrEF patients with ICD[J]. Pacing Clin Electrophysiol. 2021;44(3):490–6. Hayıroğlu Mİ, Keskin M, Keskin T, et al. A Novel Independent Survival Predictor in Pulmonary Embolism: Prognostic Nutritional Index[J]. Clin Appl Thromb Hemost. 2018;24(4):633–9. Xu D, Shen R, Hu M, et al. Prognostic impact of CONUT score in older patients with chronic heart failure[J]. BMC Geriatr. 2024;24(1):738. Ishikawa Y, Sattler E. Nutrition as treatment modality in heart failure[J]. Curr Atheroscler Rep. 2021;23(4):13. McDonagh TA, Metra M, Adamo M et al. 2023 Focused Update of the 2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure: Developed by the task force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC) With the special contribution of the Heart Failure Association (HFA) of the ESC[J]. European Heart Journal, 2023, 44(37): 3627–3639. Liao L, Pant A, Marschner S, et al. A Focus on Heart Failure Management through Diet and Nutrition: A Comprehensive Review[J]. Hearts. 2024;5(3):293–307. Akbulut M, Halil G, Emre O, et al. A novel echocardiographic index for prediction of malnutrition in acute heart failure[J]. Acta Cardiol. 2023;78(2):260–6. Fan X, He Q, Zhang K, et al. Comparison of the value of four objective nutritional indices in assessing the long-term prognosis of elderly patients with heart failure with preserved ejection fraction[J]. Rev Cardiovasc Med. 2024;25(6):201. Tirandi A, Montecucco F, Liberale L. Malnutrition predicts mortality in heart failure patients[J]. Intern Emerg Med. 2023;18(4):979–80. Kinugasa Y, Sota T, Kamitani H, et al. Diagnostic performance of nutritional indicators in patients with heart failure[J]. ESC Heart Fail. 2022;9(4):2096–106. Carbone S, Billingsley HE, Rodriguez-Miguelez P, et al. Lean Mass Abnormalities in Heart Failure: The Role of Sarcopenia, Sarcopenic Obesity, and Cachexia[J]. Curr Probl Cardiol. 2020;45(11):100417. Liu J, Liu J, Wang J, et al. Prevalence and impact of malnutrition on readmission among hospitalized patients with heart failure in China[J]. ESC Heart Fail. 2022;9(6):4271–9. Ni J, Fang Y, Zhang J, et al. Predicting prognosis of heart failure using common malnutrition assessment tools: a systematic review and meta- analysis[J]. Scott Med J. 2022;67(4):157–70. Kato T, Yaku H, Morimoto T, et al. Association with controlling nutritional status (CONUT) score and in-hospital mortality and infection in acute heart failure[J]. Sci Rep. 2020;10(1):3320. Li H, Zhou P, Zhao Y, et al. Prediction of all-cause mortality with malnutrition assessed by controlling nutritional status score in patients with heart failure: a systematic review and meta-analysis[J]. Public Health Nutr. 2022;25(7):1799–806. Zhang S, Liu Y, Feng Z, et al. A review of the application of nutritional assessment tools in elderly patients with heart failure[J]. Chin Gen Pract Nurs. 2023;21(2):1342–6. Hayıroğlu Mİ, Cınar T, Cinier G, et al. Cardiac variables associated with atrial fibrillation occurrence and mortality in octogenarians implanted with dual chamber permanent pacemakers[J]. Aging Clin Exp Res. 2022;34(10):2533–9. Hayıroğlu Mİ, Çınar T, Çinier G, et al. Prognostic value of serum albumin for long-term mortality in patients with dual-chamber permanent pacemakers[J]. Biomark Med. 2022;16(5):341–8. Hayıroğlu Mİ, Altay S. Overlap Between Nutritional Indices in Patients with Acute Coronary Syndrome: A Focus on Albumin[J]. Balkan Med J. 2024;41(5):324–5. Liu L, Chen Y, Xie J. Association of GNRI, NLR, and FT3 with the clinical prognosis of older patients with heart failure[J]. Int Heart J. 2022;63(6):1048–54. Lu H, Claggett B, Minamisawa M, et al. Predictors and prognosis of incident poor nutritional status in patients with heart failure with preserved ejection fraction: insights from the PARAGON-HF trial[J]. J Am Coll Cardiol. 2024;83(13):362. Liang W, Dong Y, Liu C. Exploration of the role of nutritional status scores in heart failure prognosis[J]. Chin J Cardiol. 2024;52(11):1296–301. Prokopidis K, Irlik K, Ishiguchi H, et al. Natriuretic peptides and C-reactive protein in in heart failure and malnutrition: a systematic review and meta‐analysis[J]. ESC Heart Fail. 2024;11:3052–64. Turen S, Sancar KM. Predictive value of the prognostic nutritional index for long-term mortality in patients with advanced heart failure[J]. Acta Cardiol Sinica. 2023;39(4):599. Stumpf F, Keller B, Gressies C, et al. Inflammation and nutrition: friend or foe?[J]. Nutrients. 2023;15(5):1159. Muscaritoli M, Imbimbo G, Jager- Wittenaar H, et al. Disease related malnutrition with inflammation and cachexia[J]. Clin Nutr. 2023;42(8):1475–9. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9073716","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":625160985,"identity":"75d6ecf5-e07c-44c0-9b67-a66059c86de3","order_by":0,"name":"Yao Li","email":"","orcid":"","institution":"Jinling hospital, Medical School of Nanjing University","correspondingAuthor":false,"prefix":"","firstName":"Yao","middleName":"","lastName":"Li","suffix":""},{"id":625160988,"identity":"09950bff-cd92-4e2c-84a2-b06655ebbe5c","order_by":1,"name":"Hairui Shao","email":"","orcid":"","institution":"Jinling hospital, Medical School of Nanjing University","correspondingAuthor":false,"prefix":"","firstName":"Hairui","middleName":"","lastName":"Shao","suffix":""},{"id":625160991,"identity":"04e12fe7-6227-475e-89ca-d69c62a59de2","order_by":2,"name":"Ying Liu","email":"","orcid":"","institution":"Jinling hospital, Medical School of Nanjing University","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Liu","suffix":""},{"id":625160995,"identity":"f9da3c5e-c3e6-4771-9eaf-7cb01ae1b1a1","order_by":3,"name":"Jingyu Wang","email":"","orcid":"","institution":"Jinling hospital, Medical School of Nanjing University","correspondingAuthor":false,"prefix":"","firstName":"Jingyu","middleName":"","lastName":"Wang","suffix":""},{"id":625160996,"identity":"309abbd7-7698-4936-ad88-a99e1e00d4ae","order_by":4,"name":"Yugang Yin","email":"","orcid":"","institution":"Jinling hospital, Medical School of Nanjing University","correspondingAuthor":false,"prefix":"","firstName":"Yugang","middleName":"","lastName":"Yin","suffix":""},{"id":625160998,"identity":"a60c24e0-b37d-43ef-ab3d-87cf866f75cc","order_by":5,"name":"Chun Yang","email":"","orcid":"","institution":"Jinling hospital, Medical School of Nanjing University","correspondingAuthor":false,"prefix":"","firstName":"Chun","middleName":"","lastName":"Yang","suffix":""},{"id":625161001,"identity":"58635e6a-3622-47d0-945b-a5e3011b1743","order_by":6,"name":"Xian Wang","email":"","orcid":"","institution":"Jinling hospital, Medical School of Nanjing University","correspondingAuthor":false,"prefix":"","firstName":"Xian","middleName":"","lastName":"Wang","suffix":""},{"id":625161008,"identity":"69e93144-0786-4290-929e-b603d3371ae0","order_by":7,"name":"Yong Zhong","email":"","orcid":"","institution":"Jinling hospital, Medical School of Nanjing University","correspondingAuthor":false,"prefix":"","firstName":"Yong","middleName":"","lastName":"Zhong","suffix":""},{"id":625161010,"identity":"f585074b-68cf-49e8-a4fa-42be92deee5f","order_by":8,"name":"Lei Lv","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYBACPgbmBoYEBgY5AzDXwIKwFjYGRrAWYwMGZpAWCSK1AEHiBrAWBmK0SCQ2PniYsy19O3v/0Q0/CiQY+Nu7EwhpaTZI3HY7d2fPYbabPUCHSZw5uwG/Fp6DbRIgLRtuJLPd4AFqMZDIJail/QdQS7oBUMvNP0RpYW9sYwBqSQBpuU2cLeyNzSCHGW44c9jstoyBBA9Bv/AzMx/8+HPbbXmD443Pbr75YyPH396LXwsG4CFN+SgYBaNgFIwCrAAAsfdHOjjsYRoAAAAASUVORK5CYII=","orcid":"","institution":"Jinling hospital, Medical School of Nanjing University","correspondingAuthor":true,"prefix":"","firstName":"Lei","middleName":"","lastName":"Lv","suffix":""}],"badges":[],"createdAt":"2026-03-09 13:40:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9073716/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9073716/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107706898,"identity":"2989bd21-2f3e-4d2d-8e9c-759bd97a957b","added_by":"auto","created_at":"2026-04-24 09:19:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":50280,"visible":true,"origin":"","legend":"\u003cp\u003ePatient flowchart. HFpEF, heart failure with preserved ejection fraction; CONUT, Controlling Nutritional Status; GNRI, Geriatric Nutritional Risk Index.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9073716/v1/372753304a71bca224b14cc4.png"},{"id":107617679,"identity":"6964faa8-3429-48d7-bbb5-f488046f2787","added_by":"auto","created_at":"2026-04-23 09:21:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":166270,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier curves. For survival probability under CONUT score (A); for risk probability under CONUT score (B); for survival probability under GNRI score (C); for risk probability under GNRI score (D). CONUT, Controlling Nutritional Status; GNRI, Geriatric Nutritional Risk Index.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9073716/v1/57cdd58c813a0dc9a4c6d6f5.png"},{"id":107617680,"identity":"b7c48a16-79ef-407c-a27e-2366ae4524b5","added_by":"auto","created_at":"2026-04-23 09:21:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":38403,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver Operating Characteristic (ROC) curve of CONUT score, GNRI score and the combine of the two scores. AUC, the area under the curve; CONUT, Controlling Nutritional Status; GNRI, Geriatric Nutritional Risk Index.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9073716/v1/710320ca70912830fe0ba475.png"},{"id":107711407,"identity":"3e4aebf1-2d2f-4500-bd9d-428ae8e1dd59","added_by":"auto","created_at":"2026-04-24 09:45:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":959485,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9073716/v1/4051b389-4089-48fe-b349-40289ce34afd.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The prognostic impact of malnutrition in elderly patients with HFpEF, as assessed using GNRI and CONUT score","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHeart failure (HF) is the severe and terminal stage of various heart diseases. Elderly HF patients usually have poorer prognosis due to comorbidities and physiological decline\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Heart failure with preserved ejection fraction (HFpEF) is the most common in elderly patients, characterized by left ventricular ejection fraction\u0026thinsp;\u0026ge;\u0026thinsp;50%\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Now, improving their prognosis and establishing effective long-term management remain critical challenges in geriatric cardiology.\u003c/p\u003e \u003cp\u003eMalnutrition is common in elderly HFpEF patients, linked to metabolic disorders, gastrointestinal dysfunction, and underlying diseases. Controlling Nutritional Status (CONUT) and Geriatric Nutritional Risk Index (GNRI) scores are common tools for evaluating nutritional status\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e, with the former derived from serum albumin, total cholesterol and lymphocyte count\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e, and the latter from body mass index (BMI) and serum albumin\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Nutritional assessment is emphasized in various diseases and has been proven to be associated with disease prognosis\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Current evidence also indicates that malnutrition is linked to poor prognosis in elderly HF patients\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. However, few studies have specifically explored its impact on elderly HFpEF patients, nor have any compared the utility of CONUT and GNRI score in this population. Therefore, this study aimed to compare GNRI and CONUT scores for malnutrition assessment in elderly HFpEF patients, and investigate the prognostic role of malnutrition, to identify suitable assessment tools, guide early intervention, and improve patient outcomes.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003ePatients aged\u0026thinsp;\u0026ge;\u0026thinsp;75 admitted to the geriatric ward of Jinling Hospital from November 2021 to June 2024 were included if diagnosed with HFpEF per the 2023 ESC Guidelines\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. To ensure the reliability of research data, all patients must complete a nutritional status assessment within 24 hours of admission, with complete clinical data and follow-up information available. Exclusion criteria were applied for the following reasons: ① Malignant tumors, hematological diseases, end-stage hepatic/renal insufficiency, or severe infections (these conditions independently alter nutritional status and prognosis, potentially confounding study results); ② Incomplete clinical data or loss to follow-up (to minimize bias and maintain statistical power). For missing data management: Missing values of key variables (e.g., nutritional assessment indices, prognostic outcomes) were imputed using multiple imputation (MI) with 5 iterations. Cases were excluded if the missing rate of any single critical variable exceeded 20%, so as to preserve the validity of statistical analyses.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eNutritional status assessment and clinical data collection\u003c/h3\u003e\n\u003cp\u003eThe general information and clinical data of the patients upon admission were documented. The former encompassed gender, age, BMI, comorbidities and baseline drug. The latter included cardiac Doppler ultrasound findings and early morning fasting blood tests within 24 hours of admission such as lymphocyte count (LY), hemoglobin (HGB), creatinine (CRE), albumin (ALB), prealbumin (PA), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), NT-pro B-type natriuretic peptide (NT-pro BNP) and C-reactive protein (CRP).\u003c/p\u003e \u003cp\u003eThe nutritional status of enrolled patients was assessed by CONUT score and GNRI. Patients were categorized into four groups based on their CONUT scores which ranges from 0 to 12 points\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e: normal (0\u0026ndash;1), mild (2\u0026ndash;4), moderate (5\u0026ndash;8), and severe malnutrition (9\u0026ndash;12). GNRI\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e was calculated from BMI and ALB according to the following calculation: GNRI\u0026thinsp;=\u0026thinsp;1.489\u0026times;ALB (g/L)\u0026thinsp;+\u0026thinsp;41.7\u0026times;actual body mass (Kg)/ideal body mass (Kg). Ideal body mass was calculated based on Lorentz equation, Male: 0.75\u0026times;height (cm) -62.5 while Female: 0.6\u0026times;height (cm)-40. If actual body mass was greater than ideal body mass, the ratio between them was defined as 1. Patients were divided into four groups: No risk of malnutrition (GNRI\u0026thinsp;\u0026gt;\u0026thinsp;98), low (92\u0026thinsp;\u0026le;\u0026thinsp;GNRI\u0026thinsp;\u0026le;\u0026thinsp;98), medium (82\u0026thinsp;\u0026le;\u0026thinsp;GNRI\u0026thinsp;\u0026lt;\u0026thinsp;92) and high risk (GNRI\u0026thinsp;\u0026lt;\u0026thinsp;82). General and clinical data of patients with different nutritional status were compared. The consistency and difference of the two tools for assessing malnutrition was analyzed.\u003c/p\u003e\n\u003ch3\u003eFollow-up and endpoint events\u003c/h3\u003e\n\u003cp\u003ePatients were followed up by telephone or outpatient, and the rates of all-cause death, cardiovascular death, readmission and heart failure readmission were recorded. All-cause death was the primary outcome indicator in this study, patients who did not reach the primary endpoint were followed up until September 2024. The prognosis of the elderly patients with HFpEF under different nutritional status was compared.\u003c/p\u003e\n\u003ch3\u003eEthical review\u003c/h3\u003e\n\u003cp\u003e The study\u0026rsquo;s protocol was approved by the hospital\u0026rsquo;s ethics committee, and patient enrolment was carried out according to the principles of the Declaration of Helsinki.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eContinuous variables with normal distribution were presented as\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\stackrel{-}{x}\\)\u003c/span\u003e\u003c/span\u003e\u0026plusmn;s, the difference of which among groups were compared by T test analysis. Continuous variables with non-normal distribution were expressed as median and interquartile range [M (P25, P75)], and the differences of which were compared using the Mann-Whitney U test. Categorical variables were expressed as numbers (percentages) and were compared by the Fisher test. Kaplan-Meier survival analysis was used to evaluate the effect of nutritional status on the endpoint. Agreement between diagnoses of the two tools for assessing malnutrition was assessed using the κ coefficient. The diagnostic performance of each nutritional indicator for nutritional status and clinical outcomes were assessed by the receiver operating curves (ROC), and the differences in diagnostic performance were compared by the area under the curve (AUC). Statistical analyses were performed with the software SPSS 26.0. \u003cem\u003eP\u003c/em\u003e values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of patients\u003c/h2\u003e \u003cp\u003eA total of 196 patients over 75 years with HFpEF were enrolled, including 182 males and 14 females, an mean age of 92.12\u0026thinsp;\u0026plusmn;\u0026thinsp;4.28 years. The patient flowchart is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eClinical data of different groups patients under CONUT score and GNRI score (Table )\u003c/h3\u003e\n\u003cp\u003ePatients in the higher CONUT group had longer hospital stays, higher proportion of anemia and nutritional support therapy. Nutrition-related indicators (e.g. albumin, prealbumin, hemoglobin, TC, LDL-C, SF, TSAT), immune indicators (lymphocytes) were significantly lower, while inflammatory indicators (CRP, PCT, IL-6) and cardiac function indicators (NT-pro BNP) were significantly higher (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). No significant differences were observed in echocardiographic parameters among CONUT groups. A similar trend in clinical characteristics was observed with decreasing GNRI scores (higher nutritional risk), consistent with CONUT groups. However, distinct differences included smaller left atrial diameter (LAD) and left ventricular diastolic diameter (LVDD) in the high nutritional risk group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), as well as significant variations in HDL-C, interventricular septum (IVS), and left ventricular posterior wall (LVPW) across GNRI groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical data of different groups patients under CONUT score and GNRI score\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eCONUT score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003cp\u003e(0\u0026ndash;1, n\u0026thinsp;=\u0026thinsp;21)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMild malnutrition\u003c/p\u003e \u003cp\u003e(2\u0026ndash;4, n\u0026thinsp;=\u0026thinsp;73)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModerate malnutrition\u003c/p\u003e \u003cp\u003e(5\u0026ndash;8, n\u0026thinsp;=\u0026thinsp;90)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSevere malnutrition\u003c/p\u003e \u003cp\u003e(9\u0026ndash;12, n\u0026thinsp;=\u0026thinsp;12)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGeneral situation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHospitalization days (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (10, 33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (17, 41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31 (20, 47) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46 (22, 55) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93 (91, 95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92 (9, 94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e92 (91 94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e94 (92, 96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.318\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (95.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67 (91.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85 (94.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10 (83.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.413\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart rate (beats/min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70 (66, 78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72 (66, 81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73 (65, 80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78 (75, 89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic blood pressure (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e132 (120, 158)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e132 (120, 144)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e132 (120, 145)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e123 (115, 145)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.560\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiastolic blood pressure (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71 (62, 80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71 (61, 78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68 (62, 76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e73 (63, 77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.452\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.0 (21.5, 24.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.0 (22.5, 25.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.4 (20.8, 24.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.5 (22.0, 24.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.289\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConut score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0, 1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (2.5, 4) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (5, 7) \u003csup\u003e*#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9 (9, 9) \u003csup\u003e*#^\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGNRI score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98.4 (96.1, 102.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96.5 (91.6, 105.0) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e88.0 (83.9, 91.4) \u003csup\u003e*#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e82.1 (76.5, 87.1) \u003csup\u003e*#^\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLaboratory data\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0 \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.0 \u003csup\u003e*#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6 \u003csup\u003e*#^\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocyte (*10^9/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.8 (1.7, 2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5 (1.2, 1.9) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.0 (0.7, 1.2) \u003csup\u003e*#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.7 (0.6, 1.4) \u003csup\u003e*#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesterol (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.6 (3.9, 5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.7 (2.9, 4.0) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.3 (2.9, 3.8) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.6 (2.5, 3.3) \u003csup\u003e*#^\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e124 (116, 131)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120 (100.5, 130)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e107 (96.5, 120) \u003csup\u003e*#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e99 (85.3, 121) \u003csup\u003e*#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-reactive protein (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5 (0.5, 3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.2 (0.5, 15.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.5 (3.7, 52.2) \u003csup\u003e*#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.6 (3.3, 50.6) \u003csup\u003e*#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrea nitrogen (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (6.7, 12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.1 (7.6, 14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.2 (7.1, 13.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.0 (6.7, 14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.419\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine (umol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e101.6 (80.2, 124.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e110.4 (80.8, 174.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e102.2 (79.6, 141.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100.8 (63.8, 135.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.535\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrealbumin (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e229.2\u0026thinsp;\u0026plusmn;\u0026thinsp;43.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e209.3\u0026thinsp;\u0026plusmn;\u0026thinsp;51.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e185.3\u0026thinsp;\u0026plusmn;\u0026thinsp;58.5 \u003csup\u003e*#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e150.5\u0026thinsp;\u0026plusmn;\u0026thinsp;80.1 \u003csup\u003e*#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglyceride (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.1 (0.9, 1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0 (0.7, 1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9 (0.6, 1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.7 (0.6, 1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.173\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.4 (2.0, 3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.7 (1.3, 2.1) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.6 (1.3, 2.0) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.4 (1.1, 1.7) \u003csup\u003e*#^\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.2 (1.0, 1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0 (0.8, 1.2) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9 (0.8, 1.2) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9 (0.8, 1.1) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCT (ug/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.05 (0.03, 0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07 (0.04, 0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.11 (0.06, 0.22) \u003csup\u003e*#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.10 (0.06, 0.27) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL-6 (ng/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.5 (3.7, 12.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.7 (7.5, 20.9) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.9 (8.9, 37.8) \u003csup\u003e*#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36.6 (13.3, 56.7) \u003csup\u003e*#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNT-pro BNP (pmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e230.8 (143.8, 341.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e269.5 (195, 392.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e329.0 (225.5, 519.3) \u003csup\u003e*#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e398.4 (242.8, 600.8) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSAT (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.31 (0.24, 0.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.26 (0.19, 0.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.24 (0.18, 0.33) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.20 (0.15, 0.27) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSF (umol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.2 (11.6, 18.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.6 (8.13.8) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.1 (6.5, 11.5) \u003csup\u003e*#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.1 (5.4, 10.3) \u003csup\u003e*#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUIBC (umol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.8 (27.5, 38.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.4 (23.5, 37.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.5 (20.6, 32.1) \u003csup\u003e*#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.4 (22.5, 32.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTIBC (umol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.7 (42.9, 52.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.9 (34.2, 51.1) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.7 (30.3, 41.4) \u003csup\u003e*#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36.1 (30.8, 41.9) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEchocardiographic data\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVEF (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63 (58.5, 69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60 (57, 64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61 (56, 66.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59.5 (55.8, 64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.180\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAOD (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34 (32, 37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (32, 37.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35 (32.8, 38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34.5 (33.3, 36.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.797\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLAD (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43 (37.5, 45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44 (38.5, 48.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41 (37, 45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41 (39.3, 44.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVDD (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47 (42.5, 49.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47 (43.5, 50.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47 (44, 51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45 (42.3, 46.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.167\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRVD (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 (21, 26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (23, 26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25 (22, 26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24 (22.3, 26.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.417\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIVS (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (10, 12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (10, 12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (10, 12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11 (11, 13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.155\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVPW (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (9, 11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (10, 11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (10, 11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11 (10, 13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.284\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComorbidity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (71.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59 (80.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70 (77.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9 (75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.776\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (23.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (43.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40 (44.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5 (41.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.373\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoronary heart disease (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (95.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70 (95.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85 (94.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.940\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOPD (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (9.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (39.7) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31 (34.3) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5 (41.7) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic renal insufficiency (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (38.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 (45.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41 (45.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5 (41.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.931\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnaemia (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (9.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (38.4) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54 (60.0) \u003csup\u003e*#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7 (58.3) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtrial fibrillation (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (47.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40 (54.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28 (31.1) \u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7 (58.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedications\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiuretic (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (47.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48 (65.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55 (61.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9 (75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.366\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACEI/ARB/ARNI (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (52.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (37.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (17.8) \u003csup\u003e*#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ-Blocker (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (52.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45 (61.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37 (41.1) \u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7 (58.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpironolactone (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (46.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34 (37.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.552\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSGLT2i (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (9.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (8.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiotonic (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (8.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (33.3) \u003csup\u003e*#^\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFerralia (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (15.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (13.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.128\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNutrition support (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (19.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (26.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38 (42.2) \u003csup\u003e*#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9 (75) \u003csup\u003e*#^\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eGNRI score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003cp\u003e(\u0026gt;\u0026thinsp;98, n\u0026thinsp;=\u0026thinsp;57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow malnutrition risk\u003c/p\u003e \u003cp\u003e(92\u0026thinsp;\u0026le;\u0026thinsp;GNRI\u0026thinsp;\u0026le;\u0026thinsp;98, n\u0026thinsp;=\u0026thinsp;38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMedium malnutrition risk\u003c/p\u003e \u003cp\u003e(82\u0026thinsp;\u0026le;\u0026thinsp;GNRI\u0026thinsp;\u0026lt;\u0026thinsp;92, n\u0026thinsp;=\u0026thinsp;78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh malnutrition risk\u003c/p\u003e \u003cp\u003e(\u0026lt;\u0026thinsp;82, n\u0026thinsp;=\u0026thinsp;23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGeneral situation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHospitalization days (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (16, 40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.5 (16.8, 39.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29 (18, 43.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42 (25, 56) \u003csup\u003e*#^\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92 (90, 94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92.5 (90.5, 94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93 (90, 94.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e94 (91, 97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53 (93.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35 (92.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75 (96.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19 (82.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.175\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart rate (beats/min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73 (68, 79.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70 (63.5, 83.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73 (65., 79.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e77 (70, 84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.559\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic blood pressure (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e134 (119.5, 150)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e138.5 (121, 150)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e126 (120, 144)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e135 (115, 140)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.515\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiastolic blood pressure (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70 (63, 79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70 (61.75, 77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68 (61.75, 76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70 (63, 79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.9 (23.7, 28.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.7 (21.3, 24.4) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (20.6, 22.9) \u003csup\u003e*#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.3 (19.0, 22.1) \u003csup\u003e*#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConut score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (2, 4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (2, 5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (4, 7) \u003csup\u003e*#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7 (6, 9) \u003csup\u003e*#^\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGNRI score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e102.6 (99.6, 106.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94.5 (93.1, 96.3) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e87.8 (86.2, 89.8) \u003csup\u003e*#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78.7 (76, 3, 80.5) \u003csup\u003e*#^\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLaboratory data\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.63\u0026thinsp;\u0026plusmn;\u0026thinsp;4.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.66 \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.63\u0026thinsp;\u0026plusmn;\u0026thinsp;2.49 \u003csup\u003e*#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.78\u0026thinsp;\u0026plusmn;\u0026thinsp;2.44 \u003csup\u003e*#^\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocyte (*10^9/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.51 (1.10, 1.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.40 (0.905, 1.835)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.1 (0.735, 1.54) \u003csup\u003e*#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.11 (0.66, 1.56) \u003csup\u003e*#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesterol (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.39 (2.70, 4.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.75 (3.33, 4.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.25 (2.91, 3.8525)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.63 (3.16, 4.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e118 (99.5\u0026thinsp;\u0026plusmn;\u0026thinsp;135.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e117 (106.5, 126.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e112 (100.75, 123.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e99 (87, 109) \u003csup\u003e*#^\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-reactive protein (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.6 (0.5, 14.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.35 (0.5, 16.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.1 (2, 44.325) \u003csup\u003e*#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.2 (7.5, 55.1) \u003csup\u003e*#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrea nitrogen (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.2 (7.65, 13.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.4 (7.2, 12.425)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.9 (6.8, 13.375)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.5 (7.2, 14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.454\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine (umol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e122.7 (81.4, 176.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96.9 (78.4, 136.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98.9 (76.7, 135.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e102.9 (78.4, 125.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrealbumin (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e221.79\u0026thinsp;\u0026plusmn;\u0026thinsp;56.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e210.03\u0026thinsp;\u0026plusmn;\u0026thinsp;51.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e184.5\u0026thinsp;\u0026plusmn;\u0026thinsp;49.98 \u003csup\u003e*#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e149\u0026thinsp;\u0026plusmn;\u0026thinsp;73.7 \u003csup\u003e*#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglyceride (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.02 (0.715, 1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.03 (0.675, 1.6325)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.89 (0.66, 1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.79 (0.61, 1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.419\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.63 (1.26, 2.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.82 (1.21, 2.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.68 (1.37, 2.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.81 (1.42, 2.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.916\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.96 (0.79, 1.145)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.15 (0.93, 1.47) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.97 (0.81, 1.14) \u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (0.85, 1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCT (ug/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.08 (0.04, 0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05 (0.04, 0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.10 (0.05,0.21) \u003csup\u003e*#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.16 (0.10, 0.39) \u003csup\u003e*#^\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL-6 (ng/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.65 (5.77, 20.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.94 (5.62, 17.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.74 (9.91, 37.84) \u003csup\u003e*#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.6 (13.0, 52.23) \u003csup\u003e*#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNT-pro BNP (pmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e249 (208.5, 380.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e278.5 (170.8, 373.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e316.9 (209.9, 500.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e361.4 (270.9, 724.4) \u003csup\u003e*#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSAT (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.27 (0.20, 0.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.26 (0.23, 0.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.27 (0.18, 0.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.22 (0.14, 0.26) \u003csup\u003e*#^\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSF (umol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.6 (8.4, 15.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.4 (8.65, 13.525)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.6 (6.75, 12.3) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.9 (5.4, 9) \u003csup\u003e*#^\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUIBC (umol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.2 (25.95, 38.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.35 (23.5, 37.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.75 (20.18, 31.33) \u003csup\u003e*#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.9 (22.4, 32.3) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTIBC (umol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45.1 (37.2, 51.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.45 (34.85, 51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.85 (30.28, 40.43) \u003csup\u003e*#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33.8 (28.2, 42.2) \u003csup\u003e*#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEchocardiographic data\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVEF (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59 (56, 63.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61 (58, 64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61 (57.66.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60 (57, 66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.558\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAOD (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35 (42, 50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35 (32, 37.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34 (31.75, 38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34 (33, 36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.653\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLAD (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45 (42, 50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43 (39.75, 45.5) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41 (37, 44) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39 (35, 41) \u003csup\u003e*#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVDD (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48 (45, 50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.5 (44.75, 53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45 (43, 49) \u003csup\u003e*#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46 (42, 49) \u003csup\u003e*#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRVD (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (23, 26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (22.5, 26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25 (22, 27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23 (18, 26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIVS (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (10, 12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (10, 11) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (10, 12) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11 (10, 12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVPW (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (10, 12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (10, 11) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (9, 11) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10 (10, 11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComorbidity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47 (82.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (73.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61 (78.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17 (73.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.729\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (47.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (42.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32 (41.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7 (30.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.579\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoronary heart disease (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55 (96.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37 (97.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75 (96.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20 (87.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.264\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOPD (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (38.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (34.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 (30.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8 (34.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.825\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic renal insufficiency (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32 (56.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (31.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33 (42.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10 (43.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.119\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnaemia (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 (42.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (34.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37 (46.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17 (73.9) \u003csup\u003e*#^\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtrial fibrillation (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34 (59.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (36.8) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31 (39.7) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 (26.1) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedications\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiuretic (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37 (64.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49 (62.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17 (73.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.268\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACEI/ARB/ARNI (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (45.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (23.7) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (26.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (4.3) \u003csup\u003e*#^\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ-Blocker (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35 (61.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (44.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35 (44.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13 (56.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.211\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpironolactone (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (47.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (26.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32 (41.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10 (43.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.223\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSGLT2i (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.217\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiotonic (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (6.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (17.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.176\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFerralia (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (15.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (12.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5 (21.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.447\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNutrition support (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (17.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (28.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31 (39.7)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18 (78.3) \u003csup\u003e*#^\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e*\u003c/b\u003e \u003cem\u003eP\u003c/em\u003e values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 vs normal malnutrition or no risk of malnutrition; \u003cb\u003e#\u003c/b\u003e \u003cem\u003eP\u003c/em\u003e values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 vs mild malnutrition or low malnutrition risk; \u003cb\u003e^\u003c/b\u003e \u003cem\u003eP\u003c/em\u003e values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 vs moderate malnutrition or medium malnutrition risk. BMI, body mass index; CONUT, Controlling Nutritional Status; GNRI, Geriatric Nutritional Risk Index; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; PCT, procalcitonin; IL-6, interleukin-6; NT-pro BNP, N-terminal probrain natriuretic peptide; TSAT, transferrin saturation; SF, serum ferritin; UIBC, unsaturated iron-binding capacity; TIBC, total iron-binding capacity; LVEF, left ventricular ejection fraction; AOD, aortic diameter; LAD, left atrial diameter; LVDD, left ventricular diastolic diameter; RVD, right ventricular diameter; IVS, interventricular septum; LVPW, left ventricular posterior wall; COPD, chronic obstructive pulmonary disease; ACEI, angiotensin-converting-enzyme inhibitor; ARB, angiotensin II receptor blocker; ARNI, angiotensin receptor enkephalase inhibitors; SGLT2i, sodium-dependent glucose transporters 2 inhibitor.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePrognosis of different groups patients under CONUT score and GNRI score (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/h2\u003e \u003cp\u003eAfter a follow-up of 11.62\u0026thinsp;\u0026plusmn;\u0026thinsp;9.49 months, 83 (42.3%) patients died. As the CONUT scores increased, the rates of all-cause death, cardiovascular death, readmission, and heart failure readmission rose significantly (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Patients in the higher nutritional risk group based on the GNRI scores had notably higher rates of all-cause death, readmission, and heart failure readmission (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) but cardiovascular death (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.136).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrognosis of different groups patients under CONUT and GNRI score. \u003cb\u003e*\u003c/b\u003e \u003cem\u003eP\u003c/em\u003e values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 vs normal malnutrition or no risk of malnutrition; \u003cb\u003e#\u003c/b\u003e \u003cem\u003eP\u003c/em\u003e values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 vs mild malnutrition or low malnutrition risk; \u003cb\u003e^\u003c/b\u003e \u003cem\u003eP\u003c/em\u003e values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 vs moderate malnutrition or medium malnutrition risk. CONUT, Controlling Nutritional Status; GNRI, Geriatric Nutritional Risk Index.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePrognosis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eCONUT score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003cp\u003e(0\u0026ndash;1, n\u0026thinsp;=\u0026thinsp;21)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMild malnutrition\u003c/p\u003e \u003cp\u003e(2\u0026ndash;4, n\u0026thinsp;=\u0026thinsp;73)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModerate malnutrition\u003c/p\u003e \u003cp\u003e(5\u0026ndash;8, n\u0026thinsp;=\u0026thinsp;90)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSevere malnutrition\u003c/p\u003e \u003cp\u003e(9\u0026ndash;12, n\u0026thinsp;=\u0026thinsp;12)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll-cause death (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (17.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57 (63.3) \u003csup\u003e*#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12 (100) \u003csup\u003e*#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiovascular death (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (15.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReadmission (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (52.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59 (80.8) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84 (93.3) \u003csup\u003e*#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10 (83.3) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eheart failure readmission (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (9.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (31.5) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55 (61.1) \u003csup\u003e*#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10 (83.3) \u003csup\u003e*#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eGNRI score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003cp\u003e(\u0026gt;\u0026thinsp;98, n\u0026thinsp;=\u0026thinsp;57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow malnutrition risk\u003c/p\u003e \u003cp\u003e(92\u0026thinsp;\u0026le;\u0026thinsp;GNRI\u0026thinsp;\u0026le;\u0026thinsp;98, n\u0026thinsp;=\u0026thinsp;38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMedium malnutrition risk\u003c/p\u003e \u003cp\u003e(82\u0026thinsp;\u0026le;\u0026thinsp;GNRI\u0026thinsp;\u0026lt;\u0026thinsp;92, n\u0026thinsp;=\u0026thinsp;78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh malnutrition risk\u003c/p\u003e \u003cp\u003e(\u0026lt;\u0026thinsp;82, n\u0026thinsp;=\u0026thinsp;23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll-cause death (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (19.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (23.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41 (52.6) \u003csup\u003e*#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22 (95.7) \u003csup\u003e*#^\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiovascular death (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (15.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (13.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReadmission (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43 (75.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (76.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71 (91.0) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21 (91.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eheart failure readmission (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (26.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (31.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46 (59.0) \u003csup\u003e*#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17 (73.9) \u003csup\u003e*#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSurvival analysis of different groups patients under CONUT score and GNRI score (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/h2\u003e \u003cp\u003eFor CONUT scores, higher scores were linked to increased all-cause mortality risk and decreased survival probability, and for GNRI scores, lower scores were associated with increased all-cause mortality risk and decreased survival probability.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eConsistency and difference between CONUT score and GNRI score in evaluating nutritional status of elderly patients with HFpEF (\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe two scores showed poor consistency in assessing nutritional status of elderly HFpEF patients (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, k\u0026thinsp;=\u0026thinsp;0.235).\u003c/p\u003e \u003cp\u003eBoth scores effectively predicted all-cause mortality. The area under the curve (AUC) was 0.86 [95%CI 0.80\u0026ndash;0.91] for CONUT (optimal cut-off: 5.5, maximum Youden\u0026rsquo;s index: 0.593), 0.79 [95%CI 0.73\u0026ndash;0.86] for GNRI (optimal cut-off: 83.83, maximum Youden\u0026rsquo;s index: 0.498), and 0.87 [95%CI 0.82\u0026ndash;0.92] for the combined score. Diagnostic performance of CONUT alone and the combined score was significantly superior to GNRI alone (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), with no significant difference between CONUT and the combined score (P\u0026thinsp;=\u0026thinsp;0.078).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eHFpEF is the most prevalent subtype of HF in the elderly population and a major contributor to mortality among hospitalized elderly patients\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Due to systemic metabolic alterations, diminished gastrointestinal function, underlying diseases and other, the HFpEF elderly often experience increased nutrient intake and absorption disorders, which can easily lead to malnutrition\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Malnutrition compromises myocardial energy supply, exacerbates heart failure, forming a vicious cycle that leads to poor prognosis\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e Despite this critical interplay, previous studies have largely overlooked elderly patients with HFpEF as a distinct subgroup, failing to address their unique nutritional challenges and the specific prognostic implications of malnutrition in this population\u0026mdash;this represents a key research gap that the present study aims to fill.\u003c/p\u003e \u003cp\u003eCurrently, various clinical tools are available for nutritional risk screening\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. The Nutritional Risk Screening 2002 (NRS 2002) is widely used for inpatients but significantly influenced by the reliability of patient medical history. Subjective Global Assessment (SGA) and Mini Nutritional Assessment (MNA) require professional medical intervention and involve subjective questions, making it challenging to obtain accurate data from patients with unclear expressions, particularly the elderly\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Compared with these existing tools, this study specifically selected two objective, clinically feasible scoring systems\u0026mdash;CONUT and GNRI scores\u0026mdash;to assess the nutritional status of elderly HFpEF patients, which addresses the limitations of subjective tools and ensures suitability for the elderly, a key advantage not fully explored in previous HF-related nutritional studies.\u003c/p\u003e \u003cp\u003eCONUT score collectively reflects protein reserves, caloric status, and immune function without being influenced by subjective factors or fluid retention. Lower levels of these parameters result in a higher CONUT score, indicating poorer nutritional status\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. The prevalence of coronary heart disease among elderly patients with HFpEF is notably higher, and the majority are undergoing lipid-lowering therapy, which could potentially influence the accuracy of cholesterol-based nutritional assessments\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. While previous studies have not accounted for this confounding factor, the present study explicitly analyzed the proportion of combined CHD across different CONUT score groups. Results showed that all four CONUT groups had high CHD prevalence with no statistically significant differences, confirming that lipid-lowering treatment did not bias inter-group comparisons of cholesterol levels. This methodological rigor enhances the reliability of our findings, a strength lacking in earlier research.\u003c/p\u003e \u003cp\u003eIn this study, elderly HFpEF patients with higher CONUT scores exhibited decreased the levels of nutrition-related and immune indices, increased the levers of NT-pro BNP and inflammatory indicators, and significantly longer hospital stays. Previous studies have demonstrated that the CONUT score is a robust predictor of readmission and all-cause mortality in HF patients\u003csup\u003e[\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. A meta-analysis has also indicated that malnutrition, defined by a CONUT score\u0026thinsp;\u0026ge;\u0026thinsp;2, is associated with an elevated risk of all-cause mortality, with HF patients experiencing a 1.92-fold increased risk of death from any cause\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. However, these studies neither stratified analyses by age (i.e., focusing on the elderly) nor distinguished HF subtypes. This study investigated the association between the CONUT scores and prognosis in elderly patients with heart failure with HFpEF. The results revealed that patients with higher CONUT scores exhibit significantly increased rates of all-cause mortality, cardiovascular mortality, and heart failure readmission, leading to a poorer overall prognosis.\u003c/p\u003e \u003cp\u003eGNRI score is a nutritional assessment tool that incorporates serum albumin levels and BMI\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e, the most frequently utilized nutritional assessment metric for elderly patients with HF. Serum albumin, a critical component of the GNRI, plays a pivotal role in cardiovascular health beyond its nutritional significance. In the context of cardiovascular disease, serum albumin reflects systemic inflammation, endothelial function, and myocardial tissue integrity\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e\u0026mdash;all key determinants of disease progression and prognosis. In patients with acute coronary syndrome, serum albumin also overlaps with other nutritional indices to predict disease severity and outcomes, underscoring its role as a multifaceted biomarker in cardiovascular care\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Numerous studies have demonstrated that the GNRI score effectively predicts prognosis in elderly patients\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. This study found that patients with low GNRI scores, indicative of high malnutrition risk, experienced prolonged hospital stays, significantly higher all-cause mortality, and increased rates of heart failure rehospitalization. Additionally, research by Liang confirmed that the GNRI score serves as an independent predictor of all-cause mortality in patients with heart failure with HFpEF\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCurrently, the pathophysiology of malnutrition in heart failure patients remains incompletely understood. Existing theories propose that fluid retention may cause intestinal edema, nausea, anorexia, and other symptoms, thereby impacting nutrient intake and absorption\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. This study revealed that elderly HFpEF patients with higher CONUT scores exhibited more severe iron deficiency, and lower levels of cholesterol, triglycerides, LDL-C, and other lipid metabolism indices, indirectly supporting this hypothesis. Furthermore, alterations in intestinal morphology and function in heart failure patients compromise the intestinal wall's immune barrier, triggering the release of pro-inflammatory cytokines. Chronic inflammation and neurohormonal activation lead to protein and adipose tissue degradation, resulting in body mass loss and cachexia, which contribute to poor prognosis\u003csup\u003e[\u003cspan additionalcitationids=\"CR29 CR30\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. In this study, under both nutritional assessment methods, patients with poorer nutritional status showed significantly elevated levels of inflammatory markers.\u003c/p\u003e \u003cp\u003eIn addition, this study showed that CONUT score and GNRI score in elderly HFpEF patients were poorly consistent, suggesting that the two cannot be completely replaced in clinical application. This study also evaluated the predictive value of the two nutritional status scores for all-cause death in elderly HFpEF patients through ROC curve analysis. The area under the combined ROC curve of the two scores was the largest and the highest predictive value, followed by the CONUT score and the lowest GNRI score, which may be related to the susceptibility of heart failure patients to water and sodium retention affecting BMI.\u003c/p\u003e \u003cp\u003eAs the aging population increases, greater attention must be given to elderly heart failure patients with malnutrition. Current heart failure guidelines\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e recommend that all HF patients undergo nutritional risk assessment, with nutritional intervention advised when such risks are identified. However, there remains a gap between clinical practice and these guidelines. Therefore, it is imperative to establish and refine management strategies for the elderly HFpEF patients with malnutrition.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eMalnutrition is a prevalent complication among elderly patients with HFpEF, significantly impacting patient prognosis, correlating with prolonged hospital stays, increased mortality rates, and higher readmission rates for heart failure. CONUT score and GNRI score are widely utilized in clinical nutrition assessments and cannot be entirely substituted for one another, combined evaluation of malnutrition using the two demonstrates superior efficacy in predicting adverse prognoses for elderly patients with HFpEF.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThis study was a single-center retrospective analysis, and the findings warrant further validation through multi-center studies with larger sample sizes.\u003c/p\u003e \u003c/div\u003e"},{"header":"Abbreviations","content":" \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHFpEF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eheart failure with preserved ejection fraction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCONUT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eControlling Nutritional Status\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGNRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGeriatric Nutritional Risk Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLDL-C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003elow-density lipoprotein cholesterol\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHDL-C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ehigh-density lipoprotein cholesterol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ebody mass index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eprocalcitonin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIL-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003einterleukin-6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNT-pro BNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eN-terminal probrain natriuretic peptide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTSAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003etransferrin saturation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eserum ferritin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUIBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eunsaturated iron-binding capacity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTIBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003etotal iron-binding capacity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLVEF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eleft ventricular ejection fraction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAOD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eaortic diameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eleft atrial diameter\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLVDD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eleft ventricular diastolic diameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRVD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eright ventricular diameter\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIVS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003einterventricular septum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLVPW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eleft ventricular posterior wall\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCOPD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003echronic obstructive pulmonary disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eACEI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eangiotensin-converting-enzyme inhibitor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eARB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eangiotensin II receptor blocker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eARNI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eangiotensin receptor enkephalase inhibitors\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSGLT2i\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003esodium-dependent glucose transporters 2 inhibitor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eROC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eReceiver Operating Characteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ethe area under the curve\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study\u0026rsquo;s protocol was approved by ethics committee(2023DZKY-051-01) of Affiliated Jinling Hospital, Medical School of Nanjing University, and patient enrolment was carried out according to the principles of the Declaration of Helsinki. Informed consent to participate was obtained from all of the participants in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cbr /\u003e Consent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003cstrong\u003e\u003cbr /\u003e\u003cbr /\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analysed during the current study are available from the corresponding author on reasonable request.\u003cstrong\u003e\u003cbr /\u003e\u003cbr /\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by Jiangsu Provincial Health Committee (LKZ 2023011), Clinical Diagnosis and Treatment New Technology Projects of Eastern Theater General Hospital (2023LCZLXB042) and General Project of the Basic Research Program of the General Hospital of Eastern Theater Command (22JCYYYB48).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eY.L. conceived and designed the study, collected and analyzed the data, drafted the original manuscript.\u003c/p\u003e\n\u003cp\u003eH.R.S. assisted in data collection and statistical analysis.\u003c/p\u003e\n\u003cp\u003eY.L. contributed to data management and patient information collection.\u003c/p\u003e\n\u003cp\u003eJ.Y.W. helped with patient recruitment and data interpretation.\u003c/p\u003e\n\u003cp\u003eY.G.Y., C.Y., X.W.,provided clinical resources, revised the manuscript critically。\u003c/p\u003e\n\u003cp\u003eY.Z. revised the manuscript critically, and gave intellectual input.\u003c/p\u003e\n\u003cp\u003eL.L. revised the manuscript critically, and gave intellectual input, and supervised the project。\u003c/p\u003e\n\u003cp\u003eAll authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHeidenreich P, Bozkurt B, Aguilar D, et al. 2022 AHA/ACC/HFSA guideline for the management of heart failure: a report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines[J]. 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Predictors and prognosis of incident poor nutritional status in patients with heart failure with preserved ejection fraction: insights from the PARAGON-HF trial[J]. J Am Coll Cardiol. 2024;83(13):362.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiang W, Dong Y, Liu C. Exploration of the role of nutritional status scores in heart failure prognosis[J]. Chin J Cardiol. 2024;52(11):1296\u0026ndash;301.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eProkopidis K, Irlik K, Ishiguchi H, et al. Natriuretic peptides and C-reactive protein in in heart failure and malnutrition: a systematic review and meta‐analysis[J]. ESC Heart Fail. 2024;11:3052\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTuren S, Sancar KM. Predictive value of the prognostic nutritional index for long-term mortality in patients with advanced heart failure[J]. Acta Cardiol Sinica. 2023;39(4):599.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStumpf F, Keller B, Gressies C, et al. Inflammation and nutrition: friend or foe?[J]. Nutrients. 2023;15(5):1159.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMuscaritoli M, Imbimbo G, Jager- Wittenaar H, et al. Disease related malnutrition with inflammation and cachexia[J]. Clin Nutr. 2023;42(8):1475\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Malnutrition, Geriatric Nutritional Risk Index, CONUT score, Heart failure with preserved ejection fraction, Prognosis","lastPublishedDoi":"10.21203/rs.3.rs-9073716/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9073716/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground and aims: This study aimed to compare the application of the Geriatric Nutritional Risk Index (GNRI) and Controlling Nutritional Status (CONUT) score in assessing malnutrition in elderly patients with heart failure with preserved ejection fraction (HFpEF), and to investigate the impact of malnutrition on their prognosis.\u003c/p\u003e\n\u003cp\u003eMethods and Results: A total of 196 HFpEF patients aged over 75 years were enrolled, with an average age of 92.12±4.28 years. Patients were grouped based on their CONUT and GNRI scores. These with the higher CONUT scores or lower GNRI scores were under the worse nutritional status, had longer hospital stays, lower levels of nutrition-related indices (albumin, TC, LDL-C, SF, TSAT), lower immune indices (lymphocytes), and higher levels of NT-pro BNP and inflammatory markers (CRP, PCT, IL-6). After a follow-up of 11.62±9.49 months, 83 patients (42.3%) died. Patients with worse nutritional status had significantly higher rates of all-cause death, readmission and heart failure readmission (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05). along with a higher risk of all-cause mortality and lower survival probability. CONUT and GNRI scores showed poor consistency in elderly HFpEF patients (\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.01, k= 0.235). The area under the curve (AUC) for predicting all-cause mortality was 0.86 [95%CI 0.80-0.91] for CONUT, 0.79 [95%CI 0.73-0.86] for GNRI, and 0.87[95%CI 0.82-0.92] for their combination.\u003c/p\u003e\n\u003cp\u003eConclusions: Malnutrition significantly impacts the prognosis of elderly HFpEF patients. Combined assessment using CONUT and GNRI scores provides superior efficacy in predicting adverse prognosis in this population.\u003c/p\u003e","manuscriptTitle":"The prognostic impact of malnutrition in elderly patients with HFpEF, as assessed using GNRI and CONUT score","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-23 09:21:39","doi":"10.21203/rs.3.rs-9073716/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-04-15T06:37:30+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-13T06:53:50+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-23T07:44:28+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-21T05:29:01+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2026-03-21T05:23:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"18e92755-1fe8-4e9d-83ae-c7ac10d53323","owner":[],"postedDate":"April 23rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-23T09:21:39+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-23 09:21:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9073716","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9073716","identity":"rs-9073716","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}