Startseite Combination of C-reactive protein and fibrinogen-to-albumin ratio as a novel predictor of all-cause mortality in heart failure patients
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Combination of C-reactive protein and fibrinogen-to-albumin ratio as a novel predictor of all-cause mortality in heart failure patients

  • Sirui Yang , Hongyan Cai , Zhao Hu , Wei Huang , Qin Fu , Ping Xia , Wenyi Gu , Tao Shi , Fazhi Yang und Lixing Chen EMAIL logo
Veröffentlicht/Copyright: 18. November 2024

Abstract

Heart failure (HF) is a common cardiovascular disease that is related to systemic inflammation. This study aimed to assess the role of C-reactive protein (CRP) combined with fibrinogen-to-albumin ratio (C-FAR) on the prognosis of all-cause mortality in different types of HF. A total of 1,221 hospitalized HF patients from the First Affiliated Hospital of Kunming Medical University between January 2017 and October 2021 were retrospectively analyzed. Patients were categorized into a low C-FAR group (C-FAR < 0.69) and a high C-FAR group (C-FAR ≥ 0.69) according to the median C-FAR value. We used Kaplan–Meier plots, restricted cubic spline regression, Cox survival analyses, and time-dependent receiver operating characteristic (ROC) analyses to evaluate the prognostic role of C-FAR on all-cause mortality in different types of HF. After excluding patients lost to follow-up and those with missing data, we ultimately included 1,196 patients with HF. The Kaplan–Meier plots showed that HF patients with high C-FAR levels had a significantly greater risk of all-cause mortality. In all four Cox proportional risk models, C-FAR was an independent predictor of all-cause mortality. Based on the ROC curve, the area under the curve (AUC) for C-FAR was greater than the AUC for Lg BNP. In the subgroup analyses, patients had the highest risk of all-cause mortality when FAR ≥ 0.091 and CRP ≥ 7.470. Regardless of the type of HF, C-FAR can be a good predictor of prognosis for all-cause mortality in HF patients, and patients with high C-FAR had a significantly increased risk of death compared to those with low C-FAR.

1 Introduction

Heart failure (HF) remains a rising global epidemic and the fastest-growing cardiovascular disease (CVD) in the world [1]. Despite improved survival, patients with HF are still at risk of substantial mortality or recurrent decompensation requiring hospitalization [2]. The 2021 ESC HF Guidelines state that patients with HF can be divided into three categories according to left ventricular ejection fraction (LVEF): HF with reduced ejection fraction (HFrEF) is defined as an LVEF <40%; HF with mildly reduced ejection fraction (HFmrEF) is defined as an LVEF of 40–49%; and HF with preserved ejection fraction (HFpEF) is defined as an LVEF ≥50%. There are also differences in risk factors, pathophysiology, and treatments among different types of HF [3]. HF is characterized by chronic inflammation, both locally in the heart and in the circulation, so chronic systemic inflammation is associated with an increased risk of HF. More precisely, the high plasma concentrations of several proinflammatory cytokines are closely related to disease progression and poor prognosis [4,5].

C-reactive protein (CRP), an acute-phase reactant predominantly released from hepatocytes, is a nonspecific inflammatory marker clearly associated with adverse CVD outcomes [6]. Previous observational studies reported that CRP, as a representative biomarker of systemic inflammation, can predict the development and prognosis of HF [7,8,9].

Another inflammatory marker, fibrinogen (FIB), is an important determinant of blood viscosity and platelet aggregation and is also considered to be related to HF risk [10]. Some studies have confirmed the relationship between low serum albumin (ALB) and an increase in CVD incidence and mortality [11,12,13]. The fibrinogen-to-ALB ratio (FAR), which comprises these two indicators, is independently associated with the presence and severity of coronary artery disease and may serve as a potential prognostic indicator for patients with CVD [14,15]. A study by Rong Huang et al. demonstrated that FAR was independently associated with adverse prognosis in patients with acute decompensated HF in diabetes mellitus [16]. Xu et al. found that the FAR is an independent prognostic risk factor for 90-day and 1-year all-cause mortality among HF patients [17].

Several studies have shown that FAR and CRP are associated with the prognosis of HF patients [16,18,19], so we speculate that FAR combined with CRP may also affect the prognosis of patients with HF. The aim of our study was to evaluate the clinical prognostic impact of FAR combined with CRP in worsening HF patients with different ejection fractions.

2 Materials and methods

2.1 Study population

A retrospective analysis of 1,221 consecutive patients with acute exacerbation of chronic HF admitted to the First Affiliated Hospital of Kunming Medical University from January 2017 to October 2021 was carried out. The enrolled patients with HF had a New York Heart Association (NYHA) functional class of III or IV and brain natriuretic peptide (BNP) levels of at least 500 pg/mL on admission. After excluding patients with missing data (blood test results, echocardiographic data), other serious illnesses (malignancy, infections, or severe renal or hepatic impairment), and those who were lost to follow-up, 1,196 patients remained and were included in this study.

2.2 Data collection and definitions

Patient demographics (including age and sex), clinical history, anthropometric data, laboratory test results, echocardiographic data, and information on medication use during hospitalization were collected at admission. Some peripheral venous blood samples were collected on admission for routine blood tests, while some were collected after overnight fasting (>8 h), and the different variables were measured in the laboratory. FIB, ALB, CRP, D-dimer, troponin, creatine kinase-MB, BNP, hemoglobin, uric acid (UA), creatinine, white blood cell (WBC) count, fasting plasma glucose (FPG), total cholesterol (TC), triglycerides (TGs), low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, serum sodium, serum potassium, alanine aminotransferase (ALT), and aspartate transaminase (AST) levels were measured.

Patients were followed up primarily through telephone contact. If no response was received, follow-up was terminated at the time of the patient’s last available medical record. The endpoint of this study was all-cause mortality in patients with HF.

FAR was defined as the ratio of the plasma FIB level (g/L) to the plasma ALB level (g/L). C-FAR was calculated as the serum CRP level (mg/L) × FAR. Because the raw values of BNP had a highly skewed distribution, they were logarithmically transformed.

2.3 Statistical analysis

We divided the patients into a low C-FAR group (C-FAR <0.69) and a high C-FAR group (C-FAR ≥ 0.69) according to the median C-FAR value. Continuous variables were classified as normally or nonnormally distributed by the normality test. The independent samples t test was used to compare differences in normally distributed continuous variables, and the Mann‒Whitney U rank sum test was used to compare differences in nonnormally distributed data. Continuous variables are presented as the mean ± standard deviation if normally distributed or as the median with interquartile range if nonnormally distributed. Categorical variables are summarized as numbers and percentages. Between-group differences in categorical variables were compared using the χ 2 test.

The cumulative incidence of all-cause death was calculated using Kaplan–Meier plots and the log-rank test. The restricted cubic spline models allowed us to determine the association of C-FAR with the risk of all-cause mortality. Unadjusted univariate Cox proportional hazard regression analyses were applied to show the estimated impact of each variable on all-cause mortality. Multivariate analysis using COX regression model tested variables that were significant (P < 0.05) in the univariate analysis to determine independent predictors of all-cause mortality. Multivariate Cox proportional hazards models were applied to determine the associations of C-FAR with the incidence rates of all-cause mortality. The first unadjusted group was regarded as the reference group. The results are expressed as hazard ratios (HRs) with 95% confidence intervals (CIs). Time-dependent receiver operating characteristic (ROC) curves and the corresponding area under the curve (AUC) were calculated to compare the predictive ability of C-FAR in HF patients.

The correlation coefficient was calculated for statistical correlations between continuous variables based on Spearman’s nonparametric test. In addition, we performed stratification analysis to confirm whether the effect of FAR and CRP differed in each of the subgroups. Patients with HF were divided into group 1 (FAR < 0.091 + CRP < 7.470), group 2 (FAR < 0.091 + CRP ≥ 7.470), group 3 (FAR ≥ 0.091 + CRP < 7.470), and group 4 (FAR ≥ 0.091 + CRP ≥ 7.470) according to the median FAR and CRP values.

Data were analyzed statistically using SPSS ver. 25.0 and R 4.3.1. A double-sided P value <0.05 was considered statistically significant.

  1. Informed consent: Informed written consent was obtained from all patients before the intervention.

  2. Ethical approval: This study was endorsed by the medical ethics committee of the First Affiliated Hospital of Kunming Medical University and complied with the Declaration of Helsinki.

3 Results

3.1 Baseline patient characteristics

After excluding patients lost to follow-up and those with missing data, 1,196 HF patients were enrolled in the study. The median age of the patients was 67 years, and 38.0% were women. Compared to the low C-FAR group, patients in the high C-FAR group had higher values of WBC, FIB, CRP, FPG, UA, creatinine, and sodium and worse NYHA class, while they had lower values of RBC, hemoglobin, and GFR and a higher proportion of combined diabetes (all P values <0.05) (Table 1).

Table 1

Baseline characteristics according to C-FAR

Characteristics Total (n = 1,196) Low C-FAR group (n = 598) High C-FAR group (n = 598) P value
Age, years 66.83 ± 12.51 65.55 ± 12.90 68.11 ± 12.00 0.113
Female, n (%) 454 (38.0) 239 (40.0) 215 (47.4) 0.153
BMI, kg/m2 23.02 ± 3.81 22.98 ± 3.96 23.06 ± 3.66 0.304
NYHA, n (%) 0.001
 Class Ⅳ 444 (37.1) 194 (32.4) 250 (41.8)
Heart rate (bpm) 85.24 ± 21.02 83.05 ± 20.08 87.43 ± 21.71 0.023
SBP, mmHg 122.10 ± 22.94 122.20 ± 22.65 122.00 ± 23.26 0.927
DBP, mmHg 76.23 ± 15.04 76.79 ± 15.20 75.67 ± 14.88 0.212
Medical history, n (%)
 Smoking status 409 (34.2) 195 (32.6) 214 (35.8) 0.247
 Drinking status 201 (16.8) 100 (16.7) 101 (16.9) 0.983
 Coronary heart disease 619 (51.8) 298 (49.8) 321 (53.7) 0.183
 Hypertension 660 (55.2) 319 (53.3) 341 (57.0) 0.201
 Diabetes 341 (28.5) 147 (24.6) 194 (32.4) 0.003
 Atrial fibrillation 406 (33.9) 202 (33.8) 204 (34.1) 0.903
LVEF 0.812
 HFrEF 499 (41.7) 249 (41.6) 250 (41.8)
 HFmrEF 233 (19.5) 113 (18.9) 120 (20.1)
 HFpEF 464 (38.8) 236 (39.5) 228 (38.1)
Laboratory data
 WBC (109/L) 6.93 (5.54, 9.07) 6.47 (5.34, 8.21) 7.39 (5.78, 10.09) <0.001
 RBC (1012/L) 4.55 ± 0.77 4.61 ± 0.72 4.48 ± 0.80 0.041
 PLT (109/L) 192.00 (148.00, 243.00) 192.50 (152.00, 240.00) 191.00 (145.75, 247.25) 0.730
 Fibrinogen (g/L) 3.57 ± 1.30 3.12 ± 0.96 4.01 ± 1.43 <0.001
 ALB (g/L) 36.69 ± 4.55 37.93 ± 4.23 35.46 ± 4.53 0.465
 Hemoglobin (g/L) 138.11 ± 24.17 140.15 ± 22.70 136.06 ± 25.41 0.006
 Lg BNP 3.17 ± 0.28 3.13 ± 0.27 3.20 ± 0.29 0.155
 CRP (mg/L) 7.47 (3.00, 21.78) 3.00 (1.60, 5.00) 21.69 (12.60,47.89) <0.001
 FPG (mmol/L) 5.03 (4.16, 6.50) 4.89 (4.12, 5.99) 5.12 (4.22, 7.15) <0.001
 UA (μmol/L) 477.10 (370.50, 588.10) 467.90 (365.00, 567.10) 493.35 (382.08, 606.55) 0.016
 Creatinine (μmol/L) 103.50 (83.20, 134.10) 98.85 (81.13, 125.35) 108.70 (86.68, 140.90) <0.001
 GFR (ml/min) 44.10 (32.33, 56.69) 46.13 (34.87, 59.36) 42.16 (29.74, 54.13) <0.001
 Sodium (mmol/L) 141.01 ± 4.43 141.55 ± 4.01 140.46 ± 4.75 0.001
 Potassium (mmol/L) 3.94 ± 0.60 3.96 ± 0.58 3.92 ± 0.62 0.203
 Chlorine (mmol/L) 102.92 ± 4.68 103.62 ± 4.49 102.21 ± 4.76 0.148
 Total cholesterol (mmol/L) 3.64 ± 1,01 3.73 ± 0.99 3.56 ± 1.03 0.552
 TG (mmol/L) 1.10 (0.86, 1.50 1.10 (0.85, 1.53) 1.10 (0.87, 1.46) 0.768
 AST (U/L) 28.60 (20.03, 43.28) 28.20 (20.28,40.00) 28.85 (20.00, 48.93) 0.081
 ALT (U/L) 25.05 (16.70, 42.30) 25.00 (16.88, 41.00) 25.80 (16.25, 45.40) 0.513
Medications at admission, n (%)
 CRT/ICD 116 (9.7) 54(9.0) 62(10.4) 0.895
 SGLT2i 263 (22.0) 145 (24.2) 118 (19.7) 0.059
 Beta-blocker 690 (57.7) 365 (61.1) 325 (54.4) 0.615
 Aldosterone antagonist 947 (79.2) 435 (72.7) 474 (79.3) 0.943
 ACEI/ARB/ARNI 587 (49.1) 330 (55.2) 257 (43.0) 0.321
 Diuretics 930 (77.8) 397 (66.4) 533 (89.1) 0.838

BMI: body mass index; NYHA: New York Heart Association; SBP: systolic blood pressure; DBP: diastolic blood pressure; LVEF: left ventricular ejection fraction; WBC: white blood cell; RBC: red blood cell; PLT: blood platelet; ALB: albumin; BNP: B-type natriuretic peptide; CRP: C-reactive protein; FPG: fasting plasma glucose; UA: uric acid; GFR: glomerular filtration rate; AST: alanine aminotransferase; ALT: aspartate transaminase; SGLT2i: sodium-glucose cotransporter 2 inhibitor; ACEI: angiotensin-converting enzyme inhibitor; ARB: angiotensin receptor inhibitor; ARNI: angiotensin receptor neprilysin inhibitor.

Note: low C-FAR: C-FAR < 0.69, high C-FAR: C-FAR ≥ 0.69.

Differences in normally distributed continuous variables were compared using variance analyses, and those in nonnormally distributed data were compared using Mann‒Whitney U tests. chi-square tests were used to compare differences in categorical variables between groups.

3.2 Prediction of the incidence of all-cause mortality by C-FAR

Kaplan‒Meier analysis showed that the cumulative incidence of all-cause mortality was higher in the high C-FAR group (C-FAR ≥ 0.69) than in the low C-FAR group (C-FAR <0.69) for all patients (log-rank test, chi-square 122.113, P < 0.001) (Figure 1a), HFrEF + HFmrEF patients (log-rank test, chi-square 77.427, P < 0.001) (Figure 1b), and HFpEF patients (log-rank test, chi-square 44.780, P < 0.001) (Figure 1c).

Figure 1 
                  Kaplan‒Meier analysis for all-cause mortality according to different C-FAR levels. (a) All HF patients, (b) HFrEF plus HFmrEF patients, and (c) HFpEF patients. Note: Q1: low C-FAR, C-FAR < 0.69, Q2: high C-FAR, C-FAR ≥ 0.69. The line represents the reference value of the survival rate, and the corresponding color area represents the CI. The all-cause mortality of the high C-FAR group was higher than the low C-FAR group for all patients, regardless of the type of HF.
Figure 1

Kaplan‒Meier analysis for all-cause mortality according to different C-FAR levels. (a) All HF patients, (b) HFrEF plus HFmrEF patients, and (c) HFpEF patients. Note: Q1: low C-FAR, C-FAR < 0.69, Q2: high C-FAR, C-FAR ≥ 0.69. The line represents the reference value of the survival rate, and the corresponding color area represents the CI. The all-cause mortality of the high C-FAR group was higher than the low C-FAR group for all patients, regardless of the type of HF.

As shown in Figure 2, we used restricted cubic splines to flexibly model and visualize the relation of C-FAR levels with all-cause mortality in all HF patients and different types of HF patients. C-FAR was roughly positively correlated with the risk rate of all-cause death for all patients. In HFpEF patients, C-FAR was also roughly positively correlated with the risk rate of all-cause death. However, in HFrEF + HFmrEF patients, C-FAR was positively correlated with the risk of all-cause death at first and then negatively correlated.

Figure 2 
                  Restricted cubic spline analysis for the association of C-FAR and all-cause mortality. (a) All HF patients, (b) HFrEF plus HFmrEF patients, and (c) HFpEF patients. Note: HRs are indicated by solid lines and 95% CIs by shaded areas.
Figure 2

Restricted cubic spline analysis for the association of C-FAR and all-cause mortality. (a) All HF patients, (b) HFrEF plus HFmrEF patients, and (c) HFpEF patients. Note: HRs are indicated by solid lines and 95% CIs by shaded areas.

3.3 C-FAR as an independent predictor

Table 2 shows the four multivariate Cox proportional hazard models used to determine the correlation between the C-FAR groups and all-cause mortality. In model 4, the covariates adjusted for included age, body mass index, NYHA cardiac function classification, Lg BNP, creatinine, serum UA, and GFR. With the low C-FAR group as a reference, high C-FAR was associated with a higher incidence of all-cause mortality in all HF patients and in different types of HF patients. Compared with low-level C-FAR patients, the HR of high-level C-FAR patients increased by 1.168 times in all HF patients, by 1.152 times in HFrEF plus HFmrEF patients, and by 0.915 times in HFpEF patients (P < 0.001).

Table 2

Cox proportional hazards models for the association of C-FAR and the risk of all-cause mortality

Model All HF patients HFrEF plus HFmrEF HFpEF
HR (95% CI) P HR (95% CI) P HR (95% CI) P
Unadjusted 2.601 (2.181, 3.101) <0.001 2.582 (2.074,3.216) <0.001 2.641 (1.965, 3.551) <0.001
Adjusted model 1 2.464 (2.066, 2.940) <0.001 2.466 (1.979, 3.073) <0.001 2.437 (1.812, 3.278) <0.001
Adjusted model 2 2.512 (2.105, 2.998) <0.001 2.516 (2.019,3.136) <0.001 2.479 (1.843, 3.335) <0.001
Adjusted model 3 2.267 (1.897, 2.709) <0.001 2.207 (1.757, 2.772) <0.001 1.996 (1.471, 2.707) <0.001
Adjusted model 4 2.168 (1.808, 2.600) <0.001 2.152 (1.710, 2.707) <0.001 1.915 (1.407, 2.606) <0.001

Ref.: low C-FAR (C-FAR <0.69).

Adjusted model 1: adjusted for age.

Adjusted model 2: adjusted for age and BMI.

Adjusted model 3: model 2 + NYHA cardiac function classification and Lg BNP.

Adjusted model 4: model 3 + creatinine, serum UA, and GFR.

3.4 Predictive ability of C-FAR

We constructed time-dependent ROC curves to investigate the ability of C-FAR to predict all-cause mortality in HF patients. In ROC curve analysis, C-FAR had an AUC of 0.717 (95% CI 0.688–0.746), with a sensitivity of 63.9% and a specificity of 70.0%, for predicting the prognosis of all HF patients. C-FAR was significantly better than Lg BNP (AUC 0.639, 95% CI 0.608–0.671, P < 0.001) (Figure 3a).

Figure 3 
                  Time-dependent ROC curves of C-FAR with the reference line for all-cause mortality. (a) All HF patients. (b) HFrEF plus HFmrEF patients. (c) HFpEF patients.
Figure 3

Time-dependent ROC curves of C-FAR with the reference line for all-cause mortality. (a) All HF patients. (b) HFrEF plus HFmrEF patients. (c) HFpEF patients.

C-FAR was also better than Lg BNP in predicting the prognosis of patients with different types of HF. Among patients with HFrEF plus HFmrEF, the AUC of C-FAR was 0.721 (95% CI 0.684–0.758) and that of Lg BNP was 0.638 (95% CI 0.598–0.678) (Figure 3b). Among patients with HFrEF, the AUC of C-FAR was 0.717 (95% CI 0.670–0.763) and that of Lg BNP was 0.627 (95% CI 0.575–0.679) (Figure 3c) (all P values were <0.001).

3.5 Correlation and subgroup analysis

The Spearman’s rank correlation analysis results are reported in Table A1. FAR was positively correlated with CRP in the total participant population [Spearman’s correlation coefficient (r): 0.318, P < 0.001]. In addition, C-FAR was positively correlated with age, LVEF, Lg BNP, FPG, WBC, PLT, AST, and creatinine but negatively correlated with RBC, HB, serum sodium, serum chlorine, GFR, and TC (P < 0.05).

There was an interaction between FAR and CRP, so subgroup analysis was performed to determine the association. All patients with HF were divided into four groups (group 1: FAR < 0.091 + CRP < 7.470, group 2: FAR < 0.091 + CRP ≥ 7.470, group 3: FAR ≥ 0.091 + CRP < 7.470, group 4: FAR ≥ 0.091 + CRP ≥ 7.470). Using group 1 as a reference, group 4 patients had the highest risk of all-cause mortality (Table 3).

Table 3

Subgroup analysis

Unadjusted Adjusted
HR (95% CI) P HR (95% CI) P
All HF patients
FAR < 0.091 Ref. Ref.
FAR ≥ 0.091 1.252 (1.061, 1.478) 0.008 1.195 (1.003, 1.425) 0.046
CRP < 7.470 Ref. Ref.
CRP ≥ 7.470 2.900 (2.427, 3.465) <0.001 2.182 (1.811, 2.629) <0.001
Combined categories
Group 1: FAR < 0.091 + CRP < 7.470 Ref. Ref.
Group 2: FAR < 0.091 + CRP ≥ 7.470 2.468 (1.928, 3.158) <0.001 1.591 (1.226, 2.066) <0.001
Group 3: FAR ≥ 0.091 + CRP < 7.470 0.818 (0.602, 1.111) 0.199 0.723 (0.530, 0,988) 0.042
Group 4: FAR ≥ 0.091 + CRP ≥ 7.470 2.827 (2.262, 3.532) <0.001 2.117 (1.674, 2.675) <0.001
HFrEF plus HFmrEF patients
FAR < 0.091 Ref. Ref.
FAR ≥ 0.091 1.187 (0.996, 1.459) 0.102 1.142 (0.919, 1.419) 0.230
CRP < 7.470 Ref. Ref.
CRP ≥ 7.470 2.986 (2.386, 3.736) <0.001 2.369 (1.870, 3.000) <0.001
Combined categories
Group 1: FAR < 0.091 + CRP < 7.470 Ref. Ref.
Group 2: FAR < 0.091 + CRP ≥ 7.470 2.617 (1.954, 3.504) <0.001 1.768 (1.291, 2.422) <0.001
Group 3: FAR ≥ 0.091 + CRP < 7.470 0.728 (0.483, 1.098) 0.130 0.620 (0.408, 0,944) 0.026
Group 4: FAR ≥ 0.091 + CRP ≥ 7.470 2.757 (2.095, 3.626) <0.001 2.170 (1.628, 2.893) <0.001
HFpEF patients
FAR < 0.091 Ref. Ref.
FAR ≥ 0.091 1.461 (1.093, 1.952) 0.010 1.306 (0.956, 1.784) 0.093
CRP < 7.470 Ref. Ref.
CRP ≥ 7.470 2.753 (2.052, 3.693) <0.001 1.826 (1.332, 2.503) <0.001
Combined categories
Group 1: FAR < 0.091 + CRP < 7.470 Ref. Ref.
Group 2: FAR < 0.091 + CRP ≥ 7.470 2.082 (1.303, 3.328) 0.002 1.215 (0.738, 2.000) 0.445
Group 3: FAR ≥ 0.091 + CRP < 7.470 0.992 (0.617, 1.597) 0.975 0.878 (0.542, 1.424) 0.598
Group 4: FAR ≥ 0.091 + CRP ≥ 7.470 3.107 (2.107, 4.580) <0.001 2.038 (1.335, 3.109) 0.001

Note: Adjusted for age, BMI, NYHA cardiac function classification, Lg BNP, PLT, HB, chlorine, AST, and UA.

4 Discussion

This study suggested that C-FAR plays an important role in predicting the prognosis of patients with different types of HF. Kaplan‒Meier analysis showed that the all-cause mortality of the high C-FAR group was higher regardless of the type of HF. In all four Cox proportional risk models, for all HF patients and for different HF subgroups (HFrEF plus HFmrEF and HFpEF), C-FAR was an independent predictor of all-cause mortality. Subgroup analysis showed that patients had the highest risk of all-cause mortality when FAR ≥ 0.091 and CRP ≥ 7.470. Among all HF patients, the risk of death was 2.117 times higher than that in the FAR < 0.091 and CRP < 7.470 group; among HFrEF plus HFmrEF patients, it was 2.170 times higher; and among HFpEF patients, it was 2.038 times higher. The time-dependent ROC curves showed that the AUC for C-FAR was 0.717 (P < 0.001) in all HF patients, with a sensitivity of 63.9% and a specificity of 70.0%, which provided an incremental prognostic value beyond that of plasma BNP (AUC = 0.639). In HFrEF plus HFmrEF and HFpEF patients, C-FAR was also better than Lg BNP in predicting the prognosis of patients.

FIB is a marker of thrombosis and inflammation and is associated with the prognosis of many diseases, including coronary artery disease [20], diabetes [21], and chronic kidney disease [22]. There is growing evidence that FIB is a poor prognostic predictor of CVD [23,24,25]. FIB may increase cardiovascular risk through platelet aggregation, plasma viscosity, and fibrin formation. A study by Kotbi et al. has also shown that FIB is related to the severity of coronary artery disease and cardiovascular risk in Moroccan patients [26]. ALB is a protein synthesized in the liver that influences nutrient absorption, colloidal pressure, and systemic inflammation [27]. In HF, hypoalbuminemia may be a marker of comorbid burden, inflammatory state, malnutrition, and cachexia. Low serum ALB levels are associated with an increased risk of HF onset and progression. Hypoproteinemia may promote pulmonary bruising, myocardial edema, and subsequent worsening of myocardial dysfunction, diuretic resistance, and fluid retention and reduce antioxidant function and anti-inflammatory properties [28,29,30,31]. In patients with HFpEF, the excessive activation of renin–angiotensin–aldosterone system leads to the expansion of blood volume, and the circulating blood volume continues to increase through progressive sodium and water retention, resulting in the decrease of plasma ALB concentration. In patients with HFrEF, to maintain circulatory homeostasis, the sympathetic nervous system and the renin–angiotensin–aldosterone system are continuously activated through continuous efforts [32,33]. In this process, the over-expression of bioactive molecules has toxic effects on the heart and circulation, accompanied by the further activation of inflammatory signaling pathway [34] which leads to the increase of CRP and FIB levels. The role of FAR, as a ratio of FIB to ALB, has been elucidated in several diseases. FAR has been associated with poor prognosis in a variety of cancers [35,36,37,38] and has also been directly associated with the severity of coronary artery calcification and poor prognosis in acute coronary syndromes [15,39,40], but it is less studied in HF. In addition, it is more sensitive and specific in predicting major adverse cardiovascular events than FIB and ALB alone [14].

Serum CRP level has been widely considered to be a nonspecific but sensitive marker of the acute inflammatory response. During acute inflammation, CRP levels can be increased by 100 times or even 500 times. This reaction is mainly regulated by proinflammatory cytokines, especially IL-6 [41]. Many prospective studies have shown that plasma CRP is a strong independent predictor of the risk of acute myocardial infarction, stroke, peripheral arterial disease, and vascular death [42,43,44]. Anand et al. found that higher CRP levels are associated with features of more severe HF and are independently associated with mortality and morbidity [18]. Jin et al. reported that CRP was an excellent prognostic marker for HFrEF, HFmrEF, and HFpEF [45]. The primary cause of elevated CRP is related to cardiac decompensation and ongoing damage to other organs; low cardiac output and venous stasis may induce IL-6 production. This key cytokine activates CRP through TNF-α production, which in turn activates complement and amplifies the inflammatory response; this may lead to myocardial tissue damage or dysfunction [46]. The contribution of CRP to the progression of HF may also be related to effects on organs other than the heart. The common comorbidities of HF, anemia, and renal dysfunction may be caused in part by inflammatory activation [47]. However, the specific inflammatory processes that lead to elevated CRP levels in patients with HF and their specific mechanisms for disease progression have not been clarified.

5 Conclusion

This study indicates that FAR and CRP are independent predictors of prognosis in CHF patients. C-FAR was significantly associated with the incidence of all-cause mortality in HF patients, regardless of HF subtype, and roughly positively correlated with the risk of all-cause death. Our study demonstrated that C-FAR can predict the prognosis of patients with HF.

6 Limitations

This is a retrospective observational study, and data bias could not be avoided despite correcting for multiple confounding factors. Further prospective studies are needed to validate the role of C-FAR in the prognosis of patients with CHF. The risk of selection bias in retrospective research is inevitable, which might affect the results’ generalizability. Patients with HF who were in NYHA class III or IV were the primary subjects in this study. The predictive value of the C-FAR for all-cause mortality in HF patients with NYHA class I or II was not explored.


# Sirui Yang and Hongyan Cai are co-first authors.


  1. Funding information: This study was supported by the Applied Basic Research Program of the Science and Technology Hall of Yunnan Province and Kunming Medical University (Project No. 202301AY070001-130), Yunnan health training project of high-level talents (H2019052), and Yunnan Provincial Health Commission Clinical Medical Center (ZX2019-03-01).

  2. Author contributions: Sirui Yang, Hongyan Cai, and Lixing Chen conceived and designed the survey, conducted the statistical analyses, drafted the first manuscript, and approved the final manuscript as submitted. Zhao Hu, Wei Huang, Qin Fu, and Ping Xia participated in drafting the manuscript and conducted the statistical analyses. Wenyi Gu, Tao Shi, and Fazhi Yang performed data collection and statistical analysis. All the authors have read and approved the final version of the manuscript.

  3. Conflict of interest: The authors claim that they have no competing interests.

  4. Data availability statement: The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

Appendix

Table A1

Correlation coefficients between C-FAR and other factors

Variable FAR CRP C-FAR
r p r p r p
FAR 1 0.318 <0.001
CRP 0.318 <0.001 1
Age 0.057 0.047 0.065 0.025 0.064 0.026
BMI 0.019 0.505 −0.001 0.985 0.025 0.391
LVEF 0.145 <0.001 0.029 0.323 0.071 0.014
NYHA 0.012 0.676 0.112 <0.001 0.132 <0.001
Lg BNP −0.004 0.885 0.093 0.001 0.087 0.003
FPG 0.135 <0.001 0.117 <0.001 0.137 <0.001
WBC 0.193 <0.001 0.277 <0.001 0.277 <0.001
RBC −0.181 <0.001 0.094 0.001 −0.106 <0.001
PLT 0.288 <0.001 0.008 0.784 0.071 0.013
HB −0.212 <0.001 −0.092 0.001 −0.114 <0.001
Sodium −0.111 <0.001 −0.127 <0.001 −0.111 <0.001
Potassium −0.014 0.641 0.001 0.976 −0.010 0.741
Chlorine −0.061 0.035 −0.150 <0.001 −0.131 <0.001
ALT −0.076 <0.001 0.110 <0.001 0.038 0.190
AST −0.022 0.443 0.132 <0.001 0.088 0.002
Creatinine 0.161 <0.001 0.111 <0.001 0.097 0.001
UA −0.112 <0.001 0.060 0.040 0.058 0.048
GFR −0.094 0.001 −0.109 <0.001 −0.091 0.002
TC 0.021 0.472 −0.127 <0.001 −0.114 <0.001

FAR: fibrinogen-to-albumin ratio; CRP: C-reactive protein; BMI: body mass index; LVEF: left ventricular ejection fraction; NYHA: New York Heart Association; BNP: B-type natriuretic peptide; FPG: fasting plasma glucose; WBC: white blood cell; RBC: red blood cell; PLT: blood platelet; HB: haemoglobin; ALT: aspartate transaminase; AST: alanine aminotransferase; UA: uric acid; GFR: glomerular filtration rate.

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Received: 2023-12-03
Revised: 2024-07-02
Accepted: 2024-08-27
Published Online: 2024-11-18

© 2024 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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  142. The effect of COVID-19 lockdown on admission rates in Maternity Hospital
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  144. Luteolin alleviates cerebral ischemia/reperfusion injury by regulating cell pyroptosis
  145. Therapeutic role of respiratory exercise in patients with tuberculous pleurisy
  146. Effects of CFTR-ENaC on spinal cord edema after spinal cord injury
  147. Irisin-regulated lncRNAs and their potential regulatory functions in chondrogenic differentiation of human mesenchymal stem cells
  148. DMD mutations in pediatric patients with phenotypes of Duchenne/Becker muscular dystrophy
  149. Combination of C-reactive protein and fibrinogen-to-albumin ratio as a novel predictor of all-cause mortality in heart failure patients
  150. Significant role and the underly mechanism of cullin-1 in chronic obstructive pulmonary disease
  151. Ferroptosis-related prognostic model of mantle cell lymphoma
  152. Observation of choking reaction and other related indexes in elderly painless fiberoptic bronchoscopy with transnasal high-flow humidification oxygen therapy
  153. A bibliometric analysis of Prader-Willi syndrome from 2002 to 2022
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  155. Oxidative stress regulates glycogen synthase kinase-3 in lymphocytes of diabetes mellitus patients complicated with cerebral infarction
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  158. Purinergic P2X7 receptor mediates hyperoxia-induced injury in pulmonary microvascular endothelial cells via NLRP3-mediated pyroptotic pathway
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  161. Elevated serum miR-142-5p correlates with ischemic lesions and both NSE and S100β in ischemic stroke patients
  162. Correlation between the mechanism of arteriopathy in IgA nephropathy and blood stasis syndrome: A cohort study
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  164. Predictive role of neuron-specific enolase and S100-β in early neurological deterioration and unfavorable prognosis in patients with ischemic stroke
  165. The potential risk factors of postoperative cognitive dysfunction for endovascular therapy in acute ischemic stroke with general anesthesia
  166. Fluoxetine inhibited RANKL-induced osteoclastic differentiation in vitro
  167. Detection of serum FOXM1 and IGF2 in patients with ARDS and their correlation with disease and prognosis
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  169. Differences in mortality risk by levels of physical activity among persons with disabilities in South Korea
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  173. A narrative review on adverse drug reactions of COVID-19 treatments on the kidney
  174. Emerging role and function of SPDL1 in human health and diseases
  175. Adverse reactions of piperacillin: A literature review of case reports
  176. Molecular mechanism and intervention measures of microvascular complications in diabetes
  177. Regulation of mesenchymal stem cell differentiation by autophagy
  178. Molecular landscape of borderline ovarian tumours: A systematic review
  179. Advances in synthetic lethality modalities for glioblastoma multiforme
  180. Investigating hormesis, aging, and neurodegeneration: From bench to clinics
  181. Frankincense: A neuronutrient to approach Parkinson’s disease treatment
  182. Sox9: A potential regulator of cancer stem cells in osteosarcoma
  183. Early detection of cardiovascular risk markers through non-invasive ultrasound methodologies in periodontitis patients
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  189. Utilizing reactive oxygen species-scavenging nanoparticles for targeting oxidative stress in the treatment of ischemic stroke: A review
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  193. Iron in ventricular remodeling and aneurysms post-myocardial infarction
  194. Case Reports
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  197. Successful treatment with bortezomib in combination with dexamethasone in a middle-aged male with idiopathic multicentric Castleman’s disease: A case report
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  199. Elevation of D-dimer in eosinophilic gastrointestinal diseases in the absence of venous thrombosis: A case series and literature review
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  203. Anti-HMGCR myopathy mimicking facioscapulohumeral muscular dystrophy
  204. Recurrent opportunistic infections in a HIV-negative patient with combined C6 and NFKB1 mutations: A case report, pedigree analysis, and literature review
  205. Letter to the Editor
  206. Letter to the Editor: Total parenteral nutrition-induced Wernicke’s encephalopathy after oncologic gastrointestinal surgery
  207. Erratum
  208. Erratum to “Bladder-embedded ectopic intrauterine device with calculus”
  209. Retraction
  210. Retraction of “XRCC1 and hOGG1 polymorphisms and endometrial carcinoma: A meta-analysis”
  211. Corrigendum
  212. Corrigendum to “Investigating hormesis, aging, and neurodegeneration: From bench to clinics”
  213. Corrigendum to “Frankincense: A neuronutrient to approach Parkinson’s disease treatment”
  214. Special Issue The evolving saga of RNAs from bench to bedside - Part II
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  218. Special Issue Advancements in oncology: bridging clinical and experimental research - Part I
  219. Ultrasound-guided transperineal vs transrectal prostate biopsy: A meta-analysis of diagnostic accuracy and complication rates
  220. Assessment of diagnostic value of unilateral systematic biopsy combined with targeted biopsy in detecting clinically significant prostate cancer
  221. SENP7 inhibits glioblastoma metastasis and invasion by dissociating SUMO2/3 binding to specific target proteins
  222. MARK1 suppress malignant progression of hepatocellular carcinoma and improves sorafenib resistance through negatively regulating POTEE
  223. Analysis of postoperative complications in bladder cancer patients
  224. Carboplatin combined with arsenic trioxide versus carboplatin combined with docetaxel treatment for LACC: A randomized, open-label, phase II clinical study
  225. Special Issue Exploring the biological mechanism of human diseases based on MultiOmics Technology - Part I
  226. Comprehensive pan-cancer investigation of carnosine dipeptidase 1 and its prospective prognostic significance in hepatocellular carcinoma
  227. Identification of signatures associated with microsatellite instability and immune characteristics to predict the prognostic risk of colon cancer
  228. Single-cell analysis identified key macrophage subpopulations associated with atherosclerosis
Heruntergeladen am 30.9.2025 von https://www.degruyterbrill.com/document/doi/10.1515/med-2024-1045/html
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