A novel predictive model based on inflammatory markers to assess the prognosis of patients with HBV-related acute-on-chronic liver failure: a retrospective cohort study

doi:10.21203/rs.2.14221/v1

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A novel predictive model based on inflammatory markers to assess the prognosis of patients with HBV-related acute-on-chronic liver failure: a retrospective cohort study

https://doi.org/10.21203/rs.2.14221/v1

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published 16 Sep, 2020

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Backgrounds: Systemic inflammatory response is closely related to the development and prognosis of liver failure. This study aimed to establish a new model combing the inflammatory markers including neutrophil/lymphocyte ratio (NLR) and red blood cell distribution width (RDW) with several hematological testing indicators to assess the prognosis of patients with hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF).

Methods: A derivation cohort with 421 patients and a validation cohort with 156 patients were recruited from three hospitals. Retrospectively collecting their clinical data and laboratory testing indicators . Medcalc-15.10 software was employed for Data analyses.

Results: Multivariate analysis indicated that RDW, NLR, INR, TBIL and Cr were risk factors for 90-day mortality in patients with HBV-ACLF. The risk assessment model is COX RNTIC =0.053× R DW+0.027× N LR+0.003× T BIL+0.317× I NR+0.003× C r (RNTIC) with a cut-off value of 3.082 (sensitivity: 77.89%, specificity: 86.04%). The area under the receiver operating characteristics curve (AUC) of the RNTIC was 0.873 [95%CI (0.837–0.903)], better than the predictive value of RDW [0.675, 95%CI (0.628–0.719)], NLR [0.693, 95%CI (0.646–0.737)] and MELD score [0.732, 95%CI (0.687–0.774)]. In the validation cohort, RNTIC also performed a better prediction value than RDW, NLR, and MELD score with the AUC of [0.845, 95%CI (0.778-0.898)], [0.670, 95%CI (0.591-0.744)], [0.732, 95%CI (0.655-0.799)] and [0.768, 95%CI (0.694-0.832)], respectively.

Conclusions: The inflammatory markers RDW and NLR could be used as independent predictors of 90-day mortality in patients with HBV-ACLF. Compared with MELD score, RNTIC had a more powerful predictive value for prognosis of patients with HBV-ACLF.

Gastroenterology & Hepatology

Liver failure

Hepatitis B virus

Prediction model

inflammatory markers

Red Blood Cell Distribution Width

Neutrophil/Lymphocyte ratio

Acute-on-chronic liver failure (ACLF), a series of clinical syndrome resulted from culmination of chronic liver disease leading to single or multiple organ failures，has been shown to carry poor prognosis with a short-term mortality of > 50%[1]. At present, conservative medical treatment usually has been adopted, due to the artificial liver support system is poorly effective for end-stage liver failure, whereas stem cell therapy is still in development and faced with ethical issues[2]. Moreover, most patients with end-stage liver failure are suffering with multi-system organ failures resulting in many limitations in liver transplantation[3]. Therefore, reliable, user-friendly, inexpensive and reproducible predictors of survival are important to evaluate the risk of death early and choose treatment appropriately in those patients.

Currently, amounts of predictive scoring systems are available for assessing the prognosis in patients with ACLF, including chronic liver failure sequential organ failure assessment (CLIF-SOFA) score, Child-Turcotte Pugh (CTP) score, model for end-stage liver disease (MELD) score, MELD-sodium (MELD-Na) score[4]. The Model for End-Stage Liver Disease (MELD) score has the advantage of objective parameters which is often used for the prognosis of the patients with ACLF. In China, most cases of ACLF are caused by hepatitis B virus (HBV) infection, but those scores were established in European and American countries, where the alcohol is the most leading cause of the ACLF. Since these kinds of scoring systems might have certain limitations for HBV-related acute-on-chronic liver failure (HBV-ACLF), this study intended to establish a new model applicable to patients in China.

Nowadays, increasing evidences showed that systemic inflammatory response played a pivotal role in the development of liver failure and cirrhosis[5, 6]. A generalized activation of the inflammatory cytokines not only resulting to an accentuation of systemic circulatory dysfunction and organ hypo-perfusion, but also directly doing harm to organ function[7]. Inflammatory cytokines could affect the survival of erythrocytes, suppress maturation, lead larger and newer reticulocytes to enter circulation and increase the RDW[8]. The elevated granulocyte colony stimulating factor and granulocyte-macrophage colony stimulating factor, key regulatory cytokines that target committed progenitors promote differentiation and activation of monocytes and neutrophils[9]. Interleukin–6 (IL–6), an increased pro-inflammation cytokine in HBV-ACLF patients, also has ability to lead amounts of young platelets in the bone marrow to be released to the bloodstream [10] thus making the mean platelet volume (MPV) elevated[11]. The occurrence of ACLF generally represents a complicated state of host immune dysregulation. Excessive immune activation could lead to a decrease in lymphocyte numbers caused by activation induced cell death and impaired lymphopoiesis[12]. Based on the large amounts of investigations on systemic inflammation, routine hematology parameters, neutrophil/lymphocyte ratio (NLR), monocyte/lymphocyte ratio (MLR), platelet/lymphocyte ratio (PLR), red cell distribution width (RDW), RDW/platelet ratio (RPR), gamma-glutamyl transpeptidase/platelet ratio (GPR), mean platelet volume (MPV), RDW/lymphocyte ratio (RLR) and prognostic nutritional index (PNI) and MPV/platelet ratio (MPR), are being considered as the inflammatory markers which could predict outcomes of various diseases[13–15]. Thus, this study aimed to identify inflammatory markers and hematological indicators associated with a short-term negative prognosis and establish a new multi-factor combined prognostic model for patients with HBV-ACLF.

2.1 Patient selection

ACLF was defined as the acute deterioration of liver function manifested as jaundice [total bilirubin (TBIL) ≥5mg/dL or ≥85μmol/L and coagulopathy with international normalized ratio of prothrombin time (INR) ≥1.5 or prothrombin activity (PTA) 40%, complicated with ascites and/or hepatic encephalopathy noted within 4 weeks in a patient diagnosed with HBV related chronic liver disease/cirrhosis[1]. The cirrhosis was diagnosed histologically proven or clearly considered on the basis of biological, clinical, and radiological features. Patients with cardiac diseases, endocrinological disorders, hematological disease and other types of cancer were excluded. Co-infection with human immunodeficiency virus, hepatitis A, C, D, and E viruses or other hepatitis viruses, autoimmune diseases, alcoholic liver disease, drug-induced liver injury, coexistent hepatocellular carcinoma, and any other serious medical illness or patients who had received any immunotherapy, liver transplantation or artificial liver support were also excluded.

421 cases of adult HBV-infected patients diagnosed with HBV-ACLF admitted to the Affiliated Hospital of Southwest Medical University were consecutively recruited as a derivation cohort to establish the new prognostic model between January 1, 2014 to February 28, 2019. Next, 56 patients in the First People’s Hospital of Neijiang and 100 patients in the Second People’s Hospital of Yibin from January 1，2017 to February 28，2019 were enrolled as a validation cohort. Retrospectively collecting their clinical data and laboratory testing indicators.

All patients admitted were given a standard medical treatment including nutritional support, antiviral therapy, intravenous infusion albumin and plasma, treatment of complications.

2.2 Laboratory analysis

Demographic and clinical characteristics of the included patients were recorded. Blood samples were collected from an antecubital vein after overnight fasting on the first day of admission, and detected the complete blood counts and biochemical tests by Mind 6800 automated blood analyzer and Mindray BS200 biochemical analyzer, respectively. Coagulation indicators were assessed using a CS–5100 automated coagulation analyzer. The HBV-DNA levels in serum were quantified by ABI 7500FAST (fluorescence quantitative PCR). The MELD score was calculated using the Kamath formula: R = 9.6×ln（Cr mg/dl）+3.8×ln（TBIL mg/ dl）+11.2×ln（INR）+6.4[15]. PNI = albumin (g/L) + 5×lymphocyte count (10⁹/L)[15].

2.3 Statistical analysis

Normally distributed variables were expressed as means± standard deviation (SD), and non-normally distributed variables were expressed as a median and interquartile range (IQR). Count and percentages were used to describe categorical variables. Two independent groups were compared using the t test for continuous normally-distributed variables and the Mann–Whitney U test for non-normally distributed variables. For categorical variables, comparisons between groups used the Chi-squared test or the Fisher test as appropriate. The Kaplan–Meier method was used to calculate the 90-day survival probability curves. The BOX-Tidwell method was used to test the wireless relationship between the independent and dependent variable. The tolerance and variance expansion factor were used to test the multicollinearity between the independent variables. Cox regression models were used for univariate and multivariate analysis of outcome predictors. Cut-off values were determined via the receiver operating characteristic (ROC) analysis. All calculations were performed by MedCalc software (version15.10). P values<0.05 based on a two-tailed test were considered with statistically significance.

3.1 Basic characteristics of patients with HBV-ACLF

A total of 462 patients were diagnosed with ACLF in our electronic medical record system between January 1, 2014 to February 28, 2019, in which 19 patients were lost to follow up and 22 patients were excluded according to the inclusion and exclusion criteria. Finally, 421 patients with HBV-ACLF were included in the study eventually, and basic characteristics of patients were listed in Table 1.

3.2 Comparison of inflammatory markers and routine hematological parameters between survivors and non-survivors.

In order to identify indicators with statistical differences, inflammatory markers and routine hematological parameters in the survivors and non-survivors were analyzed. For the inflammatory markers, compared with the survivors, the level of NLR, MLR, PLR and RDW increased (P≤0.001), while RLR decreased significantly(P<0.001) (Table 1); For the routine hematological parameters, compared with the survivors, the serum Na, PTA and lymphocytes were lower, while WBC, neutrophils, monocytes, TBIL, Cr, cyst-c, PT, INR, and MELD scores were higher (P＜0.05). Moreover, the incidence of hepatic encephalopathy was elevated in the non-survivors (P<0.05) (Table 1).

3.2 Univariate and multivariate cox regression analysis of survival and death in HBV-ACLF patients

Univariate regression analysis was performed on statistic significant indicators in table 1, and multivariate cox regression analysis was performed on the indicators with significant difference in univariate analysis (P<0.05), including TBIL, Cr, Cyst-c, INR, PTA, WBC, neutrophils, RDW, NLR, RLR, PLR, MLR. The multivariate cox regression results indicated that RDW, NLR, TBIL, INR, Cr were risk factors for 90-day death in HBV-ACLF patients (P<0.05). In addition, RDW and NLR were significantly positively correlated with MELD scores (P<0.05), suggesting that high RDW, NLR might be closely associated with the prognosis of the patients with HBV-ACLF (Figure 1A and B).

3.4 Establishing a new prognostic model combining inflammatory markers with hematological parameters in patients with HBV-ACLF by Cox regression

The two inflammatory markers RDW, NLR and other three hematological parameters TBIL, INR, Cr had been found to be related to the prognosis of patients with HBV-ACLF in forward analysis. Based on the regression coefficient (Beta coefficient) as the weight of the risk factor (Table 2), the following model was established: COX_RNTIC = 0.053×RDW+0.027×NLR+0.003×TBIL+0.317×INR+0.003×Cr with a cut-off value of 3.082 (sensitivity: 77.89%, specificity: 86.04%). The model was able to predict 190 patients alive and 154 dead, accurately classifying 81.71% of the patients in this study (Table 3).

3.5 Comparison of predictive value of RDW, NLR, MELD score and the new model for prognosis of patients with HBV-ACLF

Receiver operating characteristic (ROC) curves for parameters including RDW, NLR, MELD score and RNTIC were shown in Figure 1C. RNTIC had a higher area under the ROC curve (AUC) for identifying poor prognosis than RDW, NLR, and MELD Scores (p<0.001, Table 3). We further identified the patients with HBV-ACLF based on the cut-off values of NLR, RDW, Meld scores and RNTIC and graphed the Kaplan-Meier survival curves. The results showed RNTIC was more efficient to predict the patients’ prognosis than other indicators (Figure 1D).

3.6 External validation of the new model

In order to test the model, 180 patients were enrolled from the other two hospitals. According to the inclusion and exclusion criteria, 156 patients were admitted to the validation cohort with a 90-day mortality rate at 35.89%. Comparisons of demographics and baseline clinical characteristics of the patients in the derivation and validation cohort were summarized in Table 4. The AUC of the RNTIC was higher than RDW, NLR and MELD score (P<0.05, Figure2), which proved this model also had an efficient ability on the prediction of the 90-day death in patients with HBV-ACLF in the validation cohort.

ACLF with a high mortality is a systemic inflammatory response driven by cytokines secretion, oxidative stress, immune dysfunction and increased risk of infection, which also compromises organ function integrity[16, 17]. In this study, a triple-center retrospective research was launched to create a new prognostic model taking inflammatory markers into consideration for patients with HBV-ACLF. Compared the routine hematological inflammatory parameters listed in Table 1, We found only NLR and RDW were independent prognostic factors associated with90-day mortality in patients with HBV-ACLF, and then combined RDW and NLR with other three statistically significant indicators (TBIL, INR, Cr) to establish a new prognostic model, which performed a better predictive value both in derivation and validation cohort.

It was reported that increased neutrophil counts reflected oxidative stress and that lower lymphocyte counts reflected a deterioration of nutritional status[18]. Thus, the Neutrophil and lymphocyte counts could reflect inflammation status and general nutrition status of patients. The NLR has been researched in many diseases including liver disease. Increased NLR is predictive of mortality in advanced illnesses apart from infections including malignancy, acute coronary syndrome, intracerebral hemorrhage, chronic kidney disease and rheumatic diseases[19, 20], and elevated NLR has a tight relationship with the prognosis of hepatitis, liver cirrhosis and liver cancer[14, 21, 22]. In our study, the NLR value significantly elevated in the HBV-ACLF death group, and was an independent risk factor for 90-day death in HBV-ACLF patients, which was consistent with the study by Cai J, et al[15], but the specific mechanism of HBV-ACLF patients' poor prognosis and NLR elevation is unclear. It was reported that in patients with end-stage liver disease, the body’s immune system and inflammatory response were over-activated with a large number of inflammatory factors being released into the bloodstream (e.g., IL–6, IL–8, TNF-α, etc.)[23], which caused damage to hepatocytes. Moreover, the robust inflammatory reaction could cause amounts of lymphocyte apoptosis, and make neutrophils originally presented in the hepatic sinusoids released into the blood, thereby increasing the level of NLR[24].Thus, the hypothesis that elevated NLR reflects the severity of the potentially acute systemic inflammation following primary injury is widely accepted.

In addition, another inflammatory marker RDW also was proved to be an independent risk factor for 90-day death in HBV-ACLF patients. However, the reason why RDW elevation is closely associated with the outcome of the patients with HBV-ACLF is still unclear. It may be due to the following five reasons: (Ⅰ) Significant changes in RDW are associated with some abnormalities, such as inflammation, oxidative stress, red blood cell fragmentation, poor nutritional status, and erythropoietin dysfunction[25]. Pro-inflammatory factors could damage the maturation of red blood cells and cause immature red blood cells to enter the bloodstream simultaneously, leading to an increase in RDW[26]. (Ⅱ) Inflammatory cytokines such as tumor necrosis factor TNF-α, IL–1β and IL–6 may inhibit iron metabolism and erythropoietin production, leading to synthetic disorders or abnormal erythropoietin activity[27]. (Ⅲ) Excessive hepatocyte necrosis resulting in decreased liver reservation of vitamin B12, folic acid and iron[28], elevated the RDW. (Ⅳ) Pathological immune response to HBV can release inflammatory mediators and endotoxin etc., which affect the growth and development of red blood cells, making RDW rise[8]. (Ⅴ) Low serum antioxidant concentrations characterized by a compromise between oxidant and antioxidant defenses are associated with increased levels of RDW, which is common in liver disease [29].

Apart from the inflammatory markers NLR and RDW, we also found that Cr, TBIL, and INR, reflecting the function of liver, kidney, and coagulation in routine hematological tests, were independent risk factors for prognosis of HBV-ACLF in this study. Therefore, the new prediction Cox regression model was constructed based on the above five indicators, which showed a great predictive performance both in the derivation and validation cohort with high sensitivity and specificity.

Some limitations of our study must be considered. First, this was a retrospective study, so we did not observe the changes of RDW and NLR values dynamically. In the future, more prospective studies are needed to reveal the association between RDW, NLR longitudinal changes and outcomes in HBV-ACLF patients. Second, we did not test other pro-inflammatory cytokines, such as TNF-α, IL–1β, IL–6, and IL–8, which may contribute to revealing the mechanisms.

In summary, compared with the MELD score, our newly established model has a better predictive ability to assess the 90-day mortality in HBV-ACLF patients in the early stage.

Acknowledgements

Not applicable.

Authors contribution

LQ: collected and analyzed data, designed and wrote the first draft, and revised each version of the manuscript. JQ: collected and analyzed data. CFS, YJS and WC: revised the manuscript. GW: Conception and design, construction of the framework, revision of the manuscript. BDQ and XC: checking, and made the tables and figures, revised the manuscript. YFC and FL: Data collection.

Funding

This work was supported by the Science and Technology Project of the Health department of Sichuan (120326), Collaborative Fund of Luzhou Government and Southwest Medical University (2018LZXNYD-ZK29), Collaborative Fund of Luzhou Government and Sichuan Medical University (2015SX-W35), the Youth Fund of Southwest Medical University (2017-ZRQN–103), the Science and Technology Project of the Health Planning Committee of Sichuan (18PJ340), the Ph.D. Research Fund of the Affiliated Hospital of Southwest Medical University (16237). The recipients of the fund, Changfeng Sun revised the manuscript and Gang WU designed the study, constructed the framework and revised the manuscript.

Availability of data and materials

The datasets analyzed during the current study are not publicly available because they contain sensitive patient information, but may available from the corresponding author on reasonable request.

Ethics approval and consent to participate

The study was approved by the Institutional Ethics Committee of Affiliated Hospital of Southwest Medical University and performed in adherence with the principles of the Declaration of Helsinki. Written informed consent was obtained from the patients and at the case of the patient was unable to write or the patient was unconscious, it was obtained from their family members.

Consent for publication

Not applicable.

Competing interests

The authors declared no conflicts of interest.

NLR: neutrophil/lymphocyte ratio

RDW: red blood cell distribution width

HBV-ACLF: hepatitis B virus-related acute-on-chronic liver failure

AUC: the area under the receiver operating characteristics curve

CLIF-SOFA: chronic liver failure sequential organ failure assessment

CTP: child-Turcotte Pugh score

MELD: model for end-stage liver disease score

MELD-Na: MELD-sodium score

HBV: hepatitis B virus

MPV: mean platelet volume

MLR: monocyte/lymphocyte ratio

PLR: platelet/lymphocyte ratio

RPR: RDW/platelet ratio

GPR: gamma-glutamyl transpeptidase/platelet ratio

RLR: RDW/lymphocyte ratio

PNI: prognostic nutritional index

MPR: MPV/platelet ratio

IL–6: Interleukin–6

TBIL: total bilirubin

INR: international normalized ratio of prothrombin time

PTA: prothrombin activity

ROC: receiver operating characteristic analysis

ALT: alanine aminotransferase

AST: aspartate aminotransferase

ALB: albumin

γ-GGT: gamma-glutamyl transpeptidase

Cr: creatinine

Cyst-c: Cystatin c

Serum k+: serum potassium

Serum Na+: serum sodium

PCT: procalcitonin

HE: Hepatic encephalopathy

NPV: negative predictive value

PPV: positive predict value

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Table1: Comparisons of characteristics between survivors and non-survivors in patients with HBV-ACLF.

Variables	Survivors (n=222)	Non-survivors (n=199)	P value
Age (years)	48.10±11.20	47.75±11.64	0.735
Gender (M/F)	195/27	170/29	0.468
Cirrhosis (%)	159(71.62)	140(70.35)	0.744
HBV-DNA (10⁷IU/mL)	1.89±5.16	1.17±4.53	0.139
HBeAg(+) n (%)	67(30.18)	49(24.62)	0.203
ALT(U/L)	258.75(10.50-3840.50)	198.20(10.50-3271.50)	0.532
AST(U/L)	254.10(28.00-6000.00)	206.10(11.40-3524.20)	0.477
TBIL(μmol/L)	236.35±118.55	420.49±156.77	<0.001
ALB(g/L)	29.33±5.81	28.82±5.30	0.348
γ-GGT(U/L)	79.35(12.10-711.40)	86.30(12.00-723.00)	0.719
Cr (μmol/L)	66.10(40.3-175.4)	73.65(27.9-343.6)	0.002
Cyst-c(mg/L)	1.11(0.01-4.10)	1.38(0.31-8.89)	0.001
Serum k⁺(mmol/L)	3.99±0.60	3.95±0.70	0.510
Serum Na⁺(mmol/L)	135.79±5.70	134.86±5.66	0.042
PT(s)	23.70(16.2-58.4)	26.40(17.8-120.00)	0.271
INR	2.11(1.49-6.06)	2.40(1.51-10.48)	<0.001
PTA (%)	35.79±9.49	32.23±11.45	0.001
WBC (10⁹/L)	5.825(1.77-24.79)	7.59(2.11-43.09)	<0.001
Neutrophils (10⁹/L)	3.92(0.96-21.61)	5.98(1.55-39.98)	<0.001
Lymphocytes(10⁹/L)	1.03(0.28-3.04)	0.89(0.25-3.72)	0.030
Monocytes(10⁹/L)	0.54(0.09-11.64)	0.70(0.09-16.30)	<0.001
RDW (%)	15.00(11.20-27.70)	16.82(12.40-32.79)	<0.001
PLT (10⁹/L)	82.00(6.30-256.00)	89.00(16.00-427.00)	0.155
MPV(FL)	11.82±1.48	11.67±1.58	0.312
NLR	4.00(0.78-25.73)	6.31(0.56-68.19)	<0.001
GPR	0.98(0.21-58.47)	0.95(0.97-14.24)	0.253
MLR	0.53(0.15-9.54)	0.77(0.13-27.63)	<0.001
RPR	0.18(0.05-1.95)	0.19(0.03-1.74)	0.490
MPR	0.15(0.38-2.16)	0.13(0.22-0.55)	0.103
PNI	34.98(18.25-55.00)	33.65(20.55-54.25)	0.060
PLR	85.20(6.56-329.33)	102.15(17.59-627.27)	0.001
RLR	20.58(3.71-85.81)	15.68(5.59-73.6)	<0.001
PCT (μg/L)	0.79(0.06-24.14)	0.99(0.15-35.80)	0.064
MELD SCORE	22.47±4.67	27.94±7.52	<0.001
Ascites, n (%)			0.788
None	117(52.70）	98（49.2）
Grade 1	80(36.0）	74（37.2）
Grade 2	11(5.0）	14（7.0）
Grade 3	14(6.3）	13（6.5）
HE, n (%)			0.005
Stage 0	184(82.9)	148(74.4)
Stage 1	9(4.1)	5(2.5)
Stage 2	12(5.4)	11(5.5)
Stage 3	13(5.9)	15(7.5)
Stage 4	4(1.8)	20(10.1)

ALT=alanine aminotransferase, AST=aspartate aminotransferase, TBIL=total bilirubin, ALB=albumin, γ-GGT=gamma-glutamyl transpeptidase, Cr=creatinine, Cyst-c=Cystatin c, Serum k⁺= serum potassium, Serum Na⁺=serum sodium, PT= prothrombin time, INR=international normalized ratio, PTA=prothrombin activity, WBC=white blood cell count, RBC=red blood cells, HGB=hemoglobin, PLT=platelet, RDW=red blood cell distribution width, NLR=neutrophil/lymphocyte ratio, MLR=monocyte/lymphocyte ratio, PLR=platelet/lymphocyte ratio, RPR=RDW/platelet ratio, GPR=gamma-glutamyl transpeptidase/platelet ratio, MPV=mean platelet volume, RLR=RDW/lymphocyte ratio, PNI=prognostic nutritional index, MPR=MPV/platelet ratio, PCT=procalcitonin, MELD SCORE=model for end-stage liver disease score, HE= Hepatic encephalopathy.

Table2: Cox regression analysis for variables associated with 90-day mortality in patients with HBV-ACLF.

Variables	Univariate analysis		Multivariate analysis
Variables	HR 95%(CI)	P value	Beta coefficient	HR 95%(CI）	P value
TBIL (μmol/L)	1.005 (1.004-1.005)	<0.001	0.003	1.002 (1.001-1.003)	<0.001
Cr (μmol/L)	1.008 (1.005-1.010)	<0.001	0.003	1.003 (1.001-1.005)	0.001
Cyst-c (mg/L)	1.055 (1.014-1.097)	0.008
Serum Na⁺(mmol/L)	0.981 (0.959-1.004)	0.112
INR	1.488 (1.342-1.651)	<0.001	0.317	1.318 (1.163-1.494)	<0.001
PTA (%)	0.973 (0.959-0.988)	<0.001
WBC (10⁹/L)	1.068 (1.043-1.094)	<0.001
Neutrophils (10⁹/L)	1.082 (1.055-1.110)	<0.001
Monocytes(10⁹/L)	1.080 (0.997-1.170)	0.060
Lymphocytes(10⁹/L)	0.856 (0.640-1.144)	0.293
RDW (%)	1.112 (1.075-1.15)	<0.001	0.053	1.047 (1.009-1.086)	0.015
NLR	1.053 (1.038-1.068)	<0.001	0.027	1.027 (1.009-1.046)	0.003
MLR	1.057 (1.007-1.109)	0.025
PLR	1.003 (1.001-1.005)	0.001
RLR	1.016 (1.007-1.026)	0.001
HE	1.221
	(1.101-1.353)	<0.001

TBIL=total bilirubin, ALB=albumin, γ-GGT=gamma-glutamyl transpeptidase, Cr=creatinine, Cyst-c=Cystatin c, Serum Na+=serum sodium, PT= prothrombin time, INR=international normalized ratio, PTA=prothrombin activity, WBC=white blood cell count, RDW=red blood cell distribution width, NLR=neutrophil/lymphocyte ratio, MLR=monocyte/lymphocyte ratio, PLR=platelet/lymphocyte ratio, RLR=RDW/lymphocyte ratio, HE=Hepatic encephalopathy. HBV-ACLF=hepatitis B virus related acute-on-chronic liver failure.

Table3: Comparison of predictive value of RDW, NLR, MELD score and the RNTIC for prognosis of patients with HBV-ACLF

Variables	AUC (95%)	Cut-off Value	Sensitivity (%)	Specificity (%)	PPV (%)	NPV (%)	Overall Accuracy (%)	Youden Index
RNTIC	0.873 (0.837-0.903)	3.08	77.89	86.04	77.39	85.58	81.71	0.64
MELD	0.732 (0.687-0.774)	24.14	70.35	70.72	70.35	69.37	69.83	0.41
NLR	0.693 (0.646-0.737)	4.09	77.89	51.08	77.88	51.35	63.90	0.29
RDW	0.675 (0.628-0.719)	16.1	61.81	65.77	61.81	65.77	63.90	0.28

RNTIC=0.053×RDW+0.027×NLR+0.003×TBIL+0.317×INR+0.003×Cr, MELD=model for end-stage liver disease, NLR=neutrophil/lymphocyte ratio, RDW= red blood cell distribution width, TBIL=total bilirubin, Cr=creatinine, INR=international normalized ratio, HBV-ACLF=hepatitis B virus related acute-on-chronic liver failure, NPV=negative predictive value, PPV=positive predict value.

Table4: Comparisons of demographics and baseline clinical characteristics of the patients in the derivation and validation cohort.

Variables	Validation cohort(n=156)	derivation cohort (n=421)	P value
Age (years)	48.92±11.94	47.93±11.40	0.372
Gender (M: F)	129/27	365/56	--
Cirrhosis (%)	156(73.1%)	421(71.02%)	0.627
TBIL (μmol/L)	239.43±136.17	323.39±165.69	<0.001
INR	1.92(1.30-5.89)	2.25(1.48-10.48)	<0.001
Cr (μmol/L)	62.40(10.99-602.40)	69.30(23.3-729.0)	0.004
RDW (%)	16.10(12.0-45.5)	15.90(11.20-32.79)	0.842
MPV (FL)	11.29±1.67	11.74±1.53	0.003
NLR	1.92(0.38-65.64)	4.84(0.55-68.18)	<0.001
MLR	0.61(0.13-3.59)	0.64(0.13-27.63)	0.473
GPR	0.91(0.11-7.48)	0.97(0.097-58.48)	0.162
RPR	0.17(0.04-0.77)	0.19(0.33-1.95)	0.093
MPR	0.70(0.26-1.09)	0.14(0.02-2.16)	<0.001
PNI	35.03(18.50-54.45)	34.10(18.25-55.00)	0.033
PLR	95.92(18.09-581.48)	91.03(6.56-627.27)	0.206
RLR	17.77(3.45-115.00)	16.70(3.71-85.81)	0.939
MELD SCORE	21.41±7.84	25.05±6.75	<0.001

TBIL=total bilirubin, ALB=albumin, γ-GGT=gamma-glutamyl transpeptidase, Cr=creatinine, Cyst-c=Cystatin c, Serum Na+=serum sodium, PT= prothrombin time, INR=international normalized ratio, PTA=prothrombin activity, WBC=white blood cell count, RDW= red blood cell distribution width, NLR=neutrophil/lymphocyte ratio, MLR=monocyte/lymphocyte ratio, PLR=platelet/lymphocyte ratio, RPR=RDW/platelet ratio, GPR=gamma-glutamyl transpeptidase/platelet ratio, MPV=mean platelet volume, RLR=RDW/lymphocyte ratio, PNI=prognostic nutritional index, MPR=MPV/platelet ratio, MELD=model for end-stage liver disease.

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A novel predictive model based on inflammatory markers to assess the prognosis of patients with HBV-related acute-on-chronic liver failure: a retrospective cohort study

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