A novel prognostic model based on Prealbumin for predicting short-term prognosis of patients with HBV-related acute-on-chronic liver failure

doi:10.21203/rs.3.rs-1963380/v1

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A novel prognostic model based on Prealbumin for predicting short-term prognosis of patients with HBV-related acute-on-chronic liver failure

https://doi.org/10.21203/rs.3.rs-1963380/v1

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Background

Hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF) is a clinical syndrome associated with a high short-term mortality rate, and accurate clinical decision-making requires an accurate assessment of prognosis. The purpose of this study was to develop a simple and effective prognostic model for predicting short -term mortality in HBV-ACLF patients.

Methods

We retrospectively enrolled 170 patients to develop and validate a novel prognostic model for predicting 30-day mortality in HBV-ACLF patients. Using univariate and multivariate logistic regression analysis, laboratory and clinical data were obtained to identify independent predictors of short-term mortality.

Results

We found the Prealbumin (PAB) level at admission was a strong independent predictor for 30-day mortality, with an AUROC of 0.760. The HIAPP score, a prognostic-score model based on five independent prognostic variables, including PAB, Platelet (PLT), international normalized ratio (INR), the occurrence of hepatic encephalopathy (HE), and age, was markedly lower in survivors than in non-survivors (-2.80 ± 0.21 vs 0.97 ± 0.41, P < 0.001). Additionally, the HIAPP score was positively and strongly correlated with the CLIF‐SOFA, MELD, and CLIF‐C ACLF scores. The AUROC value for the HIAPP score was 0.899, which was found to be superior to the MELD (AUROC = 0.795), CLIF‐SOFA (AUROC = 0.731), and CLIF‐C ACLF (AUROC = 0.700) scores for 30‐day mortality. These findings were validated using a validation cohort.

Conclusion

PAB is a simple and useful predictive index for 30‐day mortality in HBV-ACLF. The HIAPP score was found to be an easy-to-use pragmatic prognostic score and superior to the CLIF‐SOFA, MELD, and CLIF‐C ACLF scores.

retrospective study

Hepatitis B virus-related acute-on-chronic liver failure

short-term prognosis

Prealbumin

independent predictors

prognostic model

Acute-on-chronic liver failure (ACLF) is a severe clinical syndrome characterized by acute liver decompensation, organ failures, and a high rate of short-term mortality that is triggered by an acute insult [1–3]. HBV-ACLF induced by chronic HBV infection is the main form in China due to the high prevalence of HBV infection [4–5]. According to statistics, there are over 70 million people with chronic hepatitis B infection in China, of which about 20 million to 30 million patients with chronic hepatitis B [6]. HBV-ACLF patients have an extremely poor prognosis, with a high short-term mortality ranging from 29.7–40% [7–8], imposing a significant burden on patients and society. Accurate assessment of the clinical condition and prognosis is critical for HBV-ACLF management.

PAB, also known as transthyretin, is involved in thyroid hormones transport and is mostly produced by the liver [9]. Apart from being a sensitive nutritional protein indicator, PAB is also closely associated with inflammation and can be consumed during an inflammatory reaction [10]. However, the potential benefit of PAB for HBV-ACLF prognosis has not been fully explored.

In this study, we initially established the prognostic power of PAB. Following that, we used logistic regression to identify independent and most powerful prognostic biomarkers for short-term mortality in HBV-ACLF. We developed and validated a prognostic score based on PAB, PLT, INR, HE, and age for the prognosis of short-term mortality in HBV-ACLF patients, then compared its predictive value with that of CLIF Consortium Acute on Chronic Liver Failure (CLIF‐C ACLF), chronic liver failure‐ sequential organ failure assessment (CLIF‐SOFA) and the Model for End‐stage Liver Disease (MELD) scores.

Study Design and Patients

Two cohorts were enrolled in this study, as shown in Fig. 1. A cross-sectional study was conducted on a cohort (cohort 1, n = 102), of 27 ACLF, 22 DCC (decompensated cirrhosis, DCC), 26 LC (liver cirrhosis, LC), and 27 CHB (chronic hepatitis B, CHB) inpatients and outpatients. A retrospective cohort 2, consisting of 170 HBV-ACLF inpatients, was used to develop and validate a prognostic score for HBV-ACLF. HBV-ACLF patients were recruited using the 2014 APASL criteria based on HBsAg, serum total bilirubin (TBil), INR [11]. All subjects were admitted to the Second Affiliated Hospital of Anhui Medical University for a period of 30 days or until death, whichever occurred first. This study was approved by the ethical committee of the Second Affiliated Hospital of Anhui Medical University and was conducted in accordance with the 1975 declaration of Helsinki. All participants provided written informed consent so that their clinical data could be used in this study.

Clinical Data And Calculation Of Scores

Clinical data and other parameters included in this study were obtained from the hospital information system. The CLIF-C ACLF, CLIF-SOFA, and MELD scores were calculated as previously reported [12].

Statistical analysis

Statistical analyses were performed using SPSS 20.0 and MEDCALC statistical software. Quantitative variables were presented as means ± SD or medians (P25-P75). Categorical variables were presented as numbers and percentages. The student's t-test or chi-square test was used to compare two groups. Multivariate logistic regression was used to identify independent prognostic factors for the 30-day mortality of HBV‐ACLF. A P ≤ 0.05 (two-tailed) was considered statistically significant. We used receiver operating characteristic (ROC) curves to determine the predictive value of several prognostic indices in HBV-ACLF patients.

PAB levels in the cross-sectional study cohort

Logistic regression revealed that PAB was a significant and independent predictor of short-term mortality for HBV-ACLF (Table 1). We conducted a cross-sectional study using cohort 1 (n = 102), including 27 CHB, 26 LC, 22 DCC, and 27 ACLF subjects. On admission, ACLF patients had significantly lower PAB levels (54.04 ± 33.65mg/L) than CHB (220.96 ± 63.09 mg/L), LC(152.08 ± 64.52 mg/L), and DCC (108.55 ± 60.30 mg/L) patients (all P < 0.05; Fig. 2).

Table 1

Baseline characteristics of the deriving cohort enrolled in this study on admission
Variables	Total population (n = 120)	Nonsurvivor (n = 38)	Survivor (n = 82)	Univariate logistic regression	P	Multivariate logistic regression
Variables	Total population (n = 120)	Nonsurvivor (n = 38)	Survivor (n = 82)	OR (95% CI)	P	0R (95% CI)	P
Clinical characteristics
Age (y)	48.68 ± 13.61	54.34 ± 12.78	46.05 ± 13.25	1.049(1.017–1.082)	0.003	1.068(1.021–1.118)	0.005
Gender (female/male)	16/104	5/33	11/71	0.978(0.314–3.042)	0.969
SBP	38(31.67%)	14 (36.8%)	24 (29.3%)	1.410(0.625–3.178)	0.408
HE (mild/serve)	5/12	4/10	1/2	15.361(4.071–57.965)	< 0.001	0.051(0.005–0.469)	0.009
Laboratory parameters
WBC (×10⁹/L)	7.11 ± 4.08	7.86 ± 4.03	6.77 ± 4.08	1.065(0.970–1.169)	0.186	–	–
Neutrophil (×10⁹/L)	5.11 ± 3.66	6.05 ± 3.59	4.69 ± 3.63	1.102(0.992–1.226)	0.071
Platelet (×10⁹/L)	86.50(63.25–125.00)	79.00(50.75–103.00)	95.00(67.25–139.50)	0.991(0.983–0.999)	0.033	0.985(0.971–0.999)	0.039
Total protein (g/L)	57.82 ± 8.07	55.91 ± 7.88	58.71 ± 8.04	0.957(0.910–1.005)	0.081	–	–
Albumin (g/L)	29.62 ± 4.77	29.40 ± 3.99	29.72 ± 5.11	0.986(0.909–1.069)	0.729
Globulin (g/L)	28.20 ± 7.81	26.51 ± 7.01	28.99 ± 8.07	0.958(0.909–1.009)	0.108
A/G	1.04(0.84–1.40)	1.05(0.92–1.43)	1.01(0.81–1.40)	1.330(0.619–2.854)	0.465	–	–
ALT (U/L)	403.50(130.25-768.25)	574.00(281.50-940.25)	349.50(99.50-654.50)	1.001(1.000-1.002)	0.027	–	–
AST (U/L)	263.50(157.00-611.50)	491.50(239.00-859.50)	214.00(121.75-424.75)	1.001(1.000-1.002)	0.046	–	–
Serum bilirubin (mg/dL)	19.52 ± 8.23	20.47 ± 6.81	19.08 ± 8.82	1.021(0.974–1.069)	0.389
BUN (mmol/L)	4.26(3.05–5.65)	4.35(3.08–6.94)	4.19(3.02–5.33)	1.048(0.957–1.147)	0.311
Creatinine (mg/dL)	0.84(0.70–1.02)	0.86(0.71–1.15)	0.81(0.70-1.00)	2.314(0.888–6.030)	0.086	–
INR	2.17(1.78–2.64)	2.59(2.06–4.13)	2.04(1.66–2.39)	3.117(1.772–5.483)	< 0.001	2.197(1.005–4.805)	0.049
Lg(DNA)	4.93 ± 1.75	5.12 ± 1.54	4.83 ± 1.84	1.120(0.897–1.397)	0.317	–
Prealbumin	34.00(20.00–61.00)	20.00(12.00-34.50)	36.00(26.00-67.25)	0.959(0.937–0.982)	0.001	0.963(0.934–0.994)	0.018
HIAPP score	-1.63 ± 2.74	0.97 ± 0.41	-2.80 ± 0.21	< 0.001
MELD	25.49 ± 6.78	29.78 ± 7.73	23.50 ± 5.25	< 0.001
CLIF-SOFA	8 (7-9)	8 (7-11)	7 (7-8)	< 0.001
CLIF-C ACLF	37.55 ± 10.01	44.78 ± 11.18	34.28 ± 7.45	< 0.001
Data presented as means ± SD or medians (P25-P75). SBP: spontaneous bacterial peritonitis; HE: Hepatic encephalopathy; WBC: White blood cell count; A/G: albumin/globulin; BUN:Blood Urea Nitrogen; INR: international normalised ratio; MELD: Model for End‐stage Liver Disease; CLIF‐C ACLF: European Association for the Study of Chronic Liver Failure; CLIF‐SOFA: chronic liver failure‐sequential organ failure assessment;

Pab For Prognostic Prediction In Hbv Aclf

A deriving cohort (n = 120) was enrolled in this study. Table 1 summarizes the cohort's characteristics. On admission, non-survivors had a significantly lower PAB level than survivors (P = 0.001) (Fig. 3A). The PAB achieved an AUC of 0.760 (sensitivity = 72.97%, specificity = 74.39%, cut-off = 26) for 30‐day mortality of HBV‐ACLF. According to the median PAB level in ACLF patients, we classified them as high-PAB (> 34mg/L) or low-PAB (≤ 34mg/L) groups. The low - PAB group had a significantly higher mortality rate than the high - PAB group (P < 0.001; Table 2).

Table 2

Mortality according to PAB levels
	Mortality	P value	Odds Ratio	95% CI
	Mortality	P value	Odds Ratio	Lower	Upper
With low levels of PAB (n = 59)	28 (47.5%)
With high levels of PAB (n = 61)	10(16.4%)	< 0.001	0.217	0.093	0.507
P values indicate statistical significance. PAB: Prealbumin;

Development And Evaluation Of A Pab -based Prognostic Model

The performance of PAB in combination with other indicators at admission to predict HBV-ACLF outcomes was examined using univariate and multivariate logistic regression analysis on the derived cohort (cohort 2). The results indicated that PAB, PLT, INR, HE, and age were all independent prognostic predictors of short-term mortality (all P < 0.05, Table 1). On this basis, a prognostic model (HIAPP) was developed as follows: HIAPP = 2.981* HE + 0.787*INR + 0.066* ages-0.038* PA-0.015* PLT − 4.119, where HE = 0 for patients without HE, and HE = 1 for patients with HE.

The HIAPP score in the non-survivor group (0.97 ± 0.41) was significantly higher than the survivor group (-2.80 ± 0.21) (P < 0.001; Fig. 4A). Meanwhile, there was a positive correlation between the HIAPP score and the MELD (r = 0.411, P < 0.001) (Fig. 4B), CLIF-SOFA (r = 0.524, P < 0.001) (Fig. 4C), and CLIF-C ACLF (r = 0.603, P < 0.001) (Fig. 4D). The HIAPP score achieved an AUC of 0.899 (sensitivity = 83.8%, specificity = 85.7%, cut‐off = -1.09) for 30-day mortality, which showed a significantly greater predictive ability than the MELD, CLIF‐SOFA, and CLIF-C ACLF scores (all P < 0.05; Fig. 5 and Table 3). We classified HBV‐ACLF patients into high- and low-risk groups based on the optimal HIAPP cut-off value. The high-risk (HIAPP >-1.09) group had a significantly higher mortality rate than the low-risk (HIAPP≤-1.09) group (70.5%vs8.0%, P < 0.001).

Table 3

HIAPP and the most updated prognostic scores for predicting the 30-day mortality of HBV‐ACLF
	AUC	SE	95% CI	P value
MELD	0.795^*	0.0434	0.712–0.863	0.029
CLIF_C_ACLF	0.700^**	0.0507	0.610–0.780	0.003
CLIF-SOFA	0.731^*	0.0515	0.643–0.808	0.025
HIAPP	0.899	0.0320	0.831–0.946	-
P < 0.05, vs. HIAPP.* P < 0.01, vs. HIAPP.**

Hiapp Performance Validation

We recruited a prospective HBV-ACLF cohort (n = 50) to validate the performance of PAB and HIAPP. In the validation cohort, non-survivors showed significantly lower PAB levels than survivors (27.46 ± 14.47 vs. 48.73 ± 30.56 mg/L, P = 0.020; Fig. 6A). The PAB levels achieved an AUC of 0.722 for 30-day mortality (sensitivity = 61.5%, specificity = 75.7%). The HIAPP score of non-survivors (-0.49 ± 0.42) was significantly higher than that of survivors (-2.42 ± 0.30) (P = 0.001; Fig. 6B). The HIAPP score model had an AUROC of 0.936, which was higher than the MELD, CLIF-SOFA, and CLIF-C ACLF scores for 30-day mortality (all P < 0.05, Table 4).

Table 4

HIAPP and the most updated prognostic scores for predicting the 30-day mortality for HBV‐ACLF patients in the validation cohort
	AUC	SE	95% CI	P value
PAB	0.722**	0.0766	0.578–0.840	0.0025
MELD	0.761*	0.0908	0.619–0.870	0.0408
CLIF_C_ACLF	0.765*	0.0901	0.624–0.873	0.0475
CLIF-SOFA	0.748*	0.0857	0.606–0.860	0.0365
HIAPP	0.936	0.0336	0.828–0.985	-
P < 0.05, vs. HIAPP.* P < 0.01, vs. HIAPP.**

HBV-ACLF is the leading cause of ACLF in China, owing to the high prevalence of chronic HBV infection, which is associated with a high rate of short-term mortality and places a heavy burden on patients’ families and society [13–14]. A simple and useful prognostic score is required for predicting short-term mortality and early patient classification to determine the appropriate management strategy. Based on the pathological course of ACLF, several clinical indicators reflecting liver function, organ failure, infection, and inflammation have been used in previous studies to develop prognostic models [15–17].

As previously noted, we found that INR, HE, and age were all-powerful and independent predictors of 30-day mortality [18]. Additionally, platelet count was found to be an independent predictor. The specific mechanism may be decreased thrombopoietin secretion in the presence of liver failure [19]. Therefore, a low platelet count correlates with impaired liver function and a poor prognosis, which is consistent with the findings of a retrospective observational cohort study [20].

Notably, PAB demonstrated potential as a predictor of HBV-ACLF prognosis, but it had not yet been incorporated into any prognostic scoring system. Because serum PAB is synthesized by the liver and has a relatively short half-life of only 1.9 days, it can reflect recent liver function. A lower serum PAB suggests decreased liver synthesis capacity and poor prognosis. Additionally, PAB may reflect the pathological process of ACLF to some extent. Previous studies have shown that uncontrolled inflammatory responses and immunological dysfunction play important roles in pathogenesis, which also affects the prognosis of ACLF [21]. Numerous cytokines, such as IL-6, were synthesized in response to inflammation, resulting in decreased synthesis and low serum concentrations of these proteins, including PAB[22]. As expected, ACLF patients had significantly lower PAB levels and the ROC curves analysis demonstrated that the serum PAB level at admission could accurately predict 30-day mortality of HBV-ACLF.

Several prognostic scoring systems have been developed to aid in the early detection and optimization of ACLF management. However, the commonly used prognostic scores for ACLF, such as MELD, CLIF-SOFA, and CLIF-C ACLF, were developed and validated using data from other pathological types of ACLF in the European population, and thus their ability to accurately predict short-term outcomes of patients with HBV‐ACLF in China requires additional research. Additionally, these scores are difficult to apply since they require many parameters and a complex evaluation of organ failure. Recently, several prognostic scoring systems were developed and validated in HBV-ACLF patients using a Chinese population cohort. While these systems showed higher predictive power [12, 18], they also evaluated the detection capabilities of primary hospitals, limiting their clinical application. Therefore, a simple and practical prognostic score for HBV-ACLF patients is required.

Based on existing scores and this study's findings, we proposed a prognostic model (HIAPP) for HBV-ACLF short-term mortality based on PAB, PLT, INR, HE, and age. Here, we demonstrated that the HIAPP model had an excellent linear relationship with the commonly used model, indicating that it may be used as a potential prognostic model for the short-term prognosis of HBV‐ACLF. Furthermore, in predicting the short-term outcome of HBV‐ACLF patients, we demonstrated HIAPP was superior to the MELD, CLIF‐SOFA, and CLIF-C ACLF score on admission. Additionally, we revealed that HIAPP requires only routine biochemical and hematological indicators, which improves the feasibility of the clinical application, particularly in primary hospitals, and reduces the patient's medical burden.

Our study had some limitations. First, the small sample size should be considered. We developed and validated the performance of HIAPP in a retrospective cohort study using a small sample size. Second, a prospective longitudinal cohort was not established to assess PAB and HIAPP performance due to incomplete follow-up data.

In this study, we demonstrated for the first time the significance of the PAB in predicting the short-term outcome of HBV-ACLF patients using logistic regression analysis. We developed a simple prognostic score (HIAPP) based on this data that was superior to commonly used prognostic models.

In conclusion, we established that PAB was a potential predictive marker and that the HIAPP score was an easy-to-use pragmatic score of short-term prognosis in HBV-ACLF patients. These results suggest that the HIAPP score can help expand the clinical applicability of early patient triage to determine appropriate management strategies, especially in primary hospitals.

HBV-ACLF	Hepatitis B virus-related acute-on-chronic liver failure
PAB	Prealbumin
PLT	Platelet
INR	International normalized ratio
HE	Hepatic encephalopathy
ACLF	Acute-on-chronic liver failure
CLIF‐C ACLF	CLIF Consortium Acute on Chronic Liver Failure
CLIF‐SOFA	Chronic liver failure‐sequential organ failure assessment
MELD	Model for End‐stage Liver Disease
DCC	Decompensated cirrhosis
LC	Liver cirrhosis
CHB	Chronic hepatitis B
TBil	Total bilirubin
ROC	Receiver operating characteristic

Acknowledgments

We would like to thank the Department of Infectious Diseases, Second Affiliated Hospital of Anhui Medical University for their strong support.

Authors’ contributions

Yuan-Hong Xu and Qiang Zhou designed the study; Hao Zhang, Ling-Mei Wang ,Xing-Yu Fan and Xiu Wang collected the data; Hao Zhang, Kai Yang and Qin Wang analyzed the data; Hao Zhang and Kai Yang drafted the manuscript; Fa-Ming Pan conducted statistical data analysis. All authors read and approved the final manuscript.

Funding

This work was supported by grants from the National Natural Science Foundation of China (81902056), Provincial Natural Science Research Project of Anhui Colleges (KJ2019A0276) and the Research Fund Project of Anhui Medical University (2021xkj050).

Availability of data and materials

The datasets generated and analyzed during the current study are available upon reasonable request to the corresponding author ([email protected]).

Ethics approval and consent to participate

This study was approved by the ethical committee of the Second Affiliated Hospital of Anhui Medical University and was conducted in accordance with the 1975 declaration of Helsinki. All participants provided written informed consent so that their clinical data could be used in this study.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Author details

1Department of Clinical Laboratory, Second Affiliated Hospital of Anhui Medical University, No. 678, Furong Road, Hefei 230601, Anhui, China

2Department of infectious diseases, Second Affiliated Hospital of Anhui Medical University, No. 678, Furong Road, Hefei 230601, Anhui, China

3Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, No. 218, Jixi Road, Hefei 230032, Anhui, China.

4Department of Clinical Laboratory, First Affiliated Hospital of Anhui Medical University, No. 218, Jixi Road, Hefei 230022, Anhui, China

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No competing interests reported.

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A novel prognostic model based on Prealbumin for predicting short-term prognosis of patients with HBV-related acute-on-chronic liver failure

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Abstract

Background

Methods

Results

Conclusion

Figures

Background

Methods

Study Design and Patients

Clinical Data And Calculation Of Scores

Statistical analysis

Results

PAB levels in the cross-sectional study cohort

Pab For Prognostic Prediction In Hbv Aclf

Development And Evaluation Of A Pab -based Prognostic Model

Hiapp Performance Validation

Discussion

Conclusions

Abbreviations

Declarations

References

Additional Declarations

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