Sun K, Li JB, Chen YF, Zhai ZJ, Chen L, Dong R. Predicting post-hepatectomy liver failure using a nomogram based on portal vein width, inflammatory indices, and the albumin-bilirubin score. World J Gastrointest Surg 2025; 17(2): 99529 [DOI: 10.4240/wjgs.v17.i2.99529]
Corresponding Author of This Article
Rui Dong, MD, Affiliate Associate Professor, Associate Chief Physician, Department of General Surgery, The Second Affiliated Hospital of Air Force Medical University, No. 569 Xinsi Road, Baqiao District, Xi’an 710000, Shaanxi Province, China. s1208532322@outlook.com
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Retrospective Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Ke Sun, Xi’an Medical College, Xi’an 710000, Shaanxi Province, China
Jiang-Bin Li, Ya-Feng Chen, Lang Chen, Rui Dong, Department of General Surgery, The Second Affiliated Hospital of Air Force Medical University, Xi’an 710000, Shaanxi Province, China
Zhong-Jie Zhai, Statistics Teaching and Research Office, Air Force Medical University, Xi’an 710038, Shaanxi Province, China
Author contributions: Sun K designed the research study; Li JB, Chen YF and Chen L collected and assembled data; Sun K and Zhai ZJ performed data analysis; Sun K wrote the manuscript; Dong R critically revised the manuscript for important intellectual content.
Supported by Shaanxi Provincial Social Development Fund, No. 2024SF-YBXM-140.
Institutional review board statement: This study was approved by the Ethics Committee of The Second Affiliated Hospital of Air Force Medical University, No. K202407-01.
Informed consent statement: This study was retrospective, and the Ethics Committee of the Second Affiliated Hospital of Air Force Medical University approved the waiver of informed consent.
Conflict-of-interest statement: All authors declare no conflicts of interest.
Data sharing statement: The data for this study is available from the corresponding author upon reasonable request at s1208532322@outlook.com.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Rui Dong, MD, Affiliate Associate Professor, Associate Chief Physician, Department of General Surgery, The Second Affiliated Hospital of Air Force Medical University, No. 569 Xinsi Road, Baqiao District, Xi’an 710000, Shaanxi Province, China. s1208532322@outlook.com
Received: July 24, 2024 Revised: October 6, 2024 Accepted: October 30, 2024 Published online: February 27, 2025 Processing time: 181 Days and 19.4 Hours
Abstract
BACKGROUND
Post-hepatectomy liver failure (PHLF) after liver resection is one of the main complications causing postoperative death in patients with hepatocellular carcinoma (HCC). It is crucial to help clinicians identify potential high-risk PHLF patients as early as possible through preoperative evaluation.
AIM
To identify risk factors for PHLF and develop a prediction model.
METHODS
This study included 248 patients with HCC at The Second Affiliated Hospital of Air Force Medical University between January 2014 and December 2023; these patients were divided into a training group (n = 164) and a validation group (n = 84) via random sampling. The independent variables for the occurrence of PHLF were identified by univariate and multivariate analyses and visualized as nomograms. Ultimately, comparisons were made with traditional models via receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).
RESULTS
In this study, portal vein width [odds ratio (OR) = 1.603, 95%CI: 1.288-1.994, P ≤ 0.001], the preoperative neutrophil-to-lymphocyte ratio (NLR) (OR = 1.495, 95%CI: 1.126-1.984, P = 0.005), and the albumin-bilirubin (ALBI) score (OR = 8.868, 95%CI: 2.144-36.678, P = 0.003) were independent risk factors for PHLF. A nomogram prediction model was developed using these factors. ROC and DCA analyses revealed that the predictive efficacy and clinical value of this model were better than those of traditional models.
CONCLUSION
A new Nomogram model for predicting PHLF in HCC patients was successfully established based on portal vein width, the NLR, and the ALBI score, which outperforms the traditional model.
Core Tip: Post-hepatectomy liver failure, a serious complication, poses a serious threat to patients’ postoperative survival rate and quality of life. Therefore, to avoid this, the preoperative prediction of post-hepatectomy liver failure is crucial. This study revealed that portal vein width, the neutrophil-to-lymphocyte ratio, and the albumin-bilirubin score are independent risk factors for post-hepatectomy liver failure (PHLF). We developed a nomogram model based on blood indicators combined with imaging indicators, and this study was the first to combine imaging indicators of portal vein width to construct a prediction model that can highly predict PHLF.
Citation: Sun K, Li JB, Chen YF, Zhai ZJ, Chen L, Dong R. Predicting post-hepatectomy liver failure using a nomogram based on portal vein width, inflammatory indices, and the albumin-bilirubin score. World J Gastrointest Surg 2025; 17(2): 99529
Primary liver cancer is one of the most common malignant tumors worldwide, with a high incidence and mortality rate; it is the sixth most common malignant tumor and the third leading cause of cancer-related death[1]. According to the GLOBOCAN database, by 2020, there will be more than 900000 new cases of liver cancer, accounting for 4.69% of the global cancer incidence, and more than 830000 deaths from liver cancer, accounting for 8.30% of the global cancer deaths[2]. Therefore, the treatment of hepatocellular carcinoma (HCC) is essential. With the progress of science and technology, in-depth research, and the continuous development of medical technology, patients with HCC have more treatment options, including surgical treatment, liver transplantation, ablation, trans-arterial chemoembolization, and systemic therapy. However, hepatic resection is still the primary radical treatment in the early clinical stage for patients with HCC[3]. Hepatic resection is often associated with severe life-threatening complications, such as post-hepatectomy liver failure (PHLF), which, despite continuous improvements in perioperative management and surgeon excellence in surgical procedures, remains an unacceptably severe complication with a potentially high risk of death[4]. The reported incidence rates range from 0.7%-39.6%[5-7]. Therefore, to avoid this severe complication as much as possible, medical researchers have used predictive models, such as the Child-Pugh scoring system[8], the model for end-stage liver disease (MELD) score[9], and the aspartate-to-platelet ratio index (APRI) score[10], to predict PHLF. These models have specific predictive value but have certain limitations. There are currently no uniform prediction criteria for PHLF, a severe complication, and this study aims to explore an individualized approach to predict prognosis after hepatic resection, thereby helping clinicians choose a more appropriate treatment for their patients to improve their prognosis. Next, we explored the correlations between preoperative test indices, patients' physical factors, tumor characteristics, and imaging indices, and the occurrence of PHLF, identified the independent influencing factors of PHLF, established a nomogram model, and analyzed and compared it with other models.
MATERIALS AND METHODS
Patients
This was a retrospective analysis of 248 patients with HCC who underwent hepatic resection from January 2014 to December 2023 at the Second Affiliated Hospital of Air Force Military Medical University.
The inclusion criteria were as follows: (1) Preoperative Child-Pugh grade A or B; (2) Radical resection and meeting the R0 resection criteria; and (3) Postoperative pathological diagnosis of primary HCC.
Exclusion criteria: (1) The combination of other malignant diseases; (2) History of relevant anticancer treatment before surgery; (3) Preoperative obstructive jaundice; or (4) Missing case data.
The Ethics Committee of The Second Affiliated Hospital of Air Force Medical University approved the study. The research flow chart is shown in Figure 1.
Figure 1 Research flow chart.
HCC: Hepatocellular carcinoma.
Clinicopathologic variables
We collected data on the patients’ underlying physical factors, including sex, body weight, hypertension, and diabetes mellitus. Patients’ radiological data included contrast-enhanced computed tomography (CT) images to determine the number of tumors and the portal vein width (PVD). The serum prothrombin time, international normalized ratio (INR), alanine aminotransferase, albumin (ALB), total bilirubin (TBil), direct bilirubin, gamma-glutamyl transferase, alkaline phosphatase, creatinine, platelet count (PLT), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio, lymphocyte, monocyte, neutrophil, and PVD were all examined preoperatively. The clinical and pathological features of the patients are presented in Table 1.
The criteria for PHLF used in this study were those proposed by the International Study Group of Liver Surgery (ISGLS) in 2011[11]: PHLF was diagnosed when the INR and TBil were elevated and greater than preoperatively on or after the fifth postoperative day, provided that biliary obstruction was excluded. In this study, we diagnosed the occurrence of PHLF as having an INR > 1.2, along with a TBil > 22 μmol/L. The ALB-bilirubin (ALBI) score[12] was calculated as (0.66 × log10 TBil) - (0.085 × ALB, g/L). The APRI score was calculated as: [aspartate aminotransferase (AST, U/L)/upper limit of normal (ULN)]/PLT (109/L) × 100. The MELD score was calculated via the formula: 11.2 × ln (INR) + 9.57 × ln (Cr, μmol/L) + 3.78 × ln (TBil, μmol/L) + 6.43. The platelet-ALB-bilirubin (PALBI) score[13] was calculated as 2.02 × log10 (TBil, μmol/L) - 0.37 × [log10 (TBil)]2 - 0.04 × ALB (g/L) - 3.48 × log10 (PLT, × 109/L) + 1.01 × log10 (PLT, × 109/L)2.
Contrast-enhanced CT
The patient was examined with Siemens's second-generation dual-source CT. The CT scanning parameters were as follows: Layer thickness of 10 mm, tube voltage of 120 kV, and tube current of 120 mA, depending on the individual, pitch of 0.984, and matrix of 512 × 512; enhanced CT scanning: 320 mg/mL iohexol (dosage of 1.3-1.5 mL/kg) was used as the contrast agent and injected intravenously at the right elbow. The injection flow rate was 3 mL/second for intravenous injections. Arterial phase images were acquired when the arterial phase reached the threshold (100 HU), and venous and delayed phase images were acquired at intervals of 30 seconds and 40 seconds, respectively. PVD (in mm) was measured at the widest point of the portal vein trunk.
Statistical analysis
The statistical software used was SPSS 25.0 and R 4.3.3 (Institute for Statistics and Mathematics). Continuous variables were first examined for normality, and those that conformed to a normal distribution were analyzed using the t test and are expressed as the mean ± SD; those that did not conform to a normal distribution were analyzed using the nonparametric rank sum test and are expressed as the interquartile spacing. The χ2 test was used, and the results are expressed as frequencies and constitutive ratios for categorical variables. Factors with P < 0.05 from the above univariate analysis were included in the multifactorial logistic regression analysis to obtain independent risk factors for PHLF, and those with P < 0.05 were included in the R software to create a nomogram prediction model. The predictive performance of the nomogram model was validated using Bootstrap equal-volume release-return replicated sampling 1000 times, calibration plots were drawn, and receiver operating characteristic (ROC) curves were plotted based on the scores. Decision curve analysis (DCA) was also performed to quantify the net benefit at different threshold probabilities to assess the clinical value of the nomogram model. The clinical efficacy of the model was evaluated by comparing it with the ALBI score, the APRI score, the MELD score, and the PALBI score, with a statistically significant difference at P < 0.05.
RESULTS
Clinicopathological characteristics
The criteria for defining PHLF in this study were adopted from those developed by the ISGLS in 2011. Among the 164 patients in the training group, 55 (33.5%) had postoperative PHLF, and 105 (66.5%) were in the group without PHLF. Among the 84 patients in the validation group, 32 (38.1%) were in the group with postoperative PHLF, and 52 (61.9%) were without PHLF.
Univariate and multivariate analyses of factors associated with PHLF
In the training set, univariate analysis revealed that PT (P ≤ 0.001), the INR (P ≤ 0.001), ALB (P ≤ 0.001), TBil (P = 0.001), the PLT (P = 0.001), lymphocytes (P ≤ 0.001), the NLR (P = 0.007), the PVD (P ≤ 0.001), and the ALBI score (P ≤ 0.001) were significantly different (P < 0.05).
The relevant clinical indicators with P < 0.05 in the above univariate analyses were included in the multivariate analyses as independent variables, and the occurrence of PHLF was included as a dependent variable. The results revealed that the NLR (P = 0.005), PVD (P < 0.001), and ALBI score (P = 0.003) were independent risk factors for the occurrence or nonoccurrence of PHLF, as shown in Table 2.
Table 2 Univariate and multivariate analyses of post hepatectomy liver failure factors.
Characteristics
Univariable logistic regression
Multivariable logistic regression
OR (95%CI)
P value
OR (95%CI)
P value
Gender, (male vs female)
1.137 (0.467, 2.769)
0.777
Body weight, kg
0.984 (0.955, 1.013)
0.262
Hypertension, (yes, no)
0.780 (0.356, 1.707)
0.534
Diabetes mellitus, (yes, no)
0.804 (0.312, 2.074)
0.652
PT, second
2.432 (1.696, 3.487)
0.000
1.346 (0.545, 3.325)
0.520
INR
4461.289 (122.808, 162067.386)
0.000
20.021 (0.003, 115173.617)
0.497
ALT, U/L
1.009 (0.994, 1.025)
0.258
ALB, g/L
0.840 (0.767, 0.921)
0.000
-
-
TBil, μmol/L
1.088 (1.036, 1.142)
0.001
-
-
DBil, μmol/L
1.064 (0.957, 1.183)
0.252
GGT, U/L
1.005 (0.999, 1.011)
0.086
ALP, U/L
1.005 (0.999, 1.011)
0.087
Cr, μmol/L
0.978 (0.951, 1.005)
0.107
PLT, 109/L
0.990 (0.983, 0.996)
0.001
1.004 (0.995, 1.013)
0.346
Lymphocyte, 109/L
0.211 (0.102, 0.438)
0.000
-
-
Monocyte, 109/L
0.197 (0.022, 1.764)
0.146
Neutrophil, 109/L
0.884 (0.696, 1.124)
0.314
NLR
1.354 (1.086, 1.689)
0.007
1.495 (1.126, 1.984)
0.005
PLR
1.002 (0.997, 1.008)
0.379
PVD, mm
1.587 (1.320, 1.907)
0.000
1.603 (1.288, 1.994)
0.000
Number of tumors, ≥ 2/1
0.543 (0.186, 1.585)
0.264
ALBI score
18.305 (5.682, 58.965)
0.000
8.868 (2.144, 36.678)
0.003
Establishment of the nomogram for PHLF
Based on the results of multifactorial logistic analysis, relevant variables with P < 0.05 were introduced to build a nomogram model in the training set, as shown in Figure 2, where each predictor variable scale corresponds to the score on the score scale. All the variable scores were summed to obtain the patient's total score, and the total score scale corresponded to the risk prediction value. The total score for all patients was followed by the creation of an ROC curve with an AUC of 0.846 (95%CI: 0.782-0.910). The accuracy of the nomogram model in predicting PHLF was greater than that of the conventional model. Please refer to Figure 3A. The "boot" package in R software was used to construct calibration curves via bootstrap self-sampling with 1000 randomized putbacks to validate the predictive performance of the nomogram model, as shown in Figure 4A, which demonstrated that the nomogram model was in good agreement with the actual observations. In the validation set, the nomogram graph demonstrated greater accuracy for PHLF, with an AUC of 0.812 (95%CI: 0.718-0.906) (Figure 3B). The calibration curves revealed good agreement between the nomogram model and the observations (Figure 4B).
Figure 2 Predictive nomogram for assessing the probability of post-hepatectomy liver failure in patients with hepatocellular carcinoma.
NLR: Neutrophil-to-lymphocyte ratio; PVD: Portal vein width; ALBI: Albumin-bilirubin.
Figure 3 Comparison of receiver operating characteristic curves between the nomogram and the conventional models (aspartate-to-platelet ratio index score, model for end-stage liver disease score, albumin-bilirubin score, and platelet-albumin-bilirubin score) for post-hepatectomy liver failure.
A: The training set; B: The validation set. APRI: Aspartate-to-platelet ratio index; ALBI: Albumin-bilirubin; MELD: Model for end-stage liver disease; PALBI: Platelet-albumin-bilirubin
Figure 4 Calibration curve of the nomogram model.
A: The training set; B: The validation set.
Comparison of predictive accuracy for PHLF between the nomogram and the conventional models in the training cohort and validation cohort respectively
A comparison of the area under the ROC curve of the nomogram model with the ALBI score, APRI score, MELD score, and PALBI score in the training set revealed that the AUC of the nomogram model was 0.846 (95%CI: 0.782-0.910), which was significantly greater than the ALBI score AUC of 0.747 (95%CI. 0.665-0.829), APRI AUC: 0.697 (95%CI: 0.611-0.783), MELD score AUC: 0.645 (95%CI: 0.560-0.731), and PALBI score AUC: 0.693 (95%CI: 0.609-0.776), and as shown by the DCA curves, the nomogram model was more reliable than the conventional model (Figure 5A).
Figure 5 Comparison of decision curve analysis between the nomogram and the conventional models (aspartate-to-platelet ratio index score, model for end-stage liver disease score, albumin-bilirubin score, and platelet-albumin-bilirubin score) for post-hepatectomy liver failure.
A: The training set; B: The validation set. APRI: Aspartate-to-platelet ratio index; ALBI: Albumin-bilirubin; MELD: Model for end-stage liver disease; PALBI: Platelet-albumin-bilirubin.
For the validation set, we obtained the same conclusion. The AUC of the nomogram model was 0.812 (95%CI: 0.718-0.906) greater than that of the ALBI score model: 0.650 (95%CI: 0.619-0.781), the APRI score AUC was 0.704 (95%CI: 0.591-0.818), and the MELD score AUC was 0.602 (95%CI: 0.475-0.729), and the AUC of the PALBI score was 0.628 (95%CI: 0.500-0.756). The DCA curves of the validation set revealed that the nomogram model was more reliable than the conventional model (Figure 5B).
DISCUSSION
HCC is a severe and life-threatening disease, and surgical resection is by far the best treatment option for HCC. However, any treatment comes with certain risks, and PHLF is a severe complication that can pose a risk of death after surgery; thus, it is crucial to be able to identify patients who may develop PHLF proactively. Currently, more individualized clinical prediction models are needed. Accordingly, it is essential to establish an individualized PHLF prediction model to inform clinical workers' decision-making. Statistical analysis revealed that PVD, the NLR, and the ALBI score independently influenced PHLF.
Currently, inflammation plays a vital role in the development of HCC. Systemic inflammation is an uninterrupted response to malignancy through interactions with various types of cytokines, chemokines, inflammatory proteins, and immune cells in an organism[14]. Neutrophils can increase tumor cell survival, invasiveness, and angiogenesis by circulating growth factors, such as vascular endothelial growth factor and secreted proteins, and promote tumor cell motility, thus facilitating tumor implantation and metastasis in distant organs[14-16]. It has also been shown that chemokine-induced lymphocytes control tumor activity by inducing cytotoxic cell death and cytokine secretion. Decreased lymphocyte counts impair the body's immune function, allowing for accelerated cancer progression and affecting the prognosis of the disease. The NLR is the ratio of neutrophils to lymphocytes. The role of the NLR in predicting the prognosis of tumors after radical resection of HCC has been widely noted[17-20]. Related studies have shown that the pretreatment NLR is a prognostic indicator after hepatectomy in HCC patients. In a meta-analysis that included 17 studies[21], an elevated preoperative NLR was significantly associated with overall survival, recurrence-free survival, and tumor-free survival in patients with HCC. In contrast, a lower preoperative NLR was associated with an improved prognosis. A study by Kuang et al[22] revealed that the NLR is considered an independent risk factor for PHLF[22]. Moreover, several researchers have reported the role of systemic inflammation in predicting the prognosis of patients with breast, esophageal, gastric, pancreatic, and colorectal cancers[23-27]. Therefore, as PHLF is a potentially severe complication after hepatectomy that affects patient prognosis, we included the NLR as a hematological predictor of PHLF and explored its predictive value. In this study, the NLR was an independent risk factor for PHLF, which is consistent with previous findings[22].
The ALBI score was initially proposed to predict overall productivity after hepatic resection in patients with HCC in 2015. The ALBI score has been widely used because of its fewer metrics and easier accessibility. In studies related to the prediction of PHLF, the ALBI scoring system avoids the use of subjective metrics and reduces the bias caused by metrics compared with the Child-Pugh score. In the study by Zou et al[28], the ALBI score was more accurate than the Child-Pugh score, ICG R15 score, and MELD score in predicting PHLF. In the study by Guo et al[29], the ALBI scoring system achieved the most accurate predictive ability among the six liver function reserve models (ALBI, APRI, Fibrosis-4, MELD, PALBI, and King's score). However, the ALBI scoring system does not consider portal hypertension a risk factor[30], so we included PVD, which responds to portal hypertension, in our study.
Hepatitis B virus infection, which progresses to cirrhosis and ultimately to liver cancer, is widespread among liver cancer patients in many Asian countries in what we call the "liver cancer trilogy". The severity of cirrhosis affects liver function, leading to a series of complications that have a severe impact on patient survival. With pathological changes in the liver parenchyma and increased resistance to transhepatic blood flow, portal pressure gradually increases, leading to the development of portal hypertension. Portal hypertension was shown to independently influence PHLF in previous studies[31]. In combination with clinical experience, as the pressure in the portal vein continues to increase, resulting in the portal vein vessels being in a state of distension, and as the pressure in the vessels rise, resulting in widening of the diameter of the portal vein, the more severe the cirrhosis is, the worse the liver function will be. PVD is a routine imaging indicator that has been reported to be an independent influence on portal hypertension[32], which in turn is a predictor of PHLF; therefore, whether PVD and PHLF are associated has not been assessed, and in this study, we included PVD and concluded that PVD is an independent risk factor for PHLF. However, few studies have focused on the safety limits of PVD for hepatic resection patients with HCC. In the future, more studies should be conducted to determine this width to reduce the risk of surgery and severe postoperative complications.
In conclusion, the present study revealed that the PVD (P < 0.001), preoperative NLR (P = 0.005), and ALBI score (P = 0.003) were independent imaging factors for PHLF, and the nomogram model established in this study was used to predict the occurrence of PHLF with good predictive results. However, this study has several shortcomings: (1) It is a retrospective study, which only collected clinical data from HCC patients who underwent "partial hepatectomy" in our hospital, a single center, and with a limited number of cases, which may have resulted in confounding bias; and (2) This study lacked intraoperative factors, such as the duration of portal vein blockade, the number of times, or the amount of bleeding, which did not take into account their effects on PHLF. Considering its effect on the PHLF. Therefore, multicenter, large sample sizes, and prospective studies are still needed for validation in the future in anticipation of the emergence of a more accurate PHLF prediction model.
CONCLUSION
In this study, by combining blood indicators with imaging indicators, it was finally concluded that PVD, the NLR, and the ALBI score were independent influencing factors for predicting liver failure after hepatectomy. A nomogram prediction model was established on this basis, and good prediction results were achieved, which can provide a reference for clinicians' clinical decisions and prevent the occurrence of liver failure, a serious complication after hepatectomy, as much as possible.
Footnotes
Provenance and peer review: Unsolicited article; Externally peer reviewed.
Peer-review model: Single blind
Specialty type: Gastroenterology and hepatology
Country of origin: China
Peer-review report’s classification
Scientific Quality: Grade C
Novelty: Grade B
Creativity or Innovation: Grade B
Scientific Significance: Grade B
P-Reviewer: Gan XJ S-Editor: Li L L-Editor: A P-Editor: Wang WB
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