Development and validation of a model for predicting short-term complications after hepatectomy in patients with primary liver cancer

Introduction

Primary liver cancer continues to be among the most prevalent malignancies globally. Recent statistics indicate that it ranks as the sixth most common cancer and the third leading cause of cancer-related mortality worldwide (1). Although the global incidence rate is gradually declining, the absolute number of cases in regions such as the United States, East Asia, and Southeast Asia is on the rise (2). Currently, surgical resection remains the preferred treatment of choice for primary liver cancer. Despite significant advancements being achieved in surgical techniques and perioperative care, postoperative complications remain significant challenges in the management of liver cancer. The incidence of complications following hepatectomy varies between 15% and 50% and is influenced by factors such as the patient’s preoperative health status and the surgeon’s expertise (3-5). These complications include a range of issues, ranging from cardiopulmonary events and infections to liver-specific complications such as post-hepatectomy liver failure (PHLF). These adverse outcomes not only greatly prolong hospitalization and increase healthcare costs but also diminish patients’ quality of life and even negatively impact prognosis (3,6). Consequently, the identification of patients at high risk for postoperative complications after hepatectomy is essential to optimizing perioperative management.

Increasing emphasis has been placed on the prediction of postoperative complications. Various scoring systems have been developed for this purpose, such as the Physiological and Operative Severity Score for the Enumeration of Mortality and Morbidity (POSSUM) (7), Surgical Outcome Risk Tool (8), and the Surgical Appearance, Pulse, Grimace, Activity, and Respiration (APGAR) Score (SAS) system (9). Compared to these traditional risk scoring systems, nomograms offer distinct advantages by integrating continuous variables and providing visual, quantitative estimates, thereby enhancing individualized risk prediction (10). Therefore, an expanding number of nomograms have been widely developed to assist in supporting personalized treatment decisions and follow-up management. However, no studies thus far have attempted to develop predictive nomograms specifically designed to assess short-term complications in patients with primary liver cancer following hepatectomy.

Thereby, the objective of this study was to develop and validate a comprehensive predictive model for short-term postoperative complications, with the aim of aiding hepatobiliary surgeons in promptly identifying patients at elevated risk after hepatectomy. The purpose of this model is to facilitate improved postoperative surveillance, the implementation of targeted therapeutic interventions, and the optimization of medical resource allocation. We present this article in accordance with the TRIPOD reporting checklist (available at https://jgo.amegroups.com/article/view/10.21037/jgo-2026-0213/rc).

Methods

Research participants

This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This study had received approval from the Ethics Committee of Nantong First People’s Hospital (No. 2026-KT121-01). Individual consent for this retrospective analysis was waived.

The clinical data of 425 patients diagnosed with liver cancer who underwent hepatectomy in Nantong First People’s Hospital from January 2015 to October 2024 were retrospectively collected. The inclusion criteria were as follows: (I) age ≥18 years; (II) histopathologically confirmed diagnosis of primary liver cancer; and (III) availability of complete perioperative data. Meanwhile, the exclusion criteria included the following: (I) history of prior liver surgery; (II) presence of distant metastases; (III) emergency surgery due to liver cancer rupture and hemorrhage; and (IV) absence of follow-up data within 30 days postoperatively. According to these criteria, 397 patients were ultimately enrolled in the study.

Data collection

Data were collected for the following categories: (I) demographic characteristics, including age, gender, history of diabetes mellitus, cirrhosis and ascites, and body mass index (BMI); (II) tumor characteristics, including maximum tumor diameter, tumor number, presence of vascular invasion and lymph node metastasis, pathological type, and tumor grade; (III) laboratory results, including complete blood count, hemoglobin (Hb), alanine aminotransferase (ALT), aspartate aminotransferase (AST), gamma-glutamyl transferase (GGT), albumin (ALB), total bilirubin (TBIL), alpha-fetoprotein (AFP), prothrombin activity (PTA), international normalized ratio (INR), prothrombin time (PT), and activated partial thromboplastin time (APTT); and (IV) intraoperative data, including surgical approach, American Society of Anesthesiologists (ASA) physical status classification, operative time, estimated blood loss, blood transfusion, lowest mean arterial pressure (MAP), and lowest heart rate (HR).

All preoperative test results were obtained from the most recent preoperative data, and the lowest MAP and the lowest HR referred to the minimum values recorded at certain points during the operation. The Child-Pugh score, tumor-node-metastasis (TNM) staging system, China Liver Cancer (CNLC) staging system, Barcelona Clinic Liver Cancer (BCLC) staging system, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), albumin-bilirubin (ALBI) score, and prognostic nutritional index (PNI) were all systematically calculated. The data collection procedures were carried out independently, with the investigators having no knowledge of their association with the outcomes.

In this study, short-term postoperative complications were defined as those occurring within 30 days after surgery. All patients underwent an outpatient follow-up assessment 1 month after surgery. Complications were classified according to the Clavien-Dindo classification (CDC) system (11) by a final diagnosis committee composed of two hepatobiliary surgeons, with only those classified as CDC class II or higher being included. The complications considered were as follows: (I) respiratory conditions, including pneumonia, pleural effusion necessitating thoracentesis, ventilator use exceeding 48 hours postoperatively, and respiratory failure; (II) cardiovascular events, including arrhythmia, myocardial infarction, heart failure, and cerebral infarction; (III) infection, including peritoneal infection, fungal infection, and sepsis; (IV) gastrointestinal reactions, including intestinal obstruction and gastrointestinal bleeding; (V) PHLF, defined by abnormal serum bilirubin and INR on or after postoperative day 5 in accordance with the criteria established by the International Study Group of Liver Surgery (ISGLS) (12); (VI) biliary fistula; (VII) renal failure; (VIII) hemorrhage; (IX) ascites requiring paracentesis and drainage; and (X) others.

Statistical analysis

The totality of the available dataset was used and randomly divided into training and validation sets at a ratio of 7:3, and the baseline variables were compared. Categorical variables are presented as frequencies and percentages and were compared with the Chi-squared test or Fisher’s exact test. Continuous variables are reported as the median (first quartile, third quartile) and were compared via the Wilcoxon rank-sum test. Within the training set, potential predictors were screened via least absolute shrinkage and selection operator (LASSO) regression analysis. Subsequently, univariate and multivariate logistic regression analyses were conducted to identify independent influencing factors. Based on these factors, a predictive nomogram was developed. The regression equation obtained from the development model was applied to both the training and validation sets to calculate the predicted probabilities. To evaluate the agreement between predicted probabilities and observed outcome frequencies, a calibration curve was generated by plotting the predicted probabilities on the X-axis against the corresponding observed frequencies on the Y-axis. The model’s performance and clinical value were evaluated through receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) in both the training and validation sets. The overall research workflow is depicted in Figure 1. All statistical analyses were carried out with R software version 4.2.2 (The R Foundation for Statistical Computing, Vienna, Austria) and MSTATA software (https://www.mstata.com/). All statistical tests were bilateral, and a P value <0.05 was considered to indicate statistical significance.

Figure 1 Patient enrollment and study procedure. LASSO, least absolute shrinkage and selection operator.

Results

Incidence of postoperative complications

Among the 397 patients diagnosed with primary liver cancer who underwent hepatectomy, 136 (34.26%) experienced postoperative complications within 30 days, as detailed in Table 1. The most frequently observed complications were respiratory complications, accounting for 13.10% of cases, especially pleural effusion and hypostatic pneumonia, occurring at rates of 5.79% and 5.54%, respectively. PHLF was the next most prevalent complication, with an incidence of 6.55%. Cardiovascular complications and infections followed closely behind, ranking third and fourth, respectively. Additionally, three patients required reoperation due to postoperative hemorrhage and biliary fistula. Two patients succumbed to pulmonary embolism and multiple organ failure secondary to liver failure after surgery, yielding a postoperative mortality rate of 0.50%.

Baseline clinical characteristics

Following randomization of all patients at a ratio of 7:3, there were 278 patients in the training set and 119 patients in the validation set. No statistically significant differences were observed between the two groups in terms of demographic characteristics, pathological features, laboratory test results, or intraoperative data (P>0.05), as detailed in Table 2.

Independent predictive factors of postoperative complications

The influencing factors were determined through univariate analysis. For further information, please refer to Table S1. Given the large number of variables involved, nine potential predictors were screened by LASSO regression and 10-fold cross-validation in the training set. These predictors included Child-Pugh grade, PNI, tumor grade, lymph node metastasis, operative time, blood loss, blood transfusion, lowest MAP, and lowest HR, as depicted in Figure 2. Subsequently, after confounding factors were excluded, univariate and multivariate logistic regression analyses ultimately identified four independent predictive factors: operative time [odds ratio (OR) =1.01; 95% confidence interval (CI): 1.00–1.01], lowest MAP (OR =0.93; 95% CI: 0.89–0.97), lowest HR (OR =1.17; 95% CI: 1.12–1.23), and PNI (OR =0.92; 95% CI: 0.86–0.98). These findings are summarized in Table 3.

Figure 2 LASSO regression and 10-fold cross-validation were used to select the potential predictors. (A) Optimal parameter selection (λ=0.0513752706565229) via LASSO regression and 10-fold cross-validation. (B) Plot of the LASSO regression coefficient profiles. (C) LASSO-selected predictors and corresponding coefficients (only variables with nonzero coefficients are displayed). G, grade; HR, heart rate; LASSO, least absolute shrinkage and selection operator; MAP, mean arterial pressure; PNI, prognostic nutritional index.

Development and validation of the nomogram

The regression equation was as follows:

Log(P1−P)=−2.317+0.009×(operativetime)−0.064×(lowestMAP)+0.144×(LowestHR)−0.086×(PNI)[1]

A nomogram was developed to predict the incidence of short-term postoperative complications in patients undergoing hepatectomy for primary liver cancer, which incorporated the four above-mentioned parameters. This predictive model is depicted in Figure 3. The probability range of short-term postoperative complications can be estimated by summing the total points based on the nomogram. The analysis indicated that the probability of complications increases with prolonged operative duration, decreased lowest MAP, elevated lowest HR, and lower PNI value.

Figure 3 Nomogram for predicting postoperative complications within 30 days after hepatectomy. Instructions: locate the patient’s operative time on its axis. Draw a vertical line upward to the “Points” axis in order to ascertain the corresponding points. Repeat the process for each variable. Subsequently, sum the points and locate the final points on the “Total points” axis. Draw a vertical line downward to determine the patient’s probability of short-term postoperative complications. HR, heart rate; MAP, mean arterial pressure; PNI, prognostic nutritional index.

The performance of the nomogram was comprehensively assessed. Figure 4 presents the calibration curves of the nomogram for both the training and validation sets. In the training set, a high degree of agreement was observed between the observed and predicted postoperative complications. This suggested a strong correlation between the predicted outcomes and actual results. Similarly, in the validation set, the calibration curve of this model closely approximated the ideal reference line, with an intercept of zero and a slope of one, thereby confirming the model’s validity within the validation set.

Figure 4 Calibration curve of the nomogram in the (A) training set and (B) validation set.

As illustrated in Figure 5, the area under the ROC curve (AUC) for the nomogram was 0.870 (95% CI: 0.824–0.916) in the training set and 0.874 (95% CI: 0.809–0.939) in the validation set. These results indicate that the predictive model provides a high level of accuracy. Moreover, the close correspondence between the AUC values obtained from the training and validation sets supports the strong generalizability of the model. The marginally broader CI observed in the validation set is consistent with the anticipated variability.

Figure 5 ROC curve of the nomogram in the training set and validation set. AUC, area under the ROC curve; CI, confidence interval; ROC, receiver operating characteristic.

Furthermore, as illustrated by the decision curve in Figure 6, when the threshold probability exceeded 10%, the application of this nomogram for predicting postoperative complications within 30 days after hepatectomy indicated a greater net benefit compared to both the treat-all and treat-none approaches.

Figure 6 DCA of the nomogram in the (A) training set and (B) validation set. DCA, decision curve analysis.

In conclusion, the nomogram developed in this study demonstrated outstanding predictive accuracy and significant potential for clinical application.

Discussion

By combining static preoperative factors with dynamic intraoperative variables, we successfully developed and validated a novel nomogram to predict the risk of short-term postoperative complications in patients undergoing hepatectomy for primary liver cancer. Multivariable logistic regression analysis identified four independent predictive factors: operative time, lowest MAP, lowest HR, and PNI. These model variables are routinely collected in most surgical environments, and thus, the nomogram is highly practicable and easy to implement. This accessibility makes the nomogram a potentially valuable tool for real-time evaluation of postoperative risk after hepatectomy.

Three out of four of the independent predictors were intraoperative factors, underscoring the critical role of intraoperative variables in determining short-term postoperative complications. Notably, intraoperative hemodynamic parameters emerged as particularly significant predictors. The results revealed that the lowest MAP (OR =0.93) and the lowest HR (OR =1.17) were significantly associated with the incidence of short-term postoperative complications. This association may be explained by the fact that these two parameters collectively reflect tissue hypoperfusion during surgery, which can trigger a cascade of physiological responses, especially affecting organs with heightened sensitivity, such as the heart and kidneys, and thereby increasing the risk of postoperative complications (13-15). Additionally, diminished hepatic blood flow may aggravate ischemia-reperfusion injury and subsequently impair the recovery of residual liver function. It is evident that these two predictors correspond to two of the three parameters of the surgical APGAR score system, as proposed by Gawande et al. (9). The predictive capacity of this scoring system in relation to complications following hepatectomy has been validated in several studies (16,17). However, given the unique characteristics of hepatectomy, including the use of hepatic vascular inflow occlusion or inadvertent compression of the inferior vena cava during surgery, transient hemodynamic fluctuations may occur. Therefore, not all three parameters reported in previous studies were independent predictive factors of postoperative complications. In our study, intraoperative blood loss did not independently predict postoperative complications. Moreover, the nomogram developed demonstrated superior discriminative ability, with the AUC values of 0.870 (95% CI: 0.824–0.916) and 0.874 (95% CI: 0.809–0.939) in the training and validation sets, respectively. This robust performance suggests that the inclusion of additional parameters provides complementary information that enhances the overall accuracy of complication risk prediction.

The findings further confirmed prolonged operative time (OR =1.01) to be significantly associated with a heightened risk of complications after hepatectomy, aligning with previous research (4,18,19). We speculate that prolonged operative time contributes to increased hepatic tissue trauma, prolonged exposure to anesthesia, and exacerbated inflammatory response. Over time, the cumulative impact of these factors may lead to a greater incidence of complications eventually. In addition, a longer operative time may reflect the inherent complexity of the surgical procedure or technical challenges encountered intraoperatively, which can directly increase the likelihood of adverse outcomes.

PNI emerged as a significant predictive factor in this study. Initially proposed by Buzby as a marker for forecasting the prognosis of patients with gastrointestinal cancers (20), PNI combines serum ALB levels and lymphocyte counts to provide a comprehensive evaluation of the complex interplay between nutritional status, immune function, and inflammatory processes. Its relevance is underscored by its correlation with compromised immune function, delayed tissue repair, and heightened vulnerability to infections (21,22). A number of studies have demonstrated a close association between PNI and postoperative complications, tumor recurrence, and overall survival in patients undergoing hepatectomy for liver cancer (23-26). The incorporation of PNI into the predictive model is consistent with the growing recognition that nutrition represents a modifiable risk factor in the surgical patient population.

The nomogram developed in this study carries several significant clinical implications. Primarily, it provides clinicians with a quantitative tool for individualized risk assessment that can more accurately predict the incidence of short-term postoperative complications of primary liver cancer than can traditional methods. This will facilitate improved risk stratification and the early identification of high-risk individuals, allowing for targeted monitoring and perioperative interventions and thus a reduction in the incidence of postoperative complications. The results further emphasize the importance of comprehensive intraoperative monitoring and optimization of hemodynamic parameters during hepatectomy, as well as the necessity of improving preoperative nutritional status. With regard to intraoperative management, anesthesiologists should vigilantly monitor hemodynamic fluctuations in order to prevent hypotension and tachycardia. The adoption of goal-directed fluid therapy, the judicious use of vasoactive agents when appropriate, and the maintenance of adequate tissue perfusion pressure may be key strategies for mitigating postoperative complications. Finally, this study verified that the occurrence of short-term complications after hepatectomy is not directly related to preoperative liver function or tumor characteristics. Therefore, we believe that this nomogram holds considerable potential in the prediction of postoperative complications across a broader range of surgical procedures.

Several limitations to this study should be acknowledged. First, while the predictive model demonstrated excellent discrimination ability, the findings should be interpretated with caution due to the relatively limited sample size. Additionally, the fact that all patients were from a single center limits the generalizability to a broader population. External validation involving a diversity of populations across multiple centers is needed. Second, despite measures being applied to control for confounding variables, the retrospective nature of the data collection could have introduced selection bias and unmeasured confounding factors. Investigating additional predictors, through, for example, acquiring detailed intraoperative management data or conducting more comprehensive nutritional assessments, may further improve the model’s performance. Finally, although this nomogram performed excellently in terms of statistical metrics, further substantial prospective validation is required to determine its clinical utility.

Conclusions

This study developed and validated a nomogram incorporating four independent predictors—including PNI, operative time, lowest MAP, and lowest HR—that can effectively predict the incidence of short-term complications in patients with primary liver cancer after hepatectomy. It can serve as a comprehensive risk assessment tool for guiding perioperative clinical decision-making, assessing postoperative risk, and ultimately contributing to improving patient outcomes through targeted preventive strategies and optimized medical resource allocation. It is recommended that future research focus on prospective external validation of the clinical utility of this nomogram in a diversity of ethnic groups and clinical settings. Additionally, there may be a need to integrate novel potential predictors or refine model-relevant parameters in order to enhance the predictive accuracy of the nomogram.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://jgo.amegroups.com/article/view/10.21037/jgo-2026-0213/rc

Data Sharing Statement: Available at https://jgo.amegroups.com/article/view/10.21037/jgo-2026-0213/dss

Peer Review File: Available at https://jgo.amegroups.com/article/view/10.21037/jgo-2026-0213/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jgo.amegroups.com/article/view/10.21037/jgo-2026-0213/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This study had received approval from the Ethics Committee of Nantong First People’s Hospital (No. 2026-KT121-01). Individual consent for this retrospective analysis was waived.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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