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. 2023 Apr 12;23(1):123.
doi: 10.1186/s12876-023-02713-7.

Influencing factors and predictive model of postoperative infection in patients with primary hepatic carcinoma

Yanan Ma  1   2 , Bing Tan  1 , Sumei Wang  1 , Chaoyi Ren  3 , Jiandong Zhang  1 , Yingtang Gao  4   5
Affiliations

Influencing factors and predictive model of postoperative infection in patients with primary hepatic carcinoma

Yanan Ma et al. BMC Gastroenterol. .

Abstract

Background: The purpose of this study was to explore the risk factors for postoperative infection in patients with primary hepatic carcinoma (PHC), build a nomogram prediction model, and verify the model to provide a better reference for disease prevention, diagnosis and treatment.

Methods: This single-center study included 555 patients who underwent hepatobiliary surgery in the Department of Hepatobiliary Surgery of Tianjin Third Central Hospital from January 2014 to December 2021, and 32 clinical indicators were selected for statistical analysis. In this study, Lasso logistic regression was used to determine the risk factors for infection after liver cancer resection, establish a predictive model, and construct a visual nomogram. The consistency index (C-index), calibration curve, and receiver operating characteristic (ROC) curve were used for internal validation, and decision curve analysis (DCA) was used to analyze the clinical applicability of the predictive model. The bootstrap method was used for intramodel validation, and the C-index was calculated to assess the model discrimination.

Results: Among the 555 patients, 279 patients met the inclusion criteria, of whom 48 had a postoperative infection, with an incidence rate of 17.2%. Body mass index (BMI) (P = 0.022), alpha-fetoprotein (P = 0.023), total bilirubin (P = 0.016), intraoperative blood loss (P < 0.001), and bile leakage (P < 0.001) were independent risk factors for infection after liver cancer surgery. The nomogram was constructed and verified to have good discriminative and predictive ability. DCA showed that the model had good clinical applicability. The C-index value verified internally by the bootstrap method results was 0.818.

Conclusion: Postoperative infection in patients undergoing hepatectomy may be related to risk factors such as BMI, preoperative AFP level, TBIL level, intraoperative blood loss and bile leakage. The prediction model of the postoperative infection nomogram established in this study can better predict and estimate the risk of postoperative infection in patients undergoing hepatectomy.

Keywords: Nomogram; Postoperative infection; Predictive model; Primary hepatic carcinoma; Risk factors.

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Conflict of interest statement

The authors declare that they have no conflicts of interest.

Figures

Fig. 1
Fig. 1
Predictor variable selection based on the LASSO regression method. (a) Optimal parameter (lambda) selection in the LASSO model. (b) LASSO coefficient profiles of the 32 features
Fig. 2
Fig. 2
Nomogram prediction model for predicting postoperative infection in patients undergoing liver resection. The red dots are the scores of a true-positive patient according to each of the nomograms, with a final total score of 245 and a predicted probability of infection of 0.926. AFP: alpha-fetoprotein; BMI: body mass index; TBIL: total bilirubin; *: P < 0.05; ***: P < 0.001
Fig. 3
Fig. 3
ROC curve of the nomogram for the prediction of infection after liver resection. AFP: alpha-fetoprotein; BMI: body mass index; TBIL: total bilirubin
Fig. 4
Fig. 4
Calibration curve of the infection nomogram after liver resection. The dashed line on the diagonal represents an ideal model, and the solid line represents the performance of the model, where a better fit to the diagonal dashed line indicates a better prediction
Fig. 5
Fig. 5
Clinical decision curve analysis of the infection prediction nomogram after liver resection. The blue solid line indicates that using the nomogram to predict the risk of postoperative infection is more beneficial than intervening in an all-patient scenario or a no-intervention scenario
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