To investigate the risk factors of early infection (≤1 month) after liver transplantation and to construct a Nomogram model for early infection prediction after liver transplantation.

The clinical data of 200 allogeneic liver transplant recipients in the Liver Transplant Center of Li Huili Hospital Affiliated to Ningbo University from January 2016 to December 2020 were retrospectively analyzed. According to the inclusion and exclusion criteria, a total of 181 recipients′ demographic data, clinical data and pathogen test results were collected. They were divided into the infection group and the uninfected group early after liver transplantation according to diagnostic criteria for infection. The distribution characteristics and related risk factors of early infection after liver transplantation were analyzed. A Nomogram was constructed and its fit, discrimination and clinical utility were evaluated. Independent samples t-test was used to compare normally distributed continuous variables and Mann-Whitney U test was used to compare non-normally distributed continuous variables. Categorical variables were analyzed using Chi-square test or Fisher′s exact test. Logistic regression analysis was used for multivariate analysis. The Nomogram model was constructed using rms package of R software (version 4.1.2) and verified by the Bootstrap internal verification method. Hosmer-Lemeshow test, calibration curve, areas under the receiver operating characteristic (ROC) curves, concordance index (C-index) and decision-curve analysis (DCA) were used to assess the calibration, discrimination and clinical utility of the nomograms. P<0.05 was considered statistically significant.

Among the 181 recipients, the incidence of early infection after liver transplantation was 53.0% (96/181). A total of 132 strains of pathogens were detected in the infection group, among which Gram-negative bacteria were the most common (42.4%). Recipients had the highest incidence of infection within two weeks after surgery (70. 8%, 68/96). The most common infection sites were as follows: lung and bloodstream. Multivariate Logistic regression analysis showed that female recipients (OR=4.235, 95% CI: 1.577-11.370), model for end-stage liver disease(MELD) score ≥20 (OR=3.742, 95% CI: 1.296-10.805); Child-Pugh grade C (OR=3.346, 95% CI: 1.263-8.862), postoperative ventilator supporting time (OR=1.036, 95% CI: 1.009-1.063) were independent risk factors associated with early infection after liver transplantation. According to the above independent risk factors, a Nomogram model was constructed and verified by the Bootstrap internal verification method, and the Hosmer-Lemeshow test was was not statistically significant (χ²=7.236, P>0.05). The calibration curve was close to the ideal curve, and the predictive model and the observed value showed good fit. The C-index and areas under the ROC curves were both 0.800 (95% CI: 0.735-0.865), and the model showed good discrimination. DCA of the model was higher than the single risk factor prediction in a wide range of threshold probability (0.2-1.0). The model showed the good clinical utility.

Female recipients, MELD score, Child-Pugh grade C and postoperative ventilator supporting time were independent risk factors for early infection after liver transplantation, and a Nomogram has good predictive effect on early infection after liver transplantation.