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ISSN 1001-5256 (Print)
ISSN 2097-3497 (Online)
CN 22-1108/R
Volume 38 Issue 2
Feb.  2022
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Article Navigation >  Journal of Clinical Hepatology  > 2022  >  38(2): 402-408

Establishment and validation of a risk prediction model for early-stage complications after liver transplantation

DOI: 10.3969/j.issn.1001-5256.2022.02.027
  • 1.

    Department of Liver Transplantation, Tianjin First Central Hospital, Tianjin 300110, China

  • 2.

    Department of Endocrinology, Tianjin Medical University Chu Hsien-I Memorial Hospital, Tianjin 300134, China

Research funding:

General Project of National Natural Science Foundation of China (81870444);

Natural Science Foundation of Tianjin (17JCQNJC12800)

More Information
  • Corresponding author: LI Jiang, E-mail: regnier@ibpc.fr
  • Received Date: 2021-07-30
  • Accepted Date: 2021-08-31
  • Published Date: 2022-02-20
    Abstract
  •   Objective  To investigate the risk factors for early-stage complications among liver transplant recipients, and to establish and validate a risk prediction model for early-stage complications after transplantation.  Methods  A retrospective analysis was performed for the clinical data of 234 patients who underwent orthotopic liver transplantation in Department of Liver Transplantation, Tianjin First Central Hospital, from January 2016 to December 2018. According to the presence or absence of Clavien-Dindo grade ≥Ⅲ complications after liver transplantation, the patients were divided into complication group with 97 patients and non-complication group with 137 patients. The two groups were compared in terms of the indices including age, sex, body mass index (BMI), blood type, psoas muscle thickness/height (PMTH), Controlling Nutritional Status (CONUT) score, Model for End-Stage Liver Disease (MELD) score, total serum bilirubin, serum creatinine, international normalized ratio of prothrombin time, blood urea nitrogen, hemoglobin, white blood cell count, platelet count, amount of intraoperative red blood cell transfusion, amount of frozen plasma transfusion, blood loss, anhepatic phase, time of operation, donor age, donor BMI, cold ischemia time of donor liver, and warm ischemia time of donor liver. The independent samples t-test was used for comparison of normally distributed continuous data between two groups, and the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between two groups; the chi-square test was used for comparison of categorical data between two groups. Univariate analysis and the binary logistic regression analysis were used to investigate the risk factors for early-stage complications after liver transplantation, and a risk prediction model for complications after liver transplantation was established based on the method for establishing a scoring system using the logistic model provided by Framingham Research Center. Internal validation of the model was performed by C-index, receiver operating characteristic (ROC) curve, calibration curve, and the Hosmer-Lemeshow test, and the decision curve was used to evaluate the clinical applicability of the model. The Kaplan-Meier method was used to compare the incidence rate of early-stage complications after liver transplantation between the patients with different risk scores.  Results  Compared with the non-complication group, the complication group had significantly higher MELD score, proportion of patients with low PMTH, total serum bilirubin, serum creatinine, blood urea nitrogen, CONUT score, amount of intraoperative red blood cell transfusion, and amount of frozen plasma transfusion, as well as a significantly lower level of hemoglobin (all P < 0.1). The multivariate binary logistic regression analysis showed that MELD score (odds ratio [OR]=1.104, 95% confidence interval [CI]: 1.057-1.154, P < 0.05), PMTH (OR=2.858, 95%CI: 1.451-5.626, P < 0.05), and CONUT score (OR=1.481, 95%CI: 1.287-1.703, P < 0.05) were independent risk factors for grade ≥Ⅲ complications in the early stage after liver transplantation. MELD score, PMTH, and CONUT score were included in a predictive model, and this model had the highest score of 24 points, a C-index of 0.828, an area under the ROC curve of 0.812(P < 0.001), a sensitivity of 0.792, and a specificity of 0.751, suggesting that this predictive model had good discriminatory ability. The calibration curve of this model was close to the reference curve, and the Hosmer-Lemeshow test obtained a chi-square value of 8.528(P=0.382), suggesting that this predictive model had a high degree of fitting. The decision curve showed that most patients were able to benefit from the predictive model and achieved a high net benefit rate, suggesting that this predictive model had good clinical applicability. The score of 11 was selected as the cut-off value according to the optimal Youden index of 0.507, and the patients were divided into low-risk (< 8 points) group with 55 patients, moderate-risk (8-10 points) group with 63 patients, high-risk (11-14 points) group with 67 patients, and extremely high-risk (≥15 points) group with 49 patients. These four groups had a 90-day cumulative incidence rate of early-stage postoperative complications of 3.6%, 28.6%, 59.7%, and 75.5%, respectively, and the incidence rate of complications increased with the increase in risk score (P < 0.001).  Conclusion  MELD score, PMTH, and CONUT score are independent risk factors for early-stage complications among liver transplant recipients, and the risk prediction model established based on these factors has a high predictive value in high-risk patients.

     

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