A proposed model on MR elastography for predicting postoperative major complications in patients with hepatocellular carcinoma

Abstract

Objective: To develop a model for predicting post-operative major complications in patients with hepatocellular carcinoma (HCC). Methods: In all, 186 consecutive patients with pre-operative MR elastography were included. Complications were categorised using Clavien‒Dindo classification, with major complications defined as ≥Grade 3. Liver-stiffness measurement (LSM) values were measured on elastogram. The indocyanine green clearance rate of liver remnant (ICG-Krem) was based on the results of CT volumetry, intraoperative data, and ICG-K value. For an easy application to the prediction model, the continuous variables were converted to categories. Moreover, logistic regression analysis and fivefold cross-validation were performed. The prediction model’s discriminative performance was evaluated using the area under the receiver operating characteristic curve (AUC), and the calibration of the model was assessed by the Hosmer‒Lemeshow test. Results: 43 of 186 patients (23.1%) had major complications. The multivariate analysis demonstrated that LSM, albumin–bilirubin (ALBI) score, intraoperative blood loss, and ICG-Krem were significantly associated with major complications. The median AUC of the five validation subsets was 0.878. The Hosmer-Lemeshow test confirmed no evidence of inadequate fit (p = 0.13, 0.19, 0.59, 0.59, and 0.73) on the fivefold cross-validation. The prediction model for major complications was as follows: −2.876 + 2.912 [LSM (>5.3 kPa)]+1.538 [ALBI score (>−2.28)]+0.531 [Intraoperative blood loss (>860 ml)]+0.257 [ICG-Krem (<0.10)]. Conclusion: The proposed prediction model can be used to predict post-operative major complications in patients with HCC. Advances in knowledge: The proposed prediction model can be used in routine clinical practice to identify post-operative major complications in patients with HCC and to strategise appropriate treatments of HCC.