Portal Vein Thrombosis in Liver Transplantation: A Retrospective Cohort Study

Development of an explainable prediction model for portal vein system thrombosis post-splenectomy in patients with cirrhosis

BACKGROUND

Portal vein system thrombosis (PVST) is a common and potentially life-threatening complication following splenectomy plus pericardial devascularisation (SPDV) in patients with cirrhosis and portal hypertension. Early prediction of PVST is critical for timely intervention. This study aimed to develop a machine learning-based prediction model for PVST occurrence within 3 months after splenectomy.

METHODS

392 patients with cirrhosis who underwent splenectomy at the Second Affiliated Hospital of Xi'an Jiaotong University between 1 July 2016 and 31 December 2022 were enrolled in this study and followed up for 3 months. The predictive model integrated 37 candidate predictors based on accessible clinical data, including demographic characteristics, disease features, imaging results, laboratory values, perioperative details and postoperative prophylactic therapies, and finally, eight predictors were selected for model construction. The five machine learning algorithms (logistic regression, Gaussian Naive Bayes, decision tree, random forest and AdaBoost) were employed to train the predictive models for assessing risks of PVST, which were validated using five fold cross-validation. Model discrimination and calibration were estimated using receiver operating characteristic curves(ROC), accuracy, sensitivity, specificity, positive predictive value, negative predictive value and Brier scores. The outcome of the predictive model was interpreted using SHapley Additive exPlanations (SHAP), which provided insights into the factors influencing PVST risk prediction.

RESULTS

During the 3-month follow-up, a total of 144 (36.73%) patients developed PVST. The AdaBoost model demonstrated the highest discriminative ability, with a mean area under the receiver operating characteristic curve (AUROC) of 0.72 (95% CI 0.60 to 0.84). Important features for predicting PVST included albumin, platelet addition, the diameter of the portal vein, γ-glutamyl transferase, length of stay, activated partial thromboplastin time, D-dimer level and history of preoperative gastrointestinal bleeding, as revealed by SHAP analysis.

CONCLUSIONS

The machine learning-based prediction models can provide an initial assessment of 3-month PVST risk after SPDV in patients with cirrhosis and portal hypertension. The AdaBoost model demonstrates moderate discriminative ability in distinguishing between high-risk and low-risk patients, with an AUROC of 0.72 (95% CI 0.60 to 0.84). By incorporating SHAP analysis, the model can offer transparent explanations for personalised risk predictions, facilitating targeted preventive interventions and reducing excessive interventions across the entire patient population.

Impossible Yet Possible-Orthotopic Liver Transplantation in a Patient With Complete Portal Vein Thrombosis: A Case Report and Literature Review

This case study presents a liver transplantation (LT) in a patient with incidentally, intraoperatively detected complete portal vein thrombosis (PVT), classified as YERDEL stage 4, challenging traditional surgical boundaries. The patient's resilience and the innovative approach adopted by the surgical team exemplify the evolving complexities of LT in the context of advanced PVT. This report underscores the significance of detailed case documentation in medical literature, especially for complex transplant scenarios. It contributes to a broader understanding of surgical techniques and patient-centered approaches in LT. The narrative highlights the dynamic interplay between surgical advancements and vascular complications, advocating for the refinement of surgical methods and a reevaluation of conventional perspectives in transplantation. This case is pivotal in illustrating medical progress and the persistent pursuit of better outcomes in complex transplant situations.