Risk Factors of Portal Vein Thrombosis after Devascularization Treatment in Patients with Liver Cirrhosis: A Nested Case-Control Study

Feasibility and long-term outcomes of hepatic vein recanalization in Budd-Chiari syndrome through intrahepatic collateral pathways

BACKGROUND AND AIMS

To investigate the feasibility and long-term outcomes of hepatic vein (HV) recanalization using intrahepatic collateral pathways in patients with Budd-Chiari syndrome (BCS) with HV obstruction.

METHODS

Clinical data of 29 BCS patients with HV obstruction and intrahepatic collateral pathways were reviewed. All patients underwent HV recanalization through the intrahepatic collaterals. Follow-up was performed at 1, 3, 6, and 12 months after treatment and annually thereafter. Cumulative patency and survival rates were assessed using Kaplan-Meier curves. The independent predictors of patency were determined using a Cox regression model.

RESULTS

HV recanalization was successful in 28 of the 29 patients (96.6%), with no complications. Of the 28 cases, simultaneous recanalization of the accessory HV and right HV was achieved in 11 patients, accessory HV and middle HV in six, accessory HV and left HV in three, right HV and middle HV in five, and left HV and middle HV in three. Twenty-eight patients were followed from 4 to 87 (mean, 53.6 ± 26.7) months after treatment, and six patients developed reocclusion. The overall cumulative 1-, 3-, 5-, and 7-year primary HV patency rates were 96.3, 82.9, 74.6, and 59.7%, respectively. The cumulative 1-, 3-, 5-, and 7-year survival rates were 100, 95.8, 95.8, and 86.3%, respectively.

CONCLUSION

Interventional treatment of HV obstruction in BCS patients through intrahepatic collateral approaches is well tolerated and feasible and can result in excellent long-term patency and survival rates.

Diagnostic Prediction of portal vein thrombosis in chronic cirrhosis patients using data-driven precision medicine model

BACKGROUND

Portal vein thrombosis (PVT) is a significant issue in cirrhotic patients, necessitating early detection. This study aims to develop a data-driven predictive model for PVT diagnosis in chronic hepatitis liver cirrhosis patients.

METHODS

We employed data from a total of 816 chronic cirrhosis patients with PVT, divided into the Lanzhou cohort (n = 468) for training and the Jilin cohort (n = 348) for validation. This dataset encompassed a wide range of variables, including general characteristics, blood parameters, ultrasonography findings and cirrhosis grading. To build our predictive model, we employed a sophisticated stacking approach, which included Support Vector Machine (SVM), Naïve Bayes and Quadratic Discriminant Analysis (QDA).

RESULTS

In the Lanzhou cohort, SVM and Naïve Bayes classifiers effectively classified PVT cases from non-PVT cases, among the top features of which seven were shared: Portal Velocity (PV), Prothrombin Time (PT), Portal Vein Diameter (PVD), Prothrombin Time Activity (PTA), Activated Partial Thromboplastin Time (APTT), age and Child-Pugh score (CPS). The QDA model, trained based on the seven shared features on the Lanzhou cohort and validated on the Jilin cohort, demonstrated significant differentiation between PVT and non-PVT cases (AUROC = 0.73 and AUROC = 0.86, respectively). Subsequently, comparative analysis showed that our QDA model outperformed several other machine learning methods.

CONCLUSION

Our study presents a comprehensive data-driven model for PVT diagnosis in cirrhotic patients, enhancing clinical decision-making. The SVM-Naïve Bayes-QDA model offers a precise approach to managing PVT in this population.

Assessment of portal system hemodynamics for the prediction of portal vein thrombosis in cirrhosis-A systematic review and meta-analysis

BACKGROUND

The pathogenesis of portal vein thrombosis (PVT) in cirrhosis is multifactorial, with altered hemodynamics being proposed as a possible contributor. The present systematic review was conducted to study the role of assessment of portal hemodynamics for the prediction of PVT in patients with cirrhosis.

METHODS

Three databases (Medline, Embase, and Scopus) were searched from inception to February 2023 for studies comparing portal venous system parameters in patients with cirrhosis developing PVT with those not. Results were presented as mean difference (MD) or odds ratio (OR) with their 95% confidence intervals (CIs).

RESULTS

A total of 31 studies (patients with cirrhosis: 19 studies, patients with cirrhosis undergoing splenectomy: 12 studies) were included. On pooling the data from multivariable analyses of the included studies, a larger portal vein diameter was a significant predictor of PVT in patients with cirrhosis without or with splenectomy with OR 1.74 (1.12-2.69) and OR 1.55 (1.26-1.92), respectively. On the other hand, a lower portal vein velocity (PVV) was a significant predictor of PVT in cirrhotics without or with splenectomy with OR 0.93 (0.91-0.96) and OR 0.71 (0.61-0.83), respectively. A PVV of <15 cm/s was the most commonly used cut-off for the prediction of PVT. Patients developing PVT also had a significantly higher splenic length, thickness, and splenic vein velocity.

CONCLUSION

The assessment of portal hemodynamic parameters at baseline evaluation in patients with cirrhosis may predict the development of PVT. Further studies are required to determine the optimal cut-offs for various parameters.

Portal vein thrombosis associates with high platelet-fibrin clot strength and platelet activation in decompensated cirrhosis: A retrospective study

BACKGROUND AND AIMS

Alteration of platelet status associates with decompensation and death in cirrhosis, while its effect on portal vein thrombosis (PVT) remains unclear. We aimed to retrospectively investigate whether PVT associates with platelet-fibrin clot strength and platelet activation in decompensated cirrhosis.

METHODS

Platelet-fibrin clot strength (G) was measured by thromboelastography (TEG). Platelet activation was reflected by plasma concentrations of soluble p-selectin (sPs) and a platelet aggregation test adjusted for platelet counts.

RESULTS

Among 166 patients, 45 had PVT. The platelet count was significantly lower in PVT. While the G value was positively correlated with platelet count (ρ = 0.74, P < 0.01), increased G was associated with PVT after adjusting for platelet count in the logistic regression (P = 0.04). The normalized G value according to the linear relation with platelet count was calculated as follows: G_platelet = [(G - 2622)/platelet count]. This coefficient had no correlation with platelet count and was an independent risk factor of PVT (OR = 1.03, CI_95%: 1.01-1.05, P = 0.012). In two subanalyses, the collagen-induced platelet aggregation (n = 37, P = 0.029) and plasma concentration of sPs (n = 56, P = 0.001) adjusted for platelet count were significantly higher in PVT.

CONCLUSION

This study showed a positive correlation of high platelet-fibrin clot strength detected via TEG and platelet activation with PVT in decompensated cirrhosis.

Machine learning predicts portal vein thrombosis after splenectomy in patients with portal hypertension: Comparative analysis of three practical models

BACKGROUND

For patients with portal hypertension (PH), portal vein thrombosis (PVT) is a fatal complication after splenectomy. Postoperative platelet elevation is considered the foremost reason for PVT. However, the value of postoperative platelet elevation rate (PPER) in predicting PVT has never been studied.

AIM

To investigate the predictive value of PPER for PVT and establish PPER-based prediction models to early identify individuals at high risk of PVT after splenectomy.

METHODS

We retrospectively reviewed 483 patients with PH related to hepatitis B virus who underwent splenectomy between July 2011 and September 2018, and they were randomized into either a training (n = 338) or a validation (n = 145) cohort. The generalized linear (GL) method, least absolute shrinkage and selection operator (LASSO), and random forest (RF) were used to construct models. The receiver operating characteristic curves (ROC), calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC) were used to evaluate the robustness and clinical practicability of the GL model (GLM), LASSO model (LSM), and RF model (RFM).

RESULTS

Multivariate analysis exhibited that the first and third days for PPER (PPER1, PPER3) were strongly associated with PVT [odds ratio (OR): 1.78, 95% confidence interval (CI): 1.24-2.62, P = 0.002; OR: 1.43, 95%CI: 1.16-1.77, P < 0.001, respectively]. The areas under the ROC curves of the GLM, LSM, and RFM in the training cohort were 0.83 (95%CI: 0.79-0.88), 0.84 (95%CI: 0.79-0.88), and 0.84 (95%CI: 0.79-0.88), respectively; and were 0.77 (95%CI: 0.69-0.85), 0.83 (95%CI: 0.76-0.90), and 0.78 (95%CI: 0.70-0.85) in the validation cohort, respectively. The calibration curves showed satisfactory agreement between prediction by models and actual observation. DCA and CIC indicated that all models conferred high clinical net benefits.

CONCLUSION

PPER1 and PPER3 are effective indicators for postoperative prediction of PVT. We have successfully developed PPER-based practical models to accurately predict PVT, which would conveniently help clinicians rapidly differentiate individuals at high risk of PVT, and thus guide the adoption of timely interventions.

Nomogram Model for Prediction of Portal Vein Thrombosis in Patients with Liver Cirrhosis After Splenectomy: A Retrospective Analysis of 2 Independent Cohorts

Background

The aim of this study was to establish and validate an easy-to-use nomogram to predict portal vein thrombosis (PVT) in patients with cirrhosis after splenectomy and to test its predictive ability.

Material/Methods

This retrospective study included 315 patients with cirrhosis who underwent splenectomy at 2 high-volume medical centers. The least absolute shrinkage and selection operator (LASSO) regression method was used to select the predictors in the training cohort, and multivariable logistic regression analysis was performed to establish the predictive nomogram model. We determined the prediction value of the nomogram by the area under the receiver operating characteristic curve (AUROC), the calibration curve, and decision curve analysis. Finally, the applicability of the nomogram was internally and independently validated.

Results

The predictors of PVT included portal vein diameter, splenic vein diameter, body mass index, and platelet count. Based on the clinical and radiomic models, the nomogram had good predictive efficiency for predicting PVT in patients with cirrhosis after splenectomy, with an AUROC of 0.887 (0.856 in internal validation and 0.796 in independent validation). The decision curve analysis revealed that the nomogram had good clinical application value.

Conclusions

We successfully developed an easy-to-use nomogram to predict the probability of PVT in patients with cirrhosis after splenectomy. The nomogram can help clinicians make timely, individualized clinical decisions for PVT in patients with cirrhosis after splenectomy.