Endovascular Alternatives to Transhepatic Shunt Creation.

Portomesenteric decompression is often necessary to treat patients with refractory symptoms of portal hypertension. When transjugular or direct intrahepatic portosystemic shunt creation is not feasible or is inadequate, surgical portosystemic shunt creation is considered, which carries significant morbidity and mortality in these high-risk patients. Surgery is further complicated in patients with portomesenteric thrombosis who require concurrent thrombectomy and long-term anticoagulation. In this article, we outline the technique for performing advanced endovascular alternatives to intrahepatic portosystemic shunt creation including mesocaval and splenorenal shunting. We will also discuss some of the clinical considerations for treating these patients with symptomatic portal hypertension and portomesenteric thrombosis.

Integrated use of PD-1 inhibition and transarterial chemoembolization for hepatocellular carcinoma: evaluation of safety and efficacy in a retrospective, propensity score-matched study.

BACKGROUND: Immune checkpoint inhibitors (ICIs) have revolutionized treatment of advanced hepatocellular carcinoma. Integrated use of transarterial chemoembolization (TACE), a locoregional inducer of immunogenic cell death, with ICI has not been formally assessed for safety and efficacy outcomes. METHODS: From a retrospective multicenter dataset of 323 patients treated with ICI, we identified 31 patients who underwent >1 TACE 60 days before or concurrently, with nivolumab at a single center. We derived a propensity score-matched cohort of 104 patients based on Child-Pugh Score, portal vein thrombosis, extrahepatic metastasis and alpha fetoprotein (AFP) who received nivolumab monotherapy. We described overall survival (OS), progression-free survival (PFS), objective responses according to modified RECIST criteria and safety in the multimodal arm in comparison to monotherapy. RESULTS: Over a median follow-up of 9.3 (IQR 4.0-16.4) months, patients undergoing multimodal immunotherapy with TACE achieved a significantly longer median (95% CI) PFS of 8.8 (6.2-23.2) vs 3.7 (2.7-5.4) months (log-rank 0.15, p<0.01) in the monotherapy group. Multimodal immunotherapy with TACE demonstrated a numerically longer OS compared with ICI monotherapy with a median 35.1 (16.1-Not Evaluable) vs 16.6 (15.7-32.6) months (log-rank 0.41, p=0.12). In the multimodal treatment group, there were three (10%) grade 3 or higher adverse events (AEs) attributed to immunotherapy compared with seven (6.7%) in the matched ICI monotherapy arm. There were no AEs grade 3 or higher attributed to TACE in the multimodal treatment arm. At 3 months following each TACE in the multimodal arm, there was an overall objective response rate of 84%. There were no significant changes in liver functional reserve 1 month following each TACE. Four patients undergoing multimodal treatment were successfully bridged to transplant. CONCLUSIONS: TACE can be safely integrated with programmed cell death 1 blockade and may lead to a significant delay in tumor progression and disease downstaging in selected patients.

Machine Learning Offers Exciting Potential for Predicting Postprocedural Outcomes: A Framework for Developing Random Forest Models in IR.

PURPOSE: To demonstrate that random forest models trained on a large national sample can accurately predict relevant outcomes and may ultimately contribute to future clinical decision support tools in IR. MATERIALS AND METHODS: Patient data from years 2012-2014 of the National Inpatient Sample were used to develop random forest machine learning models to predict iatrogenic pneumothorax after computed tomography-guided transthoracic biopsy (TTB), in-hospital mortality after transjugular intrahepatic portosystemic shunt (TIPS), and length of stay > 3 days after uterine artery embolization (UAE). Model performance was evaluated with area under the receiver operating characteristic curve (AUROC) and maximum F1 score. The threshold for AUROC significance was set at 0.75. RESULTS: AUROC was 0.913 for the TTB model, 0.788 for the TIPS model, and 0.879 for the UAE model. Maximum F1 score was 0.532 for the TTB model, 0.357 for the TIPS model, and 0.700 for the UAE model. The TTB model had the highest AUROC, while the UAE model had the highest F1 score. All models met the criteria for AUROC significance. CONCLUSIONS: This study demonstrates that machine learning models may suitably predict a variety of different clinically relevant outcomes, including procedure-specific complications, mortality, and length of stay. Performance of these models will improve as more high-quality IR data become available.