Impact d’un traitement pont par radiofréquence percutanée chez les patients porteurs d’hépatocarcinomes en attente de transplantation hépatique : modélisation de Markov et analyse de base de données locale
Hepatocellular carcinoma (CHC) is the most common liver cancer and represent the fifth most frequent tumor in the world, accounting for 7% of all cancers worldwide. There are multiple treatments of various efficacies but orthotopic liver transplantation (OLT) remains the ultimate best treatment for this pathology. Unfortunately, hepatic transplants are scarse and patients with CHC could spend several months on the waiting list before the transplantation. Percutaneous radiofrequency ablation (RFA) of CHC is an efficacious treatment but there are few studies evaluating its use for controlling the tumoral burden in a context of patients on the waiting list for OLT. Aim: The aim of this study is to evaluate the efficiency of RFA on that cohort of patients. The primary endpoint is the overall survival of these patients and the secondary endpoint is to evaluate the reduction of the dropout of these patients from the waiting list. Method: Two methods were used. The first was to create a Markov model to simulate a randomized trial between group RFA and group NT (No treatment). The estimates of the probabilities were chosen from the scientific literature after an extensive review. Analysis of the overall survival, risk of exclusion from the transplantation list and chance of transplantation were evaluated for both groups. Uncertainty analysis with One-way sensitivity tests were performed to control the robustness of the model and to evaluate the different strategies. The second method was an analysis of the the CHUM database for patients awaiting liver transplantation between 2009 and 2016 (133 patients). We also analyzed the risk of exclusion from the transplant list, the chances of transplantation and the overall survival for both subgroups (patients treated with RFA and untreated patients). Finally, we compared the results of our cohort with those of the virtual cohort from our Markov model. Results: In the Markov model, two hypotheses were tested according to a probability of survival post transplantation identical or different in the two groups (RFA and NT). For both hypotheses there is a superiority of the RFA group over the NT group for each of the three strategies (minimizing the exclusion of the list, maximizing the transplantation and the survival)  Analyse of the strategies showed a superiority for the RFA group vs the no treatment group either for a single probability after transplantation or for two different probabilities for the two groups.   In our local database, overall dropout rate was conversely slightly higher in the RFA group vs AT group (12.5% vs 9.3%). Survival on the waiting list remained better in the RFA group versus AT group (93.7% vs 87.5%) and survival after transplantation was also better in the RFA group (91% vs 87.5%). Conclusion: the results of dropout rates from the transplant list appear contradictory between the Markov model and our local database probably because of the small number of patients in the subgroup of our database, because we had to exclude all patients who had received treatment other than RFA which reduced the number of patients available for the study substantially. Nevertheless, analysis of both Markov model and local database shows better overall survival in the RFA group than the NT group especially before transplantation. There is also a superiority of the RFA for the probability of transplantation in the Markov model. It also shows that the longer the patient waits on the transplant list the more beneficial the RFA treatment will be.
transplantation Markov Hepatocellular carcinoma Liver Radiofrequency ablation