Florman, Sander S

Background: Intracardiac thrombosis (ICT) during liver transplantation (LT) is a rare but life-threatening complication with limited data regarding its risk factors, management, and outcomes. This study aimed to identify factors associated with ICT development, define predictors of intraoperative outcomes, and propose a novel classification and management algorithm for ICT. Methods: A multicenter, international retrospective case-control study was conducted on liver transplant recipients from seven centers between January 2005 and December 2023. ICT cases were identified through transesophageal echocardiography (TEE) or autopsy and matched 1:1 with controls. Results: Among 11 077 liver transplant recipients screened, 133 patients (1.2%) developed ICT. ICT occurred predominantly after reperfusion (58%). Compared to controls, ICT cases exhibited higher preoperative international normalized ratio ( P = 0.001) and fibrinogen levels ( P = 0.014). Post-reperfusion syndrome ( P < 0.001), disseminated intravascular coagulation ( P < 0.001), and primary graft failure ( P = 0.002) were also more common in the ICT group. Continuous veno-venous hemofiltration during surgery (odds ratio [OR] 8.20, 95% confidence interval [CI]: 2.09–38.1; P = 0.004) was associated with ICT development. ICT was associated with markedly higher rates of cardiac arrest ( P < 0.001) and intraoperative mortality ( P < 0.001), with thrombus location in the left heart chambers representing a significant predictor of mortality (OR 20.7, 95% CI: 6.25–81.3; P < 0.001). ICT grades 2 and 3 were associated with a markedly increased risk of intraoperative cardiac arrest ( P < 0.05). Importantly, delayed use of TEE was strongly associated with intraoperative death (OR 6.27, 95% CI: 1.45–27.1; P = 0.014). The 90-day mortality was 43% in ICT cases compared to 4.5% in controls, while 1-year mortality was 46% versus 10.5%, respectively. Conclusions: ICT during LT is a rare but severe complication associated with intraoperative mortality and poor short-term survival. Coagulation profile and intraoperative management play critical roles in ICT development. The early use of TEE enables timely diagnosis and intervention, improving patient outcomes. We propose a novel ICT classification offering a systematic framework for risk stratification and management to mitigate progression and mortality.

HCC recurrence following liver transplantation (LT) is highly morbid and occurs despite strict patient selection criteria. Individualized prediction of post-LT HCC recurrence risk remains an important need. Clinico-radiologic and pathologic data of 4981 patients with HCC undergoing LT from the US Multicenter HCC Transplant Consortium (UMHTC) were analyzed to develop a REcurrent Liver cAncer Prediction ScorE (RELAPSE). Multivariable Fine and Gray competing risk analysis and machine learning algorithms (Random Survival Forest and Classification and Regression Tree models) identified variables to model HCC recurrence. RELAPSE was externally validated in 1160 HCC LT recipients from the European Hepatocellular Cancer Liver Transplant study group. Of 4981 UMHTC patients with HCC undergoing LT, 71.9% were within Milan criteria, 16.1% were initially beyond Milan criteria with 9.4% downstaged before LT, and 12.0% had incidental HCC on explant pathology. Overall and recurrence-free survival at 1, 3, and 5 years was 89.7%, 78.6%, and 69.8% and 86.8%, 74.9%, and 66.7%, respectively, with a 5-year incidence of HCC recurrence of 12.5% (median 16 months) and non-HCC mortality of 20.8%. A multivariable model identified maximum alpha-fetoprotein (HR = 1.35 per-log SD, 95% CI,1.22–1.50, p < 0.001), neutrophil-lymphocyte ratio (HR = 1.16 per-log SD, 95% CI,1.04–1.28, p < 0.006), pathologic maximum tumor diameter (HR = 1.53 per-log SD, 95% CI, 1.35–1.73, p < 0.001), microvascular (HR = 2.37, 95%–CI, 1.87–2.99, p < 0.001) and macrovascular (HR = 3.38, 95% CI, 2.41–4.75, p < 0.001) invasion, and tumor differentiation (moderate HR = 1.75, 95% CI, 1.29–2.37, p < 0.001; poor HR = 2.62, 95% CI, 1.54–3.32, p < 0.001) as independent variables predicting post-LT HCC recurrence (C-statistic = 0.78). Machine learning algorithms incorporating additional covariates improved prediction of recurrence (Random Survival Forest C-statistic = 0.81). Despite significant differences in European Hepatocellular Cancer Liver Transplant recipient radiologic, treatment, and pathologic characteristics, external validation of RELAPSE demonstrated consistent 2- and 5-year recurrence risk discrimination (AUCs 0.77 and 0.75, respectively). We developed and externally validated a RELAPSE score that accurately discriminates post-LT HCC recurrence risk and may allow for individualized post-LT surveillance, immunosuppression modification, and selection of high-risk patients for adjuvant therapies.