Observational cohort study of long-term outcomes of liver transplantation in haemophilia.

INTRODUCTION: Gene therapy is now a reality for individuals with haemophilia, yet little is known regarding the quality-of-life impact of factor correction. As few data exist, and recognizing the analogy to liver transplantation (OLTX), we identified OLTX+ and OLTX- men in the ATHNdataset to compare post-OLTX factor VIII and IX on quality of life (QoL) by Haem-A-QoL and PROMIS-29. METHODS: OLTX- were matched to OLTX+ by age, race, and haemophilia type and severity. Deidentified demographic data, including post-transplant factor levels, genotype and target joint disease were analysed by descriptive statistics. Haem-A-Qol and PROMIS-29 were compared in OLTX+ and OLTX- by student's t-test and univariate regression models. RESULTS: Of 86 people with haemophilia A (HA) or haemophilia B (HB) cared for at 10 haemophilia treatment centers (HTCs), 21 (24.4%) OLTX+ and 65 (75.6%) OLTX- were identified. OLTX+ and OLTX- had a similar frequency of target joint disease (p = .806), HA genotypes, null versus non-null (p = .696), and HIV infection (p = .316). At a median 9.2 years post-OLTX, median FVIII, .63 IU/mL [IQR 0.52-0.97] and FIX, .91 IU/mL [IQR .63-1.32], Haem-A-QoL, PROMIS-29, and HOT scores were comparable. Severe HA/HB had lower post-OLTX 'dealing with haemophilia' scores (p = .022) and higher 'sports and leisure' (p = .010) and 'view of yourself' scores (p = .024) than OLTX+ non-severe participants. Non-caucasian OLTX+ had significantly lower scores in sports and leisure (p = .042), future expectations (p = .021) and total score (p = .010). CONCLUSION: Nine years after OLTX, QoL is comparable to OLTX-, but significantly better in OLTX+ with severe than non-severe disease and in caucasians than non-caucasians.

Application of Artificial Intelligence and Machine Learning Was Not Able to Reliably Predict Poor Outcomes in People With Hemophilia.

Background Artificial intelligence (AI) and machine learning (ML) are currently used in the clinical field to improve the outcome predictions on disease diagnosis and prognosis. However, to date, few AI/ML applications have been reported in rare diseases, such as hemophilia. In this study, taking advantage of the ATHNdataset, an extensive repository of hemostasis and thrombosis data, we aimed to demonstrate the application of AI/ML approaches to build predictive models to identify persons with hemophilia (PwH) who are at risk of poor outcome and to inform providers in clinical decision-making towards helping patients prevent long-term complications. Materials and methods This project was carried out in two steps. First, the data were mined from ATHN 7, a subset study of the ATHNdataset, to determine markers that defined "poor outcome." Second, we applied multiple AI/ML approaches on the ATHNdataset to validate our findings and to develop predictive models to identify PwH at risk of poor outcomes. The classical regression-based predictive model was used as a reference to evaluate the performance of various AI/ML models. Results Our models included features similarly distributed to response variables of interest, resulting in a limited ability to distinguish poor outcomes. Low recall (<53%) resulted in no single model reliably predicting poor outcomes out of all actual positive cases. Our results suggest that, to build a more useful AI/ML model, we may need a larger dataset size along with additional features. Furthermore, our results showed that most of the AI/ML models outperformed the classical logistic regression model in both model accuracy and precision. Conclusions Our AI and ML model showed limited ability to predict poor outcomes in people with hemophilia.

Direct oral anticoagulants in pediatric venous thromboembolism: Experience in specialized pediatric hemostasis centers in the United States.

Background: Before the official US Food and Drug Administration approval in 2021, pediatric hematologists across the United States have used direct oral anticoagulants (DOACs) "off-label" and based on extrapolation from labeling for adults with venous thromboembolism (VTE) and interim results of pediatric-specific DOAC clinical studies. Objectives: The American Thrombosis and Hemostasis Network 15 (ATHN 15) study aimed to characterize the use of DOACs from 2015 to 2021 at 15 specialized pediatric hemostasis centers in the United States, with emphasis on safety and effectiveness. Methods: Eligible participants were those aged 0 to 21 years who had a DOAC included as part of their anticoagulation regimen for the treatment of acute VTE or secondary prevention of VTE. Data were collected for up to 6 months after initiation of the DOAC. Results: A total of 233 participants were enrolled, with a mean age of 16.5 years. Rivaroxaban was the most commonly prescribed DOAC (59.1%) followed by apixaban (38.8%). Thirty-one (13.8%) participants reported bleeding complications while on a DOAC. Major or clinically relevant nonmajor bleeding events occurred in 1 (0.4%) and 5 (2.2%) participants, respectively. Worsening menstrual bleeding was reported in 35.7% of females aged >12 years and occurred more frequently in those using rivaroxaban (45.6%) compared with apixaban (18.9%). The recurrent thrombosis rate was 4%. Conclusion: Pediatric hematologists at specialized hemostasis centers in the United States have been using DOACs for the treatment and prevention of VTEs, primarily in adolescents and young adults. Reported DOAC use showed adequate safety and effectiveness rates.