Reducing donor acceptance practice variation - Learnings from a discussion forum.

PURPOSE: Although waitlist mortality is unacceptably high, nearly half of donor heart offers are rejected by pediatric heart transplant centers. The Advanced Cardiac Therapy Improving Outcome Network (ACTION) and Pediatric Heart Transplant Society (PHTS) convened a multi-institutional donor decision discussion forum (DDDF) aimed at assessing donor acceptance practices and reducing practice variation. METHODS: A 1-h-long virtual DDDF for providers across North America, the United Kingdom, and Brazil was held monthly. Each session typically included two case presentations posing a real-world donor decision challenge. Attendees were polled before the presenting center's decision was revealed. Group discussion followed, including a review of relevant literature and PHTS data. Metrics of participation, participant agreement with presenting center decisions, and impact on future decision-making were collected and analyzed. RESULTS: Over 2 years, 41 cases were discussed. Approximately 50 clinicians attended each call. Risk factors influencing decision-making included donor quality (10), size discrepancy (8), and COVID-19 (8). Donor characteristics influenced 63% of decisions, recipient factors 35%. Participants agreed with the decision made by the presenting center only 49% of the time. Post-presentation discussion resulted in 25% of participants changing their original decision. Survey conducted reported that 50% respondents changed their donor acceptance practices. CONCLUSION: DDDF identified significant variation in pediatric donor decision-making among centers. DDDF may be an effective format to reduce practice variation, provide education to decision-makers, and ultimately increase donor utilization.

Taking ACTION: A Prognostic Tool for Pediatric Ventricular Assist Device Mortality.

We sought to develop a contemporary risk assessment tool for use in pediatric ventricular assist device (VAD) candidates to estimate risk for mortality on the device using readily available preimplantation clinical data. Training and testing datasets were created from Advanced Cardiac Therapies Improving Outcomes Network (ACTION) registry data on patients supported with a VAD from 2012 to 2021. Potential risk factors for mortality were assessed and incorporated into a simplified risk prediction model utilizing an open-source, gradient-boosted decision tree machine learning library, known as random forest. Predictive performance was assessed by the area under the receiver operating characteristic curve in the testing dataset. Nine significant risk factors were included in the final predictive model which demonstrated excellent discrimination with an area under the curve of 0.95. In addition to providing a framework for establishing pediatric-specific risk profiles, our model can help inform team expectations, guide optimal patient selection, and ultimately improve patient outcomes.