Developing a tool to predict the likelihood of undergoing orthotopic cardiac transplant from the urgent waitlist - a single centre UK experience.

Abstract

Background: Orthotopic Cardiac Transplantation (OCTx) improves survival in advanced heart failure. Currently, a tool in United Kingdom from NHS Blood and Transplant (NHSBT) helps predict likelihood of OCTx from waitlist. However, it does not use predictive variables such as age, or Human Leukocyte Antibody (HLA%). We aimed to develop OCTx predictive models incorporating known prognostic variables at 3-, 6-, 9- and 12-months.

Methods: All patients who were urgent-listed for OCTx at Harefield Hospital between 2014 and 2018 (n = 125) were analysed. Variables included age, gender, blood group (BG), midline sternotomy, ventricular assist device (VAD), body mass index (BMI) and HLA%. Multivariable logistic regression models were constructed following internal validation per timepoint. A separate validation dataset was collected using 52 patients transplanted between 2019 and 2023, to compare model effectiveness against the current NHSBT tool.

Results: At 3-months, variables included were age, gender, sternotomy, BG O and HLA%=0, with model area under curve (AUC) of 0.74 (0.66-0.83 95 % confidence interval [CI]). 6-month model included variables age, gender, BG O, sternotomy, BMI and HLA%=0, model AUC of 0.80 (0.72-0.89 95 % CI). 9-month model used age, BG O, VAD, BMI and HLA%=0, giving an AUC of 0.80 (0.71-0.89 95 % CI). The final 12-month model included midline sternotomy, BMI and HLA%=0 and HLA%=1-24, with AUC 0.78 (0.68-0.88 95 % CI). Our predictive models recorded an 85 % win-ratio compared to the NHSBT tool.

Conclusion: We were able to develop models to predict urgent OCTx, with greater accuracy than the currently available tool. Multicentre external validation would help enable its wider implementation.