External validation and comparison of risk score models in pediatric heart transplants. (8th December 2021) - Dani, Alia; Heidel, Justin S.; Qiu, Tingting; Zhang, Yin; Ni, Yizhao; Hossain, Md Monir; Chin, Clifford; Morales, David L. S.; Huang, Bin; Zafar, Farhan
Abstract: Background: Pediatric heart transplant (PHT) patients have the highest waitlist mortality of solid organ transplants, yet more than 40% of viable hearts are unutilized. A tool for risk prediction could impact these outcomes. This study aimed to compare and validate the PHT risk score models (RSMs) in the literature. Methods: The literature was reviewed to identify RSMs published. The United Network for Organ Sharing (UNOS) registry was used to validate the published models identified in a pediatric cohort (<18 years) transplanted between 2017 and 2019 and compared against the Scientific Registry of Transplant Recipients (SRTR) 2021 model. Primary outcome was post‐transplant 1‐year mortality. Odds ratios were obtained to evaluate the association between risk score groups and 1‐year mortality. Area under the curve (AUC) was used to compare the RSM scores on their goodness‐of‐fit, using Delong's test. Results: Six recipient and one donor RSMs published between 2008 and 2021 were included in the analysis. The validation cohort included 1, 003 PHT. Low‐risk groups had a significantly better survival than high‐risk groups as predicted by Choudhry (OR = 4.59, 95% CI [2.36–8.93]) and Fraser III (3.17 [1.43–7.05]) models. Choudhry's and SRTR models achieved the best overall performance (AUC = 0.69 and 0.68, respectively). When adjusted for CHD and ventricular assist device support, all models reported better predictability [AUC > 0.6]. Choudhry (AUC = 0.69) and SRTR (AUC = 0.71) remained the best predicting RSMs even after adjustment. Conclusion: Although the RSMs by SRTR and Choudhry provided the best prediction for 1‐year mortality, none demonstrated a strong (AUC ≥ 0.8) concordance statistic. All published studies lacked advanced analytical approaches and were derived from an inherently limited dataset.