Kilic, Arman

BACKGROUND: A preservation system, the Organ Care System (OCS; TransMedics) uses normothermic pulsatile perfusion during organ transport for heart transplantation. This system has demonstrated favorable outcomes in hearts recovered from extended-criteria donors after brain death (DBD) and donors after circulatory death (DCD). METHODS: The OCS Heart Perfusion Registry collects data on US heart transplantations using the OCS, static cold storage (SCS), or thoracoabdominal normothermic regional perfusion (NRP) and donor hearts recovered from DBD or DCD donors. We analyzed donor and recipient characteristics and posttransplantation outcomes in patients transplanted with OCS donor hearts (either DBD or DCD) compared with SCS hearts, and with OCS hearts from DCD donors compared with those recovered with NRP followed by SCS. Propensity score matching was used in survival analyses to adjust for differences among recipient characteristics. RESULTS: Between 2021 and 2023, 3225 consecutive heart transplantations enrolled from 56 centers were analyzed in the Heart Perfusion Registry. The OCS was used in 854 of 3225 heart transplantations (26.4%), among which 340 (39.8%) were DBD and 514 (60.2%) were DCD donors, whereas 2174 DBD donors were recovered with SCS and another 197 DCD donors with NRP techniques. The OCS-DBD group experienced a greater number of organ offer refusals before final acceptance (13 versus 6; Wilcoxon rank sum, P <0.001) and a longer transport distance (667 miles versus 232 miles; Wilcoxon rank sum, P <0.001) compared with SCS-DBD. Survival at 12 months was similar between the 2 groups (89.9% for OCS-DBD versus 90.6% for SCS-DBD; marginal Cox model, P =0.54). Among the OCS-DCD and SCS-DBD groups, survival at 12 months was also similar (91.0% versus 92.5%, respectively; marginal Cox model, P =0.32). The OCS-DCD and NRP-DCD groups demonstrated similar survival (91.0% versus 91.7%, respectively; log rank, P =0.63), although the transport distance was longer in OCS-DCD compared with DCD with NRP followed by SCS (400 miles versus 223 miles; Wilcoxon rank sum, P <0.001). By 2023, 90% of all OCS donor management and recovery was performed with dedicated organ recovery teams. CONCLUSIONS: We demonstrate that real-world implementation of the OCS for DBD donors (using predominantly a dedicated recovery team) is associated with expanded donor criteria, longer transport distance, and excellent posttransplantation outcomes. In OCS-DCD donors, outcomes parallel those of donors recovered with NRP-DCD and compare favorably with DBD donor organs.

Background This study evaluated the role of supplementing Society of Thoracic Surgeons (STS) risk models for surgical aortic valve replacement with machine learning (ML). Methods and Results Adults undergoing isolated surgical aortic valve replacement in the STS National Database between 2007 and 2017 were included. ML models for operative mortality and major morbidity were previously developed using extreme gradient boosting. Concordance and discordance in predicted risk between ML and STS models were defined using equal‐size tertile‐based thresholds of risk. Calibration metrics and discriminatory capability were compared between concordant and discordant patients. A total of 243 142 patients were included. Nearly all calibration metrics were improved in cases of concordance. Similarly, concordance indices improved substantially in cases of concordance for all models with the exception of deep sternal wound infection. The greatest improvements in concordant versus discordant cases were in renal failure: ML model (concordance index, 0.660 [95% CI, 0.632–0.687] discordant versus 0.808 [95% CI, 0.794–0.822] concordant) and STS model (concordance index, 0.573 [95% CI, 0.549–0.576] discordant versus 0.797 [95% CI, 0.782–0.811] concordant) (each P <0.001). Excluding deep sternal wound infection, the concordance indices ranged from 0.549 to 0.660 for discordant cases and 0.674 to 0.808 for concordant cases. Conclusions Supplementing ML models with existing STS models for surgical aortic valve replacement may have an important role in risk prediction and should be explored further. In particular, for the roughly 25% to 50% of patients demonstrating discordance in estimated risk between ML and STS, there appears to be a substantial decline in predictive performance suggesting vulnerability of the existing models in these patient subsets.