The accuracy of initial U.S. heart transplant candidate rankings

Authors

Kenley M. Pelzer, Department of Medicine, University of Chicago, Chicago, Illinois, USA.
Kevin C. Zhang, Department of Medicine, University of Chicago, Chicago, Illinois, USA.
Kevin A. Lazenby, Pritzker School of Medicine, University of Chicago, Chicago, Illinois, USA.
Nikhil Narang, Advocate Aurora HealthFollow
Matthew M. Churpek, Department of Medicine, University of Wisconsin Madison, Madison, Wisconsin, USA.Follow
Allen S. Anderson, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA.Follow
William F. Parker, Department of Medicine, University of Chicago, Chicago, Illinois, USA; MacLean Center for Medical Ethics, University of Chicago, Chicago, Illinois, USA. Electronic address: wparker@uchicago.edu.

Recommended Citation

Pelzer KM, Zhang KC, Lazenby KA, et al. The Accuracy of Initial U.S. Heart Transplant Candidate Rankings. JACC Heart Fail. 2023;11(5):504-512. doi:10.1016/j.jchf.2023.02.005

Affiliations

Advocate Christ Medical Center

Abstract

Background: The U.S. heart allocation system ranks candidates with only 6 treatment-based categorical "statuses" and ignores many objective patient characteristics.

Objectives: This study sought to determine the effectiveness of the standard 6-status ranking system and several novel prediction models in identifying the most urgent heart transplant candidates.

Methods: The primary outcome was death before receipt of a heart transplant. The accuracy of the 6-status system was evaluated using Harrell's C-index and log-rank tests of Kaplan-Meier estimated survival by status for candidates listed postpolicy (November 2018 to March 2020) in the Scientific Registry of Transplant Recipients data set. The authors then developed Cox proportional hazards models and random survival forest models using prepolicy data (2010-2017). The predictor variables included age, diagnosis, laboratory measurements, hemodynamics, and supportive treatment at the time of listing. The performance of these models was compared with the candidate's 6-status ranking in the postpolicy data.

Results: Since policy implementation, the 6-status ranking at listing has had moderate ability to rank-order candidates (C-index: 0.67). Statuses 4 and 6 had no significant difference in survival (P = 0.8), and status 5 had lower survival than status 4 (P < 0.001). Novel multivariable prediction models derived with prepolicy data ranked candidates correctly more often than the 6-status rankings (Cox proportional hazards model C-index: 0.76; random survival forest model C-index: 0.74). Objective physiologic measurements, such as glomerular filtration rate, had high variable importance.

Conclusions: The treatment-based 6-status heart allocation system has only moderate ability to rank-order candidates by medical urgency. Predictive models that incorporate physiologic measurements can more effectively rank-order heart transplant candidates by urgency.

Document Type

Article

PubMed ID

37052549