COMPETENCY IN MEDICAL KNOWLEDGE: The 6-status ranking system for heart transplant allocation had limited ability in selecting candidates on the basis of medical urgency. CPH and RSF models that
Mini-Focus On Heart Transplantation PolicyClinical ResearchThe Accuracy of Initial U.S. Heart Transplant Candidate Rankings
Central Illustration
Section snippets
Data source and outcome
This retrospective study used data from the Scientific Registry of Transplant Recipients (SRTR). The SRTR data system includes data on all donors, wait-listed candidates, and transplant recipients in the United States, submitted by the members of the Organ Procurement and Transplantation Network. The Health Resources and Services Administration of the U.S. Department of Health and Human Services provides oversight for the activities of the network and registry contractors. This study was
Study group
A total of 33,309 adult heart transplant candidates were listed in the study periods. We excluded 418 heart-lung candidates, 483 patients with data entry errors, and 114 patients who were listed as inactive. The final data set contained 32,294 adult heart transplant candidates (mean age: 53.0 years; 73.7% male), 27,200 candidates in the prepolicy training set and 5,094 candidates in the postpolicy test set (Supplemental Figure 1). Candidate characteristics in both cohorts are shown in Table 1.
Discussion
In this registry cohort study of 32,294 heart transplant candidates, we found that the 6-status heart allocation system had limited accuracy. Only candidates listed in statuses 1 to 4 had significant differences in survival, and status 5 candidates were not correctly ordered. Both the RSF and CPH predictive models had higher Harrell’s C-indices relative to the 6-status system. Objective physiologic measurements, GFR in particular, had high variable importance for predicting waitlist mortality.
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Conclusions
This work demonstrates that both conventional CPH analysis and RSF machine learning models can outperform the 6-status ranking at listing in discriminating candidates by medical urgency. We find that objective physiologic measurements substantially improve the prediction of waitlist mortality.
Funding Support and Author Disclosures
The data reported here have been supplied by the Hennepin Healthcare Research Institute as the contractor for the Scientific Registry of Transplant Recipients (SRTR). The interpretation and reporting of these data are the responsibility of the authors and in no way should be seen as an official policy of or interpretation by the SRTR or the U.S. Government. Dr Parker has received funding from National Institutes of Health grant K08HL150291. All other authors have reported that they have no
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