Elsevier

JACC: Heart Failure

Volume 11, Issue 5, May 2023, Pages 504-512
JACC: Heart Failure

Mini-Focus On Heart Transplantation Policy
Clinical Research
The Accuracy of Initial U.S. Heart Transplant Candidate Rankings

https://doi.org/10.1016/j.jchf.2023.02.005Get rights and content

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 = 0.80), 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.

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.

A

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.

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

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

References (32)

  • M.S. Slaughter et al.

    Advanced heart failure treated with continuous-flow left ventricular assist device

    N Engl J Med

    (2009)
  • P.C. Austin et al.

    Introduction to the analysis of survival data in the presence of competing risks

    Circulation

    (2016)
  • A.S. Levey et al.

    Using standardized serum creatinine values in the Modification of Diet in Renal Disease study equation for estimating glomerular filtration rate

    Ann Intern Med

    (2006)
  • R.D. Mosteller

    Simplified calculation of body-surface area

    N Engl J Med

    (1987)
  • S. van Buuren et al.

    mice: multivariate imputation by chained equations in R

    J Stat Softw

    (2011)
  • T.M. Therneau et al.

    Modeling Survival Data

    (2013)
  • The authors attest they are in compliance with human studies committees and animal welfare regulations of the authors’ institutions and Food and Drug Administration guidelines, including patient consent where appropriate. For more information, visit the Author Center.

    View full text