Incorporating Effects of Time Accrued on the Waiting List into Lung Transplantation Survival Models

Abstract

Rationale: U.S. lung transplant mortality risk models do not account for patients' disease progression as time accrues between mandated clinical parameter updates. Objectives: To investigate the effects of accrued waitlist (WL) time on mortality in lung transplant candidates and recipients beyond those expressed by worsening clinical status and to present a new framework for conceptualizing mortality risk in end-stage lung disease. Methods: Using Scientific Registry of Transplant Recipients data (2015-2020, N = 12,616), we modeled transitions among multiple clinical states over time: WL, posttransplant, and death. Using cause-specific and ordinary Cox regression to estimate trajectories of composite 1-year mortality risk as a function of time from waitlisting to transplantation, we quantified the predictive accuracy of these estimates. We compared multistate model-derived candidate rankings against composite allocation score (CAS) rankings. Measurements and Main Results: There were 11.5% of candidates whose predicted 1-year mortality risk increased by >10% by day 30 on the WL. The multistate model ascribed lower numerical rankings (i.e., higher priority) than CAS for those who died while on the WL (multistate mean; median [interquartile range] ranking at death, 227; 154 [57-334]; CAS median [interquartile range] ranking at death, 329; 162 [11-668]). Patients with interstitial lung disease were more likely to have increasing risk trajectories as a function of time accrued on the WL compared with other lung diagnoses. Conclusions: Incorporating the effects of time accrued on the WL for lung transplant candidates and recipients in donor lung allocation systems may improve the survival of patients with end-stage lung diseases on the individual and population levels.

Keywords: health policy; lung transplantation; organ allocation; statistical methods.

Figure 1.

Predicted composite 1-year mortality risk curves as a function of time accrued for hypothetical candidates. (A) Predicted mortality trajectories for three candidates as a function of time from waitlisting to the day of transplantation. (B) The same mortality predictions with the assumption that candidate 3 is listed 100 days (dashed line) after candidates 1 and 2. In this scenario, candidate 2 remained prioritized until candidate 3’s increasing risk surpassed that of candidate 2 on day 136 (star). All candidates were 63 years old, receiving varying amounts of oxygen at rest as indicated, did not need assistance for activities of daily living, and did not have an increase in partial CO₂ pressure >15%. The remaining composite allocation score predictor variables (e.g., body mass index, creatinine, bilirubin) of the candidates were set to the median value based on the candidates’ respective diagnosis group. COPD = chronic obstructive pulmonary disease; IPF = idiopathic pulmonary fibrosis. Reprinted by permission from Reference .

Figure 2.

Illustration of the sensitivity of candidates’ mortality risk as a function of accrued time on the waiting list. Estimated predicted 1-year mortality as a function of time accrued for all candidates who were placed on the waiting list between April 1, 2018, and September 1, 2018. Patients are ranked for each day with respect to predicted risk within the 3-month interval spanning August 1, 2018, and October 31, 2018. Displayed in the top row are the trajectories of rankings for 15 randomly sampled candidates from each diagnosis group. In the bottom row, for comparison, are the trajectories of rankings using the waitlist (50% weighted) and posttransplant (50% weighted) risk components of the composite allocation score for the same candidates. A lower numerical rank indicates higher relative priority. Highlighted in color are examples of candidates who would be ranked differently under the multistate model compared with the composite allocation score. The highlighted candidate in group C saw a change in ranking from 760 on the day of being placed on the waiting list to a ranking of 30 after 36 days spent on the waiting list. The highlighted candidates in group B and D each steadily increased in priority as a function of time spent on the waiting list. This trend was not consistent for all candidates. For example, in group A, some candidates’ rankings increased over time, some were stable, and some decreased. Diagnosis groups are as follows: group A, obstructive lung disease; group B, pulmonary vascular disease; group C, cystic fibrosis and immunodeficiency disorders; and group D, restrictive lung disease. CAS = composite allocation score.

Figure 3.

Effect of accrued waitlist time on 1-year mortality risk in a population of 12,616 lung transplant candidates. Displayed are bivariate density plots of 1-year predicted mortality at the time of waitlisting (x-axis) versus change in 1-year predicted mortality associated with a 30-day increase in waitlist time (y-axis). The color saturation (density) in the figure reflects the number of patients with a particular combination of baseline risk and the risk associated with a 30-day increase in waitlist time (i.e., time accrued) delay. Shown are all 12,616 candidates (A) and distributions stratified by diagnosis group (B). Diagnosis groups are as follows: group A, obstructive lung disease; group B, pulmonary vascular disease; group C, cystic fibrosis and immunodeficiency disorders; and group D, restrictive lung disease.