Lung Transplantation Advanced Prediction Tool: Determining Recipient's Outcome for a Certain Donor. Issue 10 (6th April 2022)

• Record Type: Journal Article
• Authors: Zafar, Farhan; Hossain, Md Monir; Zhang, Yin; Dani, Alia; Schecter, Marc; Hayes, Don; Macaluso, Maurizio; Towe, Christopher; Morales, David L.S.
• Is Part Of: Transplantation. Volume 106:Issue 10(2022)
• Page Start: 2019
• Page End: 2030
• Publication Date: 2022-04-06
• Subjects: Transplantation of organs, tissues, etc -- Periodicals; Transplantation immunology -- Periodicals; 617.95
• DOI: 10.1097/TP.0000000000004131
• Languages: English
• ISSNs: 0041-1337

Abstract

Abstract : Background: Many risk-prediction models for lung transplantation are centered on recipient characteristics and do not account for impact of donor and transplant-related factors or only examine short-term outcomes (eg, predicted 1-y survival). We sought to develop a comprehensive model guiding recipient-donor matching. Methods: We identified double lung transplant recipients (≥12 y old) in the United Network for Organ Sharing Registry (2005–2020) to develop a risk scoring tool. Cohort was divided into derivation and validation sets. A total of 42 recipient, donor, and transplant factors were included in the analysis. Lasso method was used for variable selection. Survival was estimated using Cox-proportional hazard models. An interactive web-based tool was developed for clinical use. Results: A derivation cohort (n = 10 660) informed the model with 13-recipient, 4-donor, and 2-transplant variables. Adjusted risk scores were computed for every transplant and grouped into 3 clusters. Model-estimated survival probabilities were similar to the observed in the validation cohort (n = 4464) for all clusters. The mortality increases for medium- and high-risk groups was similar in both derivation and validation cohorts (C statistics for 1-, 5-, and 10-y survival were 0.67, 0.64, and 0.72, respectively). The web-based application estimated 1-, 5-, 10-y survival and half-life for low- (92%, 73%, 52%; 10.5 y), medium- (89%, 62%, 38%; 7.3 y), and high-risk clusters (85%, 52%, 26%; 5.2 y). Conclusions: Advanced methods incorporating machine/deep learning led to a risk scoring model (including recipient, donor, and transplant factors) and a web-based clinical tool providing short- and long-term survival probabilities for recipient-donor matches. This will enable risk-based matching that could improve utilization of and benefit from a limited donor pool.