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3552 Is the Youngest Donor Always the Best Choice to Optimize Outcomes for Matched Unrelated Allogeneic Transplant? Improving Precision Using Novel Statistical Methodology

Program: Oral and Poster Abstracts
Session: 722. Allogeneic Transplantation: Acute and Chronic GVHD, Immune Reconstitution: Poster II
Hematology Disease Topics & Pathways:
Sunday, December 10, 2023, 6:00 PM-8:00 PM

Rodney A. Sparapani, PhD1*, Martin J. Maiers, MS2*, Stephen R. Spellman, MBS3*, Bronwen E. Shaw, PhD, MRCP, FRCPath4,5, Purushottam W. Laud, PhD6*, Steven M. Devine, MD2, Caitrin Bupp, MPH2*, Meilun He, MPH2* and Brent R. Logan, PhD4,6*

1Division of Biostatistics, Medical College of Wisconsin, Milwaukee, WI
2CIBMTR® (Center for International Blood and Marrow Transplant Research), National Marrow Donor Program/Be The Match, Minneapolis
3CIBMTR® (Center for International Blood and Marrow Transplant Research), National Marrow Donor Program/Be The Match, Minneapolis, MN
4CIBMTR® (Center for International Blood and Marrow Transplant Research), Medical College of Wisconsin, Milwaukee
5Department of Medicine, Medical College of Wisconsin, Milwaukee
6Division of Biostatistics, Medical College of Wisconsin, Milwaukee

INTRODUCTION Our recently published research has shown younger donors have superior survival for recipients of allogeneic hematopoietic stem cell transplant (HSCT). But, previously, we had not explored the donor age impact on other outcomes, or the impact of smaller age differences (<4 years) when balanced against other factors. We report results using our recently developed machine learning survival methodology, Nonparametric Failure Time Bayesian Additive Regression Trees (NFT BART). Here we study patient-specific optimization of donor selection for overall survival (OS) and event-free survival (EFS including events of death, relapse, graft failure/rejection, or moderate/severe chronic graft vs. host disease, GVHD).

METHODS The patient data was provided by the Center for International Blood and Marrow Transplant Research. Each recipient underwent matched unrelated donor HSCT from 2016 to 2019 and was followed for OS and EFS outcomes up to three years. We trained our NFT BART model on 10,016 patients to provide predictions for OS and EFS. The model was validated on 1,802 patients among whom 699 had archived unrelated donor search records available from the National Marrow Donor Program. We examined donor age, sex/parity, CMV status, DPB1 nonpermissive mismatching, and DQB1 matching, while adjusting for relevant patient characteristics. We further restricted donor factors to those with a >1% positive impact on 3-year OS and/or EFS. Using archived search data for each patient, we examined three optimal strategies for donor selection: OS only, EFS only and a 2:1 weighted average between OS and EFS (denoted OS2:EFS1). The OS2:EFS1 effectively prioritizes optimizing OS while still allowing EFS consideration when the difference in OS predictions between two potential donors is minimal. We calculated predicted OS and EFS outcomes for each patient under all three optimal donor strategies. These predicted outcomes were benchmarked against observed practice by subtracting the predicted outcomes from the actual donor used for the transplant.

RESULTS Among donor features, only age (for OS and EFS) and sex (for EFS) had clinically important impacts on outcomes. For OS, the youngest donor provided the best result regardless of patient characteristics: see the waterfall plot presented in Figure 1. As shown, the older the donor, the greater the impact of selection by age. Compared to donors 34+, use of an 18 year-old donor is associated with a substantial increase in OS where all of the recipient OS differentials are above 0.01. Compared to donors 22 to 30, the impact of an 18 year-old donor is negligible. For EFS, donor sex was the dominant factor, with males having a median 4% improvement in EFS at 3 years compared to females, while the difference for 42 vs. 18 was <2%. Optimizing for OS led to selection of the youngest donor regardless of sex, while optimizing EFS led to selection of the youngest male donor. Optimizing OS2:EFS1 generally picked the youngest donor except for selections of a male donor that may be older than the female when the age difference is small (median is 1 year older: Q1=0, Q3=2). In said cases, the OS detriment was minimal (median=-0.0005, Q1=-0.0008, Q3=-0.0003) with a substantial improvement in EFS (median=0.038, Q1=0.031, Q3=0.042) gained by switching to the male donor. Individual patient differences (OS2:EFS1 vs. actual donor) in OS vs. EFS are plotted in Figure 2.

CONCLUSION Using our novel NFT BART methodology, we showed that donor selection to optimize HSCT outcomes can be improved by considering both OS and EFS. This weighted optimization strategy balances outcomes, prioritizing OS while also allowing for consideration of EFS when OS is likely similar between donors. Additionally, this supports previous research showing that choosing among donors from 18 to 30 with resultant minimal differences in outcomes that are negligible allowing for more flexibility in donor selection.

Disclosures: Logan: Enlivex: Consultancy.

*signifies non-member of ASH