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Optimal Donor Selection for Hematopoietic Cell Transplantation Using Bayesian Machine Learning.

Brent R Logan^1,2, Martin J Maiers³, Rodney A Sparapani¹

¹Division of Biostatistics, Institute for Health and Equity, Medical College of Wisconsin (MCW), Milwaukee, WI.

JCO Clinical Cancer Informatics

|May 5, 2021

Summary

This summary is machine-generated.

Selecting the youngest matched unrelated donor (MUD) can improve hematopoietic cell transplantation (HCT) outcomes. Machine learning models can optimize donor selection for better patient results.

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Area of Science:

Hematopoietic Cell Transplantation (HCT)
Machine Learning in Medicine
Immunology

Background:

Donor selection for matched unrelated donor (MUD) hematopoietic cell transplantation (HCT) practices vary.
The impact of optimizing donor selection using machine learning (ML) has not been studied.

Purpose of the Study:

To study the impact of optimizing donor selection in a patient-specific way using modern machine learning (ML) models for MUD HCT.
To predict clinically severe acute graft-versus-host disease or death by day 180.

Main Methods:

Trained a Bayesian ML model on 10,318 patients who underwent MUD HCT (1999-2014).
Validated the model on 3,501 patients (2015-2016) with archived donor records.
Implemented donor selection optimizing predicted outcomes from an unlimited donor pool or search archives.

Main Results:

Event rates were 33% (training) and 37% (validation).
Only donor age affected outcomes, consistently across patient features.
Selecting the youngest donor reduced event rates by approximately 5% in 14% of cases, leading to a 1% absolute population reduction.

Conclusions:

Confirmed the importance of selecting the youngest MUD, irrespective of patient features.
Identified potential for improved HCT outcomes by selecting a younger MUD.
Demonstrated transferable ML models for optimizing complex, patient-specific treatment decisions.