Machine Learning Prediction Model for Neutrophil Recovery after Unrelated Cord Blood Transplantation.

BACKGROUND

Delayed neutrophil recovery is an important limitation to the administration of cord blood transplantation (CBT) and leaves the recipient vulnerable to life-threatening infection and increases the risk of other complications.

OBJECTIVES

A predictive model for neutrophil recovery after single-unit CBT was developed by using a machine learning method, which can handle large and complex datasets, allowing for the analysis of massive amounts of information to uncover patterns and make accurate predictions.

STUDY DESIGN

Japanese registry data, the largest real-world dataset of CBT, was selected as the data source. Ninety-eight variables with observed values for more than 80% of the subjects known at the time of CBT were selected. Model building was performed with a competing risk regression model with lasso penalty. Prediction accuracy of the models was evaluated by calculating area under curve (AUC) using a test dataset. The primary outcome was neutrophil recovery at day 28 (D28), and D14 and D42 were analyzed as secondary outcomes.

RESULT

The final cord blood engraftment prediction (CBEP) models included 2,991 single-unit CBT recipients with acute leukemia. Median AUC of a D28-CBEP lasso regression model run 100 times was 0.74, and those of D14 and D42 were 0.88 and 0.68, respectively. The D28-CBEP model predictivity was higher than four different legacy models that were separately constructed.

CONCLUSIONS

A highly predictive model for neutrophil recovery by 28 days after CBT was constructed using machine learning techniques. However, identification of significant risk factors was insufficient for outcome prediction for an individual patient, which is necessary for improving therapeutic outcomes. Notably, the prediction accuracy for days 14, 28, and 42 post-transplant decreased, and the model became more complex with more associated factors with increased time after transplantation.

Informations de copyright

Copyright © 2024. Published by Elsevier Inc.

Déclaration de conflit d'intérêts

Declaration of competing interest Yku reports institutional grant funding from the Ministry of Health. RY reports institutional grant funding from Japan Agency for Medical Research and Development and the Ministry of Health; payment for manuscript writing, presentation and educational event from Amelieff Corporation; and lecture fee from Novartis Pharmaceuticals Corporation. MT reports grants from Chugai Pharmaceutical; honoraria from Abbvie GK., Kyowa-Kirin, Daiichi-Sankyo, Sumitomo Pharma, Astellas Pharma, Pfizer, Otsuka Pharmaceutical, MSD and Asahi Kasei Pharma. SI reports collaborative research grants from Daiichi Sankyo RD Novare Co., Ltd., Astellas Pharma Inc., and BrightPath Biotherapeutics Co., Ltd.; payment for manuscript writing, presentation and educational event from Amelieff Corporation; and lecture fee from Novartis Pharmaceuticals Corporation. YA reports institutional grant funding from Japan Agency for Medical Research and Development and the Ministry of Health; consulting fees from JCR Pharmaceuticals Co., Ltd. and Kyowa Kirin Co., Ltd.; lecture fees from Otsuka Pharmaceutical Co., Ltd, Chugai Pharmaceutical Co., Ltd., Novartis Pharma KK, AbbVie GK; and honorarium from Meiji Seika Pharma Co, Ltd. ST reports institutional grant funding from Japan Agency for Medical Research and Development the Ministry of Health, and Daiichi Sankyo RD Novare Co., Ltd. The other authors declare no competing financial interests.