期刊
AMERICAN JOURNAL OF HEMATOLOGY
卷 97, 期 10, 页码 1309-1323出版社
WILEY
DOI: 10.1002/ajh.26671
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资金
- Bundesministerium fur Bildung und Forschung [031L0027A-F]
- Deutsche Forschungsgemeinschaft [EXC 2064/1, 390727645, FU 356/12-1]
This study developed machine learning models to predict mortality and cytomegalovirus reactivation after allogeneic hematopoietic cell transplantation. The models integrated baseline patient data and time-dependent laboratory measurements, providing accurate risk predictions in a 21-day time window.
Allogeneic hematopoietic cell transplantation (HCT) effectively treats high-risk hematologic diseases but can entail HCT-specific complications, which may be minimized by appropriate patient management, supported by accurate, individual risk estimation. However, almost all HCT risk scores are limited to a single risk assessment before HCT without incorporation of additional data. We developed machine learning models that integrate both baseline patient data and time-dependent laboratory measurements to individually predict mortality and cytomegalovirus (CMV) reactivation after HCT at multiple time points per patient. These gradient boosting machine models provide well-calibrated, time-dependent risk predictions and achieved areas under the receiver-operating characteristic of 0.92 and 0.83 and areas under the precision-recall curve of 0.58 and 0.62 for prediction of mortality and CMV reactivation, respectively, in a 21-day time window. Both models were successfully validated in a prospective, non-interventional study and performed on par with expert hematologists in a pilot comparison.
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