Abstract

The purpose was to predict the risk of acute kidney injury (AKI) within 100 days after hematopoietic stem cell transplantation (HSCT) in patients with hematologic disease by using a new predictive nomogram. Collect clinical data of patients with hematologic disease undergoing HSCT in our hospital from August 2012 to March 2018. Parameters with non-zero coefficients were selected by the Least Absolute Selection Operator (LASSO). Then these parameters were selected to build a new predictive nomogram model. Receiver operating characteristic (ROC) curve, calibration curve, C-index, and decision curve analysis (DCA) were used for the validation of the evaluation model. Finally, the nomogram was further evaluated by internal verification. According to 2012 Kidney Disease Improving Global Guidelines (KDIGO) diagnostic criteria, among 144 patients, the occurrence of AKI within 100 days after HSCT The rate was 29.2% (42/144). The C-index of the nomogram was 0.842. The C-value calculated by the internal verification was 0.809. The AUC was 0.842, and The DCA range of the predicted nomogram was from 0.01 to 0.71. This article established a high-precision nomogram for the first time for predicting the risk of AKI within 100 days after HSCT in patients with hematologic diseases. The nomogram had good clinical validity and reliability. For clinicians, it was very important to prevent AKI after HSCT.

Details

Title
Predicting the risk of acute kidney injury after hematopoietic stem cell transplantation: development of a new predictive nomogram
Author
Gan, Zhaoping 1 ; Chen, Liyi 2 ; Wu, Meiqing 1 ; Liu, Lianjin 1 ; Shi, Lingling 1 ; Li, Qiaochuan 1 ; Zhang, Zhongming 1 ; Lai, Yongrong 1 

 Guangxi Medical University First Affiliated Hospital, Department of Hematology, Nanning, China (GRID:grid.256607.0) (ISNI:0000 0004 1798 2653) 
 Guangxi Medical University First Affiliated Hospital, Spine and Osteopathy Ward, Nanning, China (GRID:grid.256607.0) (ISNI:0000 0004 1798 2653) 
Publication year
2022
Publication date
2022
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2713142857
Copyright
© The Author(s) 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.