Prognostic analysis and predictive model construction of bleeding events in allogeneic hematopoietic stem cell transplant patients
10.3760/cma.j.issn.0253-2727.2022.06.007
- VernacularTitle:异基因造血干细胞移植后出血患者预后分析和预测模型构建
- Author:
Jiaqian QI
1
;
Tao YOU
;
Hong WANG
;
Wei HAN
;
Yi FAN
;
Jia CHEN
;
Depei WU
;
Yue HAN
Author Information
1. 苏州大学附属第一医院、江苏省血液研究所、国家血液系统疾病临床医学研究中心、苏州大学造血干细胞移植研究所、血液学协同创新中心，苏州 215006
- Keywords:
Allogeneic hematopoietic stem cell transplantation;
Hemorrhage;
Forecasting model
- From:
Chinese Journal of Hematology
2022;43(6):481-487
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To study hematopoietic stem cell transplantation-related bleeding prognosis and construct a bleeding prediction model.Methods:The clinical data of 555 patients with malignant hematologic diseases who underwent allogeneic hematopoietic stem cell transplantation between May 1 st 2004, and April 1 st 2012 was analyzed retrospectively, and a prediction model was constructed. Results:Of the 555 patients, a total of 302 (54.0% ) patients exhibited bleeding events of varying degrees, including 151 (27.0% ) with grade Ⅰ bleeding, 63 (11.0% ) with grade Ⅱ bleeding, 48 (9.0% ) with grade Ⅲ bleeding, and 40 (7.0% ) with grade Ⅳ bleeding. Multifactorial analysis showed that the overall mortality ( HR=12.53, 95% CI 7.91-19.87, P<0.001) and non-recurrence mortality ( HR=23.79, 95% CI 12.23-46.26, P<0.001) were higher in patients with higher bleeding grades (Ⅲ and Ⅳ bleeding) compared to those with lower bleeding grades. Additionally, the donor’s underlying disease, graft-versus-host disease (GVHD) score, poor platelet reconstitution, and ineffective platelet transfusion were independently associated with bleeding risk. The bleeding model constructed using the above variables showed good accuracy (C-Index=0.934) , and its efficacy was significantly higher than previous bleeding models. Conclusion:Hematopoietic stem cell transplant patients are at increased risk of death after a bleeding event. The cross-validated bleeding risk prediction model is valuable for early intervention.
