作者
Jianying Zhou,Song Wang,Hailong Yuan,Ya‐Jing Xu,Xiaobing Huang,Sujun Gao,Shouxin Zhang,Fang Liz Zhou,Yue Liu,Xianmin Song,Yu Cai,Xiao‐Liang Liu,Yi Luo,Lu‐Xin Yang,Jianmin Yang,Libing Wang,Yuhua Li,Rui Huang,Shun‐Qing Wang,Ming Zhou,Yujun Dong,Qian Wang,Xi Zhang,Yimei Feng,Xin Du,Ling Wei,Han Zhu,Zunmin Zhu,X S Chen,Shiyu Wang,Fankai Meng,Kehong Bi,Ning Huang,Ming Jiang,Ting Niu,Jie Ji,Dingming Wan,Zhilei Bian,Yi Chen,Li Liu,Xue‐Qian Yan,Xi Yang,Hai Yi,Xudong Wei,Xin Li,Qian Cheng,Chenglu Yuan,Wen Wang,Yuhong Zhou,Bao‐Dong Ye,Jing Ding,Yejun Wu,Qiu‐Sha Huang,Xiaolu Zhu,Yu‐Hong Chen,Yun He,Feng‐Rong Wang,Yuanyuan Zhang,Xiao‐Dong Mo,Wei Han,Li Wang,Yu Wang,Huan Chen,Xiaosu Zhao,Ying‐Jun Chang,Kai‐Yan Liu,Xiao‐Jun Huang,Xiaohui Zhang
摘要
Chronic myelomonocytic leukemia (CMML) is a clonal hematopoietic stem cell malignancy, and allogeneic hematopoietic stem cell transplantation (allo-HSCT) is the only curable treatment. The outcomes after transplant are influenced by both disease characteristics and patient comorbidities. To develop a novel prognostic model to predict the post-transplant survival of CMML patients, we identified risk factors by applying univariable and multivariable Cox proportional hazards regression to a derivation cohort. In multivariable analysis, advanced age (hazard ratio [HR] 3.583), leukocyte count (HR 3.499), anemia (HR 3.439), bone marrow blast cell count (HR 2.095), and no chronic graft versus host disease (cGVHD; HR 4.799) were independently associated with worse survival. A novel prognostic model termed ABLAG (Age, Blast, Leukocyte, Anemia, cGVHD) was developed and the points were assigned according to the regression equation. The patients were categorized into low risk (0-1), intermediate risk (2, 3), and high risk (4-6) three groups and the 3-year overall survival (OS) were 93.3% (95%CI, 61%-99%), 78.9% (95%CI, 60%-90%), and 51.6% (95%CI, 32%-68%; p < .001), respectively. In internal and external validation cohort, the area under the receiver operating characteristic (ROC) curves of the ABLAG model were 0.829 (95% CI, 0.776-0.902) and 0.749 (95% CI, 0.684-0.854). Compared with existing models designed for the nontransplant setting, calibration plots, and decision curve analysis showed that the ABLAG model revealed a high consistency between predicted and observed outcomes and patients could benefit from this model. In conclusion, combining disease and patient characteristic, the ABLAG model provides better survival stratification for CMML patients receiving allo-HSCT.