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Dynamic prediction of relapse in patients with acute leukemias after allogeneic transplantation: Joint model for minimal residual disease
Author(s) -
Huang Aijie,
Chen Qi,
Fei Yang,
Wang Ziwei,
Ni Xiong,
Gao Lei,
Chen Li,
Chen Jie,
Zhang Weiping,
Yang Jianmin,
Wang Jianmin,
Hu Xiaoxia
Publication year - 2021
Publication title -
international journal of laboratory hematology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.705
H-Index - 55
eISSN - 1751-553X
pISSN - 1751-5521
DOI - 10.1111/ijlh.13328
Subject(s) - medicine , minimal residual disease , myeloid leukemia , oncology , acute promyelocytic leukemia , transplantation , cumulative incidence , hematopoietic stem cell transplantation , leukemia , cohort , disease , incidence (geometry) , biology , retinoic acid , biochemistry , physics , optics , gene
Relapse remains the leading cause of treatment failure after allogeneic hematopoietic stem cell transplantation (alloHSCT) in leukemia. Numerous investigations have demonstrated that minimal residual disease (MRD) before or after alloHSCT is prognostic of relapse risk. These MRD data were collected at specific checkpoints and could not dynamically predict the relapse risk after alloHSCT, which needs serial monitoring. Methods In the present study, we retrospectively analyzed MRD measured with multi‐parameter flow cytometry in 207 acute myeloid leukemia (AML) patients (acute promyelocytic leukemia excluded), and 124 acute B lymphoblastic leukemia (ALL) patients. A three‐step method based on joint model was used to build a relapse risk prediction model. Results The 3‐year overall survival and relapse‐free survival rates of the entire cohort were 67.1% ± 2.8% and 61.6% ± 2.8%, respectively. The model included disease status before alloHSCT, acute and chronic graft‐versus‐host disease, and serial MRD data. The time‐dependent receiver operating characteristics was used to evaluate the ability of the model. It fitted well with actual incidence of relapse. The serial MRD data collected after alloHSCT had better discrimination capabilities for recurrence prediction with the area under the curve from 0.67 to 0.91 (AML: 0.66‐0.89; ALL: 0.70‐0.96). Conclusion The joint model was able to dynamically predict relapse‐free probability after alloHSCT, which would be a useful tool to provide important information to guide decision‐making in the clinic and facilitate the individualized therapy.