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Long-Term Outcome Prediction After Immunosuppressive Therapy for Severe Aplastic Anemia in Childhood by Machine Learning Methods

35 Pages Posted: 28 Jul 2020

See all articles by Lixian Chang

Lixian Chang

Chinese Academy of Medical Sciences & Peking Union Medical College (CAMS & PUMC) - Institute of Hematology & Blood Disease Hospital

Mingchen Yan

Shenzhen Digital Life Institute

Jingliao Zhang

Chinese Academy of Medical Sciences & Peking Union Medical College (CAMS & PUMC) - Institute of Hematology & Blood Disease Hospital

Binghang Liu

Shenzhen Digital Life Institute

Ye Guo

Chinese Academy of Medical Sciences & Peking Union Medical College (CAMS & PUMC) - Institute of Hematology & Blood Disease Hospital

Yang Wan

Chinese Academy of Medical Sciences & Peking Union Medical College (CAMS & PUMC) - Institute of Hematology & Blood Disease Hospital

Meihui Yi

Chinese Academy of Medical Sciences & Peking Union Medical College (CAMS & PUMC) - Institute of Hematology & Blood Disease Hospital

Yang Lan

Chinese Academy of Medical Sciences & Peking Union Medical College (CAMS & PUMC) - Institute of Hematology & Blood Disease Hospital

Yuli Cai

Chinese Academy of Medical Sciences & Peking Union Medical College (CAMS & PUMC) - Institute of Hematology & Blood Disease Hospital

Jing Feng

Chinese Academy of Medical Sciences & Peking Union Medical College (CAMS & PUMC) - Institute of Hematology & Blood Disease Hospital

Shuchun Wang

Chinese Academy of Medical Sciences & Peking Union Medical College (CAMS & PUMC) - Institute of Hematology & Blood Disease Hospital

Li Zhang

Chinese Academy of Medical Sciences & Peking Union Medical College (CAMS & PUMC) - Institute of Hematology & Blood Disease Hospital

Yong Ma

Shenzhen Digital Life Institute

Yuanyuan Ren

Chinese Academy of Medical Sciences & Peking Union Medical College (CAMS & PUMC) - Institute of Hematology & Blood Disease Hospital

Haihui Zheng

Shenzhen Digital Life Institute

Aoli Zhang

Chinese Academy of Medical Sciences & Peking Union Medical College (CAMS & PUMC) - Institute of Hematology & Blood Disease Hospital

Lipeng Liu

Chinese Academy of Medical Sciences & Peking Union Medical College (CAMS & PUMC) - Institute of Hematology & Blood Disease Hospital

Zhenyu Li

Shenzhen Digital Life Institute

Jian Wang

Shenzhen Digital Life Institute

Yingrui Li

Shenzhen Digital Life Institute

Xiaofan Zhu

Chinese Academy of Medical Sciences & Peking Union Medical College (CAMS & PUMC) - State Key Laboratory of Experimental Hematology

More...

Abstract

Background: Severe Aplastic Anemia (SAA) in children is a rare severe disease, and the prognosis after immunosuppressive therapy (IST) is heterogeneous. Few models could accurately predict the long-term outcomes of immunosuppressive therapy for children SAA patients. Previous researches mainly focused on a few pretreatment factors,because SAA patients need to test the bone marrow at 3, 6, 9, and 12 months after IST treatment, and response-based surrogates of outcome have been shown to be highly predictive in other diseases. Based on these, we collected clinical electronic medical records (EMR) from 203 children with SAA, including comprehensive clinical tests from blood routine to bone marrow examination throughout the entire treatment process, to establish a model with more effective prognostic factors to predict the prognosis of patients. Our study will provides a novel and more effective prognosis time node for reducing the times of bone marrow puncture and to guide the next treatment.

Methods: Based on the machine learning methods, we established a model with the AUC 0.962 to predict the long-term outcomes in the early stage of SAA patients with IST.

Findings: By analyzing the indicators related to long-term efficacy, we found that some of the indicators such as white blood cell count, lymphocyte count and absolute reticulocyte count (ARC) which are consistent with previous studies; but the age is not a suitable predictor for children with SAA, the lymphocyte ratio of bone marrow smear is more effective than lymphocyte count in blood, the C-reactive protein, level of vitamin B12, IL-6 and IL-8 in the early stage of the disease is highly correlated with long-term efficacy (P<0.05). Three months after IST treatment can be used as a time node to guide the next treatment.

Interpretation: Herein, we established a novel model regarding the prognosis analysis of children's SAA patients. We found that "Third month after IST treatment" can be considered as an essential time node of long-term prognosis. In addition, we further identified several new predictors (e.g. level of vitamin B12, IL-6 and IL-8, et al), but not including the factor of age. In summary, the utilization of our prediction model and identification of the effective and suitable prognostic factors are of great significance for the prognosis of children's SAA patients and the guiding of the relevant clinical treatment.

Funding Statement: This work was supported by the National Key Research and Development Program of China (2016YFC0901503), the National Natural Science Foundation of China (81500156, 81170470).

Declaration of Interests: The authors declare no competing financial interests.

Ethics Approval Statement: Our study has been were reviewed and approved by the Clinical Research Ethics Committee of Blood Diseases Hospital & Institute of Hematology, Chinese Academy of Medical Sciences.

Keywords: Severe aplastic anemia; children; Immunosuppressive therapy; Machine learning predictive models

Suggested Citation

Chang, Lixian and Yan, Mingchen and Zhang, Jingliao and Liu, Binghang and Guo, Ye and Wan, Yang and Yi, Meihui and Lan, Yang and Cai, Yuli and Feng, Jing and Wang, Shuchun and Zhang, Li and Ma, Yong and Ren, Yuanyuan and Zheng, Haihui and Zhang, Aoli and Liu, Lipeng and Li, Zhenyu and Wang, Jian and Li, Yingrui and Zhu, Xiaofan, Long-Term Outcome Prediction After Immunosuppressive Therapy for Severe Aplastic Anemia in Childhood by Machine Learning Methods (4/22/2020). Available at SSRN: https://ssrn.com/abstract=3582734 or http://dx.doi.org/10.2139/ssrn.3582734

Lixian Chang

Chinese Academy of Medical Sciences & Peking Union Medical College (CAMS & PUMC) - Institute of Hematology & Blood Disease Hospital

China

Mingchen Yan

Shenzhen Digital Life Institute

Shenzhen
China

Jingliao Zhang

Chinese Academy of Medical Sciences & Peking Union Medical College (CAMS & PUMC) - Institute of Hematology & Blood Disease Hospital

China

Binghang Liu

Shenzhen Digital Life Institute

Shenzhen
China

Ye Guo

Chinese Academy of Medical Sciences & Peking Union Medical College (CAMS & PUMC) - Institute of Hematology & Blood Disease Hospital

China

Yang Wan

Chinese Academy of Medical Sciences & Peking Union Medical College (CAMS & PUMC) - Institute of Hematology & Blood Disease Hospital

China

Meihui Yi

Chinese Academy of Medical Sciences & Peking Union Medical College (CAMS & PUMC) - Institute of Hematology & Blood Disease Hospital

China

Yang Lan

Chinese Academy of Medical Sciences & Peking Union Medical College (CAMS & PUMC) - Institute of Hematology & Blood Disease Hospital

China

Yuli Cai

Chinese Academy of Medical Sciences & Peking Union Medical College (CAMS & PUMC) - Institute of Hematology & Blood Disease Hospital

China

Jing Feng

Chinese Academy of Medical Sciences & Peking Union Medical College (CAMS & PUMC) - Institute of Hematology & Blood Disease Hospital

China

Shuchun Wang

Chinese Academy of Medical Sciences & Peking Union Medical College (CAMS & PUMC) - Institute of Hematology & Blood Disease Hospital

China

Li Zhang

Chinese Academy of Medical Sciences & Peking Union Medical College (CAMS & PUMC) - Institute of Hematology & Blood Disease Hospital

China

Yong Ma

Shenzhen Digital Life Institute

Shenzhen
China

Yuanyuan Ren

Chinese Academy of Medical Sciences & Peking Union Medical College (CAMS & PUMC) - Institute of Hematology & Blood Disease Hospital

China

Haihui Zheng

Shenzhen Digital Life Institute

Shenzhen
China

Aoli Zhang

Chinese Academy of Medical Sciences & Peking Union Medical College (CAMS & PUMC) - Institute of Hematology & Blood Disease Hospital

China

Lipeng Liu

Chinese Academy of Medical Sciences & Peking Union Medical College (CAMS & PUMC) - Institute of Hematology & Blood Disease Hospital

China

Zhenyu Li

Shenzhen Digital Life Institute

Shenzhen
China

Jian Wang

Shenzhen Digital Life Institute

Shenzhen
China

Yingrui Li

Shenzhen Digital Life Institute ( email )

Shenzhen
China

Xiaofan Zhu (Contact Author)

Chinese Academy of Medical Sciences & Peking Union Medical College (CAMS & PUMC) - State Key Laboratory of Experimental Hematology ( email )

NO. 9, Dongdan Santiao
Tianjin
China