Machine learning model for predicting physical activity related bleeding risk in Chinese boys with haemophilia A

doi:10.1016/j.thromres.2023.10.012

	Machine learning model for predicting physical activity related bleeding risk in Chinese boys with haemophilia A
	Ai, Di 1; Cui, Chang2,3 ; Tang, Yongqiang2 ; Wang, Yan 4; Zhang, Ningning 5; Zhang, Chenyang2 ; Zhen, Yingzi 1; Li, Gang 6; Huang, Kun 1; Liu, Guoqing1 ; Chen, Zhenping 6; Zhang, Wensheng2 ; Wu, Runhui 1
发表期刊	THROMBOSIS RESEARCH
ISSN	0049-3848
	2023-12-01
卷号	232 页码:43-53
通讯作者	Tang, Yongqiang(yongqiang.tang@ia.ac.cn) ; Chen, Zhenping(chenzhenping@outlook.com) ; Zhang, Wensheng(zhangwenshengia@hotmail.com) ; Wu, Runhui(runhuiwu@hotmail.com)
摘要	Background: Physical activity is a crucial part of an active lifestyle for haemophiliac children. However, the fear of bleeds has been identified as barriers to participating physical activity for haemophiliac children even with prophylaxis. Lack of evidence and metrics driven by data is key problem. Objectives: We aim to develop machine learning models based on clinical data with multiple potential factors considered to predict risk of physical activity bleeding for haemophilia children with prophylaxis. Methods: From this cohort study, we collected information on 98 haemophiliac children with adequate prophylaxis (trough FVIII:C level > 1 %). The involved potential predictor variables include demographic information, treatment information, physical activity, joint evaluation, and pharmacokinetic parameters, etc. We applied CoxPH, Random Survival Forests (RSF) and DeepSurv to construct prediction models for the risk of bleeding during physical activities. All three survival analysis models were internally and externally validated. Results: A total of 98 patients were enrolled in this study. Their median age was 7.9 (5.5, 10.2) years. The CoxPH, RSF and DeepSurv models' discriminative and calibration abilities were all high, and the RSF model had the best performance (Internal validation: C-index, 0.7648 +/- 0.0139; Brier Score, 0.1098 +/- 0.0015; External validation: C-index, 0.7260 +/- 0.0154; Brier Score, 0.0930 +/- 0.0018). The prediction curves demonstrated that the developed RSF model can distinguish the risks well between bleeding and non-bleeding patients, as well as patients with different levels of physical activity. Meanwhile, the feature importance analysis confirmed that physical activity bleeding was deduced by comprehensive effects of various factors, and the importance of different factors on bleeding outcome is discrepant. Conclusions: This study revealed from the mechanism that it is necessary to incorporate multiple factors to accurately predict physical activity related bleeding risk. In clinical practice, the designed machine learning models can provide guidance for children with haemophilia A to positively participate in physical activity.
关键词	Haemophilia Children Machine learning Bleeding predictive modelling Physical activity
DOI	10.1016/j.thromres.2023.10.012
关键词[WOS]	YOUNG-PATIENTS ; CHILDREN ; PARTICIPATION ; PROPHYLAXIS
收录类别	SCI
语种	英语
资助项目	Research on the application of clinical characteristics of the Beijing Municipal Sci-ence and Technology Commission[Z181100001718182] ; Research on the application of clinical characteristics of the Beijing Municipal Sci-ence and Technology Commission[20006429]
项目资助者	Research on the application of clinical characteristics of the Beijing Municipal Sci-ence and Technology Commission
WOS研究方向	Hematology ; Cardiovascular System & Cardiology
WOS类目	Hematology ; Peripheral Vascular Disease
WOS记录号	WOS:001156025700001
出版者	PERGAMON-ELSEVIER SCIENCE LTD
引用统计
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/55503
专题	多模态人工智能系统全国重点实验室
通讯作者	Tang, Yongqiang; Chen, Zhenping; Zhang, Wensheng; Wu, Runhui
作者单位	1.Capital Med Univ, Natl Key Discipline Pediat,Natl Ctr Childrens Hlt, Beijing Childrens Hosp,Minist Educ,Key Lab Major, Hematol Ctr,Beijing Key Lab Pediat Hematol Oncol,, Beijing 100045, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 4.Capital Med Univ, Beijing Childrens Hosp, Natl Ctr Childrens Hlth, Dept Rehabil, Beijing, Peoples R China 5.Capital Med Univ, Beijing Childrens Hosp, Natl Ctr Childrens Hlth, Dept Radiol, Beijing, Peoples R China 6.Capital Med Univ, Beijing Childrens Hosp, Beijing Pediat Res Inst, Hematol Dis Lab, Beijing 100045, Peoples R China
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Ai, Di,Cui, Chang,Tang, Yongqiang,et al. Machine learning model for predicting physical activity related bleeding risk in Chinese boys with haemophilia A[J]. THROMBOSIS RESEARCH,2023,232:43-53.
APA	Ai, Di.,Cui, Chang.,Tang, Yongqiang.,Wang, Yan.,Zhang, Ningning.,...&Wu, Runhui.(2023).Machine learning model for predicting physical activity related bleeding risk in Chinese boys with haemophilia A.THROMBOSIS RESEARCH,232,43-53.
MLA	Ai, Di,et al."Machine learning model for predicting physical activity related bleeding risk in Chinese boys with haemophilia A".THROMBOSIS RESEARCH 232(2023):43-53.