Development and Validation of a Predictive Model for Intracranial Haemorrhage in Patients on Direct Oral Anticoagulants

被引:1
作者
Liu, Yuanyuan [1 ,2 ]
Li, Linjie [1 ]
Li, Jingge [1 ]
Liu, Hangkuan [1 ]
Geru, A. [1 ]
Wang, Yulong [1 ]
Li, Yongle [1 ]
Sia, Ching-Hui [3 ,4 ]
Lip, Gregory Y. H. [5 ,6 ,7 ]
Yang, Qing [1 ]
Zhou, Xin [1 ]
机构
[1] Tianjin Med Univ, Dept Cardiol, Gen Hosp, 154 Anshan Rd, Tianjin 300052, Peoples R China
[2] Qingzhou Peoples Hosp, Dept Cardiol, Weifang 262500, Shandong, Peoples R China
[3] Natl Univ Singapore, Yong Loo Lin Sch Med, 1E,Ridge Rd, Singapore 119228, Singapore
[4] Natl Univ Heart Ctr, Dept Cardiol, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
[5] Univ Liverpool, Liverpool Ctr Cardiovasc Sci, Liverpool, England
[6] Liverpool Heart & Chest Hosp, Liverpool, England
[7] Aalborg Univ, Danish Ctr Hlth Serv Res, Dept Clin Med, Aalborg, Denmark
基金
中国国家自然科学基金;
关键词
intracranial haemorrhage; direct oral anticoagulant; predictive model; XGBoost; VITAMIN-K ANTAGONIST; INTRACEREBRAL HEMORRHAGE; ATRIAL-FIBRILLATION; RISK-FACTORS; SCORE; ASSOCIATION; WARFARIN;
D O I
10.1177/10760296241271338
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Background: Intracranial haemorrhage (ICH) poses a significant threat to patients on Direct Oral Anticoagulants (DOACs), with existing risk scores inadequately predicting ICH risk in these patients. We aim to develop and validate a predictive model for ICH risk in DOAC-treated patients. Methods: 24,794 patients treated with a DOAC were identified in a province-wide electronic medical and health data platform in Tianjin, China. The cohort was randomly split into a 4:1 ratio for model development and validation. We utilized forward stepwise selection, Least Absolute Shrinkage and Selection Operator (LASSO), and eXtreme Gradient Boosting (XGBoost) to select predictors. Model performance was compared using the area under the curve (AUC) and net reclassification index (NRI). The optimal model was stratified and compared with the DOAC model. Results: The median age is 68.0 years, and 50.4% of participants are male. The XGBoost model, incorporating six independent factors (history of hemorrhagic stroke, peripheral artery disease, venous thromboembolism, hypertension, age, low-density lipoprotein cholesterol levels), demonstrated superior performance in the development dateset. It showed moderate discrimination (AUC: 0.68, 95% CI: 0.64-0.73), outperforming existing DOAC scores (Delta AUC = 0.063, P = 0.003; NRI = 0.374, P < 0.001). Risk categories significantly stratified ICH risk (low risk: 0.26%, moderate risk: 0.74%, high risk: 5.51%). Finally, the model demonstrated consistent predictive performance in the internal validation. Conclusion: In a real-world Chinese population using DOAC therapy, this study presents a reliable predictive model for ICH risk. The XGBoost model, integrating six key risk factors, offers a valuable tool for individualized risk assessment in the context of oral anticoagulation therapy.
引用
收藏
页数:11
相关论文
共 40 条
  • [1] Development and Validation of the DOAC Score: A Novel Bleeding Risk Prediction Tool for Patients With Atrial Fibrillation on Direct-Acting Oral Anticoagulants
    Aggarwal, Rahul
    Ruff, Christian T.
    Virdone, Saverio
    Perreault, Sylvie
    Kakkar, Ajay K.
    Palazzolo, Michael G.
    Dorais, Marc
    Kayani, Gloria
    Singer, Daniel E.
    Secemsky, Eric
    Piccini, Jonathan
    Tahir, Usman A.
    Shen, Changyu
    Yeh, Robert W.
    [J]. CIRCULATION, 2023, 148 (12) : 936 - 946
  • [2] Treatment of Venous Thromboembolism With New Anticoagulant Agents
    Becattini, Cecilia
    Agnelli, Giancarlo
    [J]. JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY, 2016, 67 (16) : 1941 - 1955
  • [3] Chaiquan L., 2023, Eur Heart J Digit Health, V5, P30
  • [4] 2021 Focused Update Consensus Guidelines of the Asia Pacific Heart Rhythm Society on Stroke Prevention in Atrial Fibrillation: Executive Summary *
    Chao, Tze-Fan
    Joung, Boyoung
    Takahashi, Yoshihide
    Lim, Toon Wei
    Choi, Eue-Keun
    Chan, Yi-Hsin
    Guo, Yutao
    Sriratanasathavorn, Charn
    Oh, Seil
    Okumura, Ken
    Lip, Gregory Y. H.
    [J]. THROMBOSIS AND HAEMOSTASIS, 2022, 122 (01) : 20 - 47
  • [5] Direct Oral Anticoagulant Use: A Practical Guide to Common Clinical Challenges
    Chen, Ashley
    Stecker, Eric
    Warden, Bruce A.
    [J]. JOURNAL OF THE AMERICAN HEART ASSOCIATION, 2020, 9 (13):
  • [6] A risk score to predict postdischarge bleeding among acute coronary syndrome patients undergoing percutaneous coronary intervention: BRIC-ACS study
    Chen, Yundai
    Yin, Tong
    Xi, Shaozhi
    Zhang, Shuyang
    Yan, Hongbing
    Tang, Yida
    Qian, Juying
    Chen, Jiyan
    Su, Xi
    Du, Zhimin
    Wang, Lefeng
    Qin, Qin
    Gao, Chuanyu
    Zheng, Yang
    Zhao, Xianxian
    Cheng, Xiaoshu
    Li, Zhanquan
    Zhang, Wenqi
    Chen, Hui
    Wang, Jingping
    Yang, Zhiming
    Li, Hui
    Liu, Heping
    Zhou, Xuchen
    Qu, Baiming
    Xiang, Dingcheng
    Guo, Ying
    Wang, Lin
    Nie, Shaoping
    Fu, Guosheng
    Yang, Ming
    Cai, Shanglang
    [J]. CATHETERIZATION AND CARDIOVASCULAR INTERVENTIONS, 2019, 93 (07) : 1194 - 1204
  • [7] Opportunities and Challenges for Machine Learning in Rare Diseases
    Decherchi, Sergio
    Pedrini, Elena
    Mordenti, Marina
    Cavalli, Andrea
    Sangiorgi, Luca
    [J]. FRONTIERS IN MEDICINE, 2021, 8
  • [8] Risk score to predict serious bleeding in stable outpatients with or at risk of atherothrombosis
    Ducrocq, Gregory
    Wallace, Joshua S.
    Baron, Gabriel
    Ravaud, Philippe
    Alberts, Mark J.
    Wilson, Peter W. F.
    Ohman, Erik Magnus
    Brennan, Danielle M.
    D'Agostino, Ralph B.
    Bhatt, Deepak L.
    Steg, Philippe Gabriel
    [J]. EUROPEAN HEART JOURNAL, 2010, 31 (10) : 1257 - 1265
  • [9] A New Risk Scheme to Predict Warfarin-Associated Hemorrhage The ATRIA (Anticoagulation and Risk Factors in Atrial Fibrillation) Study
    Fang, Margaret C.
    Go, Alan S.
    Chang, Yuchiao
    Borowsky, Leila H.
    Pomernacki, Niela K.
    Udaltsova, Natalia
    Singer, Daniel E.
    [J]. JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY, 2011, 58 (04) : 395 - 401
  • [10] Advancements in predicting and modeling rare event outcomes for enhanced decision-making
    Feng, Cindy
    Li, Longhai
    Xu, Chang
    [J]. BMC MEDICAL RESEARCH METHODOLOGY, 2023, 23 (01)