A life-threatening bleeding prediction model for immune thrombocytopenia based on personalized machine learning : a nationwide prospective cohort study
Copyright © 2023 Science China Press. Published by Elsevier B.V. All rights reserved..
Rare but critical bleeding events in primary immune thrombocytopenia (ITP) present life-threatening complications in patients with ITP, which severely affect their prognosis, quality of life, and treatment decisions. Although several studies have investigated the risk factors related to critical bleeding in ITP, large sample size data, consistent definitions, large-scale multicenter findings, and prediction models for critical bleeding events in patients with ITP are unavailable. For the first time, in this study, we applied the newly proposed critical ITP bleeding criteria by the International Society on Thrombosis and Hemostasis for large sample size data and developed the first machine learning (ML)-based online application for predict critical ITP bleeding. In this research, we developed and externally tested an ML-based model for determining the risk of critical bleeding events in patients with ITP using large multicenter data across China. Retrospective data from 8 medical centers across the country were obtained for model development and prospectively tested in 39 medical centers across the country over a year. This system exhibited good predictive capabilities for training, validation, and test datasets. This convenient web-based tool based on a novel algorithm can rapidly identify the bleeding risk profile of patients with ITP and facilitate clinical decision-making and reduce the occurrence of adversities.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:68 |
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| Enthalten in: |
Science bulletin - 68(2023), 18 vom: 30. Sept., Seite 2106-2114 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
An, Zhuo-Yu [VerfasserIn] |
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| Links: |
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| Themen: |
Critical bleeding |
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| Anmerkungen: |
Date Completed 26.09.2023 Date Revised 10.10.2023 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.scib.2023.08.001 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM360979157 |
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| 245 | 1 | 2 | |a A life-threatening bleeding prediction model for immune thrombocytopenia based on personalized machine learning |b a nationwide prospective cohort study |
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| 520 | |a Copyright © 2023 Science China Press. Published by Elsevier B.V. All rights reserved. | ||
| 520 | |a Rare but critical bleeding events in primary immune thrombocytopenia (ITP) present life-threatening complications in patients with ITP, which severely affect their prognosis, quality of life, and treatment decisions. Although several studies have investigated the risk factors related to critical bleeding in ITP, large sample size data, consistent definitions, large-scale multicenter findings, and prediction models for critical bleeding events in patients with ITP are unavailable. For the first time, in this study, we applied the newly proposed critical ITP bleeding criteria by the International Society on Thrombosis and Hemostasis for large sample size data and developed the first machine learning (ML)-based online application for predict critical ITP bleeding. In this research, we developed and externally tested an ML-based model for determining the risk of critical bleeding events in patients with ITP using large multicenter data across China. Retrospective data from 8 medical centers across the country were obtained for model development and prospectively tested in 39 medical centers across the country over a year. This system exhibited good predictive capabilities for training, validation, and test datasets. This convenient web-based tool based on a novel algorithm can rapidly identify the bleeding risk profile of patients with ITP and facilitate clinical decision-making and reduce the occurrence of adversities | ||
| 650 | 4 | |a Multicenter Study | |
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Critical bleeding | |
| 650 | 4 | |a Immune thrombocytopenia | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a Prediction model | |
| 650 | 4 | |a Severe bleeding | |
| 700 | 1 | |a Wu, Ye-Jun |e verfasserin |4 aut | |
| 700 | 1 | |a Hou, Yu |e verfasserin |4 aut | |
| 700 | 1 | |a Mei, Heng |e verfasserin |4 aut | |
| 700 | 1 | |a Nong, Wei-Xia |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Wen-Qian |e verfasserin |4 aut | |
| 700 | 1 | |a Zhou, Hu |e verfasserin |4 aut | |
| 700 | 1 | |a Feng, Ru |e verfasserin |4 aut | |
| 700 | 1 | |a Shen, Jian-Ping |e verfasserin |4 aut | |
| 700 | 1 | |a Peng, Jun |e verfasserin |4 aut | |
| 700 | 1 | |a Zhou, Hai |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Yi |e verfasserin |4 aut | |
| 700 | 1 | |a Song, Yong-Ping |e verfasserin |4 aut | |
| 700 | 1 | |a Yang, Lin-Hua |e verfasserin |4 aut | |
| 700 | 1 | |a Fang, Mei-Yun |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Jian-Yong |e verfasserin |4 aut | |
| 700 | 1 | |a Cheng, Yun-Feng |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Peng |e verfasserin |4 aut | |
| 700 | 1 | |a Xu, Ya-Jing |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Zhao |e verfasserin |4 aut | |
| 700 | 1 | |a Luo, Yi |e verfasserin |4 aut | |
| 700 | 1 | |a Cai, Zhen |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Hui |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Jing-Wen |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Juan |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Xi |e verfasserin |4 aut | |
| 700 | 1 | |a Sun, Zi-Min |e verfasserin |4 aut | |
| 700 | 1 | |a Zhu, Xiao-Yu |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Xin |e verfasserin |4 aut | |
| 700 | 1 | |a Fu, Rong |e verfasserin |4 aut | |
| 700 | 1 | |a Huang, Liang |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Shao-Yuan |e verfasserin |4 aut | |
| 700 | 1 | |a Yang, Tong-Hua |e verfasserin |4 aut | |
| 700 | 1 | |a Su, Li-Ping |e verfasserin |4 aut | |
| 700 | 1 | |a Ma, Liang-Ming |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Xie-Qun |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Dai-Hong |e verfasserin |4 aut | |
| 700 | 1 | |a Yao, Hong-Xia |e verfasserin |4 aut | |
| 700 | 1 | |a Feng, Jia |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Hong-Yu |e verfasserin |4 aut | |
| 700 | 1 | |a Jiang, Ming |e verfasserin |4 aut | |
| 700 | 1 | |a Zhou, Ze-Ping |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Wen-Sheng |e verfasserin |4 aut | |
| 700 | 1 | |a Shen, Xu-Liang |e verfasserin |4 aut | |
| 700 | 1 | |a Baima, Yangjin |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Yue-Ying |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Qian-Fei |e verfasserin |4 aut | |
| 700 | 1 | |a Huang, Qiu-Sha |e verfasserin |4 aut | |
| 700 | 1 | |a Fu, Hai-Xia |e verfasserin |4 aut | |
| 700 | 1 | |a Zhu, Xiao-Lu |e verfasserin |4 aut | |
| 700 | 1 | |a He, Yun |e verfasserin |4 aut | |
| 700 | 1 | |a Jiang, Qian |e verfasserin |4 aut | |
| 700 | 1 | |a Jiang, Hao |e verfasserin |4 aut | |
| 700 | 1 | |a Lu, Jin |e verfasserin |4 aut | |
| 700 | 1 | |a Zhao, Xiang-Yu |e verfasserin |4 aut | |
| 700 | 1 | |a Chang, Ying-Jun |e verfasserin |4 aut | |
| 700 | 1 | |a Wu, Tao |e verfasserin |4 aut | |
| 700 | 1 | |a Pan, Yao-Zhu |e verfasserin |4 aut | |
| 700 | 1 | |a Qiu, Lin |e verfasserin |4 aut | |
| 700 | 1 | |a Gao, Da |e verfasserin |4 aut | |
| 700 | 1 | |a Jin, A-Rong |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Wei |e verfasserin |4 aut | |
| 700 | 1 | |a Gao, Su-Jun |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Lei |e verfasserin |4 aut | |
| 700 | 1 | |a Hou, Ming |e verfasserin |4 aut | |
| 700 | 1 | |a Huang, Xiao-Jun |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Xiao-Hui |e verfasserin |4 aut | |
| 700 | 0 | |a National Cooperative ITP Working Group |e verfasserin |4 aut | |
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