Peking University People's Hospital, Peking University Institute of Hematology, Beijing 100044, China; Collaborative Innovation Center of Hematology, Peking University, Beijing 100044, China; Beijing Key Laboratory of Hematopoietic Stem Cell Transplantation, Beijing 100044, China; National Clinical Research Center for Hematologic Disease, Beijing 100044, China.
Department of Hematology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan 250014, China.
Sci Bull (Beijing). 2023 Sep 30;68(18):2106-2114. doi: 10.1016/j.scib.2023.08.001. Epub 2023 Aug 3.
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.
原发性免疫性血小板减少症（ITP）中罕见但严重的出血事件可导致危及生命的并发症，严重影响患者的预后、生活质量和治疗决策。尽管已有多项研究探讨了 ITP 严重出血的相关危险因素，但缺乏大样本量数据、一致的定义、大规模多中心研究结果和 ITP 患者严重出血事件的预测模型。首次在本研究中，我们将国际血栓与止血学会新提出的严重 ITP 出血标准应用于大样本量数据，并开发了首个基于机器学习（ML）的在线应用程序，用于预测 ITP 严重出血。本研究采用中国多中心大样本数据，开发并外部验证了一种基于 ML 的模型，用于确定 ITP 患者发生严重出血事件的风险。回顾性数据来自全国 8 家医疗中心，前瞻性数据来自全国 39 家医疗中心，为期一年。该系统在训练、验证和测试数据集上均表现出良好的预测能力。这种基于新型算法的便捷网络工具可快速识别 ITP 患者的出血风险特征，有助于临床决策，并减少不良事件的发生。
