在肝硬化患者的脊髓切除术后的门静脉系统血栓形成的一个可解释的预测模型的开发
Dou Qu1,2,3,4, Duwei Dai3,5, Guodong Li1,3,4
1Institute for Precision Medicine, Xi'an Jiaotong University Second Affiliated Hospital, Xi'an, Shaanxi, China.
BMJ health & care informatics
|April 11, 2025
概括
机器学习模型预测了肝硬化患者脊髓切除术后3个月内门静脉系统血栓形成 (PVST) 的风险. 该模型显示适度的准确性,识别了针对性预防的关键风险因素.
科学领域:
- 肝病学 肝病学是一种肝病学.
- 医疗信息学 医疗信息学
- 手术并发症 手术并发症
背景情况:
- 门静脉系统血栓塞 (PVST) 在脊髓切除术和心脏周周神经化 (SPDV) 后在肝硬化患者中是一个显著的风险.
- 早期PVST预测对于及时干预和改善患者结果至关重要.
- 这项研究的重点是开发一种机器学习模型,用于预测脊髓切除术后三个月内PVST.
研究的目的:
- 开发和验证基于机器学习的PVST发生的预测模型.
- 确定SPDV后PVST的关键临床预测因素.
- 为早期风险评估和个性化干预策略提供一个工具.
主要方法:
- 分析了一组392名经过脊髓切除术的肝硬化患者.
- 最初考虑了37个临床预测因素,其中8个被选择用于模型构建.
- 五个机器学习算法 (逻辑回归,高斯天真贝叶斯,决策树,随机森林,AdaBoost) 通过五倍交叉验证进行了训练和验证.
- 哈普利添加式解释 (SHAP) 用于模型解释.
主要成果:
- 在392名患者中,有144名患者 (36.73%) 在3个月内发展出PVST.
- 在AdaBoost模型中,AUROC为0.72 (95%CI为0.60-0.84),实现了最高的区分能力.
- 通过SHAP分析确定的关键预测因素包括白蛋白,血小板数量,门静脉直径,玛-谷氨基转移酶,停留时间,激活的部分血栓塑时间,D-二次体和手术前胃肠道出血史.
结论:
- 机器学习模型可以提供对肝硬化患者SPDV后3个月PVST风险的初步评估.
- 该AdaBoost模型显示了对PVST的适度预测能力.
- SHAP分析提高了模型的透明度,使个性化风险预测和指导有针对性的预防措施成为可能.
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