基于代谢因子的机器学习模型用于急性肝炎E的死亡率预测:从双中心队列的开发和验证
Haoshuang Fu1, Shuying Song1, Yuelin Xiao1
1Department of Infectious Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
概括
一个新的后勤回归模型准确地预测了使用代谢因素的E型肝炎病毒 (HEV) 患者的短期死亡率. 该模型有助于早期风险分层和HEV感染的个性化管理.
科学领域:
- 肝病学 肝病学是一种肝病学.
- 机器学习在医学中的应用
- 预测建模预测建模
背景情况:
- 肝炎E病毒 (HEV) 感染是导致肝衰竭的重要原因之一,死亡率高.
- 现有的HEV预测模型缺乏全面的系统代谢因子.
研究的目的:
- 开发一种机器学习模型,用于预测HEV患者的死亡率.
- 将系统代谢参数整合到HEV的预测模型中.
主要方法:
- 在培训,内部和外部验证队伍中对510名HEV患者进行了回顾性分析.
- 使用具有代谢参数的支持向量机 (SVM) 开发新陈代谢评分.
- 使用LASSO回归选择临床变量和新陈代谢得分.
- 为28天和90天死亡率预测构建和评估5个机器学习模型.
主要成果:
- 后勤回归 (LR) 模型在所有队列中预测28天和90天死亡率方面表现出卓越的表现 (AUROCs从0.84到0.98).
- 在预测准确度方面,LR模型的表现优于MELD得分.
- 开发的LR模型显示了良好的校准,显著的临床净益处,并使用名图进行可视化.
结论:
- 包含全身代谢因素的LR模型提供了对HEV患者短期死亡率的准确预测.
- 这种模型有可能提高早期风险分层,并指导HEV感染的个性化治疗策略.
相关概念视频
Effect of Hepatic Disease on Pharmacokinetics: Pathophysiologic Assessment and Liver Function Test
225
In clinical practice, the direct measurement of hepatic blood flow to evaluate liver function presents significant challenges due to the intricate and specialized nature of the necessary techniques. Consequently, healthcare professionals often rely on empirical estimates derived from thorough patient examinations and liver function tests to gauge liver health. Among the tools at their disposal, the Child–Pugh and MELD scoring systems stand out for their ability to categorize and assess...
225
Physiological Pharmacokinetic Models: Incorporating Hepatic Transporter-Mediated Clearance
338
Drug transporters are critical in drug absorption, distribution, and excretion processes. They should be included in physiological-based pharmacokinetic (PBPK) models, which help predict human drug disposition. However, predicting this is challenging during drug development, especially when liver transport is involved. However, with a realistic representation of body transport processes, an accurate model may be possible.
A recent model describes pravastatin's hepatobiliary excretion,...
A recent model describes pravastatin's hepatobiliary excretion,...
338


