开发和验证严重药物不良反应的预测名录:双中心药监测研究
Wei Bu1, Xinjing Wu2, Chengyu Wang3
1Department of Pharmacy, Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, China.
Frontiers in pharmacology
|November 24, 2025
概括
机器学习模型可以预测严重的药物不良反应 (SADR). 这项研究确定了关键的风险因素,并开发了用于SADRs临床风险分层的后勤回归名谱.
科学领域:
- 药物监督 药物监督 药物监督
- 临床信息学 临床信息学
- 计算医学是一种计算医学.
背景情况:
- 严重的药物不良反应 (SADR) 在药物治疗中存在重大挑战.
- 机器学习 (ML) 提供了可靠SADR预测的潜力.
- 识别独立的风险因素对于开发有效的预测模型至关重要.
研究的目的:
- 使用ML技术精确确定SADR的独立风险因素.
- 构建适用于临床环境的SADR预测模型.
- 为了比较不同ML算法的预测性能.
主要方法:
- 对ADR病例的回顾性双中心队列研究 (2014-2022年).
- 使用WHO-UMC标准将病例分为SADR和常见的ADR.
- 使用后勤回归 (LR),随机森林和梯度提升机模型,并使用AUC,C指数,HL测试,DCA和CIC进行比较.
主要成果:
- 在试验组中,LR模型表现出最高的可预测性,AUC为0.707.
- 基于LR模型的名图显示了良好的校准 (H-L测试p=0.369) 和预测值.
- 关键预测因素包括年龄≥54岁,≥3种并发疾病,心力衰竭,出血性疾病,活跃恶性瘤,脑梗塞,骨折和特定药物类别.
结论:
- 使用LR.LR成功建立了SADRs的预测名录.
- 诺米图表为SADRs提供了临床上有意义的风险分层.
- 该工具有助于对高风险患者群体的预防性监测.
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