使用药物治疗方案复杂性的药剂师药物干预措施的预测
Bokai Zhao1, Ye Shen1, John W Devlin2,3
1Department of Epidemiology & Biostatistics, University of Georgia College of Public Health, Athens, GA 30602, United States.
JAMIA open
|November 5, 2025
概括
使用早期患者数据,可以预测重症患者的药物干预需求. 在ICU入院24小时内结合患者因素的模型可以预测药剂师的干预工作量.
科学领域:
- 药理学 药理学是指药理学的学科.
- 医疗信息学 医疗信息学
- 临界护理医学 临界护理医学
背景情况:
- 重症患者需要复杂的药物管理,以确保安全性和有效性.
- 临床护理药剂师 (CCP) 进行药物干预以优化患者护理.
- 预测患者药物管理需求对于定义CPT工作流程和干预及时性至关重要.
研究的目的:
- 开发对重症患者药物干预的数量和强度的预测模型.
- 在ICU入院后的前24小时内确定影响药物管理需求的患者特异性因素.
主要方法:
- 对13,373名成年ICU患者 (2020年6月 - 2023年6月) 的回顾性观察队列研究.
- 开发回归和机器学习 (随机森林,SVM,XGBoost) 模型来预测干预计数.
- 使用根平均平方误差 (RMSE),平均绝对误差 (MAE) 和对称平均绝对百分比误差 (sMAPE) 评估模型.
主要成果:
- 干预的平均数量为4.7 (SD 7.1),干预强度为24.0 (40.3).
- 机器学习模型的性能类似于逐步后勤回归,性能优于基础模型.
- 随机森林模型产生了最低的RMSE (9.26),而支持矢量机具有最低的MAE (4.71).
结论:
- 药物干预量可以通过在入院24小时内确定的患者特异因素来预测.
- 在本研究中,机器学习模型与传统回归模型相比,没有提供显著的性能优势.
- 开发的模型为机构工作负载建模提供了一个框架,可以适应干预文档的变化.
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