住院治疗药物不良事件和脆弱性 (AIME-frail) 风险预测模型
Nazanin Falconer1, Ian A Scott2, Ahmad Abdel-Hafez3
1Department of Pharmacy, Princess Alexandra Hospital, Metro South Health, 199 Ipswich Road, Brisbane, QLD, 4102, Australia; School of Pharmacy, Faculty of Health and Behavioural Sciences, The University of Queensland, Brisbane, QLD, 4102, Australia; UQ Centre for Health Services Research, Faculty of Medicine, The University of Queensland, Brisbane, QLD, 4102, Australia.
Research in social & administrative pharmacy : RSAP
|May 21, 2024
概括
一个新的风险模型,AIME-Frail,通过包括脆弱性,改善了医院患者对药物危害的预测. 该工具有助于识别高风险个体,以便及时对临床医生进行审查,减少可预防的伤害.
科学领域:
- 临床药房 临床药房
- 患者安全 患者安全
- 医疗信息学 医疗信息学
背景情况:
- 药物伤害影响到5-15%的住院患者,许多事件是可以预防的.
- 不良住院药物事件 (AIME) 模型以前预测了伤害,但不包括脆弱性.
- 虚弱是患者结果的重要因素,可能会影响药物安全.
研究的目的:
- 为了评估包含脆弱性的更新AIME模型的预测性能.
- 评估AIME-Frail模型在住院成年住院患者的多站点队列中的准确性.
主要方法:
- 在两个澳大利亚医院进行的回顾性队列研究 (n=4089).
- 从电子病历和临床编码数据库中提取的数据.
- 通过ICD-10Y代码和药剂师审查确定了药物危害;计算了医院脆弱性风险评分 (HFRS).
- 后勤回归用于开发AIME-Frail模型,将其与使用AUC和DCA的原始AIME模型进行比较.
主要成果:
- 包括HFRS和其他变量在内的AIME-Frail模型实现了0.79的AUC,高于原始AIME模型 (AUC=0.70).
- 该模型在预测药物危害方面表现出良好的灵敏度 (69%) 和特异性 (81%).
- 决策曲线分析表明AIME-Frail工具的潜在临床实用性.
结论:
- 整合一个脆弱度量显著改善了AIME模型对药物危害的预测性能.
- AIME-Frail工具可以加强对高危药物危害的住院患者的查.
- 这种改进的风险分层化有助于及时的临床医生审查和干预,可能减少不良事件.
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