术后急性损伤的预测建模:大数据提高质量还是皇帝的新衣服?
1Department of Anesthesiology, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Anaesthesia, Monash University, Melbourne, VIC, Australia.
British journal of anaesthesia
|June 20, 2024
概括
对急性损伤 (AKI) 的术前风险预测模型显示临床吸收有限. 需要进一步的研究来克服实施障碍,并证明患者和医疗保健的价值.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 医疗信息学 医疗信息学
- 临床预测建模临床预测建模
背景情况:
- 大数据和计算能力的进步刺激了许多风险预测模型的创建,这些模型可以预测外科手术后不良结果,特别是急性损伤 (AKI).
- 这些模型的主要目标是预测手术前的患者风险,帮助临床决策.
研究的目的:
- 评估术前风险预测模型对术后不良结果的现状和局限性,特别关注急性损伤 (AKI).
- 确定阻碍这些预测工具在临床上广泛采用这些预测工具的障碍.
- 引导未来的研究方向,以提高医疗保健中的预测建模的实用性和影响.
主要方法:
- 审查关于开发和实施外科手术期间风险预测模型的现有文献.
- 分析报告的局限性,包括模型歧视,准确性和临床实用性.
- 评估所谓的好处与证明对患者结果和医疗保健系统的影响.
主要成果:
- 许多风险预测模型,如AKI已经开发,利用大数据集和计算能力.
- 这些手术前模型的临床应用是有限的,与准确性,歧视性和实际影响相关的已知的障碍存在.
- 术后AKI预测建模的实际好处可能无法经受严格的检查.
结论:
- 现有的风险预测模型在临床实施中面临重大障碍,质疑它们的当前价值.
- 未来的研究必须解决这些局限性,并提供对患者和医疗保健系统有形益处的证据.
- 证明改善患者结果和医疗保健价值对于成功将预测模型集成到临床实践中至关重要.
相关概念视频
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