使用机器学习开发手术后呼吸衰竭的自动化,通用预测工具:一个回顾性队列研究
Michael E Kiyatkin1, Boudewijn Aasman2, Melissa J Fazzari3
1Department of Anesthesiology, Montefiore Medical Center and Albert Einstein College of Medicine, Bronx, NY, USA.
Journal of clinical anesthesia
|July 9, 2023
概括
一个新的机器学习工具,RESPIRE,准确地预测术后呼吸衰竭,优于现有的方法. 这一进步为手术患者提供了更好的预测,提高了质量指标和临床决策.
科学领域:
- 麻醉学和外科手术期间的医学.
- 医疗保健中的人工智能
- 手术结果研究研究.
背景情况:
- 手术后呼吸衰竭是一种重要的手术并发症,也是关键的质量指标.
- 目前手术后呼吸衰竭的预测工具受限于性能不足,特定的人口限制和手动计算要求,阻碍了它们的广泛实施.
- 需要一种改进的,自动化的预测工具来预测术后呼吸衰竭.
研究的目的:
- 开发和验证一个先进的,机器学习驱动的预测工具,用于术后呼吸衰竭.
- 创建一个适合自动计算和更广泛的临床应用的工具.
- 与现有模型相比,提高预测术后呼吸衰竭的准确性和实用性.
主要方法:
- 从2018年1月到2021年6月,对101,455次麻醉手术进行了回顾性分析.
- 在培训队伍上使用随机森林方法开发RESPIRE模型,包括26个预先识别的风险因素.
- 验证RESPIRE模型的准确性 (AUROC) 和与ARISCAT和SPORC-1在单独的验证队列中的比较.
主要成果:
- 该RESPIRE模型显示出卓越的预测准确性,AUROC为0.93,显著超过ARISCAT和SPORC-1 (AUROC为0.82).
- 在可比的灵敏度 (80-90%),RESPIRE比ARISCAT和SPORC-1取得了更高的积极预测值 (11%) 和更低的错误阳性率 (12%),而不是ARISCAT和SPORC-1.
- 该RESPIRE模型还显示,该模型可以更好地预测手术后呼吸衰竭的既定质量指标.
结论:
- 一种基于机器学习的通用预测工具 (RESPIRE) 已成功开发出用于术后呼吸衰竭.
- 在研究和基于质量的术后呼吸衰竭定义方面,RESPIRE模型表现出卓越的性能.
- 该工具在临床实践和研究中为预测和管理术后呼吸衰竭提供了显著的进步.
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