总麻醉患者的二氧化碳动脉部分压力的实时估计:预测建模研究
Ah Ra Lee1, Jun Ho Lee2, Sooyoung Yoo1
1Office of eHealth Research and Business, Seoul National University Bundang Hospital, 172, Dolma-ro, Bundang-gu, Seongnam-si, 13605, Republic of Korea.
JMIR medical informatics
|September 16, 2025
概括
这项研究开发了一种人工智能模型,用于在通风患者中持续监测二氧化碳 (PaCO2) 的动脉部分压力,从而提高了对传统方法的准确性.
科学领域:
- 麻醉学和重症监护医学
- 生物医学工程 生物医学工程
- 医疗保健中的人工智能
背景情况:
- 对于在全身麻醉下机械通风患者,不断监测二氧化碳 (PaCO2) 的动脉部分压力至关重要.
- 目前用于PaCO2监测的方法是侵入性的 (动脉血液气体分析) 或不精确的 (非侵入性估计).
研究的目的:
- 开发和评估一种机器学习模型,用于在机械通风患者中持续,准确地估计PaCO2.
- 为了提高在全身麻醉期间手术内监测的准确性.
主要方法:
- 对2304个手术病例的VitalDB数据集进行了回顾性分析.
- 使用非侵入性生理参数和临床数据训练了一种CatBoost回归模型,以预测PaCO2.
- 模型性能使用嵌套交叉验证进行评估,并与终潮二氧化碳 (ETCO2) 估计进行比较.
主要成果:
- 机器学习模型显著优于传统的基于ETCO2的方法,平均绝对误差为2.38mm Hg,ICC为0.87.
- 超过90%的估计值处于临床上可接受的范围内 (误差<±5 mm Hg).
- 在hypocapnic,normocapnic和hypercapnic PaCO2亚组中注意到性能改善.
结论:
- 开发的AI模型提供了比传统方法更准确和可靠的PaCO2估计.
- 这种方法有可能实现实时监测,并促进及时的临床干预.
- 需要进一步的前性研究来验证这种人工智能驱动的监测的临床影响.
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