机械通风重症成年人对氧气目标的个性化治疗效果
Kevin G Buell1, Alexandra B Spicer2, Jonathan D Casey3
1Division of Pulmonary and Critical Care, Department of Medicine, University of Chicago, Chicago, Illinois.
对重症成年人的个性化氧和目标可能会降低死亡率. 机器学习模型可以预测哪些患者从较低或较高的氧和目标中受益,指导个性化治疗策略.
科学领域:
- 危急护理医学
- 临床试验
- 在医疗保健中的机器学习
背景情况:
- 随机试验没有确定严重病情的成年人特定氧化目标的整体益处.
- 个体患者特征对氧化目标治疗效果的潜在影响尚不清楚.
研究的目的:
- 调查患者特征是否改变较低或较高的周围氧和 (SpO2) 目标对死亡率的影响.
- 开发和验证一个预测个性化SpO2目标有效性的模型.
主要方法:
- 一个机器学习模型是使用Pragmatic Investigation of Optimal Oxygen Targets (PILOT) 试验数据开发的.
- 该模型在重症监护室随机试验中进行了外部验证,该试验比较了两种氧气治疗方法 (ICU- ROX).
- 接受机械呼吸的重症成年人被随机分为较低或较高的SpO2目标.
主要成果:
- 机器学习模型预测了个性化SPO2目标的广泛影响,从降低死亡率到增加.
- 预计受益于较低SPO2度的患者往往患有急性脑损伤,而受益于较高度的患者则患有败血症或异常生命体征.
- 以模型为指导的个性化SpO2目标可以在验证队列中将总死亡率降低6.4%.
结论:
- 机器学习的个性化氧化目标显示出降低严重疾病成人的死亡率的潜力.
- 需要进一步的前性试验来评估个性化氧化策略的临床效用.
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