使用机器学习来评估重症患者早期肠道食不耐受症的预后价值:一项回顾性研究
Orit Raphaeli1,2,3, Liran Statlender4, Chen Hajaj1,3
1Industrial Engineering and Management, Ariel University, Ariel 40700, Israel.
Nutrients
|June 28, 2023
概括
机器学习在重症患者中确定了肠道食不耐症 (EFI) 的关键标志物,预测了90天的不良结果和早期食失败. 这有助于早期识别高风险个体.
科学领域:
- 关键护理医学 关键护理医学
- 临床营养学 临床营养学
- 医疗保健中的机器学习
背景情况:
- 在重症患者早期肠道营养 (EN) 期间的胃肠道不耐受与不良结果有着有争议的关联.
- 早期识别患有肠道食不耐症 (EFI) 风险的患者对于改善临床结果至关重要.
研究的目的:
- 评估早期重症监护室 (ICU) 住院期间EFI标记物的预后价值.
- 使用机器学习 (ML) 方法预测早期的EN故障.
主要方法:
- 对1584名成年患者的回顾性分析,在ICU中接受EN>48小时.
- 利用ML算法分析临床数据,EFI标记物和药物,在入院后72小时内进行分析.
- 通过十倍交叉验证,使用接收器操作特征下的面积 (AUCROC) 评估预测性能.
主要成果:
- 在ML模型中,90天死亡率达到0.73的AUCROC,早期EN衰竭为0.71.
- 在两种模型中,第二天的胃残留体积>250毫升是显著的预测因素.
- 确定了对重症监护患者具有预后价值的特定EFI标志物.
结论:
- ML有效地识别了与90天不良结果和早期EN失败相关的EFI标记.
- 支持早期识别和管理危急病患者的风险食不耐受.
- 建议进行进一步的前性和外部验证研究以证实研究结果.
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