开发和验证用于预测重症患者肠道食不耐受症 (NOFI) 的诺图:混合后期和前性队列研究
Youquan Wang1, Yanhua Li1, Huimei Wang2
1Department of Critical Care Medicine, The First Hospital of Jilin University, Changchun, 130021, China.
Clinical nutrition (Edinburgh, Scotland)
|October 18, 2023
概括
一种新的临床预测工具,NOFI,有助于识别患有肠道食不耐受症高风险的重症患者. 它整合了诊断,AGI等级和APACHE II评分,以获得更好的养策略.
科学领域:
- 关键护理医学 关键护理医学
- 胃肠病学 胃肠病学
- 临床预测建模模型
背景情况:
- 肠道食不耐症 (FI) 在重症患者中很常见,使营养支持复杂化.
- 准确预测FI对于及时干预和优化患者护理至关重要.
研究的目的:
- 开发和验证临床预测诺姆图,用于临重病患者的肠道食不耐受 (NOFI).
- 帮助临床医生实施预干预策略和制定个性化的养计划,以减少FI的发生.
主要方法:
- 对发展队列 (n=628) 的回顾性分析,以确定FI的预测因子.
- 在独立的外部队列 (n=143) 中对NOFI名录的前性验证.
- 采用多变量逻辑回归,倒向阶段性选择和决策曲线分析.
主要成果:
- NOFI模型确定了初级诊断,急性胃肠道损伤 (AGI) 等级和APACHE II评分作为FI的独立预测因素.
- 该模型在外部验证队列中显示出良好的区分和校准.
- 决策曲线分析表明,与标准养策略相比,净益处更高.
结论:
- 在NOFI nomogram提供了一个方便和有效的工具,用于预测肠道食不耐受性在重症患者.
- 初级诊断,AGI等级和APACHE II分数的整合有助于个性化的营养管理,并减少FI的发生率.
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