心脑346-7分数:开发和验证使用深度学习的单氧化碳中毒简单死亡率预测分数的开发和验证
Jason J Rose1,2, Michael S Zhang2, Jerry Pan3
1University of MD School of Medicine, University of Maryland, Baltimore, MA, USA.
Clinical toxicology (Philadelphia, Pa.)
|July 7, 2023
概括
一个新的临床风险评分识别了可能导致死亡的一氧化碳中毒患者. 该得分使用年龄,精神状态变化和心脏并发症来预测中毒患者的死亡率.
科学领域:
- 紧急医疗 紧急医疗
- 毒理学 毒理学 毒理学
- 临床风险预测预测
背景情况:
- 一氧化碳 (CO) 中毒具有严重的急性和长期死亡风险.
- 心脏参与是已知的因素,加剧了CO中毒幸存者的死亡风险.
- 识别高风险患者对于有针对性的干预和改善结果至关重要.
研究的目的:
- 开发和验证临床风险评分,用于预测一氧化碳中毒患者的住院和长期死亡率.
- 确定与CO中毒个体死亡风险增加相关的关键临床变量.
- 为临床医生提供一个工具,以便根据患者的不良结果风险更好地分层患者.
主要方法:
- 从衍生 (811) 和验证 (462) 队列中对成年CO中毒患者的回顾性分析.
- 使用的人口统计数据,实验室值,医院费用和临床图表数据.
- 使用Firth物流回归与Stepwise Akaike的信息标准来构建预测模型.
主要成果:
- 开发的"心脑346-7分数"确定了基于年龄,精神状态改变和心脏并发症的死亡风险.
- 在导出队列中,得分显示灵敏度为82%,特异性为80% (AUC 0.81).
- 在验证队列中,得分表现类似,灵敏度为72%,特异性为69% (AUC 0.70).
结论:
- 一个简单的,基于临床的评分系统,心脑346-7评分,被开发和验证.
- 该得分有效预测一氧化碳中毒的住院患者和长期死亡率.
- 进一步的验证可能会支持其在高风险的CO中毒患者的临床决策中使用.
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