计算机辅助风险评分系统在预测紧急医疗入院后医院死亡率方面的外部验证
Viveck Kingsley1, Lisa Fox2, David Simm2
1Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK; The Rotherham NHS Foundation Trust, Rotherham General Hospital, Rotherham, South Yorkshire, UK.
International journal of medical informatics
|May 23, 2024
概括
计算机辅助风险评分系统 (CARSS) 模型得到了外部验证,用于预测紧急入院的医院死亡率. 在重新校准后,CARSS模型在验证数据集中表现出足够的性能.
科学领域:
- 临床预测模型的临床预测模型.
- 改善医疗保健质量 改善医疗保健质量
- 患者安全结果的结果.
背景情况:
- 临床预测模型可以提高患者的安全性.
- 一个计算机辅助风险评分系统 (CARSS) 已开发,以预测住院死亡率.
- 这项研究旨在对CARSS模型进行外部验证.
研究的目的:
- 通过外部验证CARSS模型来预测住院死亡率.
- 在新数据集中评估CARSS的区分和校准性能.
- 为了确定CARSS是否需要重新校准以在不同的医疗保健环境中使用.
主要方法:
- 追溯的外部验证研究.
- 包括来自英国罗瑟汉姆基金会信托基金会 (TRFT) 的成年紧急医疗住院.
- 通过使用歧视 (c-统计) 和校准 (斜坡,图表) 评估CARSS预测性能.
主要成果:
- 卡尔斯模型显示出良好的歧视 (c-统计值0.87).
- 该模型在对基线死亡率差异进行重新校准后显示出良好的校准.
- 在TRFT数据集中,CARSS最初预测的住院死亡率不足.
结论:
- 卡尔斯模型经过外部验证,并在重新校准后得到了充分的性能.
- 根据基线死亡率差异进行校正,改善了CARSS的性能.
- 卡尔斯模型可以成为预测急诊诊所住院死亡率的宝贵工具.
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