开发和外部验证机器学习风险预测模型,用于使用英国和瑞典的国家中风登记册预测中风后30天的死亡率
Wenjuan Wang1, Josline A Otieno2, Marie Eriksson2
1Department of Population Health Sciences, King's College London, London, UK wenjuan.wang@kcl.ac.uk.
BMJ open
|November 15, 2023
概括
一个新的风险预测模型准确地识别了30天中风死亡率. 该工具在英国和瑞典的中风登记册中得到验证,支持提高中风护理质量.
科学领域:
- 心血管医学 心血管医学
- 医疗保健服务研究 医疗服务研究
- 医疗信息学 医疗信息学
背景情况:
- 预测中风死亡率对于改善中风护理质量至关重要.
- 现有的模型可能在不同的医疗保健系统中缺乏通用性.
- 大规模,高质量的中风注册表为开发强大的预测工具提供了潜力.
研究的目的:
- 开发和外部验证30天中风死亡率的可概括风险预测模型.
- 创建一个适用于中风护理质量改进分析的工具.
- 为了能够在医院和卫生系统中公平地比较中风死亡率结果.
主要方法:
- 基于注册表的队列研究,使用英国 (SSNAP) 和瑞典 (Riksstroke) 的中风注册表.
- 在SSNAP中开发和时间验证;在Riksstroke中外部验证.
- 模型使用后勤回归和极端梯度提升 (XGBoost) 开发,通过歧视,校准和决策曲线评估.
主要成果:
- XGBoost模型在时间验证 (AUC 0.852) 和外部验证 (AUC 0.861) 中表现出很高的性能.
- 该模型在英国验证集中对住院死亡率进行了良好的校准.
- 瑞典的外部验证显示表现良好,但对住院死亡风险略高估计.
结论:
- 开发了一个准确的,外部验证的风险预测模型,用于30天中风死亡率.
- 该模型利用高质量的注册表数据,适用于质量改进分析.
- 这种工具可以促进在国际医疗保健机构中对中风治疗质量的公平比较.
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