使用常规护理数据,与其他类型的中风相比,预测动脉瘤下关节出血
Jos P Kanning1,2, Hendrikus J A van Os3,4, Margot Rakers4
1UMC Utrecht Brain Center, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands.
PloS one
|May 31, 2024
概括
使用电子健康记录开发动脉瘤下关节出血 (aSAH) 预测模型显示中等性能. 目前的EHR数据不足以准确识别有aSAH高风险的个体.
科学领域:
- 神经学 神经学
- 公共卫生 公共卫生
- 医疗信息学 医疗信息学
背景情况:
- 动脉瘤下关节出血 (aSAH) 的预防依赖于在高风险人群中早期发现内动脉瘤.
- 电子健康记录 (EHR) 提供了一个潜在的数据来源,用于识别处于风险的人群.
研究的目的:
- 使用EHR数据开发和评估aSAH预测模型.
- 将aSAH模型的性能与急性缺血性中风 (AIS) 和脑内出血 (ICH) 的预测模型进行比较.
主要方法:
- 开发和交叉验证了带有弹性净处罚的Cox比例危险模型.
- 对aSAH,AIS和ICH的预测模型是使用荷兰人口 (2007-2020) 的EHR数据构建的.
- 模型性能使用c统计和10年校准进行评估.
主要成果:
- 该aSAH预测模型实现了中度的c统计值 (0.61),低于AIS (0.77) 和ICH (0.77) 模型.
- 确定了19种aSAH预测因素,包括年龄,性别和社会经济地位.
- 所有模型在10年内都表现出良好的校准.
结论:
- 目前的EHR数据不足以准确识别有aSAH风险增加的个体.
- 虽然AIS和ICH的模型显示出良好的区分性能,但aSAH模型的性能有限.
- 可能需要进一步的研究来改进使用综合数据源的SAH风险预测.
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