从基于人口的全国电子健康记录预测发生心力衰竭:模型开发和验证研究的协议
Yoko M Nakao1,2,3, Ramesh Nadarajah4,2,5, Farag Shuweihdi4
1Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK y.nakao@leeds.ac.uk.
BMJ open
|January 22, 2024
概括
使用电子健康记录 (EHR) 开发精确的心力衰竭 (HF) 预测模型可以改善早期检测. 这项研究旨在为常规临床实践创建可扩展的高频风险预测工具.
科学领域:
- 心脏病学 心脏病学
- 医疗信息学 医疗信息学
- 预测分析是一种预测分析.
背景情况:
- 心力衰竭 (HF) 是一个日益增长的全球健康挑战,以显著的发病率,死亡率和不断上升的医疗保健支出为标志.
- 目前的诊断方法往往导致HF的延迟检测,通常是在需要住院的晚期症状阶段.
- 预测事件HF的现有统计模型在性能和可扩展性方面面临广泛临床应用的局限性.
研究的目的:
- 开发和验证新发性心力衰竭 (HF) 的可靠预测模型,利用例行收集的初级保健电子健康记录 (EHR).
- 对于事件HF风险,建立1,5年和10年的预测地平线.
- 通过可实施的基于EHR的模型,使HF的早期识别和有针对性的诊断成为可能.
主要方法:
- 在大规模的初级保健电子健康记录数据集上使用后勤回归和监督机器学习技术 (CPRD-GOLD用于导出,CPRD-AURUM用于外部验证).
- 使用来自初级保健,二级保健和死亡记录的患者级联结数据.
- 通过区分,校准和临床效用指标评估模型性能,重点关注常规可访问的变量.
主要成果:
- 该研究将推导并验证发生性心力衰竭 (HF) 风险的预测模型.
- 性能评估将确定模型在1,5年和10年时间范围内预测HF的准确性.
- 将对这些模型进行评估,以评估它们在识别有风险的个体方面的潜在临床实用性.
结论:
- 经过验证的基于EHR的预测模型可以提高心力衰竭 (HF) 的早期检测.
- 这种方法提供了一种可扩展的解决方案,用于在常规临床实践中识别患有HF风险的人.
- 通过预测建模改进的早期识别可以促进及时干预,并可能改变HF疾病轨迹.
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