在观察数据库网络中开发和验证患者级模型,以预测痴呆症
Luis H John1, Egill A Fridgeirsson2, Jan A Kors2
1Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands. l.john@erasmusmc.nl.
BMC medicine
|July 29, 2024
概括
使用电子健康记录开发新的痴呆症预测模型对于早期干预至关重要. 这些模型结合了诊断和药物数据,提高了风险预测准确性和临床实用性.
科学领域:
- 老年学是一门学科.
- 流行病学 流行病学
- 生物统计学 生物统计学
背景情况:
- 许多痴呆症预测模型存在,但缺乏外部验证,阻碍了临床采用.
- 在先前的研究中报告不足限制了外部验证的成功.
- 需要使用观察数据库来预测一般人群中痴呆风险的新模型.
研究的目的:
- 开发和外部验证新的痴呆症预测模型.
- 评估规范化方法,以创建节和可实施的模型.
- 改进针对性干预措施的痴呆风险量化.
主要方法:
- 使用电子健康记录和索赔数据构建了后勤回归模型.
- 五个观察数据库被用来预测55-84岁个体的5年痴呆风险.
- 评估了规范化技术 (L1,破碎的适应) 和预测器集 (基线,完整,表型).
主要成果:
- 破碎的适应 (BAR) 正规化促进了变量选择,并产生了比L1更节的模型.
- 结合疾病诊断和药物暴露预测因素,提高了基线模型的性能.
- 一个使用临床相关预测因素的BARR规范化模型显示出一致的外部验证和改进的校准.
结论:
- 患者级痴呆症预测模型已成功开发并得到外部验证.
- 人口年龄是主要的预测因素,但诊断和药物暴露显著改善了预测.
- 在开发节和有效的痴呆症预测模型方面,BARR规范化优于L1.
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