开发和验证使用出生证明和疾病国际分类代码对唐氏综合征的诊断算法
Lin Ammar1, Kristin Bird2, Hui Nian3
1Division of Epidemiology, Vanderbilt University School of Medicine, Nashville, TN 37232, USA.
Children (Basel, Switzerland)
|October 26, 2024
概括
这项研究开发了一种准确的算法来识别患有唐氏综合征 (DS) 的儿童,使用行政数据,包括出生证明和ICD码. 该算法实现了高精度,证明了它对唐氏综合征研究的价值.
科学领域:
- 医疗信息学 医疗信息学
- 儿科遗传学 儿科遗传学
- 公共卫生监督 公共卫生监督
背景情况:
- 准确识别患有唐氏综合征 (DS) 的儿童对于研究和医疗保健至关重要.
- 行政数据来源,如出生证明和电子健康记录,为大规模的DS识别提供了潜力.
- 以前用于在行政数据中识别DS的方法在准确性和范围上有局限性.
研究的目的:
- 开发和验证一个算法,使用行政数据准确识别患有唐氏综合征 (DS) 的儿童.
- 评估开发算法的积极预测值 (PPV).
主要方法:
- 确定了一组2000-2017年间出生在田纳西州医疗补助计划 (TennCare) 中的儿童.
- 怀疑唐氏综合征的儿童是使用出生证明代码或国际疾病分类 (ICD) 代码来定义的.
- 该算法根据型确认,悬而未决的测试或在生命的前六年内为DS进行多次医疗保健接触,将儿童定义为DS.
主要成果:
- 该算法在411名儿童中发现354名 (86.1%) 怀疑患有DS的儿童患有DS.
- 医学图表审查证实了算法的准确性,具有98% (95% CI: 96.0-99.0%) 的积极预测值 (PPV) 来识别患有DS的儿童.
- 该算法还在识别没有DS的儿童方面表现出很高的准确性,确认率为97.7% (95%CI:76.8-93.9%).
结论:
- 使用出生证明数据和ICD代码的算法可以准确识别患有唐氏综合征的个体.
- 该算法是利用行政数据集进行流行病学研究和唐氏综合征研究的宝贵工具.
- 这些发现支持使用行政数据来有效和可靠地识别DS队列.
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