使用数据驱动方法来定义美国电子健康记录数据中的COVID后情况
Kathleen M Andersen1, Farid L Khan2, Peter W Park2
1Vaccines Real World Evidence, Pfizer Inc, New York, New York, United States of America.
PloS one
|April 5, 2024
概括
一个新的数据驱动的定义在COVID-19感染后的20%个体中确定了后COVID条件 (PCC). 这种基于症状变化的定义,比U09.9代码捕获了更多病例,特别是在老年人中.
科学领域:
- 医疗信息学 医疗信息学
- 公共卫生 公共卫生
- 流行病学 流行病学
背景情况:
- 后COVID状况 (PCC) 构成了重大的公共卫生挑战.
- 现有的PCC诊断标准缺乏标准化,可能无法完全捕捉患者的体验.
- 需要采用数据驱动的方法来准确地定义和识别PCC.
研究的目的:
- 利用电子健康记录 (EHR) 数据,开发一个数据驱动的后COVID条件 (PCC) 的定义.
- 根据COVID-19感染后的症状变化量化PCC的发生率.
- 将数据驱动定义的性能与用于识别PCC的ICD-10-CM代码U09.9进行比较.
主要方法:
- 从2020年4月到2021年9月,使用非识别的EHR数据集进行回顾性队列研究.
- 基于新诊断的"COVID症状得分",根据与对照组相比的发病率比重进行了加权.
- 将数据驱动的PCC定义与2021年9月诊断的病例的U09.9代码进行比较.
主要成果:
- 该队列包括588,611名COVID-19患者;20%的人患有PCC.
- 随着年龄的增长,PCC发生率增加,影响7.8% (0-17岁),17.3% (18-64岁) 和33.3% (65岁以上).
- 数据驱动的定义确定了2021年9月PCC病例的19.0%,而通过U09.9代码识别的仅为2.9%.
结论:
- 基于症状和基于U09.9代码的定义确定了不同的患者群体.
- 为了最大限度地捕获PCC病例,可能需要采用综合方法.
- 这些发现凸显了当前编码系统的局限性,以及对强大,数据驱动的定义的需求,尤其是在广泛使用和达成共识之前.
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