使用临床螺旋计数据估计肺功能变化的速率
Aparna Balasubramanian1, Christopher Cervantes2, Andrew S Gearhart3
1Pulmonary and Critical Care Medicine, Johns Hopkins University, Baltimore, Maryland, USA abalasu2@jhmi.edu.
BMJ open respiratory research
|October 3, 2024
概括
使用电子健康记录 (EHR) 估计COPD患者的肺功能变化是可行的. 建议使用休伯回归方法,以准确地分析从EHR数据中一秒钟内强迫呼气体积 (FEV1) 的下降.
科学领域:
- 肺部医学 肺部医学
- 生物统计学 生物统计学
- 医疗信息学 医疗信息学
背景情况:
- 从电子健康记录 (EHR) 中精确估计慢性阻塞性肺病 (COPD) 的肺功能下降至关重要.
- 临床获得的测试数据具有复杂性,需要强大的分析方法.
研究的目的:
- 为了比较各种分析方法来估计强迫呼气体积在1秒内变化的速度 (FEV1) 从COPD患者的EHR数据.
- 确定评估FEV1下降的最可靠方法,并考虑数据不规则.
主要方法:
- 对比非回归 (总变化,平均变化) 和回归 (Quantile,RANSAC,Huber) 方法,以估计FEV1变化率 (mL/年和%/年) 在1417名COPD患者中,具有≥3个门诊测试.
- 分析了估计的分布,专注于极端值,并使用回归来测试住院病人的关联.
- 在外部队列中验证的结果.
主要成果:
- 平均FEV1下降大约为-30毫升/年或-2%/年.
- 非回归方法由于异常值或测试间隔短,产生了错误的估计.
- 回归方法,特别是休伯回归方法,最小化了极端估计,并显示了与住院治疗,吸烟状态,并发性疾病和先前住院治疗的关联.
结论:
- 从EHR数据中估计FEV1变化率是临床相关的,但对数据质量敏感.
- 休伯回归作为一个强大的方法来定义个人肺功能变化从EHR数据,优于非回归方法.
- 这些发现在外部队列中得到了验证,支持了Huber方法的临床适用性.
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