Jennifer Moreno Veteran Affairs San Diego Healthcare System, San Diego, California; Division of Gastroenterology, University of California San Diego, San Diego, California.
Division of Gastroenterology, University of California San Diego, San Diego, California.
Clin Gastroenterol Hepatol. 2023 May;21(5):1178-1187.e1. doi: 10.1016/j.cgh.2022.08.030. Epub 2022 Sep 6.
BACKGROUND & AIMS: Achalasia is an esophageal motility disorder associated with significant morbidity, yet achalasia-associated risk factors and outcomes are not well-characterized. Our aim was to establish a national cohort of individuals with achalasia, utilizing Veterans Health Administration (VHA) data.
We iteratively developed combinations of International Classification of Diseases and Current Procedural Terminology code algorithms to validate an approach for identifying achalasia cases. We assessed algorithm accuracy for achalasia diagnosis through manual chart review of candidate achalasia cases and candidate non-achalasia controls. The prespecified end point chosen to establish algorithm performance success was achieving a 1-sided 95% confidence lower bound for a positive predictive value >85% for a random sample of 100 candidate achalasia cases. Once adequate performance was validated, we queried national VHA data to establish and characterize a cohort of individuals diagnosed with achalasia between 1999 and 2020.
Three rounds of algorithm modification and validation were conducted to achieve the prespecified performance endpoint. In the final round, a combination of 3 or more International Classification of Diseases codes for achalasia in the subject's lifetime and a Current Procedural Terminology code for esophageal manometry achieved an observed 94% positive predictive value (1-sided 95% confidence lower bound of 88.5%) for identifying achalasia. Applying the algorithm to national VHA data identified a cohort of 2100 individuals with achalasia, with a median age 65 years and who were 93% male.
Using a rigorous validation approach, we established a national cohort of 2100 individuals with achalasia within the VHA, one of the largest established to date. This cohort can be utilized to study risk factors for achalasia and outcomes over time.
贲门失弛缓症是一种与显著发病率相关的食管动力障碍疾病,但贲门失弛缓症相关的危险因素和结果尚未得到很好的描述。我们的目的是利用退伍军人健康管理局(VHA)的数据,建立一个贲门失弛缓症患者的全国性队列。
我们迭代开发了国际疾病分类和当前程序术语代码算法的组合,以验证一种用于识别贲门失弛缓症病例的方法。我们通过对候选贲门失弛缓症病例和候选非贲门失弛缓症对照病例的病历进行手动审查,评估了算法对贲门失弛缓症诊断的准确性。为了确定算法性能成功,我们选择了一个预设的终点,即对 100 例随机候选贲门失弛缓症病例进行单侧 95%置信度下限的阳性预测值>85%。一旦验证了足够的性能,我们就查询了全国 VHA 数据,以建立和描述 1999 年至 2020 年期间诊断为贲门失弛缓症的患者队列。
进行了三轮算法修改和验证,以达到预设的性能终点。在最后一轮中,在受试者一生中出现 3 个或更多的贲门失弛缓症国际疾病分类代码和食管测压的当前程序术语代码的组合,实现了 94%的阳性预测值(单侧 95%置信度下限为 88.5%),用于识别贲门失弛缓症。将该算法应用于全国 VHA 数据,确定了一个由 2100 名贲门失弛缓症患者组成的队列,中位年龄为 65 岁,其中 93%为男性。
通过严格的验证方法,我们在 VHA 内建立了一个由 2100 名贲门失弛缓症患者组成的全国性队列,这是迄今为止建立的最大队列之一。该队列可用于研究贲门失弛缓症的危险因素和随时间推移的结果。