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.

Methods

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.

Results

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.

Conclusions

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.

中文翻译：

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美国国家贲门失弛缓症队列的开发和验证：退伍军人事务贲门失弛缓症队列 (VA-AC)

 背景与目标

贲门失弛缓症是一种食管运动障碍，发病率较高，但与贲门失弛缓症相关的危险因素和结果尚不明确。我们的目标是利用退伍军人健康管理局 (VHA) 数据建立全国贲门失弛缓症患者队列。

 方法

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进行手动图表审查，评估了贲门失弛缓症诊断的算法准确性。为确定算法性能成功而选择的预先指定终点是，对于 100 个候选贲门失弛缓症病例的随机样本，阳性预测值 >85% 达到单侧 95% 置信度下限。一旦验证了足够的表现，我们就会查询国家 VHA 数据，以建立并描述 1999 年至 2020 年间诊断为贲门失弛缓症的一组患者的特征。

 结果

进行了三轮算法修改和验证以达到预定的性能终点。在最后一轮中，受试者一生中贲门失弛缓症的 3 个或更多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观察到的 94% 阳性预测值（单边 95% 置信下限为 88.5） %) 用于识别贲门失弛缓症。将算法应用于国家 VHA 数据，确定了 2100 名贲门失弛缓症患者的队列，其中位年龄为 65 岁，其中 93% 为男性。

 结论

我们采用严格的验证方法，在 VHA 内建立了一个由 2100 名失弛缓症患者组成的全国队列，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。