Center for Healthcare Organization & Implementation Research, Edith Nourse Rogers Veterans Hospital, Bedford, MA; VA Boston Healthcare System, Boston, MA; The Pulmonary Center, Boston University School of Medicine, Boston, MA.
Center for Healthcare Organization & Implementation Research, Edith Nourse Rogers Veterans Hospital, Bedford, MA; VA Boston Healthcare System, Boston, MA; The Pulmonary Center, Boston University School of Medicine, Boston, MA.
Chest. 2021 May;159(5):1986-1994. doi: 10.1016/j.chest.2020.12.010. Epub 2020 Dec 17.
Pulmonary arterial hypertension (PAH) is a rare disease, and much of our understanding stems from single-center studies, which are limited by sample size and generalizability. Administrative data offer an appealing opportunity to inform clinical, research, and quality improvement efforts for PAH. Yet, currently no standardized, validated method exists to distinguish PAH from other subgroups of pulmonary hypertension (PH) within this data source.
Can a collection of algorithms be developed and validated to detect PAH in administrative data in two diverse settings: all Veterans Health Administration (VA) hospitals and Boston Medical Center (BMC), a PAH referral center.
In each setting, we identified all adult patients with incident PH from 2006 through 2017 using International Classification of Diseases PH diagnosis codes. From this baseline cohort of all PH subgroups, we sequentially applied the following criteria: diagnosis codes for PAH-associated conditions, procedure codes for right heart catheterizations (RHCs), and pharmacy claims for PAH-specific therapy. We then validated each algorithm using a gold standard review of primary clinical data and calculated sensitivity, specificity, positive predictive values (PPVs), and negative predictive values.
From our baseline cohort, we identified 12,012 PH patients in all VA hospitals and 503 patients in BMC. Sole use of PH diagnosis codes performed poorly in identifying PAH (PPV, 16.0% in VA hospitals and 36.0% in BMC). The addition of PAH-associated conditions to the algorithm modestly improved PPV. The best performing algorithm required ICD diagnosis codes, RHC codes, and PAH-specific therapy (VA hospitals: specificity, 97.1%; PPV, 70.0%; BMC: specificity, 95.0%; PPV, 86.0%).
This set of validated algorithms to identify PAH in administrative data can be used by the PAH scientific and clinical community to enhance the reliability and value of research findings, to inform quality improvement initiatives, and ultimately to improve health for PAH patients.
肺动脉高压（PAH）是一种罕见疾病，我们的大部分认识都来自于单中心研究，这些研究受到样本量和普遍性的限制。行政数据为 PAH 的临床、研究和质量改进工作提供了一个有吸引力的机会。然而，目前在这个数据源中，还没有一种标准化、经过验证的方法可以将 PAH 与其他肺动脉高压（PH）亚组区分开来。
能否在两个不同的环境中（所有退伍军人事务部（VA）医院和波士顿医疗中心（BMC），一个 PAH 转诊中心），开发和验证一组算法来从行政数据中检测 PAH？
在每个环境中，我们使用国际疾病分类 PH 诊断代码从 2006 年到 2017 年确定所有患有新发 PH 的成年患者。从所有 PH 亚组的基线队列中，我们依次应用以下标准：PAH 相关疾病的诊断代码、右心导管术（RHC）的程序代码和 PAH 特异性治疗的药房索赔。然后，我们使用主要临床数据的黄金标准审查来验证每个算法，并计算敏感性、特异性、阳性预测值（PPV）和阴性预测值。
从我们的基线队列中，我们在所有 VA 医院中确定了 12012 名 PH 患者，在 BMC 中确定了 503 名 PH 患者。仅使用 PH 诊断代码识别 PAH 的效果不佳（PPV，VA 医院为 16.0%，BMC 为 36.0%）。将 PAH 相关疾病添加到算法中可以适度提高 PPV。性能最佳的算法需要 ICD 诊断代码、RHC 代码和 PAH 特异性治疗（VA 医院：特异性，97.1%；PPV，70.0%；BMC：特异性，95.0%；PPV，86.0%）。
这套用于在行政数据中识别 PAH 的经过验证的算法可被 PAH 科学和临床社区使用，以提高研究结果的可靠性和价值，为质量改进计划提供信息，并最终改善 PAH 患者的健康状况。
