Objectives Asthma is a heterogenous condition with significant diagnostic complexity, including variations in symptoms and temporal criteria. The disease can be difficult for clinicians to diagnose accurately. Properly identifying asthma patients from the electronic health record is consequently challenging as current algorithms (computable phenotypes) rely on diagnostic codes (e.g., International Classification of Disease, ICD) in addition to other criteria (e.g., inhaler medications)—but presume an accurate diagnosis. As such, there is no universally accepted or rigorously tested computable phenotype for asthma.

Methods We compared two established asthma computable phenotypes: the Chicago Area Patient-Outcomes Research Network (CAPriCORN) and Phenotype KnowledgeBase (PheKB). We established a large-scale, consensus gold standard (n = 1,365) from the University of California, Los Angeles Health System's clinical data warehouse for patients 5 to 17 years old. Results were manually reviewed and predictive performance (positive predictive value [PPV], sensitivity/specificity, F1-score) determined. We then examined the classification errors to gain insight for future algorithm optimizations.

Results As applied to our final cohort of 1,365 expert-defined gold standard patients, the CAPriCORN algorithms performed with a balanced PPV = 95.8% (95% CI: 94.4–97.2%), sensitivity = 85.7% (95% CI: 83.9–87.5%), and harmonized F1 = 90.4% (95% CI: 89.2–91.7%). The PheKB algorithm was performed with a balanced PPV = 83.1% (95% CI: 80.5–85.7%), sensitivity = 69.4% (95% CI: 66.3–72.5%), and F1 = 75.4% (95% CI: 73.1–77.8%). Four categories of errors were identified related to method limitations, disease definition, human error, and design implementation.

Conclusion The performance of the CAPriCORN and PheKB algorithms was lower than previously reported as applied to pediatric data (PPV = 97.7 and 96%, respectively). There is room to improve the performance of current methods, including targeted use of natural language processing and clinical feature engineering.

目标 哮喘是一种具有显着诊断复杂性的异质性疾病，包括症状和时间标准的变化。临床医生可能难以准确诊断该疾病。因此，从电子健康记录中正确识别哮喘患者具有挑战性，因为当前的算法（可计算的表型）除了其他标准（例如吸入药物）之外还依赖于诊断代码（例如，国际疾病分类，ICD）——但假定诊断准确. 因此，没有普遍接受或经过严格测试的哮喘可计算表型。

方法 我们比较了两种已建立的哮喘可计算表型：芝加哥地区患者结果研究网络 (CAPriCORN) 和表型知识库 (PheKB)。 我们从加州大学洛杉矶分校卫生系统的临床数据仓库中为 5 至 17 岁的患者建立了一个大规模的共识金标准 ( n = 1,365)。人工审查结果并确定预测性能（阳性预测值 [PPV]、敏感性/特异性、F1 评分）。然后，我们检查了分类错误，以深入了解未来的算法优化。

结果 应用到我们最终的 1,365 名专家定义的金标准患者队列中，CAPiCORN 算法以平衡的 PPV = 95.8% (95% CI: 94.4–97.2%) 执行，敏感性 = 85.7% (95% CI: 83.9–87.5 %)，并协调 F1 = 90.4% (95% CI: 89.2–91.7%)。PheKB 算法以平衡 PPV = 83.1% (95% CI: 80.5–85.7%)、灵敏度 = 69.4% (95% CI: 66.3–72.5%) 和 F1 = 75.4% (95% CI: 73.1– 77.8%）。确定了与方法限制、疾病定义、人为错误和设计实施相关的四类错误。

结论 CAPriCORN 和 PheKB 算法的性能低于先前报道的应用于儿科数据的性能（PPV 分别为 97.7 和 96%）。当前方法的性能还有改进的空间，包括有针对性地使用自然语言处理和临床特征工程。