Hsu Joy, Pacheco Jennifer A, Stevens Whitney W, Smith Maureen E, Avila Pedro C
Division of Allergy-Immunology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA.
Am J Rhinol Allergy. 2014 Mar-Apr;28(2):140-4. doi: 10.2500/ajra.2014.28.4012.
Chronic rhinosinusitis (CRS) is prevalent, morbid, and poorly understood. Extraction of electronic health record (EHR) data of patients with CRS may facilitate research on CRS. However, the accuracy of using structured billing codes for EHR-driven phenotyping of CRS is unknown. We sought to accurately identify CRS cases and controls using EHR data and to determine the accuracy of structured billing codes for identifying patients with CRS.
We developed and validated distinct algorithms to identify patients with CRS and controls using International Classification of Diseases, Ninth Revision (ICD-9) and Current Procedural Terminology codes. We used blinded clinician chart review as the reference standard to evaluate algorithm and billing code accuracy.
Our initial control algorithm achieved a control positive predictive value (PPV) of 100% (i.e., negative predictive value of 100% for CRS). Our initial algorithm for CRS cases relied exclusively on billing codes and had a low case PPV (54%). Notably, ICD-9 code 471.x was associated with a case PPV of 85%, whereas the case PPV of ICD-9 code 473.x was only 34%. After multiple algorithm iterations, we increased the case PPV of our final algorithm to 91% by adding several requirements, e.g., that ICD-9 codes occur with 1 or more evaluations by a CRS specialist to enhance availability of objective clinical data for accurately phenotyping CRS.
These algorithms are an important first step to identify patients with CRS, and may facilitate EHR-based research on CRS pathogenesis, morbidity, and management. Exclusive use of coded data for phenotyping CRS has limited accuracy, especially because CRS symptomatology overlaps with that of other illnesses. Incorporating natural language processing (e.g., to evaluate results of nasal endoscopy or sinus computed tomography) into future work may increase algorithm accuracy and identify patients whose disease status may not be ascertained by only using billing codes.
慢性鼻-鼻窦炎（CRS）普遍存在且危害较大，但人们对其了解甚少。提取CRS患者的电子健康记录（EHR）数据可能有助于CRS的研究。然而，使用结构化计费代码对CRS进行EHR驱动的表型分析的准确性尚不清楚。我们试图使用EHR数据准确识别CRS病例和对照，并确定结构化计费代码用于识别CRS患者的准确性。
我们开发并验证了不同的算法，使用国际疾病分类第九版（ICD-9）和当前程序术语代码来识别CRS患者和对照。我们使用盲法临床医生病历审查作为参考标准来评估算法和计费代码的准确性。
我们最初的对照算法实现了100%的对照阳性预测值（PPV）（即CRS阴性预测值为100%）。我们最初的CRS病例算法完全依赖计费代码，病例PPV较低（54%）。值得注意的是，ICD-9代码471.x的病例PPV为85%，而ICD-9代码473.x的病例PPV仅为34%。经过多次算法迭代，我们通过添加几个要求将最终算法的病例PPV提高到了91%，例如ICD-9代码需与CRS专科医生进行1次或更多次评估同时出现，以提高客观临床数据的可用性，从而准确对CRS进行表型分析。
这些算法是识别CRS患者的重要第一步，可能有助于基于EHR的CRS发病机制、发病率和管理研究。仅使用编码数据对CRS进行表型分析的准确性有限，特别是因为CRS症状与其他疾病的症状重叠。在未来的工作中纳入自然语言处理（例如评估鼻内镜检查或鼻窦计算机断层扫描结果）可能会提高算法准确性，并识别出仅使用计费代码无法确定疾病状态的患者。
