BACKGROUND The use of real-world data to generate evidence requires careful assessment and validation of critical variables before drawing clinical conclusions. Prospective clinical trial data suggest that anatomic origin of colon cancer impacts prognosis and treatment effectiveness. As an initial step in validating this observation in routine clinical settings, we explored the feasibility and accuracy of obtaining information on tumor sidedness from electronic health records (EHR) billing codes. METHODS Nine thousand four hundred three patients with metastatic colorectal cancer (mCRC) were selected from the Flatiron Health database, which is derived from de-identified EHR data. This study included a random sample of 200 mCRC patients. Tumor site data derived from International Classification of Diseases (ICD) codes were compared with data abstracted from unstructured documents in the EHR (e.g. surgical and pathology notes). Concordance was determined via observed agreement and Cohen's kappa coefficient (κ). Accuracy of ICD codes for each tumor site (left, right, transverse) was determined by calculating the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), and corresponding 95% confidence intervals, using abstracted data as the gold standard. RESULTS Study patients had similar characteristics and side of colon distribution compared with the full mCRC dataset. The observed agreement between the ICD codes and abstracted data for tumor site for all sampled patients was 0.58 (κ = 0.41). When restricting to the 62% of patients with a side-specific ICD code, the observed agreement was 0.84 (κ = 0.79). The specificity (92-98%) of structured data for tumor location was high, with lower sensitivity (49-63%), PPV (64-92%) and NPV (72-97%). Demographic and clinical characteristics were similar between patients with specific and non-specific side of colon ICD codes. CONCLUSIONS ICD codes are a highly reliable indicator of tumor location when the specific location code is entered in the EHR. However, non-specific side of colon ICD codes are present for a sizable minority of patients, and structured data alone may not be adequate to support testing of some research hypotheses. Careful assessment of key variables is required before determining the need for clinical abstraction to supplement structured data in generating real-world evidence from EHRs.

中文翻译：

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验证诊断代码以在电子健康记录注册表中识别结肠一侧。

背景技术使用真实世界的数据来生成证据需要在得出临床结论之前仔细评估和验证关键变量。前瞻性临床试验数据表明，结肠癌的解剖起源会影响预后和治疗效果。作为在常规临床环境中验证此观察结果的第一步，我们探索了从电子健康记录（EHR）计费代码中获取有关肿瘤方面信息的可行性和准确性。方法从Flatiron Health数据库中选择了943例转移性结直肠癌（mCRC）患者，该数据库来自未识别的EHR数据。这项研究包括200名mCRC患者的随机样本。将从国际疾病分类（ICD）代码获得的肿瘤部位数据与从EHR中非结构化文档中摘录的数据（例如手术和病理记录）进行了比较。通过观察到的一致性和科恩卡伯系数（κ）来确定一致性。使用抽象数据作为计算灵敏度，特异性，阳性预测值（PPV）和阴性预测值（NPV）以及相应的95％置信区间的方法，确定每个肿瘤部位（左，右，横向）ICD码的准确性。黄金标准。结果与完整的mCRC数据集相比，研究患者具有相似的特征和结肠一侧分布。在所有样本患者中，ICD代码与肿瘤部位抽象数据之间观察到的一致性为0.58（κ= 0.41）。如果将62％的患者使用ICD编码，则观察到的一致性为0.84（κ= 0.79）。结构化数据对肿瘤定位的特异性较高（92-98％），敏感性较低（49-63％），PPV（64-92％）和NPV（72-97％）。结肠ICD码有特定和非特定一侧的患者的人口统计学和临床​​特征相似。结论当在EHR中输入特定的位置代码时，ICD代码是肿瘤位置的高度可靠的指标。然而，结肠ICD码的非特异性方面存在于少数患者中，仅结构化数据可能不足以支持对某些研究假设的检验。