VA HSR & D Houston Center of Excellence, Michael E. DeBakey Veterans Affairs Medical Center, Baylor College of Medicine, Houston, Texas 77030, USA.
J Surg Res. 2012 Jul;176(1):141-6. doi: 10.1016/j.jss.2011.07.022. Epub 2011 Aug 10.
The ability to identify patients with colorectal cancer (CRC) liver metastasis (LM) using administrative data is unknown. The goals of this study were to evaluate whether administrative data can accurately identify patients with CRCLM and to develop a diagnostic algorithm capable of identifying such patients.
A retrospective cohort study was conducted to validate the diagnostic and procedural codes found in administrative databases of the Veterans Administration (VA) system. CRC patients evaluated at a major VA center were identified (1997-2008, n = 1671) and classified as having liver-specific ICD-9 and/or CPT codes. The presence of CRCLM was verified by primary chart abstraction in the study sample. Contingency tables were created and the positive predictive value (PPV) for CRCLM was calculated for each candidate administrative code. A multivariate logistic-regression model was used to identify independent predictors (codes) of CRCLM, which were used to develop a diagnostic algorithm. Validity of the algorithm was determined by discrimination (c-statistic) of the model and PPV of the algorithm.
Multivariate logistic regression identified ICD-9 diagnosis codes 155.2 (OR 9.7 [95% CI 2.5-38.4]) and 197.7 (84.6 [52.9-135.3]), and procedure code 50.22 (5.9 [1.3-25.5]) as independent predictors of CRCLM diagnosis. The model's discrimination was 0.89. The diagnostic algorithm, defined as the presence of any of these codes, had a PPV of 87%.
VA administrative databases reliably identify patients with CRCLM. This diagnostic algorithm is highly predictive of CRCLM diagnosis and can be used for research studies evaluating population-level features of this disease within the VA system.
利用行政数据识别结直肠癌（CRC）肝转移（LM）患者的能力尚不清楚。本研究旨在评估行政数据是否能准确识别结直肠癌肝转移患者，并开发一种能够识别此类患者的诊断算法。
本研究采用回顾性队列研究，对退伍军人事务部（VA）系统的行政数据库中的诊断和程序代码进行验证。在 VA 中心对 CRC 患者进行评估（1997-2008 年，n=1671），并对具有肝特异性 ICD-9 和/或 CPT 代码的患者进行分类。通过对研究样本的主要图表摘要，验证 CRCLM 的存在。创建列联表，并计算每个候选行政代码的 CRCLM 阳性预测值（PPV）。采用多元逻辑回归模型确定 CRCLM 的独立预测因子（代码），并用于开发诊断算法。该模型的诊断算法通过模型的判别能力（c 统计量）和算法的 PPV 来确定。
多元逻辑回归确定了 ICD-9 诊断代码 155.2（OR 9.7[95%CI 2.5-38.4]）和 197.7（84.6[52.9-135.3]），以及程序代码 50.22（5.9[1.3-25.5]）为 CRCLM 诊断的独立预测因子。该模型的判别能力为 0.89。定义为存在这些代码之一的诊断算法，其 PPV 为 87%。
VA 行政数据库能可靠地识别出结直肠癌肝转移患者。该诊断算法对结直肠癌肝转移诊断具有高度预测性，可用于评估 VA 系统中该疾病的人群特征的研究。
