Department of Medicine, University of Western Ontario, London, Canada.
Am J Kidney Dis. 2011 Jan;57(1):29-43. doi: 10.1053/j.ajkd.2010.08.031.
Information in health administrative databases increasingly guides renal care and policy.
Systematic review of observational studies.
SETTING & POPULATION: Studies describing the validity of codes for acute kidney injury (AKI) and chronic kidney disease (CKD) in administrative databases operating in any jurisdiction.
After searching 13 medical databases, we included observational studies published from database inception though June 2009 that validated renal diagnostic and procedural codes for AKI or CKD against a reference standard.
Renal diagnostic or procedural administrative data codes.
Patient chart review, laboratory values, or data from a high-quality patient registry.
25 studies of 13 databases in 4 countries were included. Validation of diagnostic and procedural codes for AKI was present in 9 studies, and validation for CKD was present in 19 studies. Sensitivity varied across studies and generally was poor (AKI median, 29%; range, 15%-81%; CKD median, 41%; range, 3%-88%). Positive predictive values often were reasonable, but results also were variable (AKI median, 67%; range, 15%-96%; CKD median, 78%; range, 29%-100%). Defining AKI and CKD by only the use of dialysis generally resulted in better code validity. The study characteristic associated with sensitivity in multivariable meta-regression was whether the reference standard used laboratory values (P < 0.001); sensitivity was 39% lower when laboratory values were used (95% CI, 23%-56%).
Missing data in primary studies limited some of the analyses that could be done.
Administrative database analyses have utility, but must be conducted and interpreted judiciously to avoid bias arising from poor code validity.
健康行政数据库中的信息越来越多地指导着肾脏护理和政策。
对观察性研究进行系统评价。
描述在任何司法管辖区运行的行政数据库中用于急性肾损伤 (AKI) 和慢性肾脏病 (CKD) 的代码有效性的研究。
在搜索了 13 个医学数据库后，我们纳入了从数据库建立开始至 2009 年 6 月发表的观察性研究，这些研究使用参考标准对 AKI 或 CKD 的肾脏诊断和程序代码进行了验证。
肾脏诊断或程序行政数据代码。
患者图表审查、实验室值或来自高质量患者登记处的数据。
纳入了来自 4 个国家的 13 个数据库中的 25 项研究。在 9 项研究中验证了 AKI 的诊断和程序代码，在 19 项研究中验证了 CKD 的诊断和程序代码。在不同的研究中，敏感性存在差异，通常较差（AKI 的中位数为 29%，范围为 15%-81%；CKD 的中位数为 41%，范围为 3%-88%）。阳性预测值通常合理，但结果也存在差异（AKI 的中位数为 67%，范围为 15%-96%；CKD 的中位数为 78%，范围为 29%-100%）。仅使用透析来定义 AKI 和 CKD 通常会导致代码有效性更好。多变量荟萃回归中与敏感性相关的研究特征是参考标准是否使用实验室值（P<0.001）；当使用实验室值时，敏感性降低 39%（95%CI，23%-56%）。
原始研究中数据缺失限制了部分可进行的分析。
行政数据库分析具有实用性，但必须谨慎进行和解释，以避免因代码有效性差而导致的偏差。
