METHODS: We developed methods to extract ten preoperative laboratory values and measurement dates from the VA Corporate Data Warehouse using Logical Observation Identifiers Names and Codes. Automated (A) versus SA information extraction was compared in terms of agreement, conformance to data definitions, proximity to surgery, and missingness.
RESULTS: For surgeries with both A and SA lab values, the intraclass correlation coefficients for the ten variables ranged from 0.90 to 0.98. For several variables, the A method resulted in much lower rates of missing data (e.g., 2.4% versus 22.5% missing data for high-density lipoprotein) and eliminated out-of-date-range entries.
CONCLUSIONS: Although SQN-extracted data are widely considered the gold standard within National Surgical Quality Improvement Programs, there may be advantages to fully automating extraction of lab values, including high congruence with SA SQN-extracted or verified values and lower rates of missingness and out-of-date-range data.
方法:我们开发了使用逻辑观察标识符名称和代码从VA公司数据仓库中提取十个术前实验室值和测量日期的方法。自动(A)和SA信息提取在协议方面进行了比较,符合数据定义,接近手术,和不幸。
结果:对于具有A和SA实验室值的手术,十个变量的组内相关系数在0.90到0.98之间。对于几个变量,A方法导致数据丢失率低得多(例如,高密度脂蛋白的缺失数据为2.4%和22.5%),并消除了超出日期范围的条目。
结论:尽管SQN提取的数据被广泛认为是国家外科质量改进计划的金标准,完全自动化提取实验室值可能有优势,包括与SASQN提取或验证值的高度一致性,以及较低的误差率和超出日期的数据。