Penz Janet F E, Wilcox Adam B, Hurdle John F
Department of Surgery, University of Utah, Salt Lake City, UT 84148, USA.
J Biomed Inform. 2007 Apr;40(2):174-82. doi: 10.1016/j.jbi.2006.06.003. Epub 2006 Jun 9.
Methods for surveillance of adverse events (AEs) in clinical settings are limited by cost, technology, and appropriate data availability. In this study, two methods for semi-automated review of text records within the Veterans Administration database are utilized to identify AEs related to the placement of central venous catheters (CVCs): a Natural Language Processing program and a phrase-matching algorithm. A sample of manually reviewed records were then compared to the results of both methods to assess sensitivity and specificity. The phrase-matching algorithm was found to be a sensitive but relatively non-specific method, whereas a natural language processing system was significantly more specific but less sensitive. Positive predictive values for each method estimated the CVC-associated AE rate at this institution to be 6.4 and 6.2%, respectively. Using both methods together results in acceptable sensitivity and specificity (72.0 and 80.1%, respectively). All methods including manual chart review are limited by incomplete or inaccurate clinician documentation. A secondary finding was related to the completeness of administrative data (ICD-9 and CPT codes) used to identify intensive care unit patients in whom a CVC was placed. Administrative data identified less than 11% of patients who had a CVC placed. This suggests that other methods, including automated methods such as phrase matching, may be more sensitive than administrative data in identifying patients with devices. Considerable potential exists for the use of such methods for the identification of patients at risk, AE surveillance, and prevention of AEs through decision support technologies.
临床环境中不良事件（AE）的监测方法受到成本、技术和适当数据可用性的限制。在本研究中，利用退伍军人管理局数据库内文本记录的两种半自动审查方法来识别与中心静脉导管（CVC）放置相关的不良事件：一种自然语言处理程序和一种短语匹配算法。然后将人工审查记录的样本与两种方法的结果进行比较，以评估敏感性和特异性。发现短语匹配算法是一种敏感但相对非特异性的方法，而自然语言处理系统的特异性明显更高但敏感性更低。每种方法的阳性预测值估计该机构CVC相关不良事件发生率分别为6.4%和6.2%。同时使用两种方法可获得可接受的敏感性和特异性（分别为72.0%和80.1%）。包括人工病历审查在内的所有方法都受到临床医生记录不完整或不准确的限制。一个次要发现与用于识别放置了CVC的重症监护病房患者的管理数据（ICD-9和CPT代码）的完整性有关。管理数据识别出放置了CVC的患者不到11%。这表明，在识别使用设备的患者方面，其他方法，包括短语匹配等自动化方法，可能比管理数据更敏感。使用此类方法通过决策支持技术识别高危患者、进行不良事件监测和预防不良事件具有相当大的潜力。
