Gustafson T L
Infection Control and Prevention Analysts, Inc, Austin, TX 78735, USA.
Am J Infect Control. 2000 Dec;28(6):406-14. doi: 10.1067/mic.2000.109883.
Control chart methodology has been widely touted for monitoring and improving quality in the health care setting. P charts and U charts are frequently recommended for rate and ratio statistics, but their practical value in infection control may be limited because they (1) are not risk-adjusted, and (2) perform poorly with small denominators. The Standardized Infection Ratio is a statistic that overcomes both these obstacles. It is risk-adjusted, and it effectively increases denominators by combining data from multiple risk strata into a single value.
The AICE National Database Initiative is a voluntary consortium of US hospitals ranging in size from 50 to 900 beds. The infection control professional submits monthly risk-stratified data for surgical site infections, ventilator-associated pneumonia, and central line-associated bacteremia.
Run charts were constructed for 51 hospitals submitting data between 1996 and 1998. Traditional hypothesis tests (P values <.05) flagged 128 suspicious points, and participating infection control professionals investigated and categorized each flag as a "real problem" or "background variation." This gold standard was used to compare the performance of 5 unadjusted and 11 risk-adjusted control charts.
Unadjusted control charts (C, P, and U charts) performed poorly. Flags based on traditional 3-sigma limits suffered from sensitivity <50%, whereas 2-sigma limits suffered from specificity <50%. Risk-adjusted charts based on the Standardized Infection Ratio performed much better. The most consistent and useful control chart was the mXmR chart. Under optimal conditions, this chart achieved a sensitivity and specificity >80%, and a receiver operating characteristic area of 0. 84 (P <.00001).
These findings suggest a specific statistic (the Standardized Infection Ratio) and specific techniques that could make control charts valuable and practical tools for infection control.
控制图方法在医疗环境中监测和提高质量方面受到广泛推崇。P图和U图常用于速率和比率统计,但它们在感染控制中的实际价值可能有限,因为它们(1)未进行风险调整,(2)在分母较小时表现不佳。标准化感染率是一种克服了这两个障碍的统计量。它经过风险调整,并且通过将多个风险层次的数据合并为一个值有效地增加了分母。
AICE国家数据库倡议是一个由美国50至900张床位不等的医院组成的自愿联盟。感染控制专业人员每月提交手术部位感染、呼吸机相关性肺炎和中心静脉导管相关菌血症的风险分层数据。
为1996年至1998年间提交数据的51家医院构建了运行图。传统假设检验(P值<.05)标记出128个可疑点,参与的感染控制专业人员对每个标记进行调查并分类为“实际问题”或“背景变异”。这个金标准用于比较5个未调整和11个风险调整控制图的性能。
未调整的控制图(C图、P图和U图)表现不佳。基于传统3西格玛界限的标记灵敏度<50%,而2西格玛界限的特异性<50%。基于标准化感染率的风险调整图表现要好得多。最一致且有用的控制图是mXmR图。在最佳条件下,该图的灵敏度和特异性>80%,受试者工作特征曲线下面积为0.84(P<.00001)。
这些发现表明了一种特定的统计量(标准化感染率)和特定技术,可使控制图成为感染控制中有价值且实用的工具。