Departments of Anesthesiology and Pain Medicine, University of Ottawa, The Ottawa Hospital, Ottawa, Ontario, Canada
Clinical Epidemiology, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada.
BMJ Qual Saf. 2020 Mar;29(3):209-216. doi: 10.1136/bmjqs-2018-008852. Epub 2019 Aug 22.
Administrative data systems are used to identify hospital-based patient safety events; few studies evaluate their accuracy. We assessed the accuracy of a new set of patient safety indicators (PSIs; designed to identify in hospital complications).
Prospectively defined analysis of registry data (1 April 2010-29 February 2016) in a Canadian hospital network. Assignment of complications was by two methods independently. The National Surgical Quality Improvement Programme (NSQIP) database was the clinical reference standard (primary outcome=any in-hospital NSQIP complication); PSI clusters were assigned using International Classification of Disease (ICD-10) codes in the discharge abstract. Our primary analysis assessed the accuracy of any PSI condition compared with any complication in the NSQIP; secondary analysis evaluated accuracy of complication-specific PSIs.
All inpatient surgical cases captured in NSQIP data.
We assessed the accuracy of PSIs (with NSQIP as reference standard) using positive and negative predictive values (PPV/NPV), as well as positive and negative likelihood ratios (±LR).
We identified 12 898 linked episodes of care. Complications were identified by PSIs and NSQIP in 2415 (18.7%) and 2885 (22.4%) episodes, respectively. The presence of any PSI code had a PPV of 0.55 (95% CI 0.53 to 0.57) and NPV of 0.93 (95% CI 0.92 to 0.93); +LR 6.41 (95% CI 6.01 to 6.84) and -LR 0.40 (95% CI 0.37 to 0.42). Subgroup analyses (by surgery type and urgency) showed similar performance. Complication-specific PSIs had high NPVs (95% CI 0.92 to 0.99), but low to moderate PPVs (0.13-0.61).
Validation of the ICD-10 PSI system suggests applicability as a first screening step, integrated with data from other sources, to produce an adverse event detection pathway that informs learning healthcare systems. However, accuracy was insufficient to directly identify or rule out individual-level complications.
行政数据系统用于识别医院内的患者安全事件；很少有研究评估其准确性。我们评估了一套新的患者安全指标（旨在识别医院内并发症）的准确性。
在加拿大医院网络中，对 2010 年 4 月 1 日至 2016 年 2 月 29 日期间的注册数据进行前瞻性定义分析。并发症的分配由两种方法独立完成。国家外科质量改进计划（NSQIP）数据库是临床参考标准（主要结果=任何院内 NSQIP 并发症）；使用出院摘要中的国际疾病分类（ICD-10）代码分配 PSI 群集。我们的主要分析评估了任何 PSI 情况与 NSQIP 中任何并发症的准确性；次要分析评估了特定并发症 PSI 的准确性。
所有纳入 NSQIP 数据的住院手术病例。
我们使用阳性和阴性预测值（PPV/NPV）以及阳性和阴性似然比（+LR/-LR）评估 PSI 的准确性（以 NSQIP 为参考标准）。
我们确定了 12898 例相关的护理记录。通过 PSI 和 NSQIP 分别在 2415 例（18.7%）和 2885 例（22.4%）记录中识别出并发症。任何 PSI 编码的存在具有 0.55（95%CI 0.53 至 0.57）的 PPV 和 0.93（95%CI 0.92 至 0.93）的 NPV；+LR 为 6.41（95%CI 6.01 至 6.84）和 -LR 为 0.40（95%CI 0.37 至 0.42）。亚组分析（按手术类型和紧急程度）显示出类似的性能。特定并发症的 PSI 具有较高的 NPV（95%CI 0.92 至 0.99），但较低至中度的 PPV（0.13 至 0.61）。
对 ICD-10 PSI 系统的验证表明，它可以作为第一步筛查应用，与来自其他来源的数据相结合，以创建一个告知学习型医疗保健系统的不良事件检测途径。然而，准确性不足以直接识别或排除个体水平的并发症。
