Intensive Care Unit, Department of Intensive Care Medicine-Ente Ospedaliero Cantonale, Ospedale Regionale Bellinzona e Valli, 6500 Bellinzona, Switzerland and Faculty of Medicine, University of Geneva, Geneva, Switzerland.
Unit of Development and Research in Medical Education, Faculty of Medicine, University of Geneva, Geneva, Switzerland.
Int J Qual Health Care. 2019 Aug 1;31(7):1-7. doi: 10.1093/intqhc/mzy249.
There is no gold standard to assess data quality in large medical registries. Data auditing may be impeded by data protection regulations.
To explore the applicability and usefulness of funnel plots as a novel tool for data quality control in critical care registries.
The Swiss ICU-Registry from all 77 certified adult Swiss ICUs (2014 and 2015) was subjected to quality assessment (completeness/accuracy). For the analysis of accuracy, a list of logical rules and cross-checks was developed. Type and number of errors (true coding errors or implausible data) were calculated for each ICU, along with noticeable error rates (>mean + 3 SD in the variable's summary measure, or >99.8% CI in the respective funnel-plot).
We investigated 164 415 patient records with 31 items each (37 items: trauma diagnosis). Data completeness was excellent; trauma was the only incomplete item in 1495 of 9871 records (0.1%, 0.0%-0.6% [median, IQR]). In 15 572 patients records (9.5%), we found 3121 coding errors and 31 265 implausible situations; the latter primarily due to non-specific information on patients' provenance/diagnosis or supposed incoherence between diagnosis and treatments. Together, the error rate was 7.6% (5.9%-11%; median, IQR).
The Swiss ICU-Registry is almost complete and data quality seems to be adequate. We propose funnel plots as suitable, easy to implement instrument to assist in quality assurance of such a registry. Based on our analysis, specific feedback to ICUs with special-cause variation is possible and may promote such ICUs to improve the quality of their data.
目前尚无评估大型医学注册研究数据质量的金标准。数据审核可能会受到数据保护法规的阻碍。
探索使用漏斗图作为一种新工具来控制重症监护注册研究的数据质量的适用性和有效性。
对瑞士所有 77 家认证成人重症监护病房的重症监护室登记处（2014 年和 2015 年）进行质量评估（完整性/准确性）。为了分析准确性，制定了一套逻辑规则和交叉核对清单。计算了每个 ICU 的错误类型和数量（真实编码错误或数据不合理），以及显著错误率（变量汇总测量值的平均值+3 个标准差，或各自漏斗图的 99.8%置信区间）。
我们研究了 164415 例患者的记录，每个记录包含 31 项（37 项：创伤诊断）。数据完整性非常好；在 9871 例记录中有 1495 例记录中仅存在一个不完整项目（0.1%，0.0%-0.6%[中位数，IQR]）。在 15572 例患者记录中，发现了 3121 个编码错误和 31265 个不合理情况；后者主要是由于患者来源/诊断的非特异性信息或假设诊断和治疗之间的不一致。总的来说，错误率为 7.6%（5.9%-11%；中位数，IQR）。
瑞士重症监护室登记处几乎完整，数据质量似乎足够。我们提出漏斗图是一种合适的、易于实施的工具，可以辅助此类登记处的质量保证。基于我们的分析，可以对有特殊原因变异的 ICU 进行特定反馈，并可能促进这些 ICU 提高其数据质量。
