Emory University School of Medicine, Department of Pediatrics, Children's Healthcare of Atlanta, Atlanta, GA, USA.
The Heart Institute, Cincinnati Children's Hospital and Department of Pediatrics, Cincinnati, OH, USA.
Cardiol Young. 2021 Nov;31(11):1829-1834. doi: 10.1017/S1047951121000974. Epub 2021 Mar 17.
Multicentre research databases can provide insights into healthcare processes to improve outcomes and make practice recommendations for novel approaches. Effective audits can establish a framework for reporting research efforts, ensuring accurate reporting, and spearheading quality improvement. Although a variety of data auditing models and standards exist, barriers to effective auditing including costs, regulatory requirements, travel, and design complexity must be considered.
The Congenital Cardiac Research Collaborative conducted a virtual data training initiative and remote source data verification audit on a retrospective multicentre dataset. CCRC investigators across nine institutions were trained to extract and enter data into a robust dataset on patients with tetralogy of Fallot who required neonatal intervention. Centres provided de-identified source files for a randomised 10% patient sample audit. Key auditing variables, discrepancy types, and severity levels were analysed across two study groups, primary repair and staged repair.
Of the total 572 study patients, data from 58 patients (31 staged repairs and 27 primary repairs) were source data verified. Amongst the 1790 variables audited, 45 discrepancies were discovered, resulting in an overall accuracy rate of 97.5%. High accuracy rates were consistent across all CCRC institutions ranging from 94.6% to 99.4% and were reported for both minor (1.5%) and major discrepancies type classifications (1.1%).
Findings indicate that implementing a virtual multicentre training initiative and remote source data verification audit can identify data quality concerns and produce a reliable, high-quality dataset. Remote auditing capacity is especially important during the current COVID-19 pandemic.
多中心研究数据库可以深入了解医疗保健流程，以改善结果并为新方法提供实践建议。有效的审核可以为报告研究工作建立框架，确保准确报告，并引领质量改进。尽管存在多种数据审核模型和标准，但必须考虑到成本、监管要求、旅行和设计复杂性等有效审核的障碍。
先天性心脏病研究协作组（Congenital Cardiac Research Collaborative）对回顾性多中心数据集进行了虚拟数据培训计划和远程源数据验证审核。来自 9 个机构的 CCRC 研究人员接受了培训，以从需要新生儿干预的法洛四联症患者中提取和输入数据到一个强大的数据集。各中心为随机抽取的 10%患者样本审核提供了去识别源文件。对两组（一期修复和分期修复）的关键审核变量、差异类型和严重程度进行了分析。
在总共 572 名研究患者中，对 58 名患者（31 名分期修复和 27 名一期修复）的源数据进行了验证。在审核的 1790 个变量中，发现了 45 个差异，总体准确率为 97.5%。所有 CCRC 机构的准确率均较高，从 94.6%到 99.4%不等，且均报告了轻微（1.5%）和主要差异类型分类（1.1%）的准确率。
研究结果表明，实施虚拟多中心培训计划和远程源数据验证审核可以识别数据质量问题，并生成可靠、高质量的数据集。在当前 COVID-19 大流行期间，远程审核能力尤为重要。
