Williamson Walter, Lee Joyce M, Gaynanova Irina
Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USA.
Susan B. Meister Child Health Evaluation and Research Center, Division of Pediatric Endocrinology, Department of Pediatrics, University of Michigan, Ann Arbor, MI, USA.
J Diabetes Sci Technol. 2025 Feb 20:19322968251319801. doi: 10.1177/19322968251319801.
Continuous glucose monitoring (CGM) data stored in data warehouses often include duplicated or time-shifted uploads from the same patient, compromising data quality and accuracy of resulting CGM metrics. We developed a processing algorithm to detect and resolve these errors. We validated the algorithm using two weeks of CGM data from 2038 patients with diabetes. Duplication errors were identified in 528 patients, with 25.7% showing significant differences in at least one metric (Time in Range, Coefficient of Variation, Glycemic Management Indicator, or Glycemic Episode counts) between raw and processed data. Eleven patients crossed clinically meaningful thresholds in one or more metrics after processing. Our results underscore the importance of real-world CGM data processing to maintain accurate and reliable CGM metrics for research and clinical care.
存储在数据仓库中的连续血糖监测（CGM）数据通常包含来自同一患者的重复或时间偏移的上传数据，这会影响数据质量以及由此产生的CGM指标的准确性。我们开发了一种处理算法来检测和解决这些错误。我们使用来自2038名糖尿病患者的两周CGM数据对该算法进行了验证。在528名患者中发现了重复错误，其中25.7%的患者在原始数据和处理后的数据之间至少有一项指标（血糖达标时间、变异系数、血糖管理指标或血糖事件计数）存在显著差异。11名患者在处理后有一项或多项指标超过了临床意义阈值。我们的结果强调了在现实世界中进行CGM数据处理对于为研究和临床护理维持准确可靠的CGM指标的重要性。
