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Abstract：
BACKGROUND: The impact of missing data on individual continuous glucose monitoring (CGM) data is unknown but can influence clinical decision-making for patients.
OBJECTIVE: We aimed to investigate the consequences of data loss on glucose metrics in individual patient recordings from continuous glucose monitors and assess its implications on clinical decision-making.
METHODS: The CGM data were collected from patients with type 1 and 2 diabetes using the FreeStyle Libre sensor (Abbott Diabetes Care). We selected 7-28 days of 24 hours of continuous data without any missing values from each individual patient. To mimic real-world data loss, missing data ranging from 5% to 50% were introduced into the data set. From this modified data set, clinical metrics including time below range (TBR), TBR level 2 (TBR2), and other common glucose metrics were calculated in the data sets with and that without data loss. Recordings in which glucose metrics deviated relevantly due to data loss, as determined by clinical experts, were defined as expert panel boundary error (εEPB). These errors were expressed as a percentage of the total number of recordings. The errors for the recordings with glucose management indicator <53 mmol/mol were investigated.
RESULTS: A total of 84 patients contributed to 798 recordings over 28 days. With 5%-50% data loss for 7-28 days recordings, the εEPB varied from 0 out of 798 (0.0%) to 147 out of 736 (20.0%) for TBR and 0 out of 612 (0.0%) to 22 out of 408 (5.4%) recordings for TBR2. In the case of 14-day recordings, TBR and TBR2 episodes completely disappeared due to 30% data loss in 2 out of 786 (0.3%) and 32 out of 522 (6.1%) of the cases, respectively. However, the initial values of the disappeared TBR and TBR2 were relatively small (<0.1%). In the recordings with glucose management indicator <53 mmol/mol the εEPB was 9.6% for 14 days with 30% data loss.
CONCLUSIONS: With a maximum of 30% data loss in 14-day CGM recordings, there is minimal impact of missing data on the clinical interpretation of various glucose metrics.
BACKGROUND: ClinicalTrials.gov NCT05584293; https://clinicaltrials.gov/study/NCT05584293.
OBJECTIVE: We aimed to investigate the consequences of data loss on glucose metrics in individual patient recordings from continuous glucose monitors and assess its implications on clinical decision-making.
METHODS: The CGM data were collected from patients with type 1 and 2 diabetes using the FreeStyle Libre sensor (Abbott Diabetes Care). We selected 7-28 days of 24 hours of continuous data without any missing values from each individual patient. To mimic real-world data loss, missing data ranging from 5% to 50% were introduced into the data set. From this modified data set, clinical metrics including time below range (TBR), TBR level 2 (TBR2), and other common glucose metrics were calculated in the data sets with and that without data loss. Recordings in which glucose metrics deviated relevantly due to data loss, as determined by clinical experts, were defined as expert panel boundary error (εEPB). These errors were expressed as a percentage of the total number of recordings. The errors for the recordings with glucose management indicator <53 mmol/mol were investigated.
RESULTS: A total of 84 patients contributed to 798 recordings over 28 days. With 5%-50% data loss for 7-28 days recordings, the εEPB varied from 0 out of 798 (0.0%) to 147 out of 736 (20.0%) for TBR and 0 out of 612 (0.0%) to 22 out of 408 (5.4%) recordings for TBR2. In the case of 14-day recordings, TBR and TBR2 episodes completely disappeared due to 30% data loss in 2 out of 786 (0.3%) and 32 out of 522 (6.1%) of the cases, respectively. However, the initial values of the disappeared TBR and TBR2 were relatively small (<0.1%). In the recordings with glucose management indicator <53 mmol/mol the εEPB was 9.6% for 14 days with 30% data loss.
CONCLUSIONS: With a maximum of 30% data loss in 14-day CGM recordings, there is minimal impact of missing data on the clinical interpretation of various glucose metrics.
BACKGROUND: ClinicalTrials.gov NCT05584293; https://clinicaltrials.gov/study/NCT05584293.
摘要：
背景：数据缺失对个体连续血糖监测（CGM）数据的影响未知，但会影响患者的临床决策。
目的：我们旨在研究数据丢失对来自连续血糖监测仪的个体患者血糖指标的影响，并评估其对临床决策的影响。
方法：使用FreeStyleLibre传感器（雅培糖尿病护理）收集1型和2型糖尿病患者的CGM数据。我们从每个患者中选择了7-28天的24小时连续数据，没有任何缺失值。为了模拟真实世界的数据丢失，从5%到50%的缺失数据被引入到数据集中.从这个修改的数据集中，临床指标，包括低于范围的时间(TBR)，TBR等级2(TBR2),和其他常见的血糖指标在有和没有数据丢失的数据集中计算。由于数据丢失而导致血糖指标相关偏差的记录，根据临床专家的判断，被定义为专家面板边界误差(εEPB)。这些误差表示为记录总数的百分比。研究了葡萄糖管理指标<53mmol/mol的记录错误。
结果：共有84名患者在28天内完成了798次记录。5％-50％的数据丢失7-28天的记录，对于TBR，εEPB从798（0.0％）中的0到736（20.0％）中的147，而对于TBR2，从612（0.0％）中的0到408（5.4％）中的22。在14天录音的情况下，由于786例中的2例（0.3％）和522例中的32例（6.1％）的数据丢失，TBR和TBR2发作完全消失，分别。然而,消失的TBR和TBR2的初始值相对较小(<0.1%)。在葡萄糖管理指标<53mmol/mol的记录中,εEPB为9.6%持续14天,数据损失为30%。
结论：在14天的CGM记录中，数据丢失最多30％，缺失数据对各种血糖指标的临床解释影响最小.
背景：ClinicalTrials.govNCT05584293;https://clinicaltrials.gov/study/NCT05584293。
目的：我们旨在研究数据丢失对来自连续血糖监测仪的个体患者血糖指标的影响，并评估其对临床决策的影响。
方法：使用FreeStyleLibre传感器（雅培糖尿病护理）收集1型和2型糖尿病患者的CGM数据。我们从每个患者中选择了7-28天的24小时连续数据，没有任何缺失值。为了模拟真实世界的数据丢失，从5%到50%的缺失数据被引入到数据集中.从这个修改的数据集中，临床指标，包括低于范围的时间(TBR)，TBR等级2(TBR2),和其他常见的血糖指标在有和没有数据丢失的数据集中计算。由于数据丢失而导致血糖指标相关偏差的记录，根据临床专家的判断，被定义为专家面板边界误差(εEPB)。这些误差表示为记录总数的百分比。研究了葡萄糖管理指标<53mmol/mol的记录错误。
结果：共有84名患者在28天内完成了798次记录。5％-50％的数据丢失7-28天的记录，对于TBR，εEPB从798（0.0％）中的0到736（20.0％）中的147，而对于TBR2，从612（0.0％）中的0到408（5.4％）中的22。在14天录音的情况下，由于786例中的2例（0.3％）和522例中的32例（6.1％）的数据丢失，TBR和TBR2发作完全消失，分别。然而,消失的TBR和TBR2的初始值相对较小(<0.1%)。在葡萄糖管理指标<53mmol/mol的记录中,εEPB为9.6%持续14天,数据损失为30%。
结论：在14天的CGM记录中，数据丢失最多30％，缺失数据对各种血糖指标的临床解释影响最小.
背景：ClinicalTrials.govNCT05584293;https://clinicaltrials.gov/study/NCT05584293。
