Kristen L Flint, Mollie Y O'Connor, Amy Sabean, Annabelle Ashley, Hui Zheng, Joyce Yan, Barbara A Steiner, Nillani Anandakugan, Melissa Calverley, Rachel Bartholomew, Evelyn Greaux, Mary Larkin, Steven J Russell, Melissa S Putman
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Multivariate regression assessed predictors of CGM-detected severe hypoglycemia and the associations between CGM metrics and clinical outcomes. Regression models using CGM data or reference glucose data were compared with receiver operating characteristic (ROC) curves. <b><i>Results:</i></b> A total of 326 hospitalized adults were enrolled with median % time in range 70-180 mg/dL 44.5% (17.1, 70.2%), % time above range >180 mg/dL 54.8% (28.8, 82.3%), and % time below range 0.6% (0, 0.2%). Predictors of severe hypoglycemia included type 1 diabetes, female gender, lower admission hemoglobin, lower A1c, and longer hospital stay. Regression analyses demonstrated an association of 30-day ED visits with increased %TAR (<i>P</i> = 0.01). 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引用次数: 0
摘要
目的:我们研究了非重症监护病房(non-ICU)连续血糖监测(CGM)指标与临床结果之间的关系。方法:在这项观察性队列研究中,非icu楼层的患者使用盲法Dexcom G6 Pro CGM。测量CGM指标和CGM检测的严重低血糖的发生情况。临床数据,包括感染,糖尿病酮症酸中毒,肾脏替代治疗,血栓形成,出院后30天再入院和急诊(ED)就诊,从医疗记录和参与者电话访谈中确定。多因素回归评估了CGM检测到的严重低血糖的预测因素以及CGM指标与临床结果之间的关系。采用CGM数据和参考血糖数据的回归模型进行受试者工作特征(ROC)曲线的比较。结果:共纳入326名住院成人,中位时间在70-180 mg/dL范围内的百分比为44.5%(17.1%,70.2%),超过180 mg/dL范围的百分比为54.8%(28.8%,82.3%),低于范围的百分比为0.6%(0,0.2%)。严重低血糖的预测因素包括1型糖尿病、女性、入院时较低的血红蛋白、较低的糖化血红蛋白和较长的住院时间。回归分析显示30天ED就诊与TAR %增加相关(P = 0.01)。ROC曲线显示,使用CGM数据或参考数据的模型预测临床结果相似。结论:CGM可用于识别有住院低血糖和30天ED就诊风险的患者。
The Association of Continuous Glucose Monitoring Metrics with Hospital-Related Clinical Outcomes in Nonintensive Care Units.
Aims: We investigated the association between continuous glucose monitoring (CGM) metrics and clinical outcomes in the nonintensive care unit (non-ICU) setting. Methods: In this observational cohort study, patients on non-ICU floors wore blinded Dexcom G6 Pro CGM. CGM metrics and occurrence of CGM-detected severe hypoglycemia were measured. Clinical data, including infection, diabetic ketoacidosis, renal replacement therapy, thrombosis, and 30-day post-discharge readmissions and emergency department (ED) visits were identified from the medical record and participant phone interview. Multivariate regression assessed predictors of CGM-detected severe hypoglycemia and the associations between CGM metrics and clinical outcomes. Regression models using CGM data or reference glucose data were compared with receiver operating characteristic (ROC) curves. Results: A total of 326 hospitalized adults were enrolled with median % time in range 70-180 mg/dL 44.5% (17.1, 70.2%), % time above range >180 mg/dL 54.8% (28.8, 82.3%), and % time below range 0.6% (0, 0.2%). Predictors of severe hypoglycemia included type 1 diabetes, female gender, lower admission hemoglobin, lower A1c, and longer hospital stay. Regression analyses demonstrated an association of 30-day ED visits with increased %TAR (P = 0.01). ROC curves showed models using CGM data or reference data predicted clinical outcomes similarly. Conclusions: CGM can be useful in identifying patients at risk of inpatient hypoglycemia and 30-day ED visits.
期刊介绍:
Diabetes Technology & Therapeutics is the only peer-reviewed journal providing healthcare professionals with information on new devices, drugs, drug delivery systems, and software for managing patients with diabetes. This leading international journal delivers practical information and comprehensive coverage of cutting-edge technologies and therapeutics in the field, and each issue highlights new pharmacological and device developments to optimize patient care.