An Electronic Health Record-Integrated Computerized Intravenous Insulin Infusion Protocol: Clinical Outcomes and in Silico Adjustment
We aimed to describe the outcome of a computerized intravenous insulin infusion (CII) protocol integrated to the electronic health record (EHR) system and to improve the CII protocol in silico using the EHR-based predictors of the outcome. Clinical outcomes of the patients who underwent the CII prot...
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Published in | Diabetes & metabolism journal Vol. 44; no. 1; pp. 56 - 66 |
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Main Authors | , , , , , , , |
Format | Journal Article |
Language | English |
Published |
Korea (South)
Korean Diabetes Association / Daehan Dangnyobyeong Hakoe
01.02.2020
Korean Diabetes Association 대한당뇨병학회 |
Subjects | |
Online Access | Get full text |
ISSN | 2233-6079 2233-6087 2233-6087 |
DOI | 10.4093/dmj.2018.0227 |
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Abstract | We aimed to describe the outcome of a computerized intravenous insulin infusion (CII) protocol integrated to the electronic health record (EHR) system and to improve the CII protocol in silico using the EHR-based predictors of the outcome.
Clinical outcomes of the patients who underwent the CII protocol between July 2016 and February 2017 and their matched controls were evaluated. In the CII protocol group (
=91), multivariable binary logistic regression analysis models were used to determine the independent associates with a delayed response (taking ≥6.0 hours for entering a glucose range of 70 to 180 mg/dL). The CII protocol was adjusted
according to the EHR-based parameters obtained in the first 3 hours of CII.
Use of the CII protocol was associated with fewer subjects with hypoglycemia alert values (
=0.003), earlier (
=0.002), and more stable (
=0.017) achievement of a glucose range of 70 to 180 mg/dL. Initial glucose level (
=0.001), change in glucose during the first 2 hours (
=0.026), and change in insulin infusion rate during the first 3 hours (
=0.029) were independently associated with delayed responses. Increasing the insulin infusion rate temporarily according to these parameters
significantly reduced delayed responses (
<0.0001) without hypoglycemia, especially in refractory patients.
Our CII protocol enabled faster and more stable glycemic control than conventional care with minimized risk of hypoglycemia. An EHR-based adjustment was simulated to reduce delayed responses without increased incidence of hypoglycemia. |
---|---|
AbstractList | Background We aimed to describe the outcome of a computerized intravenous insulin infusion (CII) protocol integrated to the electronic health record (EHR) system and to improve the CII protocol in silico using the EHR-based predictors of the outcome.* Methods Clinical outcomes of the patients who underwent the CII protocol between July 2016 and February 2017 and their matched controls were evaluated. In the CII protocol group (n=91), multivariable binary logistic regression analysis models were used to determine the independent associates with a delayed response (taking ≥6.0 hours for entering a glucose range of 70 to 180 mg/dL). The CII protocol was adjusted in silico according to the EHR-based parameters obtained in the first 3 hours of CII.* Results Use of the CII protocol was associated with fewer subjects with hypoglycemia alert values (P=0.003), earlier (P=0.002), and more stable (P=0.017) achievement of a glucose range of 70 to 180 mg/dL. Initial glucose level (P=0.001), change in glucose during the first 2 hours (P=0.026), and change in insulin infusion rate during the first 3 hours (P=0.029) were independently associated with delayed responses. Increasing the insulin infusion rate temporarily according to these parameters in silico significantly reduced delayed responses (P<0.0001) without hypoglycemia, especially in refractory patients.* Conclusion Our CII protocol enabled faster and more stable glycemic control than conventional care with minimized risk of hypoglycemia. An EHR-based adjustment was simulated to reduce delayed responses without increased incidence of hypoglycemia. We aimed to describe the outcome of a computerized intravenous insulin infusion (CII) protocol integrated to the electronic health record (EHR) system and to improve the CII protocol in silico using the EHR-based predictors of the outcome. Clinical outcomes of the patients who underwent the CII protocol between July 2016 and February 2017 and their matched controls were evaluated. In the CII protocol group ( =91), multivariable binary logistic regression analysis models were used to determine the independent associates with a delayed response (taking ≥6.0 hours for entering a glucose range of 70 to 180 mg/dL). The CII protocol was adjusted according to the EHR-based parameters obtained in the first 3 hours of CII. Use of the CII protocol was associated with fewer subjects with hypoglycemia alert values ( =0.003), earlier ( =0.002), and more stable ( =0.017) achievement of a glucose range of 70 to 180 mg/dL. Initial glucose level ( =0.001), change in glucose during the first 2 hours ( =0.026), and change in insulin infusion rate during the first 3 hours ( =0.029) were independently associated with delayed responses. Increasing the insulin infusion rate temporarily according to these parameters significantly reduced delayed responses ( <0.0001) without hypoglycemia, especially in refractory patients. Our CII protocol enabled faster and more stable glycemic control than conventional care with minimized risk of hypoglycemia. An EHR-based adjustment was simulated to reduce delayed responses without increased incidence of hypoglycemia. Background: We aimed to describe the outcome of a computerized intravenous insulin infusion (CII) protocol integrated to the electronic health record (EHR) system and to improve the CII protocol in silico using the EHR-based predictors of the outcome. Methods: Clinical outcomes of the patients who underwent the CII protocol between July 2016 and February 2017 and their matched controls were evaluated. In the CII protocol group (n=91), multivariable binary logistic regression analysis models were used to determine the independent associates with a delayed response (taking ≥6.0 hours for entering a glucose range of 70 to 180 mg/dL). The CII protocol was adjusted in silico according to the EHR-based parameters obtained in the first 3 hours of CII. Results: Use of the CII protocol was associated with fewer subjects with hypoglycemia alert values (P=0.003), earlier (P=0.002), and more stable (P=0.017) achievement of a glucose range of 70 to 180 mg/dL. Initial glucose level (P=0.001), change in glucose during the first 2 hours (P=0.026), and change in insulin infusion rate during the first 3 hours (P=0.029) were independently associated with delayed responses. Increasing the insulin infusion rate temporarily according to these parameters in silico significantly reduced delayed responses (P<0.0001) without hypoglycemia, especially in refractory patients. Conclusion: Our CII protocol enabled faster and more stable glycemic control than conventional care with minimized risk of hypoglycemia. An EHR-based adjustment was simulated to reduce delayed responses without increased incidence of hypoglycemia. KCI Citation Count: 2 We aimed to describe the outcome of a computerized intravenous insulin infusion (CII) protocol integrated to the electronic health record (EHR) system and to improve the CII protocol in silico using the EHR-based predictors of the outcome.BACKGROUNDWe aimed to describe the outcome of a computerized intravenous insulin infusion (CII) protocol integrated to the electronic health record (EHR) system and to improve the CII protocol in silico using the EHR-based predictors of the outcome.Clinical outcomes of the patients who underwent the CII protocol between July 2016 and February 2017 and their matched controls were evaluated. In the CII protocol group (n=91), multivariable binary logistic regression analysis models were used to determine the independent associates with a delayed response (taking ≥6.0 hours for entering a glucose range of 70 to 180 mg/dL). The CII protocol was adjusted in silico according to the EHR-based parameters obtained in the first 3 hours of CII.METHODSClinical outcomes of the patients who underwent the CII protocol between July 2016 and February 2017 and their matched controls were evaluated. In the CII protocol group (n=91), multivariable binary logistic regression analysis models were used to determine the independent associates with a delayed response (taking ≥6.0 hours for entering a glucose range of 70 to 180 mg/dL). The CII protocol was adjusted in silico according to the EHR-based parameters obtained in the first 3 hours of CII.Use of the CII protocol was associated with fewer subjects with hypoglycemia alert values (P=0.003), earlier (P=0.002), and more stable (P=0.017) achievement of a glucose range of 70 to 180 mg/dL. Initial glucose level (P=0.001), change in glucose during the first 2 hours (P=0.026), and change in insulin infusion rate during the first 3 hours (P=0.029) were independently associated with delayed responses. Increasing the insulin infusion rate temporarily according to these parameters in silico significantly reduced delayed responses (P<0.0001) without hypoglycemia, especially in refractory patients.RESULTSUse of the CII protocol was associated with fewer subjects with hypoglycemia alert values (P=0.003), earlier (P=0.002), and more stable (P=0.017) achievement of a glucose range of 70 to 180 mg/dL. Initial glucose level (P=0.001), change in glucose during the first 2 hours (P=0.026), and change in insulin infusion rate during the first 3 hours (P=0.029) were independently associated with delayed responses. Increasing the insulin infusion rate temporarily according to these parameters in silico significantly reduced delayed responses (P<0.0001) without hypoglycemia, especially in refractory patients.Our CII protocol enabled faster and more stable glycemic control than conventional care with minimized risk of hypoglycemia. An EHR-based adjustment was simulated to reduce delayed responses without increased incidence of hypoglycemia.CONCLUSIONOur CII protocol enabled faster and more stable glycemic control than conventional care with minimized risk of hypoglycemia. An EHR-based adjustment was simulated to reduce delayed responses without increased incidence of hypoglycemia. |
Author | Hur, Kyu Yeon Lee, Moon-Kyu Jin, Sang-Man Park, Sung Woon Cha, Won Chul Park, Sung-Min Lee, Seunghyun Kim, Jae Hyeon |
AuthorAffiliation | 1 Division of Endocrinology and Metabolism, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea 4 Department of Digital Health, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, Korea 2 Department of Creative IT Engineering, Pohang University of Science and Technology (POSTECH), Pohang, Korea 3 Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea |
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Cites_doi | 10.1197/jamia.M2292 10.1007/s10047-016-0926-5 10.1056/NEJMoa0810625 10.1097/SLA.0b013e31827b6bbc 10.1097/01.TA.0000074434.39928.72 10.1007/s00134-009-1585-2 10.1172/JCI110398 10.1016/j.mayocp.2013.07.003 10.1186/cc8129 10.2337/diacare.22.9.1408 10.1056/NEJMoa011300 10.1097/CCM.0b013e3181b083f7 10.1002/jhm.816 10.2337/diacare.22.11.1827 10.1177/014860710803200118 10.4158/EP09102.RA 10.2337/diaspect.27.3.218 10.2337/dc15-2716 10.1089/dia.2016.0101 10.4037/ajcc2012956 10.2337/diacare.28.10.2418 10.1007/s10047-005-0326-8 10.1056/NEJMoa070716 10.1089/dia.2012.0277 10.1109/IEMBS.2006.260613 10.2337/dc06-1964 10.1016/j.amjsurg.2013.07.048 10.1007/s100470300009 |
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Keywords | Medical records systems, computerized Computer simulation Electronic health records Insulin |
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SubjectTerms | Clinical outcomes Diabetes Electronic health records Enteral nutrition Glucose Hypoglycemia Insulin Intensive care Mortality Original Patients 내과학 |
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Title | An Electronic Health Record-Integrated Computerized Intravenous Insulin Infusion Protocol: Clinical Outcomes and in Silico Adjustment |
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