A Bedside Computerized Decision-Support Tool for Intravenous Insulin Infusion Management in Critically Ill Patients

Telford, E. D.; Franck, Andrew J.; Hendrickson, Andrew L.; Dietrich, Nicole Maltese

doi:10.1016/j.jcjq.2017.10.005

Cited by 3 publications

(4 citation statements)

References 16 publications

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“…Therefore, caregiver workload was substantially reduced after entering euglycemia, and specialized equipment or continuous glucose monitoring was not required. Although 40.9% of our study population was in critical care setting, clinically significant hypoglycemia was absent even in ICU patients, in contrast to the findings of the previous studies conducted in ICU showing higher rate of hypoglycemia [12,[14][15][16]18,22,23] but consistent with a recent preliminary report of a bedside computerized decision-support tool targeting the blood glucose range of 140 to 180 mg/dL in mixed ICU [25]. Although elimination of perioperative hypoglycemia has been also achieved with artificial pancreas systems even with lower blood glucose target [26][27][28][29], use of these systems in critical care settings with decreased blood perfusion would require specialized equipment and continuous peripheral blood sampling.…”

Section: Discussionsupporting

confidence: 89%

An Electronic Health Record-Integrated Computerized Intravenous Insulin Infusion Protocol: Clinical Outcomes and in Silico Adjustment

et al. 2020

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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.

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Section: Discussionsupporting

confidence: 89%

An Electronic Health Record-Integrated Computerized Intravenous Insulin Infusion Protocol: Clinical Outcomes and in Silico Adjustment

et al. 2020

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“…Results from an evaluation of our IIP have been previously reported and demonstrated adequate BG control with a low occurrence of hypoglycemia in our mixed ICU patient sample. 10 The main results from the prior evaluation of our IIP showed a median BG of 162 mg/dL and BG within the target range of 140 to 180 mg/dL for 47.9% of BG checks. Hypoglycemia occurred only once in 1640 BG checks (0.06%).…”

Section: Introductionmentioning

confidence: 82%

“…Details of our glucose control protocol have been previously reported, including information regarding the initiation and titration of patients on IIP. 10…”

Section: Methodsmentioning

confidence: 99%

“…Details of our glucose control protocol have been previously reported, including information regarding the initiation and titration of patients on IIP. 10 Hypoglycemia is typically defined as a BG less than 70 mg/dL, and a BG of 180 mg/dL is the recommended upper limit for ICU patients. 5,6,11 Additionally, for quality monitoring purposes, our institution uses these values to define normoglycemia.…”

Section: Methodsmentioning

confidence: 99%

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Intravenous Insulin Infusion Protocol Compared With Subcutaneous Insulin for the Management of Hyperglycemia in Critically Ill Adults

et al. 2019

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Background: The use of an intravenous insulin infusion protocol (IIP) is recommended for management of hyperglycemia in the intensive care unit (ICU); however, limited evidence of comparison has been made with subcutaneous (SC) insulin regimens. Objective: This study aims to evaluate the safety and effectiveness of an IIP compared with SC insulin regimens in an ICU patient sample since the implementation of an IIP using a computerized clinical-decision support tool. Methods: This investigation was a retrospective cohort study of patients who were treated for hyperglycemia while admitted to any of the ICUs at a Veterans Affairs Medical Center. Patients who were treated with either an IIP or a scheduled SC insulin regimen between May 1, 2015, and May 25, 2016, were included for evaluation. Results: Blood glucose (BG) was within the normoglycemia range (70-180 mg/dL) for 63.0% of the measurements in patients treated with an IIP (n = 171) compared with 45.7% in those treated with SC insulin regimens (n = 121; P < 0.01). Overall, patients managed with an IIP had a lower proportion of hypoglycemic BG measurements (1.2% vs 2.1%, P < 0.01), a lower proportion of hyperglycemic BG measurements (35.8% vs 52.2%, P < 0.01), and a lower mean BG (172.4 vs 194.3 mg/dL, P < 0.01). Conclusion and Relevance: The results of this study suggest that an IIP in a sample of adult ICU patients was associated with better BG control and lower occurrence of hypoglycemia compared with SC insulin regimens.

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Automation in nursing decision support systems: A systematic review of effects on decision making, care delivery, and patient outcomes

Akbar

Lyell

Magrabi

2021

Journal of the American Medical Informatics Association

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Objective The study sought to summarize research literature on nursing decision support systems (DSSs); understand which steps of the nursing care process (NCP) are supported by DSSs, and analyze effects of automated information processing on decision making, care delivery, and patient outcomes. Materials and Methods We conducted a systematic review in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement. PubMed, CINAHL, Cochrane, Embase, Scopus, and Web of Science were searched from January 2014 to April 2020 for studies focusing on DSSs used exclusively by nurses and their effects. Information about the stages of automation (information acquisition, information analysis, decision and action selection, and action implementation), NCP, and effects was assessed. Results Of 1019 articles retrieved, 28 met the inclusion criteria, each studying a unique DSS. Most DSSs were concerned with two NCP steps: assessment (82%) and intervention (86%). In terms of automation, all included DSSs automated information analysis and decision selection. Five DSSs automated information acquisition and only one automated action implementation. Effects on decision making, care delivery, and patient outcome were mixed. DSSs improved compliance with recommendations and reduced decision time, but impacts were not always sustainable. There were improvements in evidence-based practice, but impact on patient outcomes was mixed. Conclusions Current nursing DSSs do not adequately support the NCP and have limited automation. There remain many opportunities to enhance automation, especially at the stage of information acquisition. Further research is needed to understand how automation within the NCP can improve nurses’ decision making, care delivery, and patient outcomes.

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A Bedside Computerized Decision-Support Tool for Intravenous Insulin Infusion Management in Critically Ill Patients

Cited by 3 publications

References 16 publications

An Electronic Health Record-Integrated Computerized Intravenous Insulin Infusion Protocol: Clinical Outcomes and in Silico Adjustment

An Electronic Health Record-Integrated Computerized Intravenous Insulin Infusion Protocol: Clinical Outcomes and in Silico Adjustment

Intravenous Insulin Infusion Protocol Compared With Subcutaneous Insulin for the Management of Hyperglycemia in Critically Ill Adults

Automation in nursing decision support systems: A systematic review of effects on decision making, care delivery, and patient outcomes

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