Validity of bedside blood glucose measurement in critically ill patients with intensive insulin therapy

Mahmoodpoor, Ata; Hamishehkar, Hadi; Shadvar, Kamran; Sanaie, Sarvin; Iranpour, Afshin; Fattahi, Vahid

doi:10.4103/0972-5229.194009

Cited by 4 publications

(6 citation statements)

References 28 publications

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“…Research on the use of glucometers in ICU and non-ICU settings is extensive. However, most of these studies focused on precision and accuracy of the glucometers, sources of glucometer measurement errors, and the difference in sensitivity and specificity between glucometer devices from different vendors [16-29]. Nevertheless, glucometers are commonly used handheld devices to measure blood glucose at the point of care, specifically in ICUs to inform timely clinical decisions regarding insulin therapy.…”

Section: Discussionmentioning

confidence: 99%

“…Extensive literature exists on the use of glucometers in ICUs. However, most studies focused on the glucometers’ accuracy in comparison with other blood glucose analytical measures [16-24]. Research on transcription errors is also available [25-27]; however, there is a paucity of research on transcription errors of blood glucose values obtained by glucometers into the EMRs and the subsequent insulin errors [28].…”

Section: Introductionmentioning

confidence: 99%

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Transcription Errors of Blood Glucose Values and Insulin Errors in an Intensive Care Unit: Secondary Data Analysis Toward Electronic Medical Record-Glucometer Interoperability

et al. 2019

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Background Critically ill patients require constant point-of-care blood glucose testing to guide insulin-related decisions. Transcribing these values from glucometers into a paper log and the electronic medical record is very common yet error-prone in intensive care units, given the lack of connectivity between glucometers and the electronic medical record in many US hospitals. Objective We examined (1) transcription errors of glucometer blood glucose values documented in the paper log and in the electronic medical record vital signs flow sheet in a surgical trauma intensive care unit, (2) insulin errors resulting from transcription errors, (3) lack of documenting these values in the paper log and the electronic medical record vital signs flow sheet, and (4) average time for docking the glucometer. Methods This secondary data analysis examined 5049 point-of-care blood glucose tests. We obtained values of blood glucose tests from bidirectional interface software that transfers the meters’ data to the electronic medical record, the paper log, and the vital signs flow sheet. We obtained patient demographic and clinical-related information from the electronic medical record. Results Of the 5049 blood glucose tests, which were pertinent to 234 patients, the total numbers of undocumented or untranscribed tests were 608 (12.04%) in the paper log, 2064 (40.88%) in the flow sheet, and 239 (4.73%) in both. The numbers of transcription errors for the documented tests were 98 (2.21% of 4441 documented tests) in the paper log, 242 (8.11% of 2985 tests) in the flow sheet, and 43 (1.64% of 2616 tests) in both. The numbers of transcription errors per patient were 0.4 (98 errors/234 patients) in the paper log, 1 (242 errors/234 patients) in the flow sheet, and 0.2 in both (43 errors/234 patients). Transcription errors in the paper log, the flow sheet, and in both resulted in 8, 24, and 2 insulin errors, respectively. As a consequence, patients were given a lower or higher insulin dose than the dose they should have received had there been no errors. Discrepancies in insulin doses were 2 to 8 U lower doses in paper log transcription errors, 10 U lower to 3 U higher doses in flow sheet transcription errors, and 2 U lower in transcription errors in both. Overall, 30 unique insulin errors affected 25 of 234 patients (10.7%). The average time from point-of-care testing to meter docking was 8 hours (median 5.5 hours), with some taking 56 hours (2.3 days) to be uploaded. Conclusions Given the high dependence on glucometers for point-of-care blood glucose testing in intensive care units, full electronic medical record-glucometer interoperability is required for complete, accurate, and timely documentation of blood glucose values and elimination of transcription errors and the subsequent insulin-related errors in intensive care units.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Transcription Errors of Blood Glucose Values and Insulin Errors in an Intensive Care Unit: Secondary Data Analysis Toward Electronic Medical Record-Glucometer Interoperability

et al. 2019

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“…Fasting glucose was measured using pocket glucometers, which are known to have variable accuracy. 40,41 Although mean improvements in some biomarkers (hs-CRP, FBG, A1C, total cholesterol, LDL, VLDL, and triglycerides) appear to continue to improve through the 12-month follow-up, other markers (insulin and HDL) showed inconsistent patterns, with unfavorable changes noted at the 6-or 12-month follow-up. The desired effects of a nutrition education program such as the naturopathic one studied here would be relatively permanent, and any sort of ''backsliding'' toward baseline after program cessation would temper the benefits of the intervention.…”

Section: Limitationsmentioning

confidence: 99%

Impact of a Short-Term Naturopathic Whole-Foods-Based Nutrition Education Intervention on Dietary Behavior and Diabetes Risk Markers: A Pilot Study

Tippens

Erlandsen

Hanes

et al. 2019

The Journal of Alternative and Complementary Medicine

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Objective: To assess the feasibility of measuring the health impacts of a 12-week naturopathic whole foods nutrition education course among adults with or at risk for prediabetes. Design: A pilot nonrandomized pre-post intervention design investigated physiological changes associated with participation in a 12-week structured naturopathic nutrition education course. Follow-up measures were assessed at 6 and 12 months from baseline. Settings/Location: Three community-based kitchens in Oregon. Subjects: Forty-five adults with or at risk for prediabetes. Intervention: Twelve weekly 90-min workshops emphasizing the health benefits of a naturopathic whole foods diet and including collective meal preparation and communal dining. Outcome measures: Changes in biomarkers of diabetes and cardiovascular disease risk, including highsensitivity C-reactive protein (hs-CRP), hemoglobin A1c, insulin, and lipids, and changes in dietary behaviors at baseline, postintervention (12 weeks), and 6-and 12-month follow-up. Changes in biomarkers and food consumption patterns were assessed using linear mixed models with random intercept, including data from all participants who completed the end-of-intervention assessment. Results: Pre-post intervention results for 45 participants showed decreases in blood glucose and hs-CRP. Food frequency questionnaire data showed decreases in daily servings of grain, dairy, and fat postintervention. Decreases in blood glucose, hs-CRP, triglycerides, cholesterol, low-density lipoprotein, very-low-density lipoprotein, and HbA1c from baseline were observed at 12-month follow-up. Both insulin and high-density lipoprotein were decreased from baseline and 12-week levels at the 6-month follow-up but increased from all earlier levels at 12 months (all p < 0.0001). Daily servings of meat, dairy, and fat remained decreased at 6-month follow-up. Reduced grain consumption observed at 12-week and at 6-month follow-up was not sustained, and increased at 12 months, although still decreased from baseline. Conclusions: Results suggest that this naturopathic nutrition education series may promote dietary behavior change with associated changes in clinical biomarkers. Larger controlled studies are needed to confirm these results.

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“…We should also consider the correct and earlier diagnosis of patients, their glycemic status and preadmission glycemic control individually (6). Future trials should consider the discrepancies accounting for controversial points like nutritional status of patients, glucose monitoring methods (7) and insulin titration method.…”

Section: Original Articlementioning

confidence: 99%

Dysglycemia in Critically Ill Patients: Common Problems and Future Direction

Sanaie

Mahmoodpoor

2017

ABCMed

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The management of blood glucose (BG) in the critically ill became a topic of great interest following the publication of the landmark single-center surgical ICU study targeting euglycemia (80 to 110 mg/dL) in Leuven, Belgium, in 2001 (1). This study resulted in thousands of protocols and guidelines promoting 'tight' BG control . The failure to show the same results and high incidence of hypoglycemia in following trials have resulted in controversy in blood glucose management in critically ill patients. Analysis of dysglycemia in critically ill patients should include markers of three domains: hyperglycemia, hypoglycemia, and glycemic variability (2,3). Thus, hyperglycemia, hypoglycemia, and blood glucose variability should all be regarded as independent predictors of adverse outcomes in critically ill patients. Agus et al., in their multicenter study (4), showed that critically ill children with hyperglycemia did not benefit from strict glycemic control to a target glucose of 80-110 mg/dL compared to 150-180 mg/dL and patients in lower treatment target showed an insignificant 90-day mortally rate compared to other group. There are so many reasons to describe these controversies: In LEUVEN III study (5), despite a 25% hypoglycemia incidence, tight glycemic control had a significant treatment effect; nevertheless, in Agus et al. study, despite a lower incidence of hypoglycemia, treatment effect was not significant. The reasons can be explained with the fact that first trials were single centered open label studies which were terminated at early stages of the study because of observed benefits which may have exaggerated the treatment effect. Also, the observed difference was found in subgroup analysis which could have been due to chance factor. Findings from RCTs conducted on critically ill adults and children strongly suggest that the largest benefit for blood glucose control can be expected if the difference in blood glucose concentrations between the study groups is large and if the study is done in a single-centre setting where the blood glucose management is tailored to the local treatment habits. Consequently, we could not compare those single centered trials which are not externally validated with high level of adherence to protocols, lower time to target range, higher time in target range with multi centered trials with a low level of adherence to protocol higher time to target range, lower time in target range and a totally different method of energy supplementation. Finally, the era of 'one size fits all' in regard to glycemic targets in the critically ill seems to be over. We should also consider the correct and earlier diagnosis of patients, their glycemic status and preadmission glycemic control individually (6). Future trials should consider the discrepancies accounting for controversial points like nutritional status of patients, glucose monitoring methods (7) and insulin titration method.

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Validity of bedside blood glucose measurement in critically ill patients with intensive insulin therapy

Cited by 4 publications

References 28 publications

Transcription Errors of Blood Glucose Values and Insulin Errors in an Intensive Care Unit: Secondary Data Analysis Toward Electronic Medical Record-Glucometer Interoperability

Transcription Errors of Blood Glucose Values and Insulin Errors in an Intensive Care Unit: Secondary Data Analysis Toward Electronic Medical Record-Glucometer Interoperability

Impact of a Short-Term Naturopathic Whole-Foods-Based Nutrition Education Intervention on Dietary Behavior and Diabetes Risk Markers: A Pilot Study

Dysglycemia in Critically Ill Patients: Common Problems and Future Direction

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