Abstract
Context
The coronavirus disease 2019 (COVID-19) pandemic has created a need for remote blood glucose (BG) monitoring in the intensive care unit (ICU).
Objective
To evaluate feasibility and ...patient safety of a hybrid monitoring strategy of point-of-care (POC) BG plus continuous glucose monitor (CGM) in the ICU.
Design
Retrospective analysis.
Setting
ICU of an academic medical center.
Patients
Patients with COVID-19 on IV insulin.
Intervention
After meeting initial validation criteria, CGM was used for IV insulin titration and POC BG was performed every 6 hours or as needed.
Main Outcome Measures
Outcomes included frequency of POC BG, workflow, safety, and accuracy measures.
Results
The study included 19 patients, 18 with CGM data, mean age 58 years, 89% on mechanical ventilation, 37% on vasopressors, and 42% on dialysis. The median time to CGM validation was 137 minutes (interquartile range IQR 114-206). During IV insulin, the median number of POC values was 7 (IQR 6-16) on day 1, and declined slightly thereafter (71% reduction compared with standard of 24/day). The median number of CGM values used nonadjunctively to titrate IV insulin was 11.5 (IQR 0, 15) on day 1 and increased thereafter. Time in range 70 to 180 mg/dL was 64 ± 23% on day 1 and 72 ± 16% on days 2 through 7, whereas time <70 mg/dL was 1.5 ± 4.1% on day 1 and <1% on days 2 through 7.
Conclusions
This study provides data to support that CGM using a hybrid protocol is feasible, accurate, safe, and has potential to reduce nursing and staff workload.
Purpose of Review
The aim of this review is to identify the implementation approaches, strategies, and outcomes for continuous glucose monitoring (CGM) in the intensive care unit (ICU). Medline and ...Web of Science databases were searched to report relevant literature published between September 12, 2016 and September 12, 2021. Implementation outcomes and strategies, defined by the Expert Recommendations for Implementing Change (ERIC) project, were extracted.
Recent Findings
Of the 324 titles reviewed, 16 articles were included in the review. While no studies were identified as implementation research, 14 of 16 identified implementation strategies that aligned with ERIC definitions. Included studies described a multi-disciplinary approach. Clinical outcomes included Mean Absolute Relative Difference (MARD), ranging from 7.5 to 15.3%, and 33–71% reduction in frequency of point-of-care (POC) blood glucose monitoring (BGM) using hybrid protocols.
Summary
This scoping review provides valuable insight into the process of CGM implementation in the ICU. Continued research should include implementation outcomes to inform widespread utilization.
Clinical trial data demonstrates improved glycemic control with hybrid close loop (HCL) insulin delivery systems, yet limited real-world data exists. Data from the inaugural cohort of patients ...initiating a HCL system (Medtronic MiniMed
670G, Medtronic Canada, Brampton, ON) at a university medical center was used to examine real-world utilization and glycemic control following a standardized implementation process.
Data from 34 adult patients with type 1 diabetes were obtained from pump downloads at 4 time points: (
) previous insulin pump, (
) HCL in manual-mode, (
) 2 weeks after HCL auto-mode transition, and (
) 6 to 12 weeks after initiation of HCL. In-person training by certified diabetes educators was performed across 3 sessions with phone and electronic messaging following auto-mode start.
Mean self-monitored blood glucose (SMBG) per day increased from 5.15 baseline to 6.49 at 6 to 12 weeks (
<.05) with 3.26 sensor calibrations per day. Time-in-auto-mode was 79.3% at 2 weeks and 72.3% at final follow-up, with 82% of patients spending >50% of time in auto-mode. There were 8.2 auto-mode exits over the final 14-day download. Time-in-target was 67.3% in manual-mode, 73.4% at 2 weeks (
= .09), and 71.7% by 6 to 12 weeks (
= .06). Hemoglobin A1c (HbA1c) decreased by 0.51% (
= .02), while total daily dose and % basal did not change. Patients with HbA1c <7.0% (53 mmol/mol) at baseline spent more time-in-target than those with HbA1c ≥7.0% (53 mmol/mol; 78.0% versus 67.5%) despite spending less time-in-auto-mode (66.5% versus 74.8%).
These data illustrate real-world implementation of HCL technology using a structured education program within a major medical center. Overall benefit may vary based on baseline characteristics such as HbA1c.
= certified diabetes educator;
= hemoglobin A1c;
= hybrid closed loop;
= self-monitored blood glucose.
We describe our implementation of a continuous glucose monitoring (CGM) guideline to support intravenous insulin administration and reduce point of care (POC) glucose monitoring frequency in the ...coronavirus disease 2019 medical intensive care unit (MICU) and evaluate nurses' experience with implementation of CGM and hybrid POC + CGM protocol using the Promoting Action on Research in Health Services framework.
A multidisciplinary team created a guideline providing criteria for establishing initial sensor-meter agreement within each individual patient followed by hybrid use of CGM and POC. POC measures were obtained hourly during initial validation, then every 6 hours. We conducted a focus group among MICU nurses to evaluate initial implementation efforts with content areas focused on initial assessment of evidence, context, and facilitation to identify barriers and facilitators. The focus group was analyzed using a qualitative descriptive approach.
The protocol was integrated through a rapid cycle review process and ultimately disseminated nationally. The Diabetes Consult Service performed device set-up and nurses received just-in-time training. The majority of barriers centered on contextual factors, including limitations of the physical environment, complex device set-up, hospital firewalls, need for training, and CGM documentation. Nurses' perceived device accuracy and utility were exceptionally high. Solutions were devised to maximize facilitation and sustainability for nurses while maintaining patient safety.
Outpatient CGM systems can be implemented in the MICU using a hybrid protocol implementation science approach. These efforts hold tremendous potential to reduce healthcare worker exposure while maintaining glucose control during the COVID-19 pandemic.
BACKGROUNDDiabetes devices, like insulin pumps and continuous glucose monitors (CGMs), capture and store patient adherence and utilization data that can be retrieved or downloaded providing objective ...information on self-management behaviors; yet, diabetes device data remain underutilized in research.
OBJECTIVEThe aim of the study was to examine the usability and feasibility of personal diabetes device data collected using a clinical download platform retooled for research purposes.
METHODSInvestigators evaluated the feasibility of raw diabetes device data collection. One hundred eight preteens and adolescents with Type 1 diabetes and their parents provided consent/assent.
RESULTSData were successfully collected from the diabetes devices (insulin pumps and CGM) of 97 youth using a clinical download software adapted for research, including data from all three commercially available CGM systems and insulin pumps brands, which contained all current and previous models of each insulin pump brand. The time required to download, mode of connection, and process varied significantly between brands. Despite the use of an agnostic download software, some outdated device brands and cloud-based CGM data were unsupported during data collection. Within the download software, dummy clinical accounts were created for each study participant, which were then linked back to a master study account for data retrieval. Raw device data were extracted into seven to eight Excel files per participant, which were then used to develop aggregate daily measures.
DISCUSSIONOur analysis is the first of its kind to examine the feasibility of raw diabetes device data using a clinical download software. The investigators highlight issues encountered throughout the research process, along with mitigating strategies to inform future inquiry.
CONCLUSIONThis study demonstrates the feasibility of raw data collection, from a wide variety of insulin pump and CGM brands, through the retooling of a clinical download software. Data from these personal devices provide a unique opportunity to study self-management behavior and the glycemic response of individuals in their everyday environments.
The coronavirus disease 2019 pandemic resulted in the need for remote glucose monitoring in intensive care unit (ICU) patients requiring insulin infusions. While rarely reported, perceptions from ...nursing staff are critical for successful implementation and expanded use. A hybrid continuous glucose monitoring (CGM) and intermittent point of care (POC) glucose monitoring was developed at the study institution. After 1.5 years of protocol use, an anonymous survey was distributed electronically to ICU nurses to obtain their perspectives on CGM utilization. The survey employed a 1-5 Likert scale regarding CGM sensor insertion, accuracy, acceptability, and usability as well as perceptions on reduced room entry and reduced workload. Of the 51 surveys completed, 50 (98%) nurses reported they cared for an ICU patient enrolled in the CGM protocol and 31 (62%) started or replaced a CGM sensor. A representative sample of questions and responses is shown in table 1. ICU nurses overwhelmingly reported CGM was accurate, reduced their workload, provided safer patient care, and was preferred over POC glucose testing alone. These results confirm nursing acceptance and preference for CGM use within a hybrid glucose monitoring protocol in the ICU setting.
Disclosure
J. Pattison: None. K. M. Dungan: Advisory Panel; Dexcom, Inc., Consultant; Boehringer Ingelheim International GmbH, Eli Lilly and Company, Elsevier, Nova Biomedical, Novo Nordisk, Tolerion, Inc., Other Relationship; UpToDate, Research Support; Abbott, Dexcom, Inc., Novo Nordisk, Sanofi, ViaCyte, Inc., Speaker’s Bureau; Academy for Continued Healthcare Learning, Cardiometabolic Health Congress, Medscape. E. Buschur: Advisory Panel; Beta Bionics, Inc., Research Support; Dexcom, Inc. M. C. Exline: Other Relationship; Abbott. L. G. Jones: None. C. May: None. M. Mcnett: None. K. Smetana: None. E. R. Faulds: Advisory Panel; Dexcom, Inc., Research Support; Dexcom, Inc., Speaker’s Bureau; Medscape.
Patients with diabetes are experts in the lived experience of self-management, making patient engagement beyond the role of research “subject” imperative for the creation of health care solutions ...that meaningfully address the problems they identify. We discuss our research team’s relationship with our university’s College Diabetes Network (CDN), an advocacy and support group for emerging adults with diabetes. Our collaborative research relationship has spanned three years, and multiple research studies with members serving as co-designers, consultants, and co-investigators. We discuss the CDN’s role in two particular studies in which CDN members made substantive contributions to study design, instrument adaptation, and interpretation of findings. Key CDN members played a larger role in research activities and facilitated sustained engagement with the larger university CDN chapter. Barriers encountered included navigating research regulatory requirements while engaging CDN members in research and facilitating sustained engagement as CDN membership changes.
Background:
The COVID-19 pandemic necessitated rapid implementation of continuous glucose monitoring (CGM) in the intensive care unit (ICU). Although rarely reported, perceptions from nursing staff ...who used the systems are critical for successful implementation and future expanded use of CGM in the inpatient setting.
Methods:
A 22-item survey focused on CGM use was distributed to ICU nurses at two large academic medical centers in the United States in 2022. Both institutions initiated inpatient CGM in the spring of 2020 using the same CGM+point of care (POC) hybrid protocol. The survey employed a 1- to 5-point Likert scale regarding CGM sensor insertion, accuracy, acceptability, usability, training, and perceptions on workload.
Results:
Of the 71 surveys completed, 68 (96%) nurses reported they cared for an ICU patient on CGM and 53% reported they had independently performed CGM sensor insertion. The ICU nurses overwhelmingly reported that CGM was accurate, reduced their workload, provided safer patient care, and was preferred over POC glucose testing alone. Interestingly, nearly half of nurses (49%) reported that they considered trend arrows in dosing decisions although trends were not included in the CGM+POC hybrid protocol. Nurses received training through multiple modalities, with the majority (80%) of nurses reporting that CGM training was sufficient and prepared them for its use.
Conclusion:
These results confirm nursing acceptance and preference for CGM use within a hybrid glucose monitoring protocol in the ICU setting. These data lay a blueprint for successful implementation and training strategies for future widespread use.
There is limited experience with continuous glucose monitoring (CGM) in intensive care units (ICUs). We assessed the effectiveness, safety, and accuracy of real-time CGM use in COVID-19 ICUs. We ...pooled data from three academic centers using hybrid protocols combining point of care (POC) blood glucose (BG) with non-adjunctive CGM. At hospitals A and B, CGM was used non-adjunctively with POC at least every 6 hours. At hospital C, POC BG was performed every two hours. We also examined CGM performance during lowest pAO2, oxygen saturation, pH, and mean arterial pressure. Of 169 patients, mean age was 60.9±12.2 years, and 82%, 93%, and 62% received corticosteroids, mechanical ventilation, and vasopressors respectively. Median duration of IV insulin was 118, 156, and 50 hours at hospitals A, B, and C respectively. Mean POC BG frequency was 10.2±6.1, 7.0±5.2, and 16±6.5 times/day on day 1 of IV insulin and 6.7±3.1, 6.0±3.5, and 12.2±5.8 times/day thereafter. The median percent CGM time in range (TIR, 70-180mg/dl) was 72%, 70% and 46% during IV insulin. Median time <70mg/dl was <0.1% for all hospitals. The absolute relative difference between CGM and POC did not correlate with hemodynamic instability, Figure. This data indicates hybrid monitoring can reduce POC frequency while safely maintaining glucose. Sensor-meter agreement was not associated with abnormal physiologic parameters and requires further study.
Disclosure
E.R.Faulds: Advisory Panel; Dexcom, Inc., Other Relationship; A1Control, LLC, Research Support; Dexcom, Inc., Speaker's Bureau; Dexcom, Inc., Medscape. M.Garcia: None. S.Chandra: None. F.J.Pasquel: Consultant; Dexcom, Inc., Medscape, Research Support; Dexcom, Inc., Ideal Medical Technologies. K.M.Dungan: Board Member; Elsevier, Consultant; Eli Lilly and Company, Dexcom, Inc., Other Relationship; UpToDate, Research Support; Dexcom, Inc., Abbott, ViaCyte, Inc., Sanofi, Speaker's Bureau; Academy for Continued Healthcare Learning, Cardiometabolic Health Congress, Medscape, Integritas. Y.Badakhshi: None. A.Boutsicaris: None. M.C.Exline: Other Relationship; Abbott Nutrition. J.Hester: None. L.G.Jones: None. M.Mcnett: Research Support; Dexcom, Inc., National Institutes of Health. J.D.Miller: Advisory Panel; MannKind Corporation, Employee; Medtronic, Research Support; Dexcom, Inc. R.Shah: None.
Background:
Specialized education is critical for optimal insulin pump use but is not widely utilized or accessible. We aimed to (1) test the usability and acceptability of A1Control, a simulation ...platform supporting insulin pump education, and (2) determine predictors of performance.
Method:
Rural adult insulin pump users with type 1 diabetes (T1D) participated in a mixed methods usability study in 2 separate rounds. Participants navigated 3 simulations (ie, infusion site occlusion, hypoglycemia, exercise). Net Promoter Score (NPS) and Systems Usability Scale (SUS) were administered. Semi-structured interviews and direct observation were used to assess perceived usability, acceptability and performance. Synthetic Minority Oversampling Technique was used to fit predictive models for visualization of patterns leading to good or poor A1Control performance.
Results:
Participants (N = 13) were 28-70 years old, 10 used automated insulin delivery and 12 used continuous glucose monitoring (CGM). Mean NPS was 9.5 (range 9-10) and positive sentiment during interviews indicated very high acceptability. SUS (mean 88.5, range 70-100) indicted a high perceived usability. CGM percent wear ≥ 94%, time spent in hypoglycemia ≤ 54 mg/dl of <0.01%, and <70 mg/dl of 0.5% predicted successful site-occlusion scenario performance with 100% accuracy. BOLUS score ≥ 2, TDD ≥ 34, and technology brand predicted exercise scenario success with 100% accuracy. There were an insufficient number of failed hypoglycemia scenarios to assess predictors.
Conclusion:
A1Control shows potential to increase access and frequency of self-management and technology education. Additional study is needed to determine sustained engagement and benefit.
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