Expanding Use of Continuous Glucose Monitoring Beyond COVID in Critical Care: Study Protocol for a Hybrid Implementation Trial

Expanding Use of Continuous Glucose Monitoring Beyond COVID in Critical Care: Study Protocol for a Hybrid Implementation Trial | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Study protocol Expanding Use of Continuous Glucose Monitoring Beyond COVID in Critical Care: Study Protocol for a Hybrid Implementation Trial Eileen Faulds, Brooke Lee, Amanie Rasul, Laureen Jones, Molly McNett, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4243392/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Background Over the last 2 decades, continuous glucose monitoring (CGM) has been shown to improve glycemic control and patient outcomes in the outpatient setting, yet these technologies are not approved for inpatient use. Both hyperglycemia and hypoglycemia, which are common in the inpatient setting, are associated with increased morbidity, mortality, length of stay, and hospital costs. Point-of-care (POC) glucose monitoring has been the standard of care in the hospital setting since the late 1980’s, yet POC glucose monitoring implementation remains insufficient with frequent missed testing and missed timing of testing. Capabilities inherent to CGM (e.g., continuous measurement, hypoglycemia/hyperglycemia threshold alarms, predictive hypoglycemia alarms, trend data) hold the promise of improving glycemic control, patient outcomes, and nursing burden in the inpatient setting, yet ongoing research is needed to examine both outcomes and implementation of CGM in the inpatient environment. Methods This mixed methods hybrid II effectiveness-implementation study will examine patient outcomes and the feasibility of CGM implementation using a CGM plus (+) POC protocol among 100 patients on IV insulin in a single Midwest academic medical center’s medical intensive care unit (MICU). In this single arm clinical trial, we are pursuing 4 research questions (RQ): RQ 1. Establish the clinical utility, fidelity, and adoption of Dexcom G6 CGM as a tool for making dosing decisions within a CGM + POC protocol among medical intensive care unit (MICU) patients; RQ2. Assess the effects of CGM implementation on nursing workload and factors influencing nursing care delivery through surveys administered to MICU nurses and through a MICU staff nurse focus group (N = 10); RQ3. To assess glycemic control among patients receiving CGM in the CGM enabled MICU compared to historical control patients who received POC glucose monitoring in the MICU; RQ4. (exploratory) To assess hospitalization outcomes and conduct economic evaluation of the costs of delivering CGM implementation in the MICU. Discussion This trial that combines elements of effectiveness and implementation research will provide valuable data simultaneously evaluating patient outcomes and feasibility to enable more rapid adoption of CGM as standard of care. Trial registration: ClinicalTrials.gov, NCT03576989; Registered on 13 June 2018. Continuous glucose monitoring hospital inpatient diabetes implementation Background During the COVID-19 pandemic, continuous glucose monitoring (CGM) provided a means for reducing healthcare worker exposure to the virus and care delivery burden during a period of staffing crisis. In April 2020, the United States Food and Drug Administration (FDA) issued a statement indicating they would permit the use of these systems within the hospital setting during the pandemic. 2 Since then, CGM has been integrated into routine care within The Ohio State University Wexner Medical Center (OSUWMC) medical intensive care unit (MICU) and with safe and effective use demonstrated in over 180 patients. 3,4 OSUWMC crafted a Hybrid CGM + point-of-care (POC) protocol in which initial and ongoing POC glucose measurements were used to validate CGM accuracy. 3 In concert, other health systems across the United States have used inpatient CGM and disseminated valuable real-world data that demonstrates safe and effective inpatient use of the technology. 5–11 Across all of these studies, no adverse events were reported. While these studies did demonstrate a reduction in POC glucose testing, reasonable accuracy, and safe use among critically ill COVID-19 patient, they were limited by small sample sizes and a restricted patient population. At our institution we examined data from the initial cohort of 19 COVID-19 patients to use CGM in OSUWMC MICU. Despite the severity of critical illness, mean absolute relative difference (MARD) was 13.9% with no apparent association with oxygen saturation, mean arterial pressure, vasopressor use, renal replacement, anticoagulation, or ventilator support. Time in range (70–180 mg/dL ) on day 1 was 64+/- 23%, and on days 2–7 was 72+/-16%. Time below range (< 70 mg/dL ) was 1.5+/-4.1% on day 1 and 0.16+/-0.35% on days 2–7 12 . We were also able to conduct a multi-site retrospective analysis examining CGM safety and clinical outcomes for COVID-19 patients using a Hybrid POC + CGM protocol. We analyzed data from 169 MICU patients hospitalized at 3 medical centers. The median percent CGM time in range (TIR, 70–180 mg/dL ) was 64% and median time below 70 mg/dL was < 0.1%. The absolute relative difference between CGM and POC did not correlate with the lowest pAO2, oxygen saturation, pH, or mean arterial pressure. Across other studies, MARD ranged between 11.1% and 13.9% for Dexcom G6 7,11,12 and Clarke error grid analysis showed around 98% of values in zones A + B. 7,11 In a recent larger study there was no association between decline in accuracy with hyperglycemia but MARD was slightly higher (18.8%) with glucose < 70mg/dL . 13 However, there is evidence that accuracy can be offset by increased frequency of monitoring, as is the case with CGM. 14 In a study by Boom et al. using an older technology focused on hypoglycemia reduction, 177 medical ICU patients were randomized to CGM or standard of care. In this study, hypoglycemia was reduced from 12 times per day to fewer than 1 time per day and overall glucose control was similar between groups. 15 Inpatient CGM holds great promise to improve patient outcomes, hospital workflow, and reduce hospital staff burden. This research will help expand our understanding of CGM safety and efficacy in the hospital setting and will help create a blueprint for successful hospital CGM implementation. Objectives The objectives of this clinical trial are as follows: ( 1 ) Establish the clinical utility, fidelity, and adoption of Dexcom G6 CGM as a tool for making dosing decisions within a CGM plus (+) POC protocol among MICU patients; ( 2 ) Assess the effects of CGM implementation on factors influencing nursing care delivery through surveys administered to MICU nurses and through a MICU staff nurse focus group (N = 10); ( 3 ) To assess glycemic control among patients receiving CGM in the CGM enabled MICU compared to historical control patients who received POC glucose monitoring in the MICU; and ( 4 ) (exploratory) To assess hospitalization outcomes and conduct economic evaluation of the costs to deliver CGM implementation in the MICU. Methods Trial design This is a single arm mixed methods hybrid II effectiveness-implementation study that will examine patient outcomes and feasibility of CGM implementation using a CGM + POC protocol among 100 patients on IV insulin in a single Midwest academic medical center MICU. This hybrid research design combines elements of effectiveness and implementation research that will be used to enable more rapid adoption of CGM as standard of care. The use of this design allows us to craft a blueprint for successful deployment of CGM within health systems, while at the same time gathering valuable real-world effectiveness data. It also allows us to examine how clinical outcomes relate to levels of adoption and fidelity. Regulatory processes Because CGM does not have expanded indication for inpatient use an Investigational Device Exemption (IDE) was obtained. In collaboration with the FDA, several modifications were made to the protocol to enhance patient safety resulting in a curtailed Hybrid CGM + POC protocol. The most significant changes included ( 1 ) increased POC glucose monitoring frequency from Q6 to Q4 hours; ( 2 ) more frequent testing at Q2 or Q1 hours during periods of clinical change that could precede a decline in CGM accuracy, and ( 3 ) exclusion of patients with more severe illness (i.e., diabetic ketoacidosis [DKA], hyperosmolar non-ketoacidosis [HONK], refractory shock. In addition, increased surveillance measures were added with daily data collection to facilitate rapid identification of protocol deviations. Setting OSUWMC has approximately 13,000 patient hospitalizations with a diagnosis code for diabetes each year. OSUWMC has implemented a comprehensive, hospital-wide inpatient diabetes program since 2006, including computerized diabetes related order sets, carbohydrate-based insulin dosing, nursing education, and ongoing review of outcomes. The study will take place in the MICU which contains 48 beds and employs 152 staff nurses. Eligibility criteria Eligible individuals include: adults > 18 years old, who are admitted to OSUWMC MICU and have hyperglycemia (glucose > 250mg/dL) or are currently on IV insulin. Exclusion criteria include: current COVID-19 infection, refractory shock (Levophed dose > 0.5 mcg/kg/min or equivalent), active treatment for DKA or ( 4 ) HONK, pitting edema, anasarca, or discoloration to left upper extremity, high dose acetaminophen treatment (> 1 gram Q6 hours), hydroxyurea use, pregnancy, home insulin pump therapy during hospitalization, or residence in a corrections institution. Recruitment Critical care clinical research coordinators will identify patients meeting eligibility criteria through daily screening of the MICU census and forward potential participant names to the study team using a secure medical record in-basket process. The study team will determine eligibility and seek permission from the critical care team to approach potential patients. The study team will obtain informed consent or assent from the patient or the patient’s legally authorized representative prior to any study procedures. If the patient is alert but does not have the capacity to provide consent the patient may still be approached to provide assent. If over the course of participation, the participant regains capacity they will be approached to provide consent in the study. Recruitment is expected at a rate of 5 patients per week over seven months. Additional permission will be sought from patients or legally authorized representative for the continued use and storage of de-identified data for research purposes by the funder, Dexcom LLC. All MICU staff nurses (N = 152) will be invited to participate in surveys via email using REDCap. Nurses will also be invited via email to participate in a focus group (N = 10) to gauge their experience using CGM. Sample size The sample size is 100 participants. The primary outcome for the power analysis is the percentage of time spent in target (70-180mg/dL). We estimate from previous studies 15,16 a standard deviation as high as 25 percentage points, in which case 100 patients in each group we will provide 80% power to detect a difference of 10 percentage points in time in range (e.g., 65 vs. 75); a moderate effect size of 0.4. All power calculations assume an alpha of 0.05 and minimal statistical effects of clustering within the hospitals (e.g., unit or nurse level). Interventions The Dexcom G6 CGM intervention is used within the hybrid CGM + POC glucose monitoring protocol and compared to 100 historical control patients from the same MICU who receive only POC glucose monitoring. Continuous Glucose Monitoring Technology . The Dexcom G6 measures interstitial fluid glucose and consists of 3 key parts: the sensor, a transmitter, and a display device. The sensor is inserted just under the skin, is worn for up to 10 days, and measures glucose levels. In this study the sensor will be worn on the back of the arm. The transmitter attaches to the sensor pod and sends glucose information to the display device using Bluetooth. Interstitial glucose concentration estimates are sent from the transmitter to the receiving device (Android phone) at 5minute intervals in real time. The G6 app on the receiver is Bluetooth paired with the transmitter before use. The app continuously and automatically sends data to the Dexcom remote server and is accessible through the CLARITY diabetes management software. Alarms for hypoglycemia, hyperglycemia and predicted hypoglycemia will be set per the CGM + POC protocol. Android phones are kept just outside of the patient rooms. Treatment decisions will not be made using data displayed on this software. Hybrid CGM + POC Protocol. The CGM + POC protocol requires comparison of paired sensor-meter readings. The comparison standard method (Novo StatStrip POC meter) and source (capillary, arterial, venous) are FDA approved for inpatient ICU use. The standard is compared to the CGM value obtained within 5 minutes. The threshold criterion for nonadjunctive (stand-alone) use of the CGM to inform insulin dosing decisions appears in Table 1 . Alert thresholds are set at 100 mg/dl (lower threshold) and 300 mg/dl (upper threshold). In addition, the Urgent Low Soon alert will be activated and designed to provide a 20-minute advance warning of impending hypoglycemia. Table 1 Initial and Ongoing CGM Validation Stage POC glucose testing procedures CGM validation POC glucose testing Q1 hour compared to CGM glucose Proceed to Q4 hour POC testing when two consecutive sensor-meter pairs approximately 1-hour apart meet either of the following criteria: 1) CGM < 20% difference from the POC when the glucose is ≥ 100 mg/dl 2) CGM < 20 mg/dl difference from the POC when the glucose is 20% difference from the POC when the glucose is ≥ 100 mg/dl 2) CGM > 20 mg/dl difference from the POC when the glucose is < 100 mg/dl 3) Revert back to Q4 hour POC testing when two consecutive sensor-meter pairs approximately 1-hour apart meet the initial validation criteria Revert from Q4 hour to Q2 hour POC testing for a duration of 6 hours for one of the following clinical status events occurring in isolation: 4) Intubation 5) Pressor support initiated (Levophed dose < 0.5 mcg/kg/min or equivalent) 6) New cardiovascular event (MI, CVA) 7) Initiation or discontinuation of nutrition support (i.e., enteral feed, total parenteral nutrition) 8) Hemoglobin < 7g/dL 9) CGM or POC glucose < 70mg/dl (follow OSUWMC hypoglycemia policy for initial treatment and monitoring) 10) Predicted low alert (glucose predicted to be < 55mg/dl in the following 20 minutes) 11) Acidosis with pH < 7.3 12) Signs and symptoms do not match glucose readings, particularly for hypoglycemia Revert from Q4 hour to Q1 hour POC testing for a duration of 6 hours for two or more of the above clinical status events occurring together (example: patient is intubated and starts pressor support) If after 6 hours no additional clinical scenarios featured above have developed then Q4 hour POC testing can resume after initial validation using two consecutive sensor-meter pairs. Obtain 1 time POC glucose if: 13) No glucose value appears on android screen (due to signal loss, Low/High measure) 14) Low threshold alert (< 100mg/dl) Stop CGM use Stop use of CGM for insulin titration or glucose monitoring and revert from Q4 hour to Q1 hour POC testing ( do not remove CGM sensor until sensor expires ) for the following conditions: 1) Refractory shock (Levophed dose > 0.5 mcg/kg/min or equivalent) 2) Cardiac arrest 3) Newly developed diabetic ketoacidosis (DKA) (pH < 7.3 or serum bicarbonate < 15 mEq/L in the setting of elevated ketones) 4) Newly developed hyperosmolar non-ketoacidosis (HONK) 5) Pitting edema, anasarca, blue or purple discoloration to bilateral upper extremity 6) Initiation of treatment with high dose acetaminophen (> 1 gram Q6 hours) 7) Initiation of treatment with hydroxyurea Participant timeline Once consent is obtained, the MICU staff nurse will insert the Dexcom G6 sensor, set up the system, and pair the transmitter. The patient will remain on CGM for the duration of their MICU. The period of data collection and trial participation is isolated to only the MICU stay. The Dexcom G6 sensor is replaced every 10 days or sooner in case of removal for radiographic procedures or sensor failure. Criteria for discontinuing or modifying the intervention We will discontinue study participation for reasons related to unacceptable sensor error or serious safety concerns. Sensor errors are defined as: Failure to meet initial validation criteria within 24 hours in 20% of participants or episodes of the following after initial sensor validation: ( 1 ) Failure to detect clinically significant hypoglycemia (CGM value > 100 mg/dL with no Urgent Low Soon alert while POC BG < 70 mg/dL) (≥ 2 episodes); ( 2 ) Failure to detect severe hyperglycemia (CGM value 400 mg dL) (≥ 2 episodes); or ( 3 ) Inappropriate treatment CGM value triggers the opposite action than the POC BG value (CGM > 180 mg/dL while POC BG < 70 mg/dL or vice versa). Serious safety concerns are defined as: ( 1 ) inappropriate insulin dose resulting in clinically significant hypoglycemia (glucose < 55mg/dL) after initial sensor validation (≥ 2 episodes), ( 2 ) iatrogenic DKA or HONK (≥ 2 episodes), ( 3 ) severe adverse events resulting in prolonged hospitalization (≥ 2 episodes), any severe adverse event resulting in death, or ( 4 ) other life-threatening complication that is attributed to the study intervention. Patient participation will cease if there are two sensors fail to validate, if the patient transitions to hospice care, or the participant or the participant’s legally authorized representative withdraws consent. Strategies to improve adherence to intervention The study will be conducted using a staggered enrolment of 20 participants in each wave (20% of target enrolment) with a respite between waves to allow analysis of adherence, safety, and fidelity data prior to continued enrolment. The initial wave (n = 20) will be conducted as a “pilot” with full analysis including examination of benchmark criteria for study continuation. Study team oversight of nursing use of the CGM system will be performed through a combination of daily ( 1 ) evaluation of EHR and CGM Clarity data and ( 2 ) rounding on CGM patients. The study staff will evaluate CGM use and nursing adherence to Hybrid CGM + POC protocol criteria (Table 1 ). Each nurse in the MICU receives education and training on CGM set-up, insertion, pairing, and use as part of their initial and annual critical care competencies. In addition, nurses will receive information to delineate distinctions between the original COVID-19 protocol and the study protocol. New criteria to stop clinical use will be reviewed in detail with nurses. Nursing education and training will be conducted during staff meetings and nursing huddles. Additional implementation strategies to support fidelity to the treatment protocol include use of audit and feedback, local champions, daily clinical reminders and supervision, real time relay of clinical data, and ongoing consultation with the study team. The study protocol, along with education and training materials, will be kept at the bedside of all patient participants. Relevant concomitant care permitted or prohibited during the trial Other than changes to the frequency of POC testing according to the Hybrid CGM + POC protocol, participants will receive all standard care provisions. All OSUWMC standard insulin infusion and subcutaneous insulin dosing guidelines, protocols, order sets, and hypoglycemia guidelines will be followed. Outcomes This hybrid effectiveness- implementation study will evaluate both implementation and patient clinical outcomes. Implementation outcome measures Our implementation outcomes include: RQ1, establishing the clinical utility, fidelity, and adoption of CGM as a tool for making dosing decisions within a Hybrid CGM + POC protocol and RQ2, assess CGM implementation effects on nursing care delivery. In RQ1 clinical utility outcomes include time to CGM validation, mean percent of dosing decisions determined by CGM, changes in insulin dosing in response to alarm and/or trend data. Fidelity outcomes include the proportion of times CGM is used non-adjunctively/number of times non-adjunctive use indicated per protocol. Adoption criteria outcomes include proportion of patients approached to receive CGM monitoring/number of patients eligible. We hypothesize that the majority of insulin dosing decisions will be made using non-adjunctive CGM and nursing will alter dosing decisions based on CGM data (e.g., glucose value, glucose trend/trajectory, alarms and alerts). For RQ2, we will conduct a mixed method analysis to evaluate nursing care delivery factors. Nursing care delivery factors, including acceptance and appropriateness of CGM, perceived feasibility, and CGM satisfaction and knowledge, will be evaluated using a combination of questionnaires and focus groups. Perceived acceptance, appropriateness, and feasibility will be measured using the Acceptability of Intervention Measure (AIM), Intervention Appropriateness Measure (IAM), and Feasibility of Intervention Measure (FIM). The AIM, IAM, and FIM are brief 5 item Likert-scale questionnaires with each showing a high degree of content validity with alphas for the 3 questionnaires between 0.87 and 0.89. 17 Satisfaction will be measured using the CGM Satisfaction Questionnaire (CGM-SAT), a 44-item tool measuring satisfaction with CGM over the previous 6 months. The survey has a high degree of validity (a \(\ge\) 0.94) in adults with type 1 diabetes using CGM. 18 Several items will be modified for application in healthcare workers. For these quantitative surveys, all MICU staff nurses (N=152) will be informed of the study and invited to participate via email. Upon e-consent, the questionnaires will be administered via a REDCap online survey. In addition to this quantitative information, additional qualitative information on knowledge and experiences with CGM will be gathered via a focus group among MICU staff nurses (N = 10). A semi-structured interview guide will focus on evaluation of knowledge regarding CGM, as well as areas of perceived CGM implementation support and education, and recommendations for future best practice. All RQ1 and RQ2 variables and measures appear in Table 2 and Table 3 . Clinical outcome measures In RQ3 we assess glycemic control among patients receiving CGM compared to historical control patients who received POC glucose monitoring in the MICU. Our primary outcome for RQ3 is time in glucose range (100–180 mg/dl, 70-180mg/dl, 140-180mg/dl). We hypothesize that patients using CGM will exhibit greater time in range and experience less frequent time in hypoglycemia than patients on fingerstick POC. Individual participant discrete data will be downloaded directly from the Clarity site. Historical controls meeting the gateway criteria (glucose > 250mg/dl or on IV insulin during admission) from the same MICU will be matched on the following factors: Diagnosis and type of diabetes, home insulin use, steroid use during hospitalization, vasopressor use, and North vs. South MICU wings. POC glucose values for historical controls (N = 100) will be obtained directly from OSUWMC EHR. EHR data will be obtained through an information warehouse request providing time stamped, POC glucose values for each matched patient. Hyperglycemic and hypoglycemic events will be identified for CGM and non-CGM users. Discrete CGM glucose values will be used to aggregate derived measures of glucose variability, including time in target (blood glucose value of 70-180mg/dl), time 100-180mg/dl (representing a more conservative target used inpatient), time 140-180mg/dl (representing institutions IV insulin guideline glycemic target), time above target (> 180mg/dl & >250mg/dl), time in hypoglycemia (< 70mg/dl & <55mg/dl), glucose standard deviation, and coefficient of variation. Daily percent time in target as well as cumulative time in target will be assessed. 19–22 Measures of hypoglycemia will be in accordance with ADA recommendations for measurement at < 70mg/dl and < 55mg/dl which is a marker of clinically significant hypoglycemia. 23 Days with \(\ge\) 70% CGM percent wear will be included in daily time in target analysis. 23 For matched historical control patients, percent POC in target (70-180mg/dl), 100-180mg/dl, 140-180mg/dl, >180mg/dl, >250mg/dl, <70mg/dl and <55mg/dl will be evaluated consistent with other recent studies. 24,25 All RQ3 variables and measures appear in Table 2 . Exploratory outcome In our exploratory aim, RQ4, we will examine the impact of CGM therapy on patient hospital outcomes and costs. In this aim, we seek to analyze the economic aspects of inpatient CGM on patient health and healthcare interactions, with a focus on the costs (inputs) and consequences (outcomes) of CGM. Data will be retrospectively extracted from the EHR for patients using CGM in the MICU (N = 100) along with 100 historical controls. Hospital outcomes criteria include the following: Length of stay [ICU, total stay], cost of stay, discharge level of care [home, SNF], morbidity, mortality and readmission rate at 30 days. We will conduct economic evaluation of the costs to deliver CGM implementation in the MICU. We will track resources needed for CGM implementation including personnel, training, facilities, materials, equipment, and other necessary inputs. In addition to resources used, we will expand analysis to evaluate nursing productivity and burden. Resource outcomes criteria include cost of CGM vs. standard POC glucose monitoring materials and frequency of use. The exploratory variables and measures appear in Table 2 . Statistical analysis Descriptive statistics of central tendency (range, mean, median, and standard deviation) will describe variable characteristics and examine data distribution for normality and outliers. Descriptive statistics will summarize the sample characteristics and distribution of each variable. Data will be screened for normality, outliers, and homogeneity. Continuous variables with normal distribution will be reported as mean (standard deviation) while non-normal distribution will be reported as median (interquartile range). For RQ1, multivariate linear models will be used to evaluate clinical utility and implementation variables while examining the effects of patient condition as covaries. For RQ 2, For qualitative analysis of focus group data, a code book will be developed a priori based on the semi-structured interview questions. Interview data, field notes and memos will be imported into NVivo 12.0 (Doncaster, Australia) for data management and analyzed using a qualitative descriptive approach. 26,27 Two researchers (Faulds and McNett) will perform qualitative analysis. Portions of text will be coded with terms that were low inference (“data close”); then grouped into thematic categories and subthemes. 26 For RQ3, for CGM daily glycemic control (i.e., time in range [70-180mg/dl], linear mixed effect modeling (LMM) for repeated measures will be used to adjust for between subject and within subject variance. A 3-tiered LMM will be used to control for patient, unit, and nurse specific variability. LMM will be repeated for analysis of daily POC glucose control for matched historical control patients for days with \(\ge\) 3 POC fingersticks performed. Two-sided significance level of 0.05 was used for all the statistical tests. For RQ4, the cost analysis for the proposed study will be conducted from the provider perspective. Cost estimation involves three major steps: ( 1 ) identify the relevant cost items; ( 2 ) measure the use of resources; and ( 3 ) place a value on the resources used. We will obtain estimated costs for proposed CGM implementation. Direct benefits and savings tend to fall into one of two categories: either savings from enhanced efficiency and productivity, or savings from outcomes improvement (e.g., reducing length of stay, lower 30-day readmissions). Costs that do not vary with the number of patients will be categorized as fixed costs, whereas those that vary by the number of patients will be defined as variable costs. The costs of the CGM implementation will be compared to costs under a control scenario. We will estimate the potential costs and cost savings resulting from the CGM implementation. Protocol amendments and regulatory oversight A Data Safety Monitoring Board (DSMB) will oversee safety activity and evaluating all salient participant outcome, protocol fidelity, and recruitment data after each round of 20 participants and at a minimum of every 6 months. Any protocol changes will be communicated by the PI to the study sponsor, the FDA, and the DSMB. Proposed protocol changes will be submitted to the institution’s Institutional Review Board (IRB) for review and approval. Table 2 Patient Participant Schedule of Activities Research Question Concept Variable Frequency Measure RQ1 Clinical utility criteria Time to CGM validation (RQ1 only) Sensor placement (Q10 days) • EHR • Clarity download Mean percent of dosing decisions determined by CGM Daily Changes in insulin dosing from guideline or outside standard times in response to alarm and/or trend data Daily RQ1 Fidelity & Adoption Implementation criteria Proportion of times CGM initiated or patient approached for study inclusion according to protocol/total number of initiations Admission • EHR • Study recruitment log Proportion of patients approached to received GCM monitoring/number of patients eligible to receive initial CGM monitoring Admission RQ3 Patient condition Demographic data: Age, race/ethnicity, gender Admission • EHR Height, weight, and BMI Admission Diagnosis and type of diabetes Admission Home diabetes regimen Admission Total daily insulin dose Daily Past medical history: tobacco use, COPD, hypertension, heart failure, coronary artery disease Admission Clinical condition: Admission/inpatient glucose, HbA1c, admission diagnosis, admitting service, MICU location (North vs. South), DKA/HHS, sepsis, acute liver failure, acute heart failure Admission Clinical condition: SOFA score, dialysis, thromboembolic events, ECMO mechanical ventilation, ICU/hospital length of stay, cardiac arrest, cerebrovascular accident, mortality Daily Medications: vasopressor use, steroids, anticoagulants, acetaminophen dose Daily Creatinine, eGFR, ALT, AST, TBR, WBC, procalcitonin, ferritin, CRP, IL6, D-dimer, PTT, INR, troponin, pH, BHB, bicarbonate Daily Enteral or parenteral nutrition Daily O2 sat, pAO2, blood pressure Daily RQ3 Glycemic control Time in target for CGM patients (70-180mg/dl) Daily • Clarity • EHR Time 100-180mg/dl for CGM patients Daily Time 140-180mg/dl for CGM patients Daily Time above range for CGM patients (> 180mg/d & >250mg/dl) Daily Time in hypoglycemia for CGM patients (< 70mg/dl & <55mg/dl) Daily Percent POC in target (70-180mg/dl) (matched controls) Daily Percent POC 100-180mg/dl (matched controls) Daily Percent POC 140-180mg/dl (matched controls) Daily Percent POC above range (> 180mg/dl & >250mg/dl) (matched controls) Daily Percent POC in hypoglycemia (< 70mg/dl & <55mg/dl) (matched controls) Daily Glucose standard deviation Daily Glucose coefficient of variation Daily RQ4 Hospitalization outcomes and costs Length of stay [ICU, total stay] Discharge • EHR • Clarity download Cost of stay Discharge Discharge level of care [home, SNF] Discharge Morbidity Discharge Mortality Discharge Readmission rate [30 days] 30d s/p discharge Cost of CGM vs. standard POC glucose monitoring materials. Discharge Table 3 Nurse Participant Schedule of Activities Research Question Concept Variables Frequency Measure RQ2 Nursing care delivery factors Evaluation of CGM support Once • Acceptability of Intervention Measure (AIM), Intervention Appropriateness Measure (IAM), and Feasibility of Intervention Measure (FIM) • CGM Satisfaction Questionnaire • Focus groups Discussion Despite rapid innovation in glucose monitoring in the outpatient setting, inpatient glucose monitoring has changed little over the last 30 years. POC glucose monitoring has been the standard of care in the hospital. While accuracy of critical care specific meters is excellent, there remains long standing implementation challenges associated with inpatient POC use where missed and mistimed testing remain common occurrences. 28,29 This trial’s dual focus on clinical and implementation outcomes facilitates the collection of data essential for widespread deployment of CGM in the hospital setting. The inpatient setting is a distinct use case from traditional patient use of CGM in the outpatient environment. Little is known about the way healthcare workers, and particularly nurses, consume CGM data or utilize CGM technology. Furthermore, CGM will be integrated into the busy hospital technology milieu, making it essential that we study nursing interaction with CGM within the hospital environment. How nurses respond to alarms and alerts is of particular importance. Alarm fatigue is a concerning phenomenon in the inpatient setting and can lead to alarm unresponsiveness or desensitization. 30 Our study examines nurse response to alerts and alarms and will lay the foundation for ongoing work focused on optimal alarm frequency, threshold settings, and response. Additionally, our study’s focus on economic impact is important for healthcare systems as they consider the costs and benefits associated with inpatient CGM utilization, if and when expanded indication to the hospital setting is approved. The study’s requisite real-world design is essential for the evaluation of CGM implementation; however, it presents limitations in evaluating CGM accuracy. Our study uses POC glucose testing for validating CGM accuracy within our Hybrid CGM + POC protocol. The gold-standard for evaluating accuracy of glucose monitoring technologies is YSI serum glucose testing; however, this type of evaluation is not feasible in bedside patient environments. OSUWMC uses the NovoStat meter, the most accurate FDA approved critical care meter on the market; however, there is still error within the technology which will limit our ability to draw conclusions regarding inpatient CGM accuracy. The regulatory impact on study design is certainly worth discussing and should be a consideration as researchers work to design other implementation studies. Several aspects of the study, including POC frequency, exclusion criteria [e.g., DKA, HHS], and training techniques, were shaped in concert with the FDA. While patient safety is of the upmost importance in clinical trials, especially those involving technologies without FDA approval, the requisite oversight and safety provisions may limit real-world application. By the end of the trial, we expect to have a blueprint for successful deployment of CGM use within health systems. Our evaluation of training and implementation strategies, nursing workflow, and nurses’ CGM-based clinical decision making will guide future efforts to deploy CGM as the standard of care in the inpatient setting. At the same time, we will have valuable data on real-world effectiveness of inpatient CGM with an examination of changes in patient outcomes, such as glycemic control, morbidity, mortality, and length of stay. Declarations Prior to starting the study, the protocol was reviewed by both the FDA and The Ohio State University’s IRB. The researchers worked with the FDA during the approval process to ensure measures were taken to minimize risk and avoid patient harm, especially given the vulnerability of the patient population. As detailed, participants or their legally authorized representative are required to provide written consent prior to study participation. Data will be disseminated to ClinicalTrials.gov and to the community through peer-reviewed journals and presentations at national and international diabetes conferences. The study is sponsored by a peer reviewed, investigator initiated, industry award through Dexcom LLC. The funding source did not play any role in designing the trial, implementing the trial, writing the report or making the decision to submit the report for publication. Author Contributions Conceptualization: ERF and KD. Methodology: ERF, KD, MM, ME, AS, and CL. Statistical plan: ERF and AS. Coordination of the study implementation: ERF, MM, BL, AR, RM and LJ. Investigation: ERF, KD, and MM. Data curation: ERF and AS. Writing (original draft preparation): BL and ERF. Writing (review and editing): ERF, KD, MM, ME, CL, LJ, and BL. Project administration: BL and AR. Funding acquisition: ERF. All named authors have read and approved the final manuscript, adhere to the authorship guidelines of Trials and have agreed to publication. References Yang X YY, Xu J, et al. , doi: FpS--. Clinical course and outcomes of critically ill patients with SARSCoV-2 pneumonia in Wuhan, China: a single-centered, retrospective, observational study. Lancet Respir Med. 20:S2213-2600. Administration. USFaD. FDA news release: FDA expands indication for continuous glucose monitoring system, first to replace fingerstick testing for diabetes treatment decisions, 2016. Accessed March 13, 2020, 2020. Faulds ER, Jones L, McNett M, et al. Facilitators and Barriers to Nursing Implementation of Continuous Glucose Monitoring (CGM) in Critically Ill Patients With COVID-19. Endocr Pract. 2021;27(4):354-361. Faulds ER, Boutsicaris A, Sumner L, et al. Use of Continuous Glucose Monitor in Critically Ill COVID-19 Patients Requiring Insulin Infusion: An Observational Study. J Clin Endocrinol Metab. 2021. Chow KW, Kelly DJ, Gupta R, Miller JD. Use of Continuous Glucose Monitoring to Assess Parenteral Nutrition-Induced Hyperglycemia in an Adult Patient With Severe COVID-19. JPEN J Parenter Enteral Nutr. 2021;45(1):208-211. Davis GM, Faulds E, Walker T, et al. Remote Continuous Glucose Monitoring With a Computerized Insulin Infusion Protocol for Critically Ill Patients in a COVID-19 Medical ICU: Proof of Concept. Diabetes Care. 2021;44(4):1055-1058. Agarwal S, Mathew J, Davis GM, et al. Continuous Glucose Monitoring in the Intensive Care Unit During the COVID-19 Pandemic. Diabetes Care. 2021;44(3):847-849. Chow KW, Kelly DJ, Rieff MC, et al. Outcomes and Healthcare Provider Perceptions of Real-Time Continuous Glucose Monitoring (rtCGM) in Patients With Diabetes and COVID-19 Admitted to the ICU. J Diabetes Sci Technol. 2021;15(3):607-614. Gómez AM, Henao DC, Muñoz OM, et al. Glycemic control metrics using flash glucose monitoring and hospital complications in patients with COVID-19. Diabetes Metab Syndr. 2021;15(2):499-503. Longo RR, Elias H, Khan M, Seley JJ. Use and Accuracy of Inpatient CGM During the COVID-19 Pandemic: An Observational Study of General Medicine and ICU Patients. J Diabetes Sci Technol. 2021:19322968211008446. Sadhu AR, Serrano IA, Xu J, et al. Continuous Glucose Monitoring in Critically Ill Patients With COVID-19: Results of an Emergent Pilot Study. J Diabetes Sci Technol. 2020;14(6):1065-1073. Faulds ER BA, Sumner L, Jones L, McNett M, Smetana KS, May CC, Buschur E, Exline MC, Ringel MD, Dungan K. Use of Continuous Glucose Monitor in Critically Ill COVID-19 Patients Requiring Insulin Infusion: An Observational Study. J Clin Endocrinol Metab. 2021. Davis GM, Spanakis EK, Migdal AL, et al. Accuracy of Dexcom G6 Continuous Glucose Monitoring in Non-Critically Ill Hospitalized Patients With Diabetes. Diabetes Care. 2021;44(7):1641-1646. Krinsley JS, Bruns DE, Boyd JC. The impact of measurement frequency on the domains of glycemic control in the critically ill--a Monte Carlo simulation. J Diabetes Sci Technol. 2015;9(2):237-245. Boom DT, Sechterberger MK, Rijkenberg S, et al. Insulin treatment guided by subcutaneous continuous glucose monitoring compared to frequent point-of-care measurement in critically ill patients: a randomized controlled trial. Crit Care. 2014;18(4):453. De Block C, Manuel YKB, Van Gaal L, Rogiers P. Intensive insulin therapy in the intensive care unit: assessment by continuous glucose monitoring. Diabetes Care. 2006;29(8):1750-1756. Weiner BJ, Lewis CC, Stanick C, et al. Psychometric assessment of three newly developed implementation outcome measures. Implement Sci. 2017;12(1):108. Juvenile Diabetes Research Foundation Continuous Glucose Monitoring Study G. Validation of measures of satisfaction with and impact of continuous and conventional glucose monitoring. Diabetes Technol Ther. 2010;12(9):679-684. Bergenstal RM, Garg S, Weinzimer SA, et al. Safety of a Hybrid Closed-Loop Insulin Delivery System in Patients With Type 1 Diabetes. JAMA. 2016;316(13):1407-1408. Beck RW, Riddlesworth T, Ruedy K, et al. Effect of Continuous Glucose Monitoring on Glycemic Control in Adults With Type 1 Diabetes Using Insulin Injections: The DIAMOND Randomized Clinical Trial. JAMA. 2017;317(4):371-378. Lind M, Polonsky W, Hirsch IB, et al. Continuous Glucose Monitoring vs Conventional Therapy for Glycemic Control in Adults With Type 1 Diabetes Treated With Multiple Daily Insulin Injections: The GOLD Randomized Clinical Trial. JAMA. 2017;317(4):379-387. Davidson MB. Continuous Glucose Monitoring in Patients With Type 1 Diabetes Taking Insulin Injections. JAMA. 2017;317(4):363-364. American Diabetes A. 6. Glycemic Targets: Standards of Medical Care in Diabetes-2020. Diabetes Care. 2020;43(Suppl 1):S66-S76. Vinals C, Quiros C, Gimenez M, Conget I. Real-Life Management and Effectiveness of Insulin Pump with or Without Continuous Glucose Monitoring in Adults with Type 1 Diabetes. Diabetes Ther. 2019;10(3):929-936. Beato-Vibora PI, Quiros-Lopez C, Lazaro-Martin L, et al. Impact of Sensor-Augmented Pump Therapy with Predictive Low-Glucose Suspend Function on Glycemic Control and Patient Satisfaction in Adults and Children with Type 1 Diabetes. Diabetes Technol Ther. 2018;20(11):738-743. Sandelowski M. Whatever happened to qualitative description? Res Nurs Health. 2000;23(4):334-340. Colorafi KJ, Evans B. Qualitative Descriptive Methods in Health Science Research. HERD. 2016;9(4):16-25. Juneja R, Roudebush CP, Nasraway SA, et al. Computerized intensive insulin dosing can mitigate hypoglycemia and achieve tight glycemic control when glucose measurement is performed frequently and on time. Crit Care. 2009;13(5):R163. Marvin MR, Inzucchi SE, Besterman BJ. Computerization of the Yale insulin infusion protocol and potential insights into causes of hypoglycemia with intravenous insulin. Diabetes Technol Ther. 2013;15(3):246-252. Patterson ES, Rayo MF, Edworthy JR, Moffatt-Bruce SD. Applying Human Factors Engineering to Address the Telemetry Alarm Problem in a Large Medical Center. Hum Factors. 2022;64(1):126-142. Additional Declarations Competing interest reported. ERF has received consultation and speaker funding from Dexcom LLC; KD has received consultation and speaker funding from Dexcom LLC Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 10 Sep, 2024 Submission checks completed at journal 11 Apr, 2024 Editor assigned by journal 11 Apr, 2024 First submitted to journal 09 Apr, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4243392","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Study protocol","associatedPublications":[],"authors":[{"id":290024215,"identity":"6d79e216-0a75-4113-b5ba-5160829eaacd","order_by":0,"name":"Eileen Faulds","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIie3Pv2vCQBTA8fcItEtj1hcE+y+ccwL5V3oI6dJZOpQSObgula4KpX+Dk3RseKBLcCxKlvgf2M1QhJ6C0A4XHTvcl4P7wX04DsDl+o95XrafArPKCO6PxxdNBA8kzAANKc4h8JugPoMElziovt6BgpEcrOq3OBGfXMGmz1YSKlTdcQFES6kif5rKSZkKHC3sRDDqtq/hEZZSt3HKN6K8A8/XdpIwPn3vNNC1IWH9ysmB7BqI8FCbASQMIT9jnOwJNhBiVOFQE3WLtYquZqkcm7/kz4tbKwleON/UOqbOvJevtg9x0ip762rbj6zk+Njf7cep+y6Xy+Vq7gdQ9VX4q8wh6QAAAABJRU5ErkJggg==","orcid":"","institution":"The Ohio State University College of Nursing","correspondingAuthor":true,"prefix":"","firstName":"Eileen","middleName":"","lastName":"Faulds","suffix":""},{"id":290024216,"identity":"611e1d06-8a63-4174-b7fc-9c3d670d4c2f","order_by":1,"name":"Brooke Lee","email":"","orcid":"","institution":"The Ohio State University Wexner Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Brooke","middleName":"","lastName":"Lee","suffix":""},{"id":290024217,"identity":"53640df7-5f4c-4b95-a0cb-6fcc64928af0","order_by":2,"name":"Amanie Rasul","email":"","orcid":"","institution":"The Ohio State University Wexner Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Amanie","middleName":"","lastName":"Rasul","suffix":""},{"id":290024218,"identity":"f4a2e1d9-53d2-4e8c-8c84-107df0db1a53","order_by":3,"name":"Laureen Jones","email":"","orcid":"","institution":"The Ohio State University Wexner Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Laureen","middleName":"","lastName":"Jones","suffix":""},{"id":290024219,"identity":"a4e79230-d8f0-4fb5-be26-aac692c68973","order_by":4,"name":"Molly McNett","email":"","orcid":"","institution":"The Ohio State University College of Nursing","correspondingAuthor":false,"prefix":"","firstName":"Molly","middleName":"","lastName":"McNett","suffix":""},{"id":290024220,"identity":"dcd7c218-39ac-4d62-9286-82947dc4e99e","order_by":5,"name":"Matthew Exline","email":"","orcid":"","institution":"The Ohio State University Wexner Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Matthew","middleName":"","lastName":"Exline","suffix":""},{"id":290024221,"identity":"8258886a-9dc1-4ecb-96d4-77a5691fce05","order_by":6,"name":"Abigail Shoben","email":"","orcid":"","institution":"The Ohio State University Wexner Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Abigail","middleName":"","lastName":"Shoben","suffix":""},{"id":290024222,"identity":"9dcaba48-1821-434d-881a-41904235412a","order_by":7,"name":"Chyongchiou Lin","email":"","orcid":"","institution":"The Ohio State University College of Nursing","correspondingAuthor":false,"prefix":"","firstName":"Chyongchiou","middleName":"","lastName":"Lin","suffix":""},{"id":290024223,"identity":"12ba082b-3504-4731-bcdc-9a05bc454056","order_by":8,"name":"Rushil Madan","email":"","orcid":"","institution":"The Ohio State University Wexner Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Rushil","middleName":"","lastName":"Madan","suffix":""},{"id":290024224,"identity":"216b05a7-34cb-4cb5-af85-aad2a1e0ef53","order_by":9,"name":"Kathleen Dungan","email":"","orcid":"","institution":"The Ohio State University Wexner Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Kathleen","middleName":"","lastName":"Dungan","suffix":""}],"badges":[],"createdAt":"2024-04-09 17:14:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4243392/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4243392/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54789208,"identity":"44041acf-4b67-4544-9791-c84a44ef2be7","added_by":"auto","created_at":"2024-04-16 20:17:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":405784,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4243392/v1/8fe45547-ff9b-4e36-9d25-5337987778df.pdf"}],"financialInterests":"Competing interest reported. ERF has received consultation and speaker funding from Dexcom LLC; KD has received consultation and speaker funding from Dexcom LLC","formattedTitle":"Expanding Use of Continuous Glucose Monitoring Beyond COVID in Critical Care: Study Protocol for a Hybrid Implementation Trial","fulltext":[{"header":"Background","content":"\u003cp\u003eDuring the COVID-19 pandemic, continuous glucose monitoring (CGM) provided a means for reducing healthcare worker exposure to the virus and care delivery burden during a period of staffing crisis. In April 2020, the United States Food and Drug Administration (FDA) issued a statement indicating they would permit the use of these systems within the hospital setting during the pandemic.\u003csup\u003e2\u003c/sup\u003e Since then, CGM has been integrated into routine care within The Ohio State University Wexner Medical Center (OSUWMC) medical intensive care unit (MICU) and with safe and effective use demonstrated in over 180 patients.\u003csup\u003e3,4\u003c/sup\u003e OSUWMC crafted a Hybrid CGM\u0026thinsp;+\u0026thinsp;point-of-care (POC) protocol in which initial and ongoing POC glucose measurements were used to validate CGM accuracy.\u003csup\u003e3\u003c/sup\u003e In concert, other health systems across the United States have used inpatient CGM and disseminated valuable real-world data that demonstrates safe and effective inpatient use of the technology.\u003csup\u003e5\u0026ndash;11\u003c/sup\u003e Across all of these studies, no adverse events were reported. While these studies did demonstrate a reduction in POC glucose testing, reasonable accuracy, and safe use among critically ill COVID-19 patient, they were limited by small sample sizes and a restricted patient population.\u003c/p\u003e \u003cp\u003eAt our institution we examined data from the initial cohort of 19 COVID-19 patients to use CGM in OSUWMC MICU. Despite the severity of critical illness, mean absolute relative difference (MARD) was 13.9% with no apparent association with oxygen saturation, mean arterial pressure, vasopressor use, renal replacement, anticoagulation, or ventilator support. Time in range (70\u0026ndash;180 mg/dL ) on day 1 was 64+/- 23%, and on days 2\u0026ndash;7 was 72+/-16%. Time below range (\u0026lt;\u0026thinsp;70 mg/dL ) was 1.5+/-4.1% on day 1 and 0.16+/-0.35% on days 2\u0026ndash;7 \u003csup\u003e12\u003c/sup\u003e. We were also able to conduct a multi-site retrospective analysis examining CGM safety and clinical outcomes for COVID-19 patients using a Hybrid POC\u0026thinsp;+\u0026thinsp;CGM protocol. We analyzed data from 169 MICU patients hospitalized at 3 medical centers. The median percent CGM time in range (TIR, 70\u0026ndash;180 mg/dL ) was 64% and median time below 70 mg/dL was \u0026lt;\u0026thinsp;0.1%. The absolute relative difference between CGM and POC did not correlate with the lowest pAO2, oxygen saturation, pH, or mean arterial pressure. Across other studies, MARD ranged between 11.1% and 13.9% for Dexcom G6\u003csup\u003e7,11,12\u003c/sup\u003e and Clarke error grid analysis showed around 98% of values in zones A\u0026thinsp;+\u0026thinsp;B.\u003csup\u003e7,11\u003c/sup\u003e In a recent larger study there was no association between decline in accuracy with hyperglycemia but MARD was slightly higher (18.8%) with glucose\u0026thinsp;\u0026lt;\u0026thinsp;70mg/dL .\u003csup\u003e13\u003c/sup\u003e However, there is evidence that accuracy can be offset by increased frequency of monitoring, as is the case with CGM.\u003csup\u003e14\u003c/sup\u003e In a study by Boom et al. using an older technology focused on hypoglycemia reduction, 177 medical ICU patients were randomized to CGM or standard of care. In this study, hypoglycemia was reduced from 12 times per day to fewer than 1 time per day and overall glucose control was similar between groups.\u003csup\u003e15\u003c/sup\u003e Inpatient CGM holds great promise to improve patient outcomes, hospital workflow, and reduce hospital staff burden. This research will help expand our understanding of CGM safety and efficacy in the hospital setting and will help create a blueprint for successful hospital CGM implementation.\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eThe objectives of this clinical trial are as follows: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Establish the clinical utility, fidelity, and adoption of Dexcom G6 CGM as a tool for making dosing decisions within a CGM plus (+) POC protocol among MICU patients; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Assess the effects of CGM implementation on factors influencing nursing care delivery through surveys administered to MICU nurses and through a MICU staff nurse focus group (N\u0026thinsp;=\u0026thinsp;10); (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e3\u003c/span\u003e) To assess glycemic control among patients receiving CGM in the CGM enabled MICU compared to historical control patients who received POC glucose monitoring in the MICU; and (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e4\u003c/span\u003e) (exploratory) To assess hospitalization outcomes and conduct economic evaluation of the costs to deliver CGM implementation in the MICU.\u003c/p\u003e \u003c/div\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eTrial design\u003c/h2\u003e \u003cp\u003eThis is a single arm mixed methods hybrid II effectiveness-implementation study that will examine patient outcomes and feasibility of CGM implementation using a CGM\u0026thinsp;+\u0026thinsp;POC protocol among 100 patients on IV insulin in a single Midwest academic medical center MICU. This hybrid research design combines elements of effectiveness and implementation research that will be used to enable more rapid adoption of CGM as standard of care. The use of this design allows us to craft a blueprint for successful deployment of CGM within health systems, while at the same time gathering valuable real-world effectiveness data. It also allows us to examine how clinical outcomes relate to levels of adoption and fidelity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eRegulatory processes\u003c/h2\u003e \u003cp\u003eBecause CGM does not have expanded indication for inpatient use an Investigational Device Exemption (IDE) was obtained. In collaboration with the FDA, several modifications were made to the protocol to enhance patient safety resulting in a curtailed Hybrid CGM\u0026thinsp;+\u0026thinsp;POC protocol. The most significant changes included (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) increased POC glucose monitoring frequency from Q6 to Q4 hours; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2\u003c/span\u003e) more frequent testing at Q2 or Q1 hours during periods of clinical change that could precede a decline in CGM accuracy, and (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e3\u003c/span\u003e) exclusion of patients with more severe illness (i.e., diabetic ketoacidosis [DKA], hyperosmolar non-ketoacidosis [HONK], refractory shock. In addition, increased surveillance measures were added with daily data collection to facilitate rapid identification of protocol deviations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eSetting\u003c/h2\u003e \u003cp\u003eOSUWMC has approximately 13,000 patient hospitalizations with a diagnosis code for diabetes each year. OSUWMC has implemented a comprehensive, hospital-wide inpatient diabetes program since 2006, including computerized diabetes related order sets, carbohydrate-based insulin dosing, nursing education, and ongoing review of outcomes. The study will take place in the MICU which contains 48 beds and employs 152 staff nurses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eEligibility criteria\u003c/h2\u003e \u003cp\u003eEligible individuals include: adults\u0026thinsp;\u0026gt;\u0026thinsp;18 years old, who are admitted to OSUWMC MICU and have hyperglycemia (glucose\u0026thinsp;\u0026gt;\u0026thinsp;250mg/dL) or are currently on IV insulin. Exclusion criteria include: current COVID-19 infection, refractory shock (Levophed dose\u0026thinsp;\u0026gt;\u0026thinsp;0.5 mcg/kg/min or equivalent), active treatment for DKA or (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e4\u003c/span\u003e) HONK, pitting edema, anasarca, or discoloration to left upper extremity, high dose acetaminophen treatment (\u0026gt;\u0026thinsp;1 gram Q6 hours), hydroxyurea use, pregnancy, home insulin pump therapy during hospitalization, or residence in a corrections institution.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eRecruitment\u003c/h2\u003e \u003cp\u003eCritical care clinical research coordinators will identify patients meeting eligibility criteria through daily screening of the MICU census and forward potential participant names to the study team using a secure medical record in-basket process. The study team will determine eligibility and seek permission from the critical care team to approach potential patients. The study team will obtain informed consent or assent from the patient or the patient\u0026rsquo;s legally authorized representative prior to any study procedures. If the patient is alert but does not have the capacity to provide consent the patient may still be approached to provide assent. If over the course of participation, the participant regains capacity they will be approached to provide consent in the study. Recruitment is expected at a rate of 5 patients per week over seven months. Additional permission will be sought from patients or legally authorized representative for the continued use and storage of de-identified data for research purposes by the funder, Dexcom LLC.\u003c/p\u003e \u003cp\u003eAll MICU staff nurses (N\u0026thinsp;=\u0026thinsp;152) will be invited to participate in surveys via email using REDCap. Nurses will also be invited via email to participate in a focus group (N\u0026thinsp;=\u0026thinsp;10) to gauge their experience using CGM.\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eSample size\u003c/h2\u003e \u003cp\u003eThe sample size is 100 participants. The primary outcome for the power analysis is the percentage of time spent in target (70-180mg/dL). We estimate from previous studies\u003csup\u003e15,16\u003c/sup\u003e a standard deviation as high as 25 percentage points, in which case 100 patients in each group we will provide 80% power to detect a difference of 10 percentage points in time in range (e.g., 65 vs. 75); a moderate effect size of 0.4. All power calculations assume an alpha of 0.05 and minimal statistical effects of clustering within the hospitals (e.g., unit or nurse level).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003eInterventions\u003c/h2\u003e \u003cp\u003eThe Dexcom G6 CGM intervention is used within the hybrid CGM\u0026thinsp;+\u0026thinsp;POC glucose monitoring protocol and compared to 100 historical control patients from the same MICU who receive only POC glucose monitoring.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eContinuous Glucose Monitoring Technology\u003c/span\u003e. The Dexcom G6 measures interstitial fluid glucose and consists of 3 key parts: the sensor, a transmitter, and a display device. The sensor is inserted just under the skin, is worn for up to 10 days, and measures glucose levels. In this study the sensor will be worn on the back of the arm. The transmitter attaches to the sensor pod and sends glucose information to the display device using Bluetooth. Interstitial glucose concentration estimates are sent from the transmitter to the receiving device (Android phone) at 5minute intervals in real time.\u003c/p\u003e \u003cp\u003eThe G6 app on the receiver is Bluetooth paired with the transmitter before use. The app continuously and automatically sends data to the Dexcom remote server and is accessible through the CLARITY diabetes management software. Alarms for hypoglycemia, hyperglycemia and predicted hypoglycemia will be set per the CGM\u0026thinsp;+\u0026thinsp;POC protocol. Android phones are kept just outside of the patient rooms. Treatment decisions will not be made using data displayed on this software.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eHybrid CGM\u0026thinsp;+\u0026thinsp;POC Protocol.\u003c/span\u003e The CGM\u0026thinsp;+\u0026thinsp;POC protocol requires comparison of paired sensor-meter readings. The comparison standard method (Novo StatStrip POC meter) and source (capillary, arterial, venous) are FDA approved for inpatient ICU use. The standard is compared to the CGM value obtained within 5 minutes. The threshold criterion for nonadjunctive (stand-alone) use of the CGM to inform insulin dosing decisions appears in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Alert thresholds are set at 100 mg/dl (lower threshold) and 300 mg/dl (upper threshold). In addition, the Urgent Low Soon alert will be activated and designed to provide a 20-minute advance warning of impending hypoglycemia.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInitial and Ongoing CGM Validation\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePOC glucose testing procedures\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCGM validation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePOC glucose testing Q1 hour compared to CGM glucose\u003c/p\u003e \u003cp\u003eProceed to Q4 hour POC testing when \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003etwo\u003c/span\u003e consecutive sensor-meter pairs approximately 1-hour apart meet either of the following criteria:\u003c/p\u003e \u003cp\u003e1) CGM\u0026thinsp;\u0026lt;\u0026thinsp;20% difference from the POC when the glucose is \u0026ge;\u0026thinsp;100 mg/dl\u003c/p\u003e \u003cp\u003e2) CGM\u0026thinsp;\u0026lt;\u0026thinsp;20 mg/dl difference from the POC when the glucose is \u0026lt;\u0026thinsp;100 mg/dl\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSustained use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRevert from Q4 hour to Q1 hour POC testing if:\u003c/p\u003e \u003cp\u003e1) CGM\u0026thinsp;\u0026gt;\u0026thinsp;20% difference from the POC when the glucose is \u0026ge;\u0026thinsp;100 mg/dl\u003c/p\u003e \u003cp\u003e2) CGM\u0026thinsp;\u0026gt;\u0026thinsp;20 mg/dl difference from the POC when the glucose is \u0026lt;\u0026thinsp;100 mg/dl\u003c/p\u003e \u003cp\u003e3) Revert back to Q4 hour POC testing when \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003etwo\u003c/span\u003e consecutive sensor-meter pairs approximately 1-hour apart meet the initial validation criteria\u003c/p\u003e \u003cp\u003eRevert from Q4 hour to Q2 hour POC testing for a duration of 6 hours for one of the following clinical status events occurring in isolation:\u003c/p\u003e \u003cp\u003e4) Intubation\u003c/p\u003e \u003cp\u003e5) Pressor support initiated (Levophed dose\u0026thinsp;\u0026lt;\u0026thinsp;0.5 mcg/kg/min or equivalent)\u003c/p\u003e \u003cp\u003e6) New cardiovascular event (MI, CVA)\u003c/p\u003e \u003cp\u003e7) Initiation or discontinuation of nutrition support (i.e., enteral feed, total parenteral nutrition)\u003c/p\u003e \u003cp\u003e8) Hemoglobin\u0026thinsp;\u0026lt;\u0026thinsp;7g/dL\u003c/p\u003e \u003cp\u003e9) CGM or POC glucose\u0026thinsp;\u0026lt;\u0026thinsp;70mg/dl (follow OSUWMC hypoglycemia policy for initial treatment and monitoring)\u003c/p\u003e \u003cp\u003e10) Predicted low alert (glucose predicted to be \u0026lt;\u0026thinsp;55mg/dl in the following 20 minutes)\u003c/p\u003e \u003cp\u003e11) Acidosis with pH\u0026thinsp;\u0026lt;\u0026thinsp;7.3\u003c/p\u003e \u003cp\u003e12) Signs and symptoms do not match glucose readings, particularly for hypoglycemia\u003c/p\u003e \u003cp\u003eRevert from Q4 hour to Q1 hour POC testing for a duration of 6 hours for two or more of the above clinical status events occurring together (example: patient is intubated and starts pressor support)\u003c/p\u003e \u003cp\u003eIf after 6 hours no additional clinical scenarios featured above have developed then Q4 hour POC testing can resume after initial validation using two consecutive sensor-meter pairs.\u003c/p\u003e \u003cp\u003eObtain 1 time POC glucose if:\u003c/p\u003e \u003cp\u003e13) No glucose value appears on android screen (due to signal loss, Low/High measure)\u003c/p\u003e \u003cp\u003e14) Low threshold alert (\u0026lt;\u0026thinsp;100mg/dl)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStop CGM use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStop use of CGM for insulin titration or glucose monitoring and revert from Q4 hour to Q1 hour POC testing (\u003cb\u003edo not remove CGM sensor until sensor expires\u003c/b\u003e) for the following conditions:\u003c/p\u003e \u003cp\u003e1) Refractory shock (Levophed dose\u0026thinsp;\u0026gt;\u0026thinsp;0.5 mcg/kg/min or equivalent)\u003c/p\u003e \u003cp\u003e2) Cardiac arrest\u003c/p\u003e \u003cp\u003e3) Newly developed diabetic ketoacidosis (DKA) (pH\u0026thinsp;\u0026lt;\u0026thinsp;7.3 or serum bicarbonate\u0026thinsp;\u0026lt;\u0026thinsp;15 mEq/L in the setting of elevated ketones)\u003c/p\u003e \u003cp\u003e4) Newly developed hyperosmolar non-ketoacidosis (HONK)\u003c/p\u003e \u003cp\u003e5) Pitting edema, anasarca, blue or purple discoloration to bilateral upper extremity\u003c/p\u003e \u003cp\u003e6) Initiation of treatment with high dose acetaminophen (\u0026gt;\u0026thinsp;1 gram Q6 hours)\u003c/p\u003e \u003cp\u003e7) Initiation of treatment with hydroxyurea\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eParticipant timeline\u003c/h2\u003e \u003cp\u003eOnce consent is obtained, the MICU staff nurse will insert the Dexcom G6 sensor, set up the system, and pair the transmitter. The patient will remain on CGM for the duration of their MICU. The period of data collection and trial participation is isolated to only the MICU stay. The Dexcom G6 sensor is replaced every 10 days or sooner in case of removal for radiographic procedures or sensor failure.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCriteria for discontinuing or modifying the intervention\u003c/h2\u003e \u003cp\u003eWe will discontinue study participation for reasons related to unacceptable sensor error or serious safety concerns. Sensor errors are defined as: Failure to meet initial validation criteria within 24 hours in 20% of participants or episodes of the following after initial sensor validation: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Failure to detect clinically significant hypoglycemia (CGM value\u0026thinsp;\u0026gt;\u0026thinsp;100 mg/dL with no Urgent Low Soon alert while POC BG\u0026thinsp;\u0026lt;\u0026thinsp;70 mg/dL) (\u0026ge; 2 episodes); (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Failure to detect severe hyperglycemia (CGM value\u0026thinsp;\u0026lt;\u0026thinsp;180 mg/dL when POC BG\u0026thinsp;\u0026gt;\u0026thinsp;400 mg dL) (\u0026ge; 2 episodes); or (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e3\u003c/span\u003e) Inappropriate treatment CGM value triggers the opposite action than the POC BG value (CGM\u0026thinsp;\u0026gt;\u0026thinsp;180 mg/dL while POC BG\u0026thinsp;\u0026lt;\u0026thinsp;70 mg/dL or vice versa).\u003c/p\u003e \u003cp\u003eSerious safety concerns are defined as: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) inappropriate insulin dose resulting in clinically significant hypoglycemia (glucose\u0026thinsp;\u0026lt;\u0026thinsp;55mg/dL) after initial sensor validation (\u0026ge; 2 episodes), (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2\u003c/span\u003e) iatrogenic DKA or HONK (\u0026ge; 2 episodes), (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e3\u003c/span\u003e) severe adverse events resulting in prolonged hospitalization (\u0026ge; 2 episodes), any severe adverse event resulting in death, or (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e4\u003c/span\u003e) other life-threatening complication that is attributed to the study intervention.\u003c/p\u003e \u003cp\u003ePatient participation will cease if there are two sensors fail to validate, if the patient transitions to hospice care, or the participant or the participant\u0026rsquo;s legally authorized representative withdraws consent.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStrategies to improve adherence to intervention\u003c/h2\u003e \u003cp\u003eThe study will be conducted using a staggered enrolment of 20 participants in each wave (20% of target enrolment) with a respite between waves to allow analysis of adherence, safety, and fidelity data prior to continued enrolment. The initial wave (n\u0026thinsp;=\u0026thinsp;20) will be conducted as a \u0026ldquo;pilot\u0026rdquo; with full analysis including examination of benchmark criteria for study continuation. Study team oversight of nursing use of the CGM system will be performed through a combination of daily (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) evaluation of EHR and CGM Clarity data and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2\u003c/span\u003e) rounding on CGM patients. The study staff will evaluate CGM use and nursing adherence to Hybrid CGM\u0026thinsp;+\u0026thinsp;POC protocol criteria (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEach nurse in the MICU receives education and training on CGM set-up, insertion, pairing, and use as part of their initial and annual critical care competencies. In addition, nurses will receive information to delineate distinctions between the original COVID-19 protocol and the study protocol. New criteria to stop clinical use will be reviewed in detail with nurses. Nursing education and training will be conducted during staff meetings and nursing huddles. Additional implementation strategies to support fidelity to the treatment protocol include use of audit and feedback, local champions, daily clinical reminders and supervision, real time relay of clinical data, and ongoing consultation with the study team. The study protocol, along with education and training materials, will be kept at the bedside of all patient participants.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eRelevant concomitant care permitted or prohibited during the trial\u003c/h2\u003e \u003cp\u003eOther than changes to the frequency of POC testing according to the Hybrid CGM\u0026thinsp;+\u0026thinsp;POC protocol, participants will receive all standard care provisions. All OSUWMC standard insulin infusion and subcutaneous insulin dosing guidelines, protocols, order sets, and hypoglycemia guidelines will be followed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eOutcomes\u003c/h2\u003e \u003cp\u003eThis hybrid effectiveness- implementation study will evaluate both implementation and patient clinical outcomes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eImplementation outcome measures\u003c/h2\u003e \u003cp\u003eOur implementation outcomes include: RQ1, establishing the clinical utility, fidelity, and adoption of CGM as a tool for making dosing decisions within a Hybrid CGM\u0026thinsp;+\u0026thinsp;POC protocol and RQ2, assess CGM implementation effects on nursing care delivery.\u003c/p\u003e \u003cp\u003eIn RQ1 \u003cem\u003eclinical utility outcomes include\u003c/em\u003e time to CGM validation, mean percent of dosing decisions determined by CGM, changes in insulin dosing in response to alarm and/or trend data. \u003cem\u003eFidelity outcomes include\u003c/em\u003e the proportion of times CGM is used non-adjunctively/number of times non-adjunctive use indicated per protocol. \u003cem\u003eAdoption criteria outcomes include\u003c/em\u003e proportion of patients approached to receive CGM monitoring/number of patients eligible. We hypothesize that the majority of insulin dosing decisions will be made using non-adjunctive CGM and nursing will alter dosing decisions based on CGM data (e.g., glucose value, glucose trend/trajectory, alarms and alerts).\u003c/p\u003e \u003cp\u003eFor RQ2, we will conduct a mixed method analysis to evaluate nursing care delivery factors. Nursing care delivery factors, including acceptance and appropriateness of CGM, perceived feasibility, and CGM satisfaction and knowledge, will be evaluated using a combination of questionnaires and focus groups. Perceived acceptance, appropriateness, and feasibility will be measured using the Acceptability of Intervention Measure (AIM), Intervention Appropriateness Measure (IAM), and Feasibility of Intervention Measure (FIM). The AIM, IAM, and FIM are brief 5 item Likert-scale questionnaires with each showing a high degree of content validity with alphas for the 3 questionnaires between 0.87 and 0.89.\u003csup\u003e17\u003c/sup\u003e Satisfaction will be measured using the CGM Satisfaction Questionnaire (CGM-SAT), a 44-item tool measuring satisfaction with CGM over the previous 6 months. The survey has a high degree of validity (a\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\ge\\)\u003c/span\u003e\u003c/span\u003e0.94) in adults with type 1 diabetes using CGM.\u003csup\u003e18\u003c/sup\u003e Several items will be modified for application in healthcare workers. For these quantitative surveys, all MICU staff nurses (N=152) will be informed of the study and invited to participate via email. Upon e-consent, the questionnaires will be administered via a REDCap online survey. In addition to this quantitative information, additional qualitative information on knowledge and experiences with CGM will be gathered via a focus group among MICU staff nurses (N\u0026thinsp;=\u0026thinsp;10). A semi-structured interview guide will focus on evaluation of knowledge regarding CGM, as well as areas of perceived CGM implementation support and education, and recommendations for future best practice. All RQ1 and RQ2 variables and measures appear in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eClinical outcome measures\u003c/h2\u003e \u003cp\u003eIn RQ3 we assess glycemic control among patients receiving CGM compared to historical control patients who received POC glucose monitoring in the MICU. Our primary outcome for RQ3 is time in glucose range (100\u0026ndash;180 mg/dl, 70-180mg/dl, 140-180mg/dl). We hypothesize that patients using CGM will exhibit greater time in range and experience less frequent time in hypoglycemia than patients on fingerstick POC. Individual participant discrete data will be downloaded directly from the Clarity site. Historical controls meeting the gateway criteria (glucose\u0026thinsp;\u0026gt;\u0026thinsp;250mg/dl or on IV insulin during admission) from the same MICU will be matched on the following factors: Diagnosis and type of diabetes, home insulin use, steroid use during hospitalization, vasopressor use, and North vs. South MICU wings. POC glucose values for historical controls (N\u0026thinsp;=\u0026thinsp;100) will be obtained directly from OSUWMC EHR. EHR data will be obtained through an information warehouse request providing time stamped, POC glucose values for each matched patient. Hyperglycemic and hypoglycemic events will be identified for CGM and non-CGM users. Discrete CGM glucose values will be used to aggregate derived measures of glucose variability, including time in target (blood glucose value of 70-180mg/dl), time 100-180mg/dl (representing a more conservative target used inpatient), time 140-180mg/dl (representing institutions IV insulin guideline glycemic target), time above target (\u0026gt;\u0026thinsp;180mg/dl \u0026amp; \u0026gt;250mg/dl), time in hypoglycemia (\u0026lt;\u0026thinsp;70mg/dl \u0026amp; \u0026lt;55mg/dl), glucose standard deviation, and coefficient of variation. Daily percent time in target as well as cumulative time in target will be assessed.\u003csup\u003e19\u0026ndash;22\u003c/sup\u003e Measures of hypoglycemia will be in accordance with ADA recommendations for measurement at \u0026lt;\u0026thinsp;70mg/dl and \u0026lt;\u0026thinsp;55mg/dl which is a marker of clinically significant hypoglycemia.\u003csup\u003e23\u003c/sup\u003e Days with \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\ge\\)\u003c/span\u003e\u003c/span\u003e70% CGM percent wear will be included in daily time in target analysis.\u003csup\u003e23\u003c/sup\u003e For matched historical control patients, percent POC in target (70-180mg/dl), 100-180mg/dl, 140-180mg/dl, \u0026gt;180mg/dl, \u0026gt;250mg/dl, \u0026lt;70mg/dl and \u0026lt;55mg/dl will be evaluated consistent with other recent studies.\u003csup\u003e24,25\u003c/sup\u003e All RQ3 variables and measures appear in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eExploratory outcome\u003c/h2\u003e \u003cp\u003eIn our exploratory aim, RQ4, we will examine the impact of CGM therapy on patient hospital outcomes and costs. In this aim, we seek to analyze the economic aspects of inpatient CGM on patient health and healthcare interactions, with a focus on the costs (inputs) and consequences (outcomes) of CGM. Data will be retrospectively extracted from the EHR for patients using CGM in the MICU (N\u0026thinsp;=\u0026thinsp;100) along with 100 historical controls. \u003cem\u003eHospital outcomes criteria\u003c/em\u003e include the following: Length of stay [ICU, total stay], cost of stay, discharge level of care [home, SNF], morbidity, mortality and readmission rate at 30 days. We will conduct economic evaluation of the costs to deliver CGM implementation in the MICU. We will track resources needed for CGM implementation including personnel, training, facilities, materials, equipment, and other necessary inputs. In addition to resources used, we will expand analysis to evaluate nursing productivity and burden. \u003cem\u003eResource outcomes criteria\u003c/em\u003e include cost of CGM vs. standard POC glucose monitoring materials and frequency of use. The exploratory variables and measures appear in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics of central tendency (range, mean, median, and standard deviation) will describe variable characteristics and examine data distribution for normality and outliers. Descriptive statistics will summarize the sample characteristics and distribution of each variable. Data will be screened for normality, outliers, and homogeneity. Continuous variables with normal distribution will be reported as mean (standard deviation) while non-normal distribution will be reported as median (interquartile range). For RQ1, multivariate linear models will be used to evaluate clinical utility and implementation variables while examining the effects of patient condition as covaries. For RQ 2, For qualitative analysis of focus group data, a code book will be developed a priori based on the semi-structured interview questions. Interview data, field notes and memos will be imported into NVivo 12.0 (Doncaster, Australia) for data management and analyzed using a qualitative descriptive approach.\u003csup\u003e26,27\u003c/sup\u003e Two researchers (Faulds and McNett) will perform qualitative analysis. Portions of text will be coded with terms that were low inference (\u0026ldquo;data close\u0026rdquo;); then grouped into thematic categories and subthemes.\u003csup\u003e26\u003c/sup\u003e For RQ3, for CGM daily glycemic control (i.e., time in range [70-180mg/dl], linear mixed effect modeling (LMM) for repeated measures will be used to adjust for between subject and within subject variance. A 3-tiered LMM will be used to control for patient, unit, and nurse specific variability. LMM will be repeated for analysis of daily POC glucose control for matched historical control patients for days with \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\ge\\)\u003c/span\u003e\u003c/span\u003e3 POC fingersticks performed. Two-sided significance level of 0.05 was used for all the statistical tests. For RQ4, the cost analysis for the proposed study will be conducted from the provider perspective. Cost estimation involves three major steps: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) identify the relevant cost items; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2\u003c/span\u003e) measure the use of resources; and (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e3\u003c/span\u003e) place a value on the resources used. We will obtain estimated costs for proposed CGM implementation. Direct benefits and savings tend to fall into one of two categories: either savings from enhanced efficiency and productivity, or savings from outcomes improvement (e.g., reducing length of stay, lower 30-day readmissions). Costs that do not vary with the number of patients will be categorized as fixed costs, whereas those that vary by the number of patients will be defined as variable costs. The costs of the CGM implementation will be compared to costs under a control scenario. We will estimate the potential costs and cost savings resulting from the CGM implementation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eProtocol amendments and regulatory oversight\u003c/h2\u003e \u003cp\u003eA Data Safety Monitoring Board (DSMB) will oversee safety activity and evaluating all salient participant outcome, protocol fidelity, and recruitment data after each round of 20 participants and at a minimum of every 6 months. Any protocol changes will be communicated by the PI to the study sponsor, the FDA, and the DSMB. Proposed protocol changes will be submitted to the institution\u0026rsquo;s Institutional Review Board (IRB) for review and approval.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePatient Participant Schedule of Activities\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResearch Question\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConcept\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMeasure\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eRQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eClinical utility criteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTime to CGM validation (RQ1 only)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSensor placement (Q10 days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026bull; EHR\u003c/p\u003e \u003cp\u003e\u0026bull; Clarity download\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean percent of dosing decisions determined by CGM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDaily\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChanges in insulin dosing from guideline or outside standard times in response to alarm and/or trend data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDaily\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFidelity \u0026amp; Adoption Implementation criteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProportion of times CGM initiated or patient approached for study inclusion according to protocol/total number of initiations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdmission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026bull; EHR\u003c/p\u003e \u003cp\u003e\u0026bull; Study recruitment log\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProportion of patients approached to received GCM monitoring/number of patients eligible to receive initial CGM monitoring\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdmission\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"11\" rowspan=\"12\"\u003e \u003cp\u003eRQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"11\" rowspan=\"12\"\u003e \u003cp\u003ePatient condition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDemographic data: Age, race/ethnicity, gender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdmission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"11\" rowspan=\"12\"\u003e \u003cp\u003e\u0026bull; EHR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHeight, weight, and BMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdmission\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDiagnosis and type of diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdmission\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHome diabetes regimen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdmission\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal daily insulin dose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDaily\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePast medical history: tobacco use, COPD, hypertension, heart failure, coronary artery disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdmission\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eClinical condition: Admission/inpatient glucose, HbA1c, admission diagnosis, admitting service, MICU location (North vs. South), DKA/HHS, sepsis, acute liver failure, acute heart failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdmission\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eClinical condition: SOFA score, dialysis, thromboembolic events, ECMO mechanical ventilation, ICU/hospital length of stay, cardiac arrest, cerebrovascular accident, mortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDaily\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedications: vasopressor use, steroids, anticoagulants, acetaminophen dose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDaily\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCreatinine, eGFR, ALT, AST, TBR, WBC, procalcitonin, ferritin, CRP, IL6, D-dimer, PTT, INR, troponin, pH, BHB, bicarbonate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDaily\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEnteral or parenteral nutrition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDaily\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eO2 sat, pAO2, blood pressure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDaily\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"11\" rowspan=\"12\"\u003e \u003cp\u003eRQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"11\" rowspan=\"12\"\u003e \u003cp\u003eGlycemic control\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTime in target for CGM patients (70-180mg/dl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDaily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"11\" rowspan=\"12\"\u003e \u003cp\u003e\u0026bull; Clarity\u003c/p\u003e \u003cp\u003e\u0026bull; EHR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTime 100-180mg/dl for CGM patients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDaily\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTime 140-180mg/dl for CGM patients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDaily\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTime above range for CGM patients (\u0026gt;\u0026thinsp;180mg/d \u0026amp; \u0026gt;250mg/dl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDaily\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTime in hypoglycemia for CGM patients (\u0026lt;\u0026thinsp;70mg/dl \u0026amp; \u0026lt;55mg/dl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDaily\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercent POC in target (70-180mg/dl) (matched controls)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDaily\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercent POC 100-180mg/dl (matched controls)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDaily\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercent POC 140-180mg/dl (matched controls)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDaily\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercent POC above range (\u0026gt;\u0026thinsp;180mg/dl \u0026amp; \u0026gt;250mg/dl) (matched controls)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDaily\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercent POC in hypoglycemia (\u0026lt;\u0026thinsp;70mg/dl \u0026amp; \u0026lt;55mg/dl) (matched controls)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDaily\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGlucose standard deviation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDaily\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGlucose coefficient of variation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDaily\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eRQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eHospitalization outcomes and costs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLength of stay [ICU, total stay]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDischarge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e\u0026bull; EHR\u003c/p\u003e \u003cp\u003e\u0026bull; Clarity download\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCost of stay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDischarge\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDischarge level of care [home, SNF]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDischarge\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMorbidity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDischarge\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDischarge\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReadmission rate [30 days]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30d s/p discharge\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCost of CGM vs. standard POC glucose monitoring materials.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDischarge\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNurse Participant Schedule of Activities\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResearch Question\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConcept\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMeasure\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNursing care delivery factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEvaluation of CGM support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOnce\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026bull; Acceptability of Intervention Measure (AIM), Intervention Appropriateness Measure (IAM), and Feasibility of Intervention Measure (FIM)\u003c/p\u003e \u003cp\u003e\u0026bull; CGM Satisfaction Questionnaire\u003c/p\u003e \u003cp\u003e\u0026bull; Focus groups\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eDespite rapid innovation in glucose monitoring in the outpatient setting, inpatient glucose monitoring has changed little over the last 30 years. POC glucose monitoring has been the standard of care in the hospital. While accuracy of critical care specific meters is excellent, there remains long standing implementation challenges associated with inpatient POC use where missed and mistimed testing remain common occurrences.\u003csup\u003e28,29\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThis trial\u0026rsquo;s dual focus on clinical and implementation outcomes facilitates the collection of data essential for widespread deployment of CGM in the hospital setting. The inpatient setting is a distinct use case from traditional patient use of CGM in the outpatient environment. Little is known about the way healthcare workers, and particularly nurses, consume CGM data or utilize CGM technology. Furthermore, CGM will be integrated into the busy hospital technology milieu, making it essential that we study nursing interaction with CGM within the hospital environment. How nurses respond to alarms and alerts is of particular importance. Alarm fatigue is a concerning phenomenon in the inpatient setting and can lead to alarm unresponsiveness or desensitization.\u003csup\u003e30\u003c/sup\u003e Our study examines nurse response to alerts and alarms and will lay the foundation for ongoing work focused on optimal alarm frequency, threshold settings, and response. Additionally, our study\u0026rsquo;s focus on economic impact is important for healthcare systems as they consider the costs and benefits associated with inpatient CGM utilization, if and when expanded indication to the hospital setting is approved.\u003c/p\u003e \u003cp\u003eThe study\u0026rsquo;s requisite real-world design is essential for the evaluation of CGM implementation; however, it presents limitations in evaluating CGM accuracy. Our study uses POC glucose testing for validating CGM accuracy within our Hybrid CGM\u0026thinsp;+\u0026thinsp;POC protocol. The gold-standard for evaluating accuracy of glucose monitoring technologies is YSI serum glucose testing; however, this type of evaluation is not feasible in bedside patient environments. OSUWMC uses the NovoStat meter, the most accurate FDA approved critical care meter on the market; however, there is still error within the technology which will limit our ability to draw conclusions regarding inpatient CGM accuracy.\u003c/p\u003e \u003cp\u003eThe regulatory impact on study design is certainly worth discussing and should be a consideration as researchers work to design other implementation studies. Several aspects of the study, including POC frequency, exclusion criteria [e.g., DKA, HHS], and training techniques, were shaped in concert with the FDA. While patient safety is of the upmost importance in clinical trials, especially those involving technologies without FDA approval, the requisite oversight and safety provisions may limit real-world application.\u003c/p\u003e \u003cp\u003eBy the end of the trial, we expect to have a blueprint for successful deployment of CGM use within health systems. Our evaluation of training and implementation strategies, nursing workflow, and nurses\u0026rsquo; CGM-based clinical decision making will guide future efforts to deploy CGM as the standard of care in the inpatient setting. At the same time, we will have valuable data on real-world effectiveness of inpatient CGM with an examination of changes in patient outcomes, such as glycemic control, morbidity, mortality, and length of stay.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003ePrior to starting the study, the protocol was reviewed by both the FDA and The Ohio State University\u0026rsquo;s IRB. The researchers worked with the FDA during the approval process to ensure measures were taken to minimize risk and avoid patient harm, especially given the vulnerability of the patient population. As detailed, participants or their legally authorized representative are required to provide written consent prior to study participation. Data will be disseminated to ClinicalTrials.gov and to the community through peer-reviewed journals and presentations at national and international diabetes conferences. The study is sponsored by a peer reviewed, investigator initiated, industry award through Dexcom LLC. The funding source did not play any role in designing the trial, implementing the trial, writing the report or making the decision to submit the report for publication.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthor Contributions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: ERF and KD. Methodology: ERF, KD, MM, ME, AS, and CL. Statistical plan: ERF and AS. Coordination of the study implementation: ERF, MM, BL, AR, RM and LJ. Investigation: ERF, KD, and MM. Data curation: ERF and AS. Writing (original draft preparation): BL and ERF. Writing (review and editing): ERF, KD, MM, ME, CL, LJ, and BL. Project administration: BL and AR. Funding acquisition: ERF. All named authors have read and approved the final manuscript, adhere to the authorship guidelines of Trials and have agreed to publication.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eYang X YY, Xu J, et al. , doi: FpS--. Clinical course and outcomes of critically ill patients with SARSCoV-2 pneumonia in Wuhan, China: a single-centered, retrospective, observational study. \u003cem\u003eLancet Respir Med.\u003c/em\u003e20:S2213-2600.\u003c/li\u003e\n\u003cli\u003eAdministration. USFaD. FDA news release: FDA expands indication for continuous glucose monitoring system, first to replace fingerstick testing for diabetes treatment decisions, 2016. Accessed March 13, 2020, 2020.\u003c/li\u003e\n\u003cli\u003eFaulds ER, Jones L, McNett M, et al. Facilitators and Barriers to Nursing Implementation of Continuous Glucose Monitoring (CGM) in Critically Ill Patients With COVID-19. \u003cem\u003eEndocr Pract. \u003c/em\u003e2021;27(4):354-361.\u003c/li\u003e\n\u003cli\u003eFaulds ER, Boutsicaris A, Sumner L, et al. Use of Continuous Glucose Monitor in Critically Ill COVID-19 Patients Requiring Insulin Infusion: An Observational Study. \u003cem\u003eJ Clin Endocrinol Metab. \u003c/em\u003e2021.\u003c/li\u003e\n\u003cli\u003eChow KW, Kelly DJ, Gupta R, Miller JD. Use of Continuous Glucose Monitoring to Assess Parenteral Nutrition-Induced Hyperglycemia in an Adult Patient With Severe COVID-19. \u003cem\u003eJPEN J Parenter Enteral Nutr. \u003c/em\u003e2021;45(1):208-211.\u003c/li\u003e\n\u003cli\u003eDavis GM, Faulds E, Walker T, et al. Remote Continuous Glucose Monitoring With a Computerized Insulin Infusion Protocol for Critically Ill Patients in a COVID-19 Medical ICU: Proof of Concept. \u003cem\u003eDiabetes Care. \u003c/em\u003e2021;44(4):1055-1058.\u003c/li\u003e\n\u003cli\u003eAgarwal S, Mathew J, Davis GM, et al. Continuous Glucose Monitoring in the Intensive Care Unit During the COVID-19 Pandemic. \u003cem\u003eDiabetes Care. \u003c/em\u003e2021;44(3):847-849.\u003c/li\u003e\n\u003cli\u003eChow KW, Kelly DJ, Rieff MC, et al. Outcomes and Healthcare Provider Perceptions of Real-Time Continuous Glucose Monitoring (rtCGM) in Patients With Diabetes and COVID-19 Admitted to the ICU. \u003cem\u003eJ Diabetes Sci Technol. \u003c/em\u003e2021;15(3):607-614.\u003c/li\u003e\n\u003cli\u003eG\u0026oacute;mez AM, Henao DC, Mu\u0026ntilde;oz OM, et al. Glycemic control metrics using flash glucose monitoring and hospital complications in patients with COVID-19. \u003cem\u003eDiabetes Metab Syndr. \u003c/em\u003e2021;15(2):499-503.\u003c/li\u003e\n\u003cli\u003eLongo RR, Elias H, Khan M, Seley JJ. Use and Accuracy of Inpatient CGM During the COVID-19 Pandemic: An Observational Study of General Medicine and ICU Patients. \u003cem\u003eJ Diabetes Sci Technol. \u003c/em\u003e2021:19322968211008446.\u003c/li\u003e\n\u003cli\u003eSadhu AR, Serrano IA, Xu J, et al. Continuous Glucose Monitoring in Critically Ill Patients With COVID-19: Results of an Emergent Pilot Study. \u003cem\u003eJ Diabetes Sci Technol. \u003c/em\u003e2020;14(6):1065-1073.\u003c/li\u003e\n\u003cli\u003eFaulds ER BA, Sumner L, Jones L, McNett M, Smetana KS, May CC, Buschur E, Exline MC, Ringel MD, Dungan K. Use of Continuous Glucose Monitor in Critically Ill COVID-19 Patients Requiring Insulin Infusion: An Observational Study. \u003cem\u003eJ Clin Endocrinol Metab. \u003c/em\u003e2021.\u003c/li\u003e\n\u003cli\u003eDavis GM, Spanakis EK, Migdal AL, et al. Accuracy of Dexcom G6 Continuous Glucose Monitoring in Non-Critically Ill Hospitalized Patients With Diabetes. \u003cem\u003eDiabetes Care. \u003c/em\u003e2021;44(7):1641-1646.\u003c/li\u003e\n\u003cli\u003eKrinsley JS, Bruns DE, Boyd JC. The impact of measurement frequency on the domains of glycemic control in the critically ill--a Monte Carlo simulation. \u003cem\u003eJ Diabetes Sci Technol. \u003c/em\u003e2015;9(2):237-245.\u003c/li\u003e\n\u003cli\u003eBoom DT, Sechterberger MK, Rijkenberg S, et al. Insulin treatment guided by subcutaneous continuous glucose monitoring compared to frequent point-of-care measurement in critically ill patients: a randomized controlled trial. \u003cem\u003eCrit Care. \u003c/em\u003e2014;18(4):453.\u003c/li\u003e\n\u003cli\u003eDe Block C, Manuel YKB, Van Gaal L, Rogiers P. Intensive insulin therapy in the intensive care unit: assessment by continuous glucose monitoring. \u003cem\u003eDiabetes Care. \u003c/em\u003e2006;29(8):1750-1756.\u003c/li\u003e\n\u003cli\u003eWeiner BJ, Lewis CC, Stanick C, et al. Psychometric assessment of three newly developed implementation outcome measures. \u003cem\u003eImplement Sci. \u003c/em\u003e2017;12(1):108.\u003c/li\u003e\n\u003cli\u003eJuvenile Diabetes Research Foundation Continuous Glucose Monitoring Study G. Validation of measures of satisfaction with and impact of continuous and conventional glucose monitoring. \u003cem\u003eDiabetes Technol Ther. \u003c/em\u003e2010;12(9):679-684.\u003c/li\u003e\n\u003cli\u003eBergenstal RM, Garg S, Weinzimer SA, et al. Safety of a Hybrid Closed-Loop Insulin Delivery System in Patients With Type 1 Diabetes. \u003cem\u003eJAMA. \u003c/em\u003e2016;316(13):1407-1408.\u003c/li\u003e\n\u003cli\u003eBeck RW, Riddlesworth T, Ruedy K, et al. Effect of Continuous Glucose Monitoring on Glycemic Control in Adults With Type 1 Diabetes Using Insulin Injections: The DIAMOND Randomized Clinical Trial. \u003cem\u003eJAMA. \u003c/em\u003e2017;317(4):371-378.\u003c/li\u003e\n\u003cli\u003eLind M, Polonsky W, Hirsch IB, et al. Continuous Glucose Monitoring vs Conventional Therapy for Glycemic Control in Adults With Type 1 Diabetes Treated With Multiple Daily Insulin Injections: The GOLD Randomized Clinical Trial. \u003cem\u003eJAMA. \u003c/em\u003e2017;317(4):379-387.\u003c/li\u003e\n\u003cli\u003eDavidson MB. Continuous Glucose Monitoring in Patients With Type 1 Diabetes Taking Insulin Injections. \u003cem\u003eJAMA. \u003c/em\u003e2017;317(4):363-364.\u003c/li\u003e\n\u003cli\u003eAmerican Diabetes A. 6. Glycemic Targets: Standards of Medical Care in Diabetes-2020. \u003cem\u003eDiabetes Care. \u003c/em\u003e2020;43(Suppl 1):S66-S76.\u003c/li\u003e\n\u003cli\u003eVinals C, Quiros C, Gimenez M, Conget I. Real-Life Management and Effectiveness of Insulin Pump with or Without Continuous Glucose Monitoring in Adults with Type 1 Diabetes. \u003cem\u003eDiabetes Ther. \u003c/em\u003e2019;10(3):929-936.\u003c/li\u003e\n\u003cli\u003eBeato-Vibora PI, Quiros-Lopez C, Lazaro-Martin L, et al. Impact of Sensor-Augmented Pump Therapy with Predictive Low-Glucose Suspend Function on Glycemic Control and Patient Satisfaction in Adults and Children with Type 1 Diabetes. \u003cem\u003eDiabetes Technol Ther. \u003c/em\u003e2018;20(11):738-743.\u003c/li\u003e\n\u003cli\u003eSandelowski M. Whatever happened to qualitative description? \u003cem\u003eRes Nurs Health. \u003c/em\u003e2000;23(4):334-340.\u003c/li\u003e\n\u003cli\u003eColorafi KJ, Evans B. Qualitative Descriptive Methods in Health Science Research. \u003cem\u003eHERD. \u003c/em\u003e2016;9(4):16-25.\u003c/li\u003e\n\u003cli\u003eJuneja R, Roudebush CP, Nasraway SA, et al. Computerized intensive insulin dosing can mitigate hypoglycemia and achieve tight glycemic control when glucose measurement is performed frequently and on time. \u003cem\u003eCrit Care. \u003c/em\u003e2009;13(5):R163.\u003c/li\u003e\n\u003cli\u003eMarvin MR, Inzucchi SE, Besterman BJ. Computerization of the Yale insulin infusion protocol and potential insights into causes of hypoglycemia with intravenous insulin. \u003cem\u003eDiabetes Technol Ther. \u003c/em\u003e2013;15(3):246-252.\u003c/li\u003e\n\u003cli\u003ePatterson ES, Rayo MF, Edworthy JR, Moffatt-Bruce SD. Applying Human Factors Engineering to Address the Telemetry Alarm Problem in a Large Medical Center. \u003cem\u003eHum Factors. \u003c/em\u003e2022;64(1):126-142.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Continuous glucose monitoring, hospital, inpatient, diabetes, implementation","lastPublishedDoi":"10.21203/rs.3.rs-4243392/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4243392/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eOver the last 2 decades, continuous glucose monitoring (CGM) has been shown to improve glycemic control and patient outcomes in the outpatient setting, yet these technologies are not approved for inpatient use. Both hyperglycemia and hypoglycemia, which are common in the inpatient setting, are associated with increased morbidity, mortality, length of stay, and hospital costs. Point-of-care (POC) glucose monitoring has been the standard of care in the hospital setting since the late 1980\u0026rsquo;s, yet POC glucose monitoring implementation remains insufficient with frequent missed testing and missed timing of testing. Capabilities inherent to CGM (e.g., continuous measurement, hypoglycemia/hyperglycemia threshold alarms, predictive hypoglycemia alarms, trend data) hold the promise of improving glycemic control, patient outcomes, and nursing burden in the inpatient setting, yet ongoing research is needed to examine both outcomes and implementation of CGM in the inpatient environment.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis mixed methods hybrid II effectiveness-implementation study will examine patient outcomes and the feasibility of CGM implementation using a CGM plus (+) POC protocol among 100 patients on IV insulin in a single Midwest academic medical center\u0026rsquo;s medical intensive care unit (MICU). In this single arm clinical trial, we are pursuing 4 research questions (RQ): RQ 1. Establish the clinical utility, fidelity, and adoption of Dexcom G6 CGM as a tool for making dosing decisions within a CGM\u0026thinsp;+\u0026thinsp;POC protocol among medical intensive care unit (MICU) patients; RQ2. Assess the effects of CGM implementation on nursing workload and factors influencing nursing care delivery through surveys administered to MICU nurses and through a MICU staff nurse focus group (N\u0026thinsp;=\u0026thinsp;10); RQ3. To assess glycemic control among patients receiving CGM in the CGM enabled MICU compared to historical control patients who received POC glucose monitoring in the MICU; RQ4. (exploratory) To assess hospitalization outcomes and conduct economic evaluation of the costs of delivering CGM implementation in the MICU.\u003c/p\u003e\u003ch2\u003eDiscussion\u003c/h2\u003e \u003cp\u003eThis trial that combines elements of effectiveness and implementation research will provide valuable data simultaneously evaluating patient outcomes and feasibility to enable more rapid adoption of CGM as standard of care.\u003c/p\u003e\u003ch2\u003eTrial registration:\u003c/h2\u003e \u003cp\u003eClinicalTrials.gov, NCT03576989; Registered on 13 June 2018.\u003c/p\u003e","manuscriptTitle":"Expanding Use of Continuous Glucose Monitoring Beyond COVID in Critical Care: Study Protocol for a Hybrid Implementation Trial","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-16 20:09:18","doi":"10.21203/rs.3.rs-4243392/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-10T06:27:47+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-11T08:39:12+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-11T08:39:12+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Health Services Research","date":"2024-04-09T17:10:10+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"908214d5-db79-4308-b913-c966c5932c5e","owner":[],"postedDate":"April 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-13T01:23:27+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-16 20:09:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4243392","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4243392","identity":"rs-4243392","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}