Advancing Continuous Glucose Monitoring for Inpatient Clinical Decision Support: Individual Algorithmic Mean Absolute Relative Difference.

<p dir="ltr">Objective: Continuous glucose monitoring (CGM) is widely used to monitor glucose levels in patients with diabetes and guide insulin dosing in outpatients. In inpatient care, special regulatory requirements necessitate CGM accuracy as a prerequisite for its integration into clinical decision support. </p><p dir="ltr">Research Design and Methods: To meet the specific demands of in-hospital care, CGM accuracy was retrospectively evaluated in 226 patients using paired CGM and point-of-care (POC) glucose measurements, assessed via Mean Absolute Relative Difference (MARD), Clarke Error Grid (CEG) analysis and the FDA agreement rule. A dynamic, patient-specific algorithm incorporating time lag correction and linear modeling was developed to minimize MARD and applied in a second cohort of 24 patients within the clinical workflow. </p><p dir="ltr">Results: Data analysis showed an initial MARD of 10.3%, with 99.02% of data points located in zones A and B of the CEG. The application of the patient-specific optimization algorithm improved the MARD by 4.33%. Evaluation of the patient-specific algorithm on an inpatient cohort (n=24) demonstrated a 5.58% reduction in intrapersonal MARD, indicating its potential applicability within clinical workflows. </p><p dir="ltr">Conclusion: Patient-specific algorithmic refinements of CGM data demonstrate the potential to adequately address the unique demands of inpatient diabetes care by reducing intrapersonal MARD, paving the way for the adoption of CGM systems into hospital environments. </p><p><br></p>

<p dir="ltr">Objective: Continuous glucose monitoring (CGM) is widely used to monitor glucose levels in patients with diabetes and guide insulin dosing in outpatients. In inpatient care, special regulatory requirements necessitate CGM accuracy as a prerequisite for its integration into clinical decision support. </p><p dir="ltr">Research Design and Methods: To meet the specific demands of in-hospital care, CGM accuracy was retrospectively evaluated in 226 patients using paired CGM and point-of-care (POC) glucose measurements, assessed via Mean Absolute Relative Difference (MARD), Clarke Error Grid (CEG) analysis and the FDA agreement rule. A dynamic, patient-specific algorithm incorporating time lag correction and linear modeling was developed to minimize MARD and applied in a second cohort of 24 patients within the clinical workflow. </p><p dir="ltr">Results: Data analysis showed an initial MARD of 10.3%, with 99.02% of data points located in zones A and B of the CEG. The application of the patient-specific optimization algorithm improved the MARD by 4.33%. Evaluation of the patient-specific algorithm on an inpatient cohort (n=24) demonstrated a 5.58% reduction in intrapersonal MARD, indicating its potential applicability within clinical workflows. </p><p dir="ltr">Conclusion: Patient-specific algorithmic refinements of CGM data demonstrate the potential to adequately address the unique demands of inpatient diabetes care by reducing intrapersonal MARD, paving the way for the adoption of CGM systems into hospital environments. </p><p><br></p>

Introduction and Objective: Continuous glucose monitoring (CGM) is a diagnostic tool widely used to monitor glucose levels in patients with diabetes and to guide insulin dose adjustments in outpatients. In the critical setting of inpatient care, however, special regulatory requirements on glucose measurements necessitate accuracy of CGM measurements as a prerequisite for its integration into clinical decision support. Methods: Strategies to optimize CGM accuracy were explored to meet the specific requirements of in-hospital care. Accuracy of glucose measurements was assessed in 226 patients using paired CGM glucose (CGM-G) and point-of-care glucose (POC-G) measurements, by calculating Mean Absolute Relative Difference (MARD) and estimation of numbers in the different zones of the Clarke Error Grid (CEG). Using raw CGM data, a dynamic, patient-specific algorithm was developed to minimize MARD through time lag optimization and linear modeling. The algorithm was integrated into the clinical workflow and applied to a second cohort of 24 patients. Results: Data analysis showed an initial MARD of 10.18% of the CGM measurements, with 99.02% of data points located in zones A and B of the CEG. The application of the patient-specific optimization algorithm improved the MARD by 4.33%. Integration of the patient-specific algorithm into the clinical workflow reduced intrapersonal MARD by 5.58%, and demonstrated significant improvements in glucose monitoring performance on an individual level. Conclusion: Patient-specific algorithmic refinements of CGM data demonstrate the potential to adequately address the unique demands of inpatient diabetes care by reducing intrapersonal MARD, paving the way for the adoption of CGM systems into hospital environments. Disclosure J. von Conta: None. F.H. Bahnsen: None. L. Heinemann: Consultant; Dexcom, Inc., Roche Diabetes Care. Board Member; Lifecare. Stock/Shareholder; Science Consulting in Diabetes GmbH, Profil Institut für Stoffwechselforschung GmbH, diateam GmbH. Consultant; Indigo. L. van Baal: None. J. Kleesiek: None. D. Fuhrer: Other Relationship; Abbott Diagnostics. Speaker's Bureau; Sanofi-Aventis Deutschland GmbH. Other Relationship; Ascendis Pharma A/S. Advisory Panel; Eli Lilly and Company, Eisai. Consultant; IPSEN, Merck & Co., Inc, Sanofi-Aventis Deutschland GmbH. S. Reger Tan: Advisory Panel; Abbott. Research Support; Boehringer-Ingelheim, AstraZeneca, Bayer Pharmaceuticals, Inc, Eli Lilly and Company, Novartis AG, Novo Nordisk. Consultant; Sinocare Inc. Other Relationship; Berlin-Chemie AG, Dexcom, Inc.

Continuous glucose monitors (CGMs) are widely used for individuals with diabetes mellitus, particularly those with type 1 diabetes (T1D). Advancements in CGM technology allow for glycemic assessment without capillary glucose measurements as many come factory calibrated. However, exercise, an essential component of diabetes care, has been reported to alter accuracy of earlier generation CGM. Considering the importance of physical activity for individuals with T1D and the progression of CGM technology, we aimed to investigate the accuracy of the Dexcom G6 during physical activity. Adolescents (ages 13-20 years) exercised on a treadmill for 40 minutes, with a 10-minute break at minute 20. We obtained paired CGM and glucometer measurements before and every 10 minutes during and after exercise. Accuracy analysis was determined by mean absolute relative difference (MARD), mean absolute difference (MAD), and Clarke Error Grid Analyses. Mean absolute relative difference and MAD increased during exercise (14%-33% and 24.3-34 mg/dL) but improved after exercise. We noted certain CGM locations produced greater changes in accuracy as MARD and MAD increased markedly when the CGM was on the buttocks (18%-46% and 30-41 mg/dL). We also noted decreased odds of Zone A in the Clarke error grid when the CGM was on the buttocks compared to the abdomen (odds ratio [OR]: 0.146; P = 0.0003; 95% CI = 0.052-0.415). This CGM system showed alterations in accuracy during exercise. Our findings additionally suggest interstitial fluid changes in muscles during exercise alter accuracy of CGM; however, additional research is required.

Diabetic ketoacidosis (DKA) is a life-threatening complication of diabetes and a leading cause of Pediatric Intensive Care Unit (PICU) admissions. The use of continuous glucose monitoring (CGM) during the acute and critical phase of DKA has been rarely explored and remains uncertain due to concerns about accuracy and utility in a setting where frequent capillary glucose measurements are standard practice. Data was collected from medical records of patients admitted to the PICU with new-onset DKA as the initial presentation of type 1 diabetes (T1D). Mean absolute relative difference (MARD) and Clarke Error Grid (CEG) analysis were used to assess CGM accuracy. Data from 19 patients (mean age 9.9 ± 3.4 years) were included. Within the first 48 h, 16 hypoglycemic episodes were recorded, with CGM detecting 14 episodes and capillary glucose detecting two. A total of 238 matched pairs of capillary and CGM interstitial glucose values were analyzed. Statistical analysis found capillary glucose values significantly higher than interstitial values (p < 0.001). The overall MARD was 14.5% and CEG analysis indicated 89.1% of matched pairs within zones A and B. Conclusions: CGM might be a useful point-of-care tool that provides valuable information that may help clinicians to make timely management decisions. The ability of CGM to indicate trends in glucose fluctuations could be its main clinical advantage, particularly in anticipating and preventing potentially dangerous hypoglycemic events, thereby optimizing patient management and safety. What is Known:• DKA emergencies require close glucose monitoring. Standard methods, such as capillary glucose monitoring or venous blood glucose measurements, have some limitations in terms of comfort, frequency, and trend detection.• CGM is currently rarely used in PICU or DKA due to a lack of clinical trials, resulting in uncertainty about its accuracy in pediatric DKA. Additionally, CGM has not been FDA-approved for use in inpatients and to manage diabetes emergencies.What is New:• CGM may benefit children with DKA from the onset.• DKA management in PICUs by showing glucose trends and enabling hypoglycemia to be detected early, supporting timely interventions, reducing workload, and minimizing patient discomfort through fewer capillary punctures.

Background: Optimal glycemic control is associated with favorable outcomes in COVID-19 hospitalized patients. Frequent capillary blood glucose (CBG) monitoring is difficult to perform. Using continuous glucose monitoring (CGM) system to assist glycemic care can reduce the exposure of healthcare personnel and PPE usage. However, data on the accuracy of CGM in this setting are limited. Objective: To evaluate the accuracy and feasibility of real-time CGM (rt-CGM) in non-ICU hospitalized adult COVID-19 patients. Methods: This is a single-center prospective study of non-ICU hospitalized adult patients with COVID-19 infection who had hyperglycemia requiring insulin therapy during admission. Medtronic Guardian Connect rt-CGM sensor and transmitter were placed on the patient’s abdomen. Paired CBG and sensor glucose values were analyzed for accuracy of CGM using mean absolute relative difference (MARD) and Clarke Error Grid Analysis (CEGA). Results: Fifteen patients were enrolled. Mean age was 48.6 ± 17.9 years. Thirteen patients (86.7%) had pre-existing diabetes. Mean HbA1c was 10.6± 3.6%. Mean duration of CGM use was 6 ± 1.2 days and mean calibration was 2.6 ± 0.7 times/day. There were 253 paired CBG and CGM measurements. The overall MARD was 9.9 ± 9.3%. The lowest MARD was observed in the CBG range of 70-180 mg/dl (9.6 ± 9.0%). The percentages of glucose readings within ±15%/15 mg/dL, ±20%/20 mg/dL, and ±30%/30 mg/dL were 80.2%, 89.7%, and 95.3%, respectively. A total of 99.2% of the data points were in zone A and B of CEGA, and none were in zone E. Percent time in range on day 1 was 57.9 ± 22.9 and improved to 64.9 ± 18.1 in the last 72 hours of sensor wear. No adverse events from the CGM were observed. CGM reduced POC testing by 30%. Conclusions: Rt-CGM use in hospitalized patients with COVID-19 infection demonstrates high accuracy and potentially improves glucose control, reduces the frequency of CBG testing, and preserves medical resources. Disclosure C.Sakjirapapong: None. S.Sirinvaravong: None. L.Preechasuk: None. N.Thongtang: None. Funding Specific League Funds (R016421005)

Objective: To assess the accuracy of the subcutaneous continuous glucose monitoring (CGM) sensor by comparing to the capillary blood glucose (CBG) and venous plasma glucose (VPG) measurements in the medical intensive care unit (MICU) patients. Subjects and Methods: In a prospective study in patients in the MICUs, Medtronic Enlite sensor was inserted in the abdominal area. Paired sensor glucose readings with reference glucose values (CBG and VPG) were collected from MICU patients receiving intravenous insulin infusion therapy. The accuracy was assessed using the mean absolute relative difference (MARD), surveillance error grid (SEG) analysis and modified Bland-Altman plot. Results: Twelve patients completed the study (age 69.3 ± 11.6 years; BMI 21.6 ± 2.9 kg/m2; APACHE II score 21.8 ± 6.3 and duration of CGM use 108.7 ± 41.1 hours). A total of 353 paired CGM and CBG glucose readings and 125 paired CGM and VPG readings were included in the analysis. Using CBG as the reference, MARD was 7.2%. The modified Bland-Altman plot showed 95% of the limit of agreement (LoA) was -18.4% to 19.2%. The SEG analysis showed that 100% of paired glucose values were within zones A or B. No clinically significant difference in the accuracy was seen between subgroups of vasopressor use (MARD 6.1% in the vasopressor group vs. 6.8% in the non-vasopressor group, p = NS). Using VPG as the reference, the MARD was 8.8%. The modified Bland-Altman plot showed 95% LoA of - 22.6% to 28.2%. The SEG analysis showed 95.2% of glucose readings were within zones A or B. Conclusion: The accuracy of the subcutaneous CGM sensor in MICU patients was comparable to that observed in non-critical care settings, with no device-related adverse events. The sensor accuracy remained within the similar range when using VPG as the references compared to using CBG as the references. No differences in terms of accuracy between the vasopressor and non-vasopressor groups were found in this study. Disclosure N. Laichuthai: None. P. Buranasupkajorn: None. W. Khovidhunkit: Speaker’s Bureau; Self; Amgen, AstraZeneca. Funding Quality Improvement Fund; King Chulalongkorn Memorial Hospital; Thai Red Cross Society (1-62-30101-A-11)

In hospitalized patients, continuous glucose monitoring (CGM) may improve glycemic control, prevent hypoglycemic events, and reduce staff workload compared with point-of-care (POC) capillary glucose monitoring. To evaluate CGM accuracy and safety of use in the inpatient setting, two versions of CGM sensors were placed on 43 and 34 adult patients with diabetes admitted to non-intensive care unit (ICU) medical wards, respectively. CGM accuracy relative to POC and safety of use were measured by calculating mean absolute relative difference (MARD) and by Clarke Error Grid (CEG) analysis. CGM version 2 had improved accuracy compared with CGM version 1 with MARD 17.7 compared with 21.4%. CGM accuracy did not change with POC value or with time of sensor wear. On CEG, 98.8% of paired values fell within acceptable zones A and B. Despite reduced accuracy compared with the outpatient setting, both versions of CGMs had acceptable safety profiles in the inpatient setting.

Continuous glucose monitors (CGMs) are an integral part of care for youth with type 1 diabetes (T1D) though lack FDA labeling for inpatient use. While some adult data on CGM use in inpatient settings is available, pediatric data are minimal. This retrospective chart review evaluated the accuracy of Dexcom G6 CGM versus point of care (POC, Nova Biomedical StatStrip [MARD 6%])) blood glucose values from pediatric inpatient encounters. Blood glucose data, diagnosis codes, and initial labs were collected from the medical record. CGM values were obtained from Dexcom Clarity CSV files. Paired glucose values (N=1191) from 83 patients with T1D (median age 12 yrs, 54% male, 69% non-Hispanic White) were used to calculate mean absolute relative difference (MARD) and Clarke Error Grid. Data from DKA admissions (N=665) had a MARD of 11.1% with 97.8% of values within A&amp;B zones, compared to 11.4% and 98.5% for non-DKA admissions (N=526). Values from severe DKA admissions (N= 307) (pH &amp;lt;7.15 and/or bicarbonate &amp;lt;5 mmol/L) had a lower MARD compared to non-severe admissions (N=358) (8.4% vs 13.4%, p=0.01). In summary, CGM accuracy is comparable between DKA and non-DKA admissions. The accuracy of CGMs, even in severe DKA, suggests potential usability during pediatric hospital encounters. Further analysis will differentiate POC versus lab glucose and the effect of medications, including IV insulin infusions. Disclosure L.A.Waterman: None. L.Pyle: None. L.Towers: None. E.Jost: Other Relationship; Tandem Diabetes Care, Inc. A.J.Karami: None. C.Berget: Consultant; Insulet Corporation, Dexcom, Inc., Other Relationship; Tandem Diabetes Care, Inc. G.P.Forlenza: Advisory Panel; Medtronic, Consultant; Dexcom, Inc., Insulet Corporation, Tandem Diabetes Care, Inc., Lilly Diabetes, Research Support; Medtronic, Abbott, Dexcom, Inc., Insulet Corporation, Tandem Diabetes Care, Inc. R.Wadwa: Consultant; Eli Lilly and Company, Other Relationship; Dexcom, Inc., Eli Lilly and Company, Research Support; Dexcom, Inc., Eli Lilly and Company, Beta Bionics, Inc., Tandem Diabetes Care, Inc. E.C.Cobry: None. Funding National Institutes of Health (5T32DK063687)

Among postsurgical and critically ill patients, malglycemia is associated with increased complications. Continuous glucose monitoring (CGM) in the inpatient population may enhance glycemic control. CGM reliability may be compromised by postsurgical complications such as edema or vascular changes. We utilized Clarke Error Grid (CEG) and Surveillance Error Grid (SEG) analysis to evaluate CGM performance after total pancreatectomy with islet autotransplantation. This subanalysis evaluated Medtronic Enlite 2 CGM values against YSI serum glucose in seven post-transplant patients (86% female; 38.6 ± 9.4 years) on artificial pancreas for 72 h at transition from intravenous to subcutaneous insulin. Sensor recalibration occurred for absolute relative difference (ARD) ≥20% x2, ≥30% x1, or by investigator discretion based on trend. Sensor analysis showed mean absolute relative difference (MARD) of 11.0% ± 11.5%. The sensors were recalibrated 8.3 times/day; active sensor was switched 1.4 times/day. Calibration factor was 7.692 ± 3.786 mg/nA·dL (target = 1.5-20 mg/nA·dL). CEG analysis showed 86.1% of pairs in Zone A (clinically accurate zone) and 99.4% of pairs in Zones A + B (low risk of error). SEG analysis of hypoglycemia/hyperglycemia risk showed 92.22% of pairs in the "no risk" zone, 5.96% of pairs in the "slight lower" risk zone, 1.01% of pairs in the "slight higher" risk zone, and only 0.81% of pairs in the "moderate lower" risk zone. Overall performance of the Medtronic Enlite 2 CGM in the post-transplant population was reasonably good with "no risk" or "slight lower" risk by SEG analysis and high CGM-YSI agreement by CEG analysis; however, frequent recalibrations were required in this intensive care population.

Studies have shown that hyper/hypoglycemia and glycemic variation are associated with adverse outcomes in critically ill patients. Currently, frequent blood point-of-care (POC) glucose measurements from an arterial or capillary sample is the only technology available to minimize glycemic excursions in the ICU. Continuous glucose monitoring (CGM) is becoming the standard of care for outpatient diabetes care and has shown improved glycemic control in the non-ICU inpatient setting. The Dexcom G6 sensor (G6) is the first CGM device approved by Health Canada for outpatient diabetes management without the need for calibration, but it has not yet been approved for inpatient use. We collected data from 23 adults who were on an insulin infusion in a medical-surgical ICU in Vancouver, British Columbia to evaluate the accuracy of uncalibrated CGM in the ICU. A blinded G6 was attached to the patient's arm and collected glucose measurements every five minutes without calibration. Nursing staff continued POC arterial glucose measurements using the AccuChek Inform II machine per standard of care. Excluding four outliers (with mean absolute relative difference (MARD) ≥ 25%), the overall MARD was 13.24% (SE 0.43) over 649 matched CGM and arterial glucose values. A Clarke Error Grid demonstrated 99.1% of CGM measurements within zones A and B. The MARD using three-point rolling averages of CGM measurements in five-minute intervals was 13.49% (SE 0.68). There were 573 matched pairs between glucose ranges of 3.9-13.9 mmol/L with two pairs <3.9 and 74 pairs >13.9 mmol/L. Eleven patients had renal replacement therapy and twelve had vasopressor use. There was no significant difference in MARD with renal replacement or across glycemic ranges ≥3.9. The MARD for patients with vasopressors was lower than for patients without (13% vs 13.55%, p<0. 01), a finding of doubtful clinical relevance. There is no expert agreement yet about the acceptable accuracy for CGM use in hospital. Our overall MARD meets the 2013 Critical Care expert consensus recommendations for MARD <14%. The FDA guidance on standard of accuracy for conventional POC glucometers require 98% of values within 15% for BG ≥ 75mg/dL. Our data showed 65.18% of values were within 15%. Previous studies using CGM in the ICU, even those using G6, all used calibration. The majority were non-blinded, and none met FDA criteria for inpatient use. Our study is the first uncalibrated and blinded study that was able to demonstrate acceptable accuracy in a large sample size. Since CGM accuracy may be affected by various factors, our comprehensive data can potentially identify specific interferences and quantify a calibration or correction criteria to improve CGM accuracy in critically ill patients. Overall, our results show that CGM shows strong potential to be an accurate, resource-efficient, and intuitive alternative to POC glucose monitoring to meet glycemic targets in the ICU.Presentation: Sunday, June 12, 2022 12:30 p.m. - 2:30 p.m.

Continuous glucose monitoring (CGM) is used clinically and for research purposes to capture glycaemic profiles. The accuracy of CGM among healthy populations has not been widely assessed. This study assessed agreement between glucose concentrations obtained from venous plasma and from CGM (FreeStyle Libre2TM, Abbott Diabetes Care, Witney, UK) in healthy women. Glucose concentrations were assessed after fasting and every 15 min after a standardized breakfast over a 4-h lab period. Accuracy of CGM was determined by Bland–Altman plot, 15/15% sensor agreement analysis, Clarke error grid analysis (EGA) and mean absolute relative difference (MARD). In all, 429 valid CGM readings with paired venous plasma glucose (VPG) values were obtained from 29 healthy women. Mean CGM readings were 1.14 mmol/L (95% CI: 0.97 to 1.30 mmol/L, p < 0.001) higher than VPG concentrations. Ratio 95% limits of agreement were from 0.68 to 2.20, and a proportional bias (slope: 0.22) was reported. Additionally, 45% of the CGM readings were within ±0.83 mmol/L (±15 mg/dL) or ±15% of VPG, while 85.3% were within EGA Zones A + B (clinically acceptable). MARD was 27.5% (95% CI: 20.8, 34.2%), with higher MARD values in the hypoglycaemia range and when VPG concentrations were falling. The FreeStyle Libre2TM CGM system tends to overestimate glucose concentrations compared to venous plasma samples in healthy women, especially during hypoglycaemia and during glycaemic swings.

Introduction: Continuous glucose monitoring (CGM) use in people with type 1 diabetes (T1D) is revolutionizing management. Use of CGM in hospital is poised to transform care, however routine use is not currently recommended due to lack of accuracy validation in acute care, including in people with T1D. We aimed to determine real-world CGM accuracy in hospitalized adults with T1D. Materials and Methods: In this multicenter retrospective observational study, we compared CGM interstitial fluid glucose with reference blood glucose (capillary/whole-blood point-of-care [POC], blood gas [GAS]) in adults with T1D requiring multiday admissions during 2020-2023 across three health services in Australia. Patients requiring dialysis or admitted under pediatric/obstetric/palliative care/psychiatry units were excluded. CGM accuracy was assessed by comparison with time-matched (±5 min) reference glucose measures, utilizing median absolute relative difference (ARD), mean ARD (MARD), and consensus error grid (CEG) analysis. Results: In total, 2,199 CGM-reference glucose pairs from 214 admissions (146 patients) were assessed. Overall, mean (SD) ARD was 12.8% (13.1) and median (IQR) ARD was 9.4% (3.7-17.7). MARD for CGM-POC pairs was 12.3%; MARD for CGM-GAS pairs was 14.3%. In CEG analysis, 99.3% of glucose pairs were within zones A/B. Accuracy was lower in critical care compared with noncritical care wards (MARD 16.1% vs. 12.0%, P < 0.001). Conclusions: In this real-world multicenter study, CGM glucose agreed well with reference blood glucose, suggesting modern CGM devices could be safely and effectively used in hospitalized adults with T1D. Further prospective studies of CGM accuracy with newer generation devices across different scenarios will further elucidate inpatient CGM accuracy and safety.

Disclosure: Q. Aziz: None. K. Batra: None. S. Fatima: None. J. Splinter: None. A.L. Champion: None. A.M. Kumar: None. K.E. Izuora: Research Investigator; Self; Novo Nordisk. Background: Continuous glucose monitoring (CGM) devices provide real time blood glucose data to guide therapy in patients with diabetes mellitus. Diabetic nephropathy is a major complication of diabetes which can eventually lead to End Stage Renal Disease (ESRD). Kidney transplant is a treatment option for ESRD and patients who undergo kidney transplant are exposed to anti-rejection therapy, including high-dose steroids, resulting in significant fluctuations in blood glucose. Having a reliable system for monitoring blood glucose continuously, such as a CGM, can provide valuable information to guide optimal diabetes management. Objective This study aims to compare the clinical accuracy of the CGM data with the Point of Care Testing (POCT) glucose values as a reference among post kidney transplant patients with Type 2 diabetes mellitus (T2DM). Methodology This study was conducted at an academic medical center from September 2023 to January 2024, enrolling subjects ≥ 18 years, diagnosed with T2DM , admitted for kidney transplant. Following informed consent, a blinded CGM (Freestyle Libre Pro®) was applied. We collected all POCT and serum glucose values and downloaded corresponding CGM glucose data. Using matched pairs between CGM and POCT glucose values, bias and absolute relative differences (ARD) were calculated. Based on the ARD, the Clarke error grid (CEG) analysis was conducted to quantify the clinical accuracy of CGM and POCT by assigning glucose data points into 5 zones A, B, C, D, and E. Summary statistics for numeric variables included mean and standard deviation, whereas categorical variables were described as counts and proportions. All analyses were performed using SAS, 9.4 version. Results: A total of 22 subjects were analyzed after excluding one patient due to CGM malfunction. Subjects were 58±9.69 years old, and 82% were males. The mean body mass index was 30±6.41 kg/m2 and mean HbA1c was 6.7 ± 1.07%, at baseline. The mean duration of diabetes and ESRD were 19±10.6 years and 3±2.27 years respectively. Average CGM wear time was 76±24.5 hours. The CEG comparing CGM values with POCT values showed 83.79% values in zone A, 15.29% in zone B (combined =99.08%) with the Mean Absolute Relative Difference (MARD) of 13.24%. The CEG comparing CGM values with serum glucose showed 83.1% values in zone A, 16.9% in zone B (combined =100%) with the MARD of 13.10%. Conclusion: Our results indicate that CGM values are comparable to the POCT and serum glucose values in post kidney transplant patients with T2DM receiving high dose steroids. Therefore, use of CGMs should be considered as a useful tool, providing timely information to guide prompt interventions and improve outcomes in this patient population. Presentation: 6/3/2024

Background: Subcutaneous continuous glucose monitoring (CGM) may facilitate glucose control in the ICU. We aimed to assess the accuracy of CGM (Dexcom G6) against arterial blood glucose (ABG) in adult critically ill patients receiving intravenous insulin infusion and vasopressor therapy. We also aimed to assess feasibility and tolerability of CGM in this setting. Methods: We included ICU patients receiving mechanical ventilation, insulin, and vasopressor therapy. Numerical accuracy was assessed by the mean absolute relative difference (MARD), overall, across arterial glucose strata, over different noradrenaline equivalent infusion rates, and over time since CGM start. MARD <14% was considered acceptable. Clinical accuracy was assessed using Clarke Error Grid (CEG) analysis. Feasibility outcome included number and duration of interrupted sensor readings due to signal loss. Tolerability outcome included skin reactions related to sensor insertion or sensor adhesives. Results: We obtained 2946 paired samples from 40 patients (18 with type 2 diabetes) receiving a median (IQR) maximum noradrenaline equivalent infusion rate of 0.18 (0.08-0.33) µg/kg/min during CGM. Overall, MARD was 12.7% (95% CI 10.7-15.3), and 99.8% of CGM readings were within CEG zones A and B. MARD values ≥14% were observed when ABG was outside target range (6-10 mmol/L [108-180 mg/dL]) and with noradrenaline equivalent infusion rates above 0.10 µg/kg/min. Accuracy improved with time after CGM start, reaching MARD values <14% after 36 h. We observed four episodes of interrupted sensor readings due to signal loss, ranging from 5 to 20 min. We observed no skin reaction related to sensor insertion or sensor adhesives. Conclusions: In our ICU cohort of patients receiving vasopressor infusion, subcutaneous CGM demonstrated acceptable overall numerical and clinical accuracy. However, suboptimal accuracy may occur outside glucose ranges of 6-10 mmol/L (108-180 mg/dL), during higher dose vasopressor infusion, and during the first 36 h after CGM start.