key: cord-0873607-ykbzv5jx
authors: Davis, Georgia M.; Faulds, Eileen; Walker, Tara; Vigliotti, Debbie; Rabinovich, Marina; Hester, Joi; Peng, Limin; McLean, Barbara; Hannon, Patricia; Poindexter, Norma; Saunders, Petrena; Perez-Guzman, Citlalli; Tekwani, Seema S.; Martin, Greg S.; Umpierrez, Guillermo; Agarwal, Shivani; Dungan, Kathleen; Pasquel, Francisco J.
title: Remote Continuous Glucose Monitoring With a Computerized Insulin Infusion Protocol for Critically Ill Patients in a COVID-19 Medical ICU: Proof of Concept
date: 2021-02-09
journal: Diabetes Care
DOI: 10.2337/dc20-2085
sha: 3ba117649dfdadcc409e57a3f0f7f9e76d262ead
doc_id: 873607
cord_uid: ykbzv5jx

OBJECTIVE: The use of remote real-time continuous glucose monitoring (CGM) in the hospital has rapidly emerged to preserve personal protective equipment and reduce potential exposures during coronavirus disease 2019 (COVID-19). RESEARCH DESIGN AND METHODS: We linked a hybrid CGM and point-of-care (POC) glucose testing protocol to a computerized decision support system for continuous insulin infusion and integrated a validation system for sensor glucose values into the electronic health record. We report our proof-of-concept experience in a COVID-19 intensive care unit. RESULTS: All nine patients required mechanical ventilation and corticosteroids. During the protocol, 75.7% of sensor values were within 20% of the reference POC glucose with an associated average reduction in POC of 63%. Mean time in range (70–180 mg/dL) was 71.4 ± 13.9%. Sensor accuracy was impacted by mechanical interferences in four patients. CONCLUSIONS: A hybrid protocol integrating real-time CGM and POC is helpful for managing critically ill patients with COVID-19 requiring insulin infusion.

Several studies have confirmed the association of in-hospital hyperglycemia with increased mortality in patients with coronavirus disease 2019 (COVID-19) (1) (2) (3) . Multiple transformations in inpatient diabetes care are occurring during this pandemic to preserve personal protective equipment (PPE) and reduce exposures while maintaining glucose control (4, 5) . In early April 2020, the U.S. Food and Drug Administration notified continuous glucose monitoring (CGM) device manufacturers that it would not object to inpatient use of CGM systems (6) , and recent reports have shown the feasibility of remote glucose monitoring in patients with COVID-19 using CGM in the hospital (7) (8) (9) (10) .

While inpatient use of CGM outside of the intensive care unit (ICU) may reduce patient-provider interactions, more significant benefit is likely to be observed among patients receiving continuous insulin infusion (CII) who require hourly point-of-care (POC) glucose testing. This POC glucose testing burden during a public health crisis could lead to the use of subcutaneous insulin regimens in situations where CII would normally be indicated. Moreover, the severity of illness and clinical characteristics of ICU patients with COVID-19 (i.e., vasopressor requirements, medical nutrition therapy, high-dose glucocorticoid therapy, renal failure), make the safety and efficacy of subcutaneous insulin regimens difficult to maintain (10) . Therefore, it is paramount to develop protocols that reduce PPE waste, nursing workload, and infectious exposures while maintaining glycemic control and reducing the risk of iatrogenic hypoglycemia.

Based on 1) our preliminary data using CGM in the cardiac ICU (11) , 2) inpatient CGM use in non-ICU units (NCT03877068, NCT03832907), 3) experience with computerized decision support algorithms in the ICU (12) (13) (14) , and 4) our inpatient clinical practice, we emergently implemented a hybrid CGM/POC glucose testing protocol linked to a computerized CII algorithm along with electronic health record (EHR) documentation to care for critically ill patients with active or suspected COVID-19 at Grady Memorial Hospital in Atlanta, GA. We report here our proof-of-concept with our first nine patients.

POC glucose values were obtained with a Food and Drug Administration-approved POC glucometer (Nova StatStrip) to measure arterial or capillary glucose. We used a factory-calibrated CGM device (Dexcom G6) including three platforms: c Dexcom G6 App for transmission of sensor data to a smartphone (with internet connectivity) placed outside (within 20 feet) the patient's room. c Follow App for remote monitoring (PharmD and endocrinologists) and glucose telemetry (tablet) at the nursing station (15) . c Clarity Dashboard for populationbased data monitoring.

We used Glucommander, a computerized algorithm integrated in our hospital's EHR (EPIC) to provide computer-guided CII adjustments. The software adjusts the multiplier according to glucose trends, insulin sensitivity, and response to therapy and has been associated with faster achievement of glucose targets and a reduction in iatrogenic hypoglycemia (13, 14) . For documentation and validation of CGM sensor values, we created a flowsheet in EPIC (see Supplementary Material). Initial CGM sensor validation was confirmed if there was ,20% variance compared with POC glucose values for 2 consecutive hours. Sensor validation was assessed once every 6 h thereafter. Only POC glucose values were used for glucose levels ,100 mg/dL. We report data for nine patients treated on this hybrid protocol, including clinical characteristics, glycemic control metrics, insulin requirements, sensor interferences, and POC testing frequency. The study was approved by the Emory Institutional Review Board.

Patients with diabetes and active or suspected COVID-19 (seven of nine had confirmed PCR testing) started the hybrid CGM/POC 1 Glucommander protocol with improvement in glycemic control within 12 h and consistent validation for most sensor values. Figure 1 shows superimposed POC and CGM glucose trends and hourly insulin doses. The arrows show conditions associated with CGM/POC discrepancies.

The mean age was 65.9 6 15.2 years, 67% were men (n 5 6), and 89% were African American (n 5 8). All patients had type 2 diabetes and blood glucose values .180 mg/dL before starting CII. All patients were on mechanical ventilation and were treated with steroids. Six (67%) were receiving renal replacement therapy. Patient characteristics are summarized in Supplementary Table 1 . Eight of nine sensors met initial validation criteria after sensor warm-up ( Supplementary Fig. 1) .

During hypoperfusion (i.e., pulseless electrical activity, shock), sensor glucose values read lower compared with arterial POC samples, with a sharp decline in sensor values and subsequent signal loss during cardiac arrest and defibrillator use. Similar findings (negative sensor glucose bias) were observed with therapeutic hypothermia protocols and during pronation or position changes causing sensor compression (e.g., bathing). All discrepancies were remotely recognized with alarms. During these interferences, glucose testing reverted to POC testing alone, and the hybrid protocol was resumed after sensor revalidation. Potential interferences are summarized in Supplementary 

Critically ill patients with diabetes and COVID-19 are commonly on vasopressors, steroids, and medical nutrition therapy and are less likely to achieve recommended glucose targets (140-180 mg/dL) on subcutaneous insulin regimens (10, 16) . A protocol involving multiple stakeholders to implement a hybrid approach (realtime CGM with POC validation every 6 h) with hourly EHR documentation guiding computerized CII is feasible in the ICU and can reduce POC glucose testing without compromising glycemic control.

Although current real-time CGM sensors are not ready to replace POC testing, given the potential for interferences during intensive care leading to discrepancies between POC and sensor glucose values (i.e., signal loss, sensor compression, hypothermia protocols, hypoperfusion, surgery, MRI), the technology has significantly advanced and can provide meaningful improvements in inpatient care. Several advantages to our approach include 1) a reduction in nursing bedside encounters and PPE use; 2) the ability to achieve and maintain adequate glycemic control; 3) minimization of patient discomfort (fingersticks) and blood loss (arterial/ venous samples); 4) remote real-time glucose monitoring by additional staff/ consult team and glucose telemetry; 5) EHR documentation to easily assess sensor function and facilitate validation; and 6) a comprehensive remote evaluation of overall performance metrics.

A careful and systematic evaluation of emergent care transformations during these unprecedented times is needed (4) . As the health care community works toward a new normal, the use of diabetes technology can help alleviate staff concerns related to work burden, exposure, and PPE consumption, while improving glycemic control during this health care crisis. thank Maina Karanja, Hang Phang, and Bimpe Umardeen (Grady Health System, Atlanta, GA), who are providing outstanding care to their patients with COVID-19 and used the sensors in the first three patients, and Makeba Lippitt (Grady Health System, Atlanta, GA) for completing the flowsheet build in EPIC. The authors would also like to thank the Dexcom COVID-19 response team for facilitating CGM devices for implementation across hospitals in the U.S. Dexcom had no role in the protocol design or approach. 

Association between achieving inpatient glycemic control and clinical outcomes in hospitalized patients with COVID-19: a multicenter, retrospective hospital-based analysis

Glycemic characteristics and clinical outcomes of COVID-19 patients hospitalized in the United States

Outcomes in patients with hyperglycemia affected by COVID-19: can we do more on glycemic control?

Individualizing inpatient diabetes management during the coronavirus disease 2019 pandemic

Implementation of continuous glucose monitoring in the hospital: emergent considerations for remote glucose monitoring during the COVID-19 pandemic

Fact Sheet for Healthcare Providers: Use of Dexcom Continuous Glucose Monitoring Systems During the COVID-19 Pandemic

StatStrip glucose meter), real-time CGM (Dexcom G6), and hourly insulin requirements in critically ill patients treated with a computerized CII algorithm (Glucommander)

Accessed 14

Feasibility of inpatient continuous glucose monitoring during the COVID-19 pandemic: early experience

Remote glucose monitoring of hospitalized, quarantined patients with diabetes and COVID-19

Usefulness and safety of remote continuous glucose monitoring for a severe COVID-19 patient with diabetes

Continuous glucose monitoring in the intensive care unit during the COVID-19 pandemic. Diabetes Care

Continuous glucose monitoring in the operating room and cardiac intensive care unit

A comparison study of continuous insulin infusion protocols in the medical intensive care unit: computer-guided vs. standard column-based algorithms

Comparison of computer-guided versus standard insulin infusion regimens in patients with diabetic ketoacidosis

Randomized controlled trial of intensive versus conservative glucose control in patients undergoing coronary artery bypass graft surgery: GLUCO-CABG trial

The effect of continuous glucose monitoring in preventing inpatient hypoglycemia in general wards: the glucose telemetry system

Managing hyperglycemia in the COVID-19 inflammatory storm