ORIGINAL ARTICLE
Continuous glucose monitoring in the surgical intensive care unit: Concordance with capillary glucose Kevin M. Schuster, MD, Kimberly Barre, RN, Silvio E. Inzucchi, MD, Robert Udelsman, MD, MBA, and Kimberly A. Davis, MD, MBA, New Haven, Connecticut
The role of intensive glycemic control (IGC) in the surgical intensive care unit (SICU) remains controversial. Continuous glucose monitoring systems (CGMSs) may mitigate the major risk of IGC, namely hypoglycemia, and improve clinical outcomes. METHODS: All patients admitted to the SICU requiring insulin infusion were eligible. CGMS (Medtronic Guardian REAL-Time CGM, Northridge, CA) was placed in the subcutaneous tissue of the abdomen or thigh and calibrated every 8 hours, based on capillary (fingerstick) blood glucose (CBG) readings. Monitors were changed every 72 hours until 144 hours of observation was complete or insulin infusion stopped. CGM data were compared with CBG at least every 2 hours. Other data collected included demographics, diagnoses, fluid balance, doses of vasopressors and/or steroids, and any intravenous or enteral glucose source. CGMS and CBG readings were compared (mean and median absolute difference, correlation coefficients, Bland-Altman plots, and Clarke error grids). RESULTS: Twenty-four patients were enrolled (11 men; mean [SD] age, 59 [14.1] years; mean [SD] body mass index 37.9 [10.1] kg/m2; mean [SD] fluid resuscitation in the first 24 hours, 6.1 [3.5] L; 17 requiring vasopressor therapy). Correlation coefficient between CGMS and CBG was 0.61 ( p G 0.001). The mean (SD) absolute difference was 22.0 [21.9] mg/dL and the median absolute difference was 16.0 mg/dL (interquartile rage, 7Y31 mg/dL). The Bland-Altman plot did not identify any trends in accuracy. Clarke error grid analysis demonstrated that 98.92% of data points were in Zone A (71.30%), indicating agreement with CBG T 20%, or Zone B (27.62%) (divergent but discrepancy would likely not lead to patient harm). Just 0.81% of data points were in Zone C (potentially dangerous overcorrection likely), and only 0.27% were in Zones D or E (potentially dangerous failure to detect hypoglycemia/hyperglycemia). CONCLUSION: CGMS seems reasonably accurate in the SICU, despite widespread use of pressors and large-volume resuscitation. Further investigation into the accuracy and precision of these devices to assist clinicians in achieving IGC is warranted. (J Trauma Acute Care Surg. 2014;76: 798Y803. Copyright * 2014 by Lippincott Williams & Wilkins) LEVEL OF EVIDENCE: Diagnostic study, level III. KEY WORDS: Glucose monitoring; critical illness; insulin. BACKGROUND:
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ntensive glycemic control (IGC) in the critical care setting has remained controversial for the past decade since the publication of the original Leuven study, which demonstrated a major mortality benefit from IGC in a surgical intensive care unit (SICU).1,2 Subsequently, several trials called into question the benefits of IGC; the NICE-SUGAR trial actually raised the possibility of a mortality risk from intervening too aggressively. Current guidelines recommend modest but no longer stringent blood glucose control in the ICU.3Y5 The reasons for inability to duplicate the results of the Leuven studies in large multinational studies remain unclear. Post hoc analysis of the NICE-SUGAR study demonstrated significant relationships
Submitted: August 1, 2013, Revised: November 8, 2013, Accepted: November 12, 2013. From the Section of Trauma, Surgical Critical Care and Surgical Critical Care and Surgical Emergencies (K.M.S., K.A.D.), Department of Surgery (R.U.), and Section of Endocrinology (S.E.I.), Department of Medicine, Yale School of Medicine; and Department of Trauma (K.B.), Yale New Haven Hospital, New Haven, Connecticut. This study was part of the poster presentation at the 72nd Annual Meeting of the American Association for the Surgery of Trauma and Clinical Congress of Acute Care Surgery, September 18Y21, 2013, in San Francisco, California. Address for reprints: Kevin M. Schuster, MD, Yale School of Medicine, 330 Cedar St, BB 310, PO Box 208062, New Haven, CT 06520-8062; email: [email protected]. DOI: 10.1097/TA.0000000000000127
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between mortality and both the occurrence of hypoglycemia and the magnitude of hypoglycemia.6 Whether hypoglycemia and its sequelae may attenuate any benefits from IGC and whether hypoglycemia prevention could enhance its proposed benefits remain unclear. This may be especially true in postoperative patients.6 A step toward hypoglycemic prevention may be continuous glucose monitoring (CGM). These devices have the potential to provide real-time measurement of systemic glucose concentrations. There are multiple prototypes for noninvasive, invasive (intravascular catheter based), or minimally invasive (subcutaneous) CGM; however, the last one is the only Food and Drug Administration (FDA)Yapproved sensor currently available.7 CGM is now a well-established adjunct to self-monitored blood glucose in the outpatient care of diabetic patients, where it has been shown to reduce hemoglobin A1C levels but not necessarily impact the incidence of hypoglycemia.8 In the critical care setting, subcutaneous CGM has been shown to be accurate in medical or mixed-patient populations.9Y11 It has also been demonstrated to reduce the incidence of hypoglycemia in the ICU but, in one randomized trial, did not improve overall glycemic control.12 Patients in the SICU may exhibit pathophysiologic responses different from those in the medical ICU populations. Surgical patients have the additional insult of surgery and tissue injury J Trauma Acute Care Surg Volume 76, Number 3
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and are more likely to have increased interstitial edema related to large-volume fluid resuscitation. These factors may compromise the accuracy and utility of using CGM to monitor critically ill surgical patients. The Guardian REAL-Time Continuous Glucose Monitoring System (CGMS, Medtronic MiniMed, Northridge, CA) is a CGM composed of a subcutaneous sensor that uses the glucose oxidase reaction for generation of an electrical current. A wireless transmitter connects to the sensor, and the signal is analyzed and converted to a measurement of the subcutaneous glucose level by a pager-sized device carried by the ambulatory user. The interstitial glucose level is measured and recorded every 5 minutes. The device carries an FDA-approved indication to supplement but not replace routine standard monitoring in ambulatory diabetic patients. Importantly, there is no current indication for therapeutic decisions based on the data provided by the device,13 which require confirmation with capillary blood glucose (CBG). As an initial step in the evaluation of CGM in critically ill surgical patients, we evaluated the feasibility of placing the CGMS in patients with recent abdominal surgery and evaluated interactions with other wireless SICU equipment. We hypothesized that, in this preliminary pilot study, subcutaneous CGM would provide accurate data compared with routine CBG measurements. These data could then be used to design larger future trials.
PATIENTS AND METHODS Setting The SICU at Yale New Haven Hospital, a 1,541-bed tertiary care medical center, is composed of two separate clinical units encompassing a total of 21 beds. Nurse-to-patient ratios are either 2:1 or 1:1, with nurses implementing orders for glycemic management. Patients in the SICU are managed according to a strict insulin infusion algorithm whose glucose target liberalized during the course of the study.14 The SICU is a comanaged unit with primary management overseen by an attending intensivist. All deviations from the glycemic management protocol are under the purview of this individual. Services who admit their patients for management in the SICU include general surgery, trauma, obstetrics and gynecology, otolaryngology, orthopedics, plastic surgery, oncologic surgery, endocrine surgery, transplant surgery, and vascular surgery. Critical care management is provided by attending physicians from the departments of surgery, anesthesia, and emergency medicine. This study was approved by the Yale University Human Investigations Committee and registered at ClinicalTrials.gov (ID# NCT01108640).
Enrollment All patients 18 years or older who were admitted to the SICU and were receiving an insulin infusion for the management of hyperglycemia were eligible to participate. Initiation of an insulin infusion was at the discretion of the attending intensivist. Protocols changed during the course of the study, which suggested an initiation of an insulin infusion after two consecutive CBG measurements more than 150 mg/dL at the start of the study to two consecutive CBG measurements more than 180 mg/dL during the second half of the study. Patients were screened daily for eligibility. The patient or a surrogate was approached, and written informed consent was obtained. At the time of study entry,
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patient demographics, comorbidities including preexisting diabetes, reason for SICU admission, body mass index, requirement for vasopressors, volume of crystalloid and blood products received, Acute Physiology and Chronic Health Evaluation II (APACHE II), and Sequential Organ Failure Assessment (SOFA) scores were recorded. The only exclusion criteria were pregnancy and the lack of a safe location to place a sensor.
Data Acquisition A subcutaneous glucose sensor was deployed using the insertion kit provided by the manufacturer. The sensors were preferentially placed in the anterior abdomen. In the event the anterior abdomen was not available owing to wound dressings, drains, or an open abdomen, with ongoing fluid leakage, the sensor was placed in the upper thigh. CBG measurements were obtained at 2 hours after insertion, and these were used for initial meter calibration. Routine CBG measurements were obtained at least every 2 hours while the patient was receiving an insulin infusion. The CGMS requires calibration every 12 hours at a minimum; however, for logistic reasons, the bedside nurse calibrated the CGMS on a set schedule at 6:00 AM, 2:00 PM, and 10:00 PM. When the routine CBGs were obtained at these time points, the data were entered into the CGMS for calibration. For logistic reasons, if patients were transported to the radiology section, the operating room, or other areas where CGMS monitoring could continue but the bedside nurse was not present, calibration delays were accepted. The nurse was partially blinded to the CGMS readings because active manipulation of the device was required to view the current reading. Alarms were set only for the glycemic extremes of less than 40 mg/dL or greater than 400 mg/dL. All therapeutic decisions were based on CBG measurements. The sensor was replaced at 72 hours, and a second sensor maintained until completion of the study at 144 hours. If a sensor failed prematurely, it was replaced, and if a second failure occurred, monitoring of that subject was terminated. Sensors were removed, and monitoring was also terminated if the attending intensivist chose to stop the insulin infusion or the patient was transferred out of the SICU. Sensors were monitored daily by an investigator for physical signs of dislodgement or contamination and removed if there was concern about the insertion site. Small amounts of bleeding at insertion were accepted. Insertion sites were monitored for a minimum of 1 day after removal. After each subject’s participation was complete, the data were uploaded to a desktop computer using Carelink software (Medtronic MiniMed) and converted to a spreadsheet.
Data Analysis Bedside glucometers upload time, date, and glucose readings directly to hospital servers. CBGs were compared with the CGMS data point that was closest in time to the CBG. Because CGMS records a value every 5 minutes, the time difference was a maximum of 2.5 minutes. In the event of a CBG falling exactly 2.5 minutes from two CGMS readings, the later reading was chosen because of the time delay of 5 minutes to 15 minutes between plasma and interstitial glucose concentration.15 Glucometer data were compared with CGMS data by various methods including calculation of mean absolute difference (MAD) and median absolute difference calculated as the mean and median of |CBG j CGMS|. Percent difference was calculated as 100 |CBG j CGMS| / CBG. Pearson correlation coefficients were calculated
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between CBG and CGMS. Linear regression was used to model agreement and time from sensor insertion and time from calibration. Bland-Altman plots and error grid analysis according to the methods of Clarke were performed.16 The Clarke data points that lie in Zone A are in agreement (T20%); those in Zone B indicate less agreement but will not lead to unsafe actions; points in Zone C may lead to dangerous overcorrection of hypoglycemia or hyperglycemia; and points in Zones D or E may lead to failure to detect hypoglycemia or hyperglycemia. Data analysis was performed with SAS 9.3 (SAS Institute, Cary, NC), and p G 0.05 was considered significant.
RESULTS Subjects Thirty-nine patients screened were eligible, and 25 of those agreed to participate and had an initial sensor placed. Of those, 21 remained on an insulin infusion after 72 hours and had a second sensor placed. One patient completed 144 hours of monitoring; however, the monitor was lost during transport, leaving 24 patients with data available for analysis. At entry, 16 patients met criteria for severe sepsis or septic shock, 3 were being treated for hypotension and a systemic inflammatory response as a result of severe blunt trauma, and 4 had received more than 10 L of intravenous crystalloid in the 24 hours before enrollment. Patient demographics (Table 1) indicate a moderate severity of illness with a mean APACHE II of 20.3 (range, 11Y36) and mean SICU TABLE 1. Study Population Characteristics Characteristics
n = 25
Age, mean (SD), y Sex, % Male Female Race, % White African American Latino Preexisting diabetes, % Body mass index, mean (SD) Admission diagnosis, n Trauma Postoperative elective surgery Postoperative emergency surgery Complication of elective surgery 24-h volume load at insertion, mean (SD), L Pressor requirement at insertion, % ICU length of stay, median (IQR), d Hospital length of stay, median (IQR), d Days of mechanical ventilation, median (IQR) Sensor placement (total number) Abdomen Thigh APACHE II score, mean (SD) SOFA score, mean (SD) Major device complications Minor device complications
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59.3 (14.3) 48.0 52.0 72.0 24.0 4.0 48.0 37.9 (10.1) 6 1 13 5 5.65 (3.46) 68.0 20.0 (15) 40.9 (33) 16.0 (15) 51 4 20.3 (6.4) 9.3 (3.7) 0 1
Figure 1. Bland-Altman plot comparing the degree of CGMS error as a function of CBG.
length of stay of 27.5 days. Seven patients died before hospital discharge, one of whom was actively being monitored during the study but died of airway loss unrelated to the study. Sensors were placed in the subcutaneous tissue of the abdominal wall (n = 51). Sensors were required to be placed in the thigh in four patients. Approximately half of the patients had known preexisting diabetes, 64% had hypertension, and 44% were smokers; other comorbidities were present less frequently. During the study period, all patients were on a continuous source of glucose. Twenty patients were receiving continuous enteral feeding, 2 others were receiving parenteral nutrition, and 20 were receiving continuous dextrose containing intravenous fluids.
CGMS Agreement There were 1,491 CBG/CGMS data pairs available for evaluation. The data were examined manually, and all discrepancies greater than 50 mg/dL were specifically reviewed. Seven data pairs were excluded when there was also extreme disagreement, greater than 50 mg/dL, between original and a repeat CBG measurement performed within 5 minutes. After exclusion of the erroneous CBG data, correlation between CBG and CGMS was good with a Pearson correlation coefficient of 0.61 ( p G 0.001). The MAD was 22.0 mg/dL, with an SD of 21.9 mg/dL, and the MedAD was 16.0, with an interquartile rage (IQR) 7 mg/dL to 31 mg/dL. The mean and median percent differences were 15.9% and 12.0%, respectively. The mean (SD) difference was 2.1 (30.9) mg/dL, and median difference was 3.0 mg/dL (IQR, j13.0 to 19.0 mg/dL). This lack of tendency toward either overestimation or underestimation of serum glucose is visually apparent on the BlandAltman plot (Fig. 1). The Bland-Altman plot also demonstrates no range of glucose readings where accuracy differs. Clarke error grid analysis (Fig. 2) demonstrated 98.92% of data points to be in Zones A or B, with Zone A (71.30%) indicating agreement equal to or less than 20% of CBG and Zone B (27.62%) indicating greater than 20% divergent but an error unlikely to lead to patient harm through overcorrection or undercorrection of serum glucose. Less than 2% were in Zone C (0.81%) where potentially dangerous corrections could be * 2014 Lippincott Williams & Wilkins
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AD after insertion. There was no interaction between time from insertion and time from calibration.
Complications
Figure 2. Clarke error grid analysis comparing CGMS and CBG.
implemented or Zones D or E (0.27%) where potentially dangerous hyperglycemia or hypoglycemia is unrecognized. The majority of the data points in Zones C, D, and E occurred in three patients. These data points likely represented calibration problems with the meter where the error persisted over multiple sequential CBG measurements. After a recalibration of the meter, the large discrepancies resolved. CGMS agreement may have been affected by time from calibration or sensor insertion. To assess both of these possibilities, time from the last meter calibration and time from sensor insertion were plotted against agreement (Fig. 3A and B). There were no obvious patterns with respect to time from calibration or insertion and agreement between CBG and CGMS. Linear regression demonstrated statistically significant, although likely clinically inconsequential, relationships between time from calibration and absolute difference (AD) between CGMS and CBG. The coefficients in the regression model were 0.942 ( p G 0.001) for time from calibration and j0.166 ( p G 0.001) for time from insertion, indicating an increase of 0.942 mg/dL per hour in AD after calibration and a decrease of 0.166 mg/dL per hour in
There were no major complications attributable to the device including significant bleeding or sensor insertion site infection. Minor complications included a superficial dermal abrasion upon removal of the dressing covering the sensor in one patient. Six sensors were prematurely removed either owing to inadvertent pulling of the sensor during routine patient care (4) or detachment of the dressing owing to leaking edema fluid (2) at the insertion site. Three sensors were placed successfully but stopped functioning within 4 hours of insertion and could not be recalibrated. Two sensors failed multiple attempts at initial calibration. All five of these sensors were replaced in similar locations with new sensors and were able to be calibrated successfully.
DISCUSSION CGM has the potential to transform the way glucose management is performed in critically ill and injured patients. Several modalities are under development including noninvasive methods. The most advanced and only FDA-approved method is CGM of subcutaneous interstitial fluid.7,17 In previous studies of mixed or medical intensive care unit patient populations, including one from our institution, the accuracy of the CGMS has met with mixed results.9,12,18Y20 Our goal was to assess the accuracy of CGMS-based interstitial glucose measurements in critically ill surgical patients. The relationship between interstitial glucose and plasma glucose may vary in surgical patients related to perfusion deficits and the common use of large volumes of crystalloid fluid leading to interstitial edema. Our patients were at particular risk for these potential problems given that 68% required vasopressor support at study entry and the average intravenous fluid load was nearly 6 L. The agreement of the CGMS in our study is best characterized as good but likely not adequate for clinical use in isolation. Previous CGM studies in the ICU have identified
Figure 3. A, Plot of difference between CGMS and CBG as a function of time from monitor instertion. B, Plot of difference between CGMS and CBG as a function of time from monitor calibration. * 2014 Lippincott Williams & Wilkins
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varying degrees of correlation ranging between 0.62 and 0.95. The correlation coefficient (0.61) for our data was at the lower end of this range,18Y20 perhaps reflecting the relative complexity of our patients, most of whom were administered with pressors and/or had large fluid shifts. Similarly, our MAD values were on the high end of the values reported in these studies; the mean percent differences were in the middle of the range.9,11,19 Our mean percent difference of 15.9% was well within the International Organization for Standardization recommended maximum difference of 20% for values greater than 75 mg/dL. We conducted an error grid analysis according to the methods of Clarke et al.16 Error grid analysis was developed to compare two tests that measure the same quantity on a continuous scale. This allows for comparison of a novel method against a reference method of measures of a continuous quantitative clinically relevant value. The grid then determines whether equivalent or discordant clinical decisions will be made without requiring strict numeric agreement. It also improves on measuring statistical correlation, which may lead to a strong correlation but disparate absolute values and results that would drive incorrect clinical decisions. By plotting the results of the two tests on a coordinate plane, areas or zones can be constructed where despite numerical discordance, appropriate clinical decisions will be made. With respect to the Clarke error grid, points in Zone A indicate an agreement of equal to or less than 20% divergent from CBG, and Zone B points indicate greater than 20% divergent but unlikely to lead to patient harm through overcorrection or undercorrection of serum glucose. Zone C points could lead to dangerous corrections, and Zone D or E points could allow dangerous hyperglycemia or hypoglycemia to go unrecognized. We demonstrated 98.92% of values within Zones A and B, which is consistent with all other studies.9,11,18,19 We did note a slightly lower percentage of points in Zone A when compared with the results of other authors. The change in agreement with respect to time from calibration and time from insertion has not been reported by other investigators in this setting. The linear regression models we constructed demonstrated only a small loss of agreement as time from calibration increased and accuracy actually improved as time from insertion increased. This may indicate improved accuracy over multiple cycles of calibration or that in critically ill patients, more calibrations will be necessary than manufacturer recommendations for outpatient monitoring. There are many limitations of this study that need to be addressed. Most important is our choice of comparison for interstitial fluid with capillary glucose. Similarly, the method of analyzing the sample has the potential to introduce significant error. Obtaining arterial whole blood samples and using a blood gas analyzer are the recommended methods for the determination of serum glucose in the critically ill.21 The accuracy of capillary glucose analyzed using a bedside glucometer has been demonstrated to be poor in multiple studies.22Y24 The International Organization for Standardization standard allows up to 5% of glucometer measurements to be more than 20% discrepant with no limit on the magnitude of discrepancy. These discrepancies were clearly present in our data where several CBG readings were immediately repeated based on judgment of the bedside nurse and found to differ by more than 50 mg/dL. We chose to eliminate 802
several readings that were clearly erroneous but also would have had a negative impact on CGMS and CBG correlation in this study. These potential problems may have led us to either overestimate or underestimate the accuracy of the CGMS. Because this was a pilot study and because CBG remains the standard glycemic measure during insulin infusion at our institution, we felt that comparison with CBG was appropriate for an initial assessment. Using the recommended blood gas analyzer would have made the study logistically impossible. Patients who did not have an indwelling vascular access for blood sampling would have been excluded, significantly increasing the time to complete the study. In addition, glucometers are the most common method of monitoring blood glucose in most ICUs, and the future use of CGMS will likely be complimentary to CBG.21 The time delay between serum and interstitial glucose may be as long as 15 minutes under normal circumstances although likely less when serum glucose is falling rapidly.15 This may have been exacerbated or improved slightly by the time between sample acquisition and analysis in bedside glucometer. The combination of these factors likely had a negative impact on CBG/CGMS agreement. There is essentially no way to mitigate against this, and control algorithms will need to account for this. Several studies have used blood gas analyzers as the comparator for CGMS, and our future investigations will also need to use this measurement to address CGM accuracy.11,12 Once there is improved certainty regarding accuracy, the next steps in the development of CGMS for use in the ICU will be to use CGM to actively control blood glucose. The ultimate goal of introduction of CGM should be to minimize glucose variability, hypoglycemia, and hyperglycemia all of which have been linked to patient mortality.3,25 Only at that point will it be possible to understand if there are patient groups that may benefit from IGC with prevention of hypoglycemia and minimizing variability. Future investigations will then need to focus on identifying more precise glucose targets than are currently safely attainable in the critically ill patient. Based on the results of this study and the work of previous investigators, we believe CGM continues to hold promise in the critical care setting. However, greater accuracy will be necessary before any future implementation of CGM to facilitate glucose management in the ICU. We have gained significant knowledge with respect to the use of CGM that can be used for the development of larger future studies. AUTHORSHIP K.M.S., S.E.I., R.U., and K.A.D. designed this study. K.M.S. and K.B. acquired and analyzed the data. K.M.S., S.E.I., R.U., and K.A.D. contributed to the data interpretation.
DISCLOSURE All continuous glucose monitoring equipment was provided by Medtronic MiniMed, Northridge, California.
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2. Van Den Berghe G, Wilmer A, Hermans G, Meersseman W, Wouters PJ, Milants I, Van Wijngaerden E, Bobbaers H, Bouillon R. Intensive insulin therapy in the medical ICU. N Engl J Med. 2006;354:449Y461. 3. The NICE-SUGAR Study Investigators. Intensive versus conventional glucose control in critically ill patients. N Engl J Med. 2009;360:1283Y1297 4. Qaseem A, Chou R, Humphrey L, Shekelle P, , for the Clinical Guidelines Committee of the American College of Physicians. Inpatient glycemic control: best practice advice from the Clinical Guidelines Committee of the American College of Physicians. Am J Med Qual. 2013. 5. Dellinger RP, Levy MM, Rhodes A, Annane D, Gerlach H, Opal SM, Sevransky JE, Sprung CL, Douglas IS, Jaeschke R, et al. Surviving sepsis campaign: international guidelines for management of severe sepsis and septic shock, 2012. Intensive Care Med. 2013;39:165Y228. 6. The NICE-SUGAR Study Investigators. Hypoglycemia and risk of death in critically ill patients. N Engl J Med. 2012;367:1108Y1118. 7. Oliver NS, Toumazou C, Cass AEG, Johnston DG. Glucose sensors: a review of current and emerging technology. Diabet Med. 2009;26:197Y210. 8. Yeh HC, Brown TT, Maruthur N, Ranasinghe P, Berger Z, Suh YD, Wilson LM, Haberl EB, Brick J, Bass EB, et al. Comparative effectiveness and safety of methods of insulin delivery and glucose monitoring for diabetes mellitus. Ann Intern Med. 2012;157:336Y347. 9. Goldberg PA, Siegel MD, Russell RR, Sherwin RS, Halickman JI, Cooper DA, Dziura JD, Inzucchi SE. Experience with the continuous glucose monitoring system in a medical intensive care unit. Diabetes Technol Ther. 2004;6:339Y347. 10. De Block C, Manuel-Y-Keenoy B, Van Gaal L, Rogiers P. Intensive insulin therapy in the intensive care unit: assessment by continuous glucose monitoring. Diabetes Care. 2006;29:1750Y1756. 11. Brunner R, Kitzberger R, Miehsler W, Herkner H, Madl C, Holzinger U. Accuracy and reliability of a subcutaneous glucose-monitoring system in critically ill patients. Crit Care Med. 2011;39:659Y664. 12. Holzinger U, Warszawska J, Kitzberger R, Wewalka M, Miehsler W, Herkner H, Madl Cl. Real-time continuous glucose monitoring in critically ill patients. Diabetes Care. 2010;33:467Y472. 13. Medtronic Mini Med. Available at: http://www.medtronicdiabetes.com/ ImportantSafetyInformation Accessed July 16, 2013. 14. Shetty S, Inzucchi SE, Goldberg PA, Cooper D, Siegel MD, Honiden S. Adapting to the new consensus guidelines for managing hyperglycemia
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Continuous glucose monitoring in the surgical intensive care unit: Concordance with capillary glucose Kevin M. Schuster, MD, Kimberly Barre, RN, Silvio E. Inzucchi, MD, Robert Udelsman, MD, MBA, and Kimberly A. Davis, MD, MBA, New Haven, Connecticut
The role of intensive glycemic control (IGC) in the surgical intensive care unit (SICU) remains controversial. Continuous glucose monitoring systems (CGMSs) may mitigate the major risk of IGC, namely hypoglycemia, and improve clinical outcomes. METHODS: All patients admitted to the SICU requiring insulin infusion were eligible. CGMS (Medtronic Guardian REAL-Time CGM, Northridge, CA) was placed in the subcutaneous tissue of the abdomen or thigh and calibrated every 8 hours, based on capillary (fingerstick) blood glucose (CBG) readings. Monitors were changed every 72 hours until 144 hours of observation was complete or insulin infusion stopped. CGM data were compared with CBG at least every 2 hours. Other data collected included demographics, diagnoses, fluid balance, doses of vasopressors and/or steroids, and any intravenous or enteral glucose source. CGMS and CBG readings were compared (mean and median absolute difference, correlation coefficients, Bland-Altman plots, and Clarke error grids). RESULTS: Twenty-four patients were enrolled (11 men; mean [SD] age, 59 [14.1] years; mean [SD] body mass index 37.9 [10.1] kg/m2; mean [SD] fluid resuscitation in the first 24 hours, 6.1 [3.5] L; 17 requiring vasopressor therapy). Correlation coefficient between CGMS and CBG was 0.61 ( p G 0.001). The mean (SD) absolute difference was 22.0 [21.9] mg/dL and the median absolute difference was 16.0 mg/dL (interquartile rage, 7Y31 mg/dL). The Bland-Altman plot did not identify any trends in accuracy. Clarke error grid analysis demonstrated that 98.92% of data points were in Zone A (71.30%), indicating agreement with CBG T 20%, or Zone B (27.62%) (divergent but discrepancy would likely not lead to patient harm). Just 0.81% of data points were in Zone C (potentially dangerous overcorrection likely), and only 0.27% were in Zones D or E (potentially dangerous failure to detect hypoglycemia/hyperglycemia). CONCLUSION: CGMS seems reasonably accurate in the SICU, despite widespread use of pressors and large-volume resuscitation. Further investigation into the accuracy and precision of these devices to assist clinicians in achieving IGC is warranted. (J Trauma Acute Care Surg. 2014;76: 798Y803. Copyright * 2014 by Lippincott Williams & Wilkins) LEVEL OF EVIDENCE: Diagnostic study, level III. KEY WORDS: Glucose monitoring; critical illness; insulin. BACKGROUND:
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ntensive glycemic control (IGC) in the critical care setting has remained controversial for the past decade since the publication of the original Leuven study, which demonstrated a major mortality benefit from IGC in a surgical intensive care unit (SICU).1,2 Subsequently, several trials called into question the benefits of IGC; the NICE-SUGAR trial actually raised the possibility of a mortality risk from intervening too aggressively. Current guidelines recommend modest but no longer stringent blood glucose control in the ICU.3Y5 The reasons for inability to duplicate the results of the Leuven studies in large multinational studies remain unclear. Post hoc analysis of the NICE-SUGAR study demonstrated significant relationships
Submitted: August 1, 2013, Revised: November 8, 2013, Accepted: November 12, 2013. From the Section of Trauma, Surgical Critical Care and Surgical Critical Care and Surgical Emergencies (K.M.S., K.A.D.), Department of Surgery (R.U.), and Section of Endocrinology (S.E.I.), Department of Medicine, Yale School of Medicine; and Department of Trauma (K.B.), Yale New Haven Hospital, New Haven, Connecticut. This study was part of the poster presentation at the 72nd Annual Meeting of the American Association for the Surgery of Trauma and Clinical Congress of Acute Care Surgery, September 18Y21, 2013, in San Francisco, California. Address for reprints: Kevin M. Schuster, MD, Yale School of Medicine, 330 Cedar St, BB 310, PO Box 208062, New Haven, CT 06520-8062; email: [email protected]. DOI: 10.1097/TA.0000000000000127
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between mortality and both the occurrence of hypoglycemia and the magnitude of hypoglycemia.6 Whether hypoglycemia and its sequelae may attenuate any benefits from IGC and whether hypoglycemia prevention could enhance its proposed benefits remain unclear. This may be especially true in postoperative patients.6 A step toward hypoglycemic prevention may be continuous glucose monitoring (CGM). These devices have the potential to provide real-time measurement of systemic glucose concentrations. There are multiple prototypes for noninvasive, invasive (intravascular catheter based), or minimally invasive (subcutaneous) CGM; however, the last one is the only Food and Drug Administration (FDA)Yapproved sensor currently available.7 CGM is now a well-established adjunct to self-monitored blood glucose in the outpatient care of diabetic patients, where it has been shown to reduce hemoglobin A1C levels but not necessarily impact the incidence of hypoglycemia.8 In the critical care setting, subcutaneous CGM has been shown to be accurate in medical or mixed-patient populations.9Y11 It has also been demonstrated to reduce the incidence of hypoglycemia in the ICU but, in one randomized trial, did not improve overall glycemic control.12 Patients in the SICU may exhibit pathophysiologic responses different from those in the medical ICU populations. Surgical patients have the additional insult of surgery and tissue injury J Trauma Acute Care Surg Volume 76, Number 3
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and are more likely to have increased interstitial edema related to large-volume fluid resuscitation. These factors may compromise the accuracy and utility of using CGM to monitor critically ill surgical patients. The Guardian REAL-Time Continuous Glucose Monitoring System (CGMS, Medtronic MiniMed, Northridge, CA) is a CGM composed of a subcutaneous sensor that uses the glucose oxidase reaction for generation of an electrical current. A wireless transmitter connects to the sensor, and the signal is analyzed and converted to a measurement of the subcutaneous glucose level by a pager-sized device carried by the ambulatory user. The interstitial glucose level is measured and recorded every 5 minutes. The device carries an FDA-approved indication to supplement but not replace routine standard monitoring in ambulatory diabetic patients. Importantly, there is no current indication for therapeutic decisions based on the data provided by the device,13 which require confirmation with capillary blood glucose (CBG). As an initial step in the evaluation of CGM in critically ill surgical patients, we evaluated the feasibility of placing the CGMS in patients with recent abdominal surgery and evaluated interactions with other wireless SICU equipment. We hypothesized that, in this preliminary pilot study, subcutaneous CGM would provide accurate data compared with routine CBG measurements. These data could then be used to design larger future trials.
PATIENTS AND METHODS Setting The SICU at Yale New Haven Hospital, a 1,541-bed tertiary care medical center, is composed of two separate clinical units encompassing a total of 21 beds. Nurse-to-patient ratios are either 2:1 or 1:1, with nurses implementing orders for glycemic management. Patients in the SICU are managed according to a strict insulin infusion algorithm whose glucose target liberalized during the course of the study.14 The SICU is a comanaged unit with primary management overseen by an attending intensivist. All deviations from the glycemic management protocol are under the purview of this individual. Services who admit their patients for management in the SICU include general surgery, trauma, obstetrics and gynecology, otolaryngology, orthopedics, plastic surgery, oncologic surgery, endocrine surgery, transplant surgery, and vascular surgery. Critical care management is provided by attending physicians from the departments of surgery, anesthesia, and emergency medicine. This study was approved by the Yale University Human Investigations Committee and registered at ClinicalTrials.gov (ID# NCT01108640).
Enrollment All patients 18 years or older who were admitted to the SICU and were receiving an insulin infusion for the management of hyperglycemia were eligible to participate. Initiation of an insulin infusion was at the discretion of the attending intensivist. Protocols changed during the course of the study, which suggested an initiation of an insulin infusion after two consecutive CBG measurements more than 150 mg/dL at the start of the study to two consecutive CBG measurements more than 180 mg/dL during the second half of the study. Patients were screened daily for eligibility. The patient or a surrogate was approached, and written informed consent was obtained. At the time of study entry,
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patient demographics, comorbidities including preexisting diabetes, reason for SICU admission, body mass index, requirement for vasopressors, volume of crystalloid and blood products received, Acute Physiology and Chronic Health Evaluation II (APACHE II), and Sequential Organ Failure Assessment (SOFA) scores were recorded. The only exclusion criteria were pregnancy and the lack of a safe location to place a sensor.
Data Acquisition A subcutaneous glucose sensor was deployed using the insertion kit provided by the manufacturer. The sensors were preferentially placed in the anterior abdomen. In the event the anterior abdomen was not available owing to wound dressings, drains, or an open abdomen, with ongoing fluid leakage, the sensor was placed in the upper thigh. CBG measurements were obtained at 2 hours after insertion, and these were used for initial meter calibration. Routine CBG measurements were obtained at least every 2 hours while the patient was receiving an insulin infusion. The CGMS requires calibration every 12 hours at a minimum; however, for logistic reasons, the bedside nurse calibrated the CGMS on a set schedule at 6:00 AM, 2:00 PM, and 10:00 PM. When the routine CBGs were obtained at these time points, the data were entered into the CGMS for calibration. For logistic reasons, if patients were transported to the radiology section, the operating room, or other areas where CGMS monitoring could continue but the bedside nurse was not present, calibration delays were accepted. The nurse was partially blinded to the CGMS readings because active manipulation of the device was required to view the current reading. Alarms were set only for the glycemic extremes of less than 40 mg/dL or greater than 400 mg/dL. All therapeutic decisions were based on CBG measurements. The sensor was replaced at 72 hours, and a second sensor maintained until completion of the study at 144 hours. If a sensor failed prematurely, it was replaced, and if a second failure occurred, monitoring of that subject was terminated. Sensors were removed, and monitoring was also terminated if the attending intensivist chose to stop the insulin infusion or the patient was transferred out of the SICU. Sensors were monitored daily by an investigator for physical signs of dislodgement or contamination and removed if there was concern about the insertion site. Small amounts of bleeding at insertion were accepted. Insertion sites were monitored for a minimum of 1 day after removal. After each subject’s participation was complete, the data were uploaded to a desktop computer using Carelink software (Medtronic MiniMed) and converted to a spreadsheet.
Data Analysis Bedside glucometers upload time, date, and glucose readings directly to hospital servers. CBGs were compared with the CGMS data point that was closest in time to the CBG. Because CGMS records a value every 5 minutes, the time difference was a maximum of 2.5 minutes. In the event of a CBG falling exactly 2.5 minutes from two CGMS readings, the later reading was chosen because of the time delay of 5 minutes to 15 minutes between plasma and interstitial glucose concentration.15 Glucometer data were compared with CGMS data by various methods including calculation of mean absolute difference (MAD) and median absolute difference calculated as the mean and median of |CBG j CGMS|. Percent difference was calculated as 100 |CBG j CGMS| / CBG. Pearson correlation coefficients were calculated
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between CBG and CGMS. Linear regression was used to model agreement and time from sensor insertion and time from calibration. Bland-Altman plots and error grid analysis according to the methods of Clarke were performed.16 The Clarke data points that lie in Zone A are in agreement (T20%); those in Zone B indicate less agreement but will not lead to unsafe actions; points in Zone C may lead to dangerous overcorrection of hypoglycemia or hyperglycemia; and points in Zones D or E may lead to failure to detect hypoglycemia or hyperglycemia. Data analysis was performed with SAS 9.3 (SAS Institute, Cary, NC), and p G 0.05 was considered significant.
RESULTS Subjects Thirty-nine patients screened were eligible, and 25 of those agreed to participate and had an initial sensor placed. Of those, 21 remained on an insulin infusion after 72 hours and had a second sensor placed. One patient completed 144 hours of monitoring; however, the monitor was lost during transport, leaving 24 patients with data available for analysis. At entry, 16 patients met criteria for severe sepsis or septic shock, 3 were being treated for hypotension and a systemic inflammatory response as a result of severe blunt trauma, and 4 had received more than 10 L of intravenous crystalloid in the 24 hours before enrollment. Patient demographics (Table 1) indicate a moderate severity of illness with a mean APACHE II of 20.3 (range, 11Y36) and mean SICU TABLE 1. Study Population Characteristics Characteristics
n = 25
Age, mean (SD), y Sex, % Male Female Race, % White African American Latino Preexisting diabetes, % Body mass index, mean (SD) Admission diagnosis, n Trauma Postoperative elective surgery Postoperative emergency surgery Complication of elective surgery 24-h volume load at insertion, mean (SD), L Pressor requirement at insertion, % ICU length of stay, median (IQR), d Hospital length of stay, median (IQR), d Days of mechanical ventilation, median (IQR) Sensor placement (total number) Abdomen Thigh APACHE II score, mean (SD) SOFA score, mean (SD) Major device complications Minor device complications
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59.3 (14.3) 48.0 52.0 72.0 24.0 4.0 48.0 37.9 (10.1) 6 1 13 5 5.65 (3.46) 68.0 20.0 (15) 40.9 (33) 16.0 (15) 51 4 20.3 (6.4) 9.3 (3.7) 0 1
Figure 1. Bland-Altman plot comparing the degree of CGMS error as a function of CBG.
length of stay of 27.5 days. Seven patients died before hospital discharge, one of whom was actively being monitored during the study but died of airway loss unrelated to the study. Sensors were placed in the subcutaneous tissue of the abdominal wall (n = 51). Sensors were required to be placed in the thigh in four patients. Approximately half of the patients had known preexisting diabetes, 64% had hypertension, and 44% were smokers; other comorbidities were present less frequently. During the study period, all patients were on a continuous source of glucose. Twenty patients were receiving continuous enteral feeding, 2 others were receiving parenteral nutrition, and 20 were receiving continuous dextrose containing intravenous fluids.
CGMS Agreement There were 1,491 CBG/CGMS data pairs available for evaluation. The data were examined manually, and all discrepancies greater than 50 mg/dL were specifically reviewed. Seven data pairs were excluded when there was also extreme disagreement, greater than 50 mg/dL, between original and a repeat CBG measurement performed within 5 minutes. After exclusion of the erroneous CBG data, correlation between CBG and CGMS was good with a Pearson correlation coefficient of 0.61 ( p G 0.001). The MAD was 22.0 mg/dL, with an SD of 21.9 mg/dL, and the MedAD was 16.0, with an interquartile rage (IQR) 7 mg/dL to 31 mg/dL. The mean and median percent differences were 15.9% and 12.0%, respectively. The mean (SD) difference was 2.1 (30.9) mg/dL, and median difference was 3.0 mg/dL (IQR, j13.0 to 19.0 mg/dL). This lack of tendency toward either overestimation or underestimation of serum glucose is visually apparent on the BlandAltman plot (Fig. 1). The Bland-Altman plot also demonstrates no range of glucose readings where accuracy differs. Clarke error grid analysis (Fig. 2) demonstrated 98.92% of data points to be in Zones A or B, with Zone A (71.30%) indicating agreement equal to or less than 20% of CBG and Zone B (27.62%) indicating greater than 20% divergent but an error unlikely to lead to patient harm through overcorrection or undercorrection of serum glucose. Less than 2% were in Zone C (0.81%) where potentially dangerous corrections could be * 2014 Lippincott Williams & Wilkins
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AD after insertion. There was no interaction between time from insertion and time from calibration.
Complications
Figure 2. Clarke error grid analysis comparing CGMS and CBG.
implemented or Zones D or E (0.27%) where potentially dangerous hyperglycemia or hypoglycemia is unrecognized. The majority of the data points in Zones C, D, and E occurred in three patients. These data points likely represented calibration problems with the meter where the error persisted over multiple sequential CBG measurements. After a recalibration of the meter, the large discrepancies resolved. CGMS agreement may have been affected by time from calibration or sensor insertion. To assess both of these possibilities, time from the last meter calibration and time from sensor insertion were plotted against agreement (Fig. 3A and B). There were no obvious patterns with respect to time from calibration or insertion and agreement between CBG and CGMS. Linear regression demonstrated statistically significant, although likely clinically inconsequential, relationships between time from calibration and absolute difference (AD) between CGMS and CBG. The coefficients in the regression model were 0.942 ( p G 0.001) for time from calibration and j0.166 ( p G 0.001) for time from insertion, indicating an increase of 0.942 mg/dL per hour in AD after calibration and a decrease of 0.166 mg/dL per hour in
There were no major complications attributable to the device including significant bleeding or sensor insertion site infection. Minor complications included a superficial dermal abrasion upon removal of the dressing covering the sensor in one patient. Six sensors were prematurely removed either owing to inadvertent pulling of the sensor during routine patient care (4) or detachment of the dressing owing to leaking edema fluid (2) at the insertion site. Three sensors were placed successfully but stopped functioning within 4 hours of insertion and could not be recalibrated. Two sensors failed multiple attempts at initial calibration. All five of these sensors were replaced in similar locations with new sensors and were able to be calibrated successfully.
DISCUSSION CGM has the potential to transform the way glucose management is performed in critically ill and injured patients. Several modalities are under development including noninvasive methods. The most advanced and only FDA-approved method is CGM of subcutaneous interstitial fluid.7,17 In previous studies of mixed or medical intensive care unit patient populations, including one from our institution, the accuracy of the CGMS has met with mixed results.9,12,18Y20 Our goal was to assess the accuracy of CGMS-based interstitial glucose measurements in critically ill surgical patients. The relationship between interstitial glucose and plasma glucose may vary in surgical patients related to perfusion deficits and the common use of large volumes of crystalloid fluid leading to interstitial edema. Our patients were at particular risk for these potential problems given that 68% required vasopressor support at study entry and the average intravenous fluid load was nearly 6 L. The agreement of the CGMS in our study is best characterized as good but likely not adequate for clinical use in isolation. Previous CGM studies in the ICU have identified
Figure 3. A, Plot of difference between CGMS and CBG as a function of time from monitor instertion. B, Plot of difference between CGMS and CBG as a function of time from monitor calibration. * 2014 Lippincott Williams & Wilkins
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varying degrees of correlation ranging between 0.62 and 0.95. The correlation coefficient (0.61) for our data was at the lower end of this range,18Y20 perhaps reflecting the relative complexity of our patients, most of whom were administered with pressors and/or had large fluid shifts. Similarly, our MAD values were on the high end of the values reported in these studies; the mean percent differences were in the middle of the range.9,11,19 Our mean percent difference of 15.9% was well within the International Organization for Standardization recommended maximum difference of 20% for values greater than 75 mg/dL. We conducted an error grid analysis according to the methods of Clarke et al.16 Error grid analysis was developed to compare two tests that measure the same quantity on a continuous scale. This allows for comparison of a novel method against a reference method of measures of a continuous quantitative clinically relevant value. The grid then determines whether equivalent or discordant clinical decisions will be made without requiring strict numeric agreement. It also improves on measuring statistical correlation, which may lead to a strong correlation but disparate absolute values and results that would drive incorrect clinical decisions. By plotting the results of the two tests on a coordinate plane, areas or zones can be constructed where despite numerical discordance, appropriate clinical decisions will be made. With respect to the Clarke error grid, points in Zone A indicate an agreement of equal to or less than 20% divergent from CBG, and Zone B points indicate greater than 20% divergent but unlikely to lead to patient harm through overcorrection or undercorrection of serum glucose. Zone C points could lead to dangerous corrections, and Zone D or E points could allow dangerous hyperglycemia or hypoglycemia to go unrecognized. We demonstrated 98.92% of values within Zones A and B, which is consistent with all other studies.9,11,18,19 We did note a slightly lower percentage of points in Zone A when compared with the results of other authors. The change in agreement with respect to time from calibration and time from insertion has not been reported by other investigators in this setting. The linear regression models we constructed demonstrated only a small loss of agreement as time from calibration increased and accuracy actually improved as time from insertion increased. This may indicate improved accuracy over multiple cycles of calibration or that in critically ill patients, more calibrations will be necessary than manufacturer recommendations for outpatient monitoring. There are many limitations of this study that need to be addressed. Most important is our choice of comparison for interstitial fluid with capillary glucose. Similarly, the method of analyzing the sample has the potential to introduce significant error. Obtaining arterial whole blood samples and using a blood gas analyzer are the recommended methods for the determination of serum glucose in the critically ill.21 The accuracy of capillary glucose analyzed using a bedside glucometer has been demonstrated to be poor in multiple studies.22Y24 The International Organization for Standardization standard allows up to 5% of glucometer measurements to be more than 20% discrepant with no limit on the magnitude of discrepancy. These discrepancies were clearly present in our data where several CBG readings were immediately repeated based on judgment of the bedside nurse and found to differ by more than 50 mg/dL. We chose to eliminate 802
several readings that were clearly erroneous but also would have had a negative impact on CGMS and CBG correlation in this study. These potential problems may have led us to either overestimate or underestimate the accuracy of the CGMS. Because this was a pilot study and because CBG remains the standard glycemic measure during insulin infusion at our institution, we felt that comparison with CBG was appropriate for an initial assessment. Using the recommended blood gas analyzer would have made the study logistically impossible. Patients who did not have an indwelling vascular access for blood sampling would have been excluded, significantly increasing the time to complete the study. In addition, glucometers are the most common method of monitoring blood glucose in most ICUs, and the future use of CGMS will likely be complimentary to CBG.21 The time delay between serum and interstitial glucose may be as long as 15 minutes under normal circumstances although likely less when serum glucose is falling rapidly.15 This may have been exacerbated or improved slightly by the time between sample acquisition and analysis in bedside glucometer. The combination of these factors likely had a negative impact on CBG/CGMS agreement. There is essentially no way to mitigate against this, and control algorithms will need to account for this. Several studies have used blood gas analyzers as the comparator for CGMS, and our future investigations will also need to use this measurement to address CGM accuracy.11,12 Once there is improved certainty regarding accuracy, the next steps in the development of CGMS for use in the ICU will be to use CGM to actively control blood glucose. The ultimate goal of introduction of CGM should be to minimize glucose variability, hypoglycemia, and hyperglycemia all of which have been linked to patient mortality.3,25 Only at that point will it be possible to understand if there are patient groups that may benefit from IGC with prevention of hypoglycemia and minimizing variability. Future investigations will then need to focus on identifying more precise glucose targets than are currently safely attainable in the critically ill patient. Based on the results of this study and the work of previous investigators, we believe CGM continues to hold promise in the critical care setting. However, greater accuracy will be necessary before any future implementation of CGM to facilitate glucose management in the ICU. We have gained significant knowledge with respect to the use of CGM that can be used for the development of larger future studies. AUTHORSHIP K.M.S., S.E.I., R.U., and K.A.D. designed this study. K.M.S. and K.B. acquired and analyzed the data. K.M.S., S.E.I., R.U., and K.A.D. contributed to the data interpretation.
DISCLOSURE All continuous glucose monitoring equipment was provided by Medtronic MiniMed, Northridge, California.
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