DIABETES TECHNOLOGY & THERAPEUTICS Volume 2, Supplement 1, 2000 Mary Ann Liebert, Inc.
Continuous Glucose Monitoring in Previously Unstudied Population Subgroups TODD M. GROSS, Ph.D., and ANNA TER VEER, M.S.
O
15, 1999, the Food and Drug Administration (FDA) approved a Pre-Market Approval (PMA) application for the MiniMed ® Continuous Glucose Monitoring System (CGMS). 1 This approval carried with it conditions recommended by an earlier FDA Advisory panel and adopted by the FDA. Among these conditions was a request to provide data on the use of the CGMS among patient groups that were not represented in previous studies. Although the CGMS had been the focus of an extensive program of research during its development and regulatory review (see Gross and Mastrototaro, “Efficacy and Reliability of the Continuous Glucose Monitoring System,” in this volume), these earlier studies were conducted at a limited number of research sites, and thus included subjects who were restricted in the breadth of their demographic characteristics. The FDA requested additional performance data in six population subgroups: patients with type 2 and gestational diabetes, pregnant patients, pediatric patients, patients from a range of ethnic backgrounds, patients with significant underlying chronic illness besides diabetes, and patients with a range of duration of diabetes. In order to satisfy the FDA’s conditions of approval, a postmarketing surveillance study was conducted upon the commercial release of the CGMS. Healthcare practitioners who prescribed the CGMS were asked to return performance data for any patient who used the device, along with anonymous demographic information on that patient. A PMA supplement reporting the results of the study was subN JUNE
mitted to the FDA on March 2, 2000. The FDA accepted this report in satisfaction of the postapproval conditions on August 9, 2000. Because the CGMS is a commercially available medical device under the control of the prescribing clinician, the postmarketing surveillance study had no restrictions on selection of patients, on the reason for prescription, nor on the conditions for use. As a result, the study confirmed the performance of the CGMS across a diverse group of patients. The study also confirmed the performance of the CGMS outside a controlled clinical setting because the patients wore the sensor at home. Thus in addition to satisfying regulatory requirements, this study was pivotal in presenting results for the first time on home-use continuous glucose monitoring in previously unstudied populations.
STUDY DESIGN AND METHODS This postmarketing study used a nonrandomized, quasi-experimental design. Prior to enrolling patients in the postmarketing study, physicians and their staff were provided with all the components of the CGMS and were given an orientation on the CGMS. They also were provided with the case report forms (CRFs) for collecting demographic and baseline data, sensor insertion history data, and reimbursement data. Healthcare practitioners responsible for managing the patients’ diabetes selected the patients for the study. At the initial office visit, the patients were trained on the CGMS according to the pack-
MiniMed Inc., Northridge, California.
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aged instructions for use. The patients were asked to take at least four fingersticks a day using their own home blood glucose meters and to enter the readings into the CGMS monitor. Following the insertion of the sensor, the patients returned home to wear the sensor for up to 3 days, though the patients could wear consecutive sensors for more than 3 days at the healthcare practitioner’s discretion. Upon the completion of 3 days of sensor use, the patients returned to the medical office to have the sensor removed and the data downloaded and interpreted. The interpretation of the CGMS data and the diabetes management activities were performed by the healthcare provider. Each clinician who enrolled patients in the postmarketing study returned the CGMS download files, along with an anonymous demographic questionnaire and sensor insertion records, to the device manufacturer. The performance of the CGMS was measured by comparing its readings to the meter readings taken with the patients’ home blood glucose meter. Each meter reading entered into the CGMS monitor was paired with the corresponding calibrated sensor glucose value obtained at the same time. Using only paired readings, two measures of agreement were calculated for each of the demographic subgroups: a correlation coefficient and an absolute percent difference. The correlation coefficient was calculated as the Pearson product moment between each meter reading and its paired sensor reading. The absolute percent difference was calculated as the absolute difference between each meter reading and its paired sensor reading, divided by the meter reading to create a percent value. Comparison of performance statistics between subgroups were performed as follows: correlation coefficients were compared using a Fisher’s Z test and median absolute percent differences were compared using a Wilcoxon rank-sum test. All tests were two-sided with a significance level of 0.05.
RESULTS The CGMS download files, demographic questionnaires, and sensor insertion forms for
238 patients from 13 different clinical sites were submitted to the device manufacturer. The sensor insertion history indicated that 278 sensors were inserted, resulting in 961 days of sensor use and 4,015 paired sensor and meter values. Demographic and baseline characteristics Table 1 presents the demographic and baseline characteristics for the 238 patients. Twentyone percent of the patients were pediatric, 57% were female (21 of the 136 females were pregnant at the time of study enrollment), 18% were of an ethnicity other than Caucasian, 12% were diagnosed with type 2 diabetes, and 55% were on continuous subcutaneous insulin infusion (CSII). The mean age (6 standard deviation, SD) was 35.6 (616.8) years. The mean duration of diabetes (6SD) was 15.4 (610.7) years, with 21% of the patients having a short-term history of diabetes (5 years or less). The mean HbA1C (6SD) was 7.9% (61.6%). Twenty-five percent of the patients reported hypoglycemic unaware-
TABLE 1.
DEMO GRAPHIC
AND
BASELINE CHARACTERISTICS n (%) or mean 6 SD
Age group Adult Pediatric (,18 years) Gender Femalea Male Ethnicity Caucasian Latino African American Other Diagnosis Type 1 Type 2 Other (gestational, hypoglycemia) Therapy CSII MDI Other (conventional, oral, diet, implantable pump) Age (years) Duration of diabetes (years) HbA1C (%) Number of patients with Hypoglycemic unawareness Severe hypoglycemia Chronic illness a Twenty-one
were pregnant.
188 (79%) 50 (21%) 136 (57%) 102 (43%) 194 20 13 11
(82%) (8%) (6%) (4%)
198 (83%) 28 (12%) 12 (5%) 132 (55%) 73 (31%) 33 (14%) 35.6 6 16.8 15.4 6 10.7 7.9 6 1.6 59 (25%) 38 (16%) 81 (34%)
CGMS IN PREVIOUSLY UNSTUDIED POPULATION SUBGROUPS
ness, 16% reported severe hypoglycemia within the last 12 months, and 34% had a significant underlying chronic illness. Overall CGMS performance The scatter plot of the 4,015 paired sensor and meter readings displays excellent agreement between the paired readings (Fig. 1), with a correlation coefficient of 0.91. The distribution of the absolute percent difference is skewed and right-tailed, and as a result the median is the more appropriate measure of central tendency. The median absolute percent difference (MAD) of 12.6% shows excellent agreement between the paired sensor and meter readings. CGMS performance by demographic subgroups The correlation coefficient in each of the demographic subgroups is well above 0.79, which is the cutoff criteria used by the CGMS software to identify optimal sensor calibration accuracy (Fig. 2). No statistically significant differences in the correlation coefficients were found when comparing adult to pediatric patients, Caucasian to non-Caucasian patients, patients without chronic illness to patients with
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chronic illness, and patients with long-term history of diabetes to those with short-term diabetes. Patients with type 2 diabetes had a statistically lower correlation coefficient when compared to patients with type 1 diabetes (r 5 0.88 vs. 0.91; p 5 0.04). Pregnant patients had a statistically lower correlation coefficient when compared to nonpregnant females (r 5 0.84 vs. 0.90; p 5 0.0001). These two observed differences in correlation coefficients are small in magnitude, and the lower correlation coefficient still is well above the cutoff criteria for optimal accuracy. Clinicians treating patients in these subgroups confirmed the clinical utility of the sensor downloads. The MAD in each of the demographic subgroups is well below 28%, the cutoff criteria for optimal accuracy (Fig. 3). No statistically significant differences in the MAD were found when comparing adult to pediatric patients, Caucasian to non-Caucasian patients, patients without chronic illness to patients with chronic illness, and patients with long-term history of diabetes to patients with short-term diabetes. Patients with type 2 diabetes had a statistically lower MAD when compared to patients with type 1 diabetes (10.6% vs. 12.6%; p 5 0.001). Pregnant patients had a statistically higher MAD when compared to nonpregnant females
FIG. 1. Scatter plot of the paired sensor and meter readings (n 5 4,015). Note that the CGMS software automatically truncates glucose levels at the limits of its operating range, which is 40–400 mg/dL. The correlation coefficient is denoted by r, and the median absolute percent difference is denoted by MAD.
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FIG. 2.
Comparison of the correlation coefficient (r) between demographic subgroups.
(16.1% vs. 12.6%; p 5 0.0001). The observed differences in MAD are of a small magnitude and of limited clinical relevance. Investigators treating these patients confirmed that the sensor profiles were considered clinically useful in these subgroups. The CGMS performance statistics, the corre-
FIG. 3.
lation and absolute percent difference, can be strongly influenced by the level and range of the standard blood glucose meter values used to calculate them. Specifically, the magnitude of the correlation coefficient is directly related to the variance of the meter values,2 and the percent absolute difference scores are a func-
Comparison of the median absolute percent difference (MAD) between demographic subgroups.
CGMS IN PREVIOUSLY UNSTUDIED POPULATION SUBGROUPS
tion of the level of meter values used in the denominator of the percent calculation. To test the hypothesis that the observed differences in CGMS performance statistics for patients with type 2 diabetes and pregnant patients could be explained by this artifact, the level and variability of the blood glucose meter values in these subgroups were compared. Patients with type 2 diabetes had less variable glucose levels when compared to patients with type 1 diabetes (SD 5 60.6 mg/dL vs. 80.7 mg/dL; p 5 0.0001). Two daily CGMS profiles, one for a patient with type 1 diabetes and the other for a patient with type 2 diabetes, illustrate the lower variability in glucose levels seen in the setting of type 2 diabetes (Fig. 4A,B). Pregnant patients had lower mean glucose levels (mean 5 112.0 mg/dL vs. 150.6 mg/dL; p 5 0.0001) and less variable glucose levels (SD 5 51.5 mg/dL vs. 77.3 mg/dL; p 5 0.0001). Two daily CGMS profiles, one for a nonpregnant female patient and the other for a pregnant patient, again display the lower variability in glucose levels clearly observed for the pregnant patient (Fig. 4C,D). All four profiles demonstrate the additional clinical information obtained by the sensor and the close agreement between the sensor and meter values.
DISCUSSION This study provides extensive data on the performance of the CGMS, derived from 238 patients wearing 278 sensors for 961 days. These patients, who included members of six demographic subgroups underrepresented in previous studies, were asked to use the CGMS as part of their diabetes management by healthcare practitioners at 13 clinical sites. The study sample is highly representative of the target population for this device, and the CGMS performance observed here is expected to generalize to this larger population. The study results describe a continuous glucose sensor that agrees quite closely with the blood glucose meters used to calibrate it. The overall correlation of 0.91 and the median absolute percent difference of 12.6% highlight the excellent performance of the system. These results are quite close to the performance statis-
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tics calculated during analyses of prior data used to refine the method of retrospective calibration and optimize the values of its parameters. Such close agreement confirms that these earlier results were not simply the result of data fitting, but rather stem from the fundamental robustness of the CGMS performance. It is important to note that the use of the same meter values for calibration and evaluation, as was done in this study, does not introduce an automatic bias towards positive results, guaranteeing excellent agreement between the two devices. In order to obtain a strong correlation and low numerical difference, the sensor must be highly sensitive to glucose concentrations, and its response must be proportional to the meter’s response. It is possible, within the present study design, to obtain a correlation of zero and difference scores equal in magnitude to the meter’s glucose values. The present results instead confirm the opposite conclusion, that the sensor’s response is consistent and predictable. Further, it is accepted practice in regression and correlation analyses to use the same predictors to produce the regression equation and to evaluate the goodness of fit. The study results also confirm that CGMS performance is largely unaffected by demographic characteristics. No significant difference in CGMS performance was observed as a function of age (pediatric vs. adult), ethnicity (Caucasian vs. non-Caucasian), chronic illness (present vs. absent), or duration of diabetes (short term vs. long term). Where differences between demographic subgroups were observed, the results point out the interdependency between the level and range of meter values and the resulting CGMS performance statistics. Mixed results were observed for the comparison of patients with type 2 diabetes to those with type 1 diabetes. A lower correlation was observed in patients with type 2 diabetes (0.88 vs. 0.91), but this difference is quite small from a practical perspective. Further, this result was coupled with significantly better agreement in MAD (10.6% vs. 12.6%). The daily CGMS profile for a patient with type 2 diabetes clearly demonstrates the lower variability in glucose levels observed in patients with type 2 diabetes (Fig. 4B), and it also demonstrates both the additional information obtained by the sen-
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FIG. 4. Daily CGMS profiles. (A) Patient with type 1 diabetes (male, 42 years old, Caucasian, no chronic illness, long-term diabetes). (B) Patient with type 2 diabetes (male, 50 years old, Caucasian, no chronic illness, short-term diabetes). (C) Nonpregnant female patient (type 1, 32 years old, Caucasian, no chronic illness, long-term diabetes). (D) Pregnant female patient (type 1, 32 years old, Caucasian, no chronic illness, long-term diabetes).
sor not captured by the blood glucose meter and the close agreement between the sensor and meter values. Pregnancy is the only demographic stratification that shows a consistent, and explainable, effect on the CGMS performance indices. Compared to nonpregnant female patients, pregnant patients show a lower correlation (0.84 vs. 0.90) and a higher MAD (16.1% vs. 12.6%).
Rather than suggesting that the sensor works differently in pregnant patients, these results are likely a by-product of the tighter control of diabetes required during gestation. Because elevated glucose levels have a much more pronounced effect on the fetus than on the mother during pregnancy, the target glucose control for pregnant women is much more stringent than outside of pregnancy.3 These patients are
CGMS IN PREVIOUSLY UNSTUDIED POPULATION SUBGROUPS
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FIG. 4. Continued
instructed to test their blood more frequently and to perform increased postmeal measurements. The postmeal glucose target is also lower than for nonpregnant patients. This treatment regimen produces much lower and much less variable glucose levels. The result is a flatter sensor profile, rarely seen in patients with diabetes. The lower range of values produced in both the meter and sensor tends to attenuate the correlation coefficient (the well-known effect of a restricted range). Further, the over-
all lower meter values maximize the percent difference scores because the meter values serve as the denominator for this calculation. The daily CGMS profile for a pregnant patient clearly demonstrates the lower variability in glucose levels observed in pregnant patients (Fig. 4D), and it also demonstrates both the additional information obtained by the sensor as compared to the blood glucose meter and the close agreement between sensor and meter values.
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These effects do not reduce the clinical utility of sensor data for pregnant women. On the contrary, because of the increased vigilance required during pregnancy, the additional information provided from continuous glucose readings is even more crucial. The sensor provides more complete postmeal and nocturnal profiles than could be obtained via meter alone. Comments from the investigator using the CGMS in her treatment of diabetes during gestation confirm that the attenuation in the performance statistics is an aberration and does not limit the ability to base meaningful clinical decisions on the extensive data the sensor provides (see Jovanovic, “The Role of Continuous Glucose Monitoring in Gestational Diabetes Mellitus,” in this volume).
CONCLUSION These results confirm that the CGMS performance was no different in an independent data set than was observed in the clinical trials on which PMA approval was based. The CGMS performance was statistically and clinically acceptable in those specific population subgroups for which the FDA requested additional data. Any observed statistical differences between population subgroups were not of sufficient practical size or, in the case of use during gestation, were artifacts of a restricted range of glucose levels and overshadowed by the clinical importance of the data obtained. Thus home-use continuous glucose monitoring can confidently be used in a wide range of patient populations to provide a more complete picture of glycemic control than what can be ob-
tained by monitoring with the blood glucose meter alone. ACKNOWLEDGMENTS Investigators were Bruce W. Bode, M.D.; Peter Chase, M.D.; Daniel Einhorn, M.D.; Lois Jovanovic, M.D.; Francine R. Kaufman, M.D.; David M. Kayne, M.D.; Richard Levy, M.D.; Jorge Mestman, M.D.; Etie Moghissi, M.D.; Chip Reed, M.D.; William V. Tamborlane, M.D.; Neil White, M.D.; and Fred Whitestone, M.D. Clinical research and statistics support were provided by Lily Jeng, M.A.; Suzanne Juth, R.N., C.D.E.; Marianne Kolopp, M.D.; Judi Spell, R.N., M.S.N.; and Kay Thornton, R.N., M.B.A. REFERENCES 1. Summary of safety and effectiveness data for the MiniMed Continuous Glucose Monitoring System (CGMS), PMA P980022, Food and Drug Administration, 1999. Available at: http://www.fda.gov/cdrh/pdf/p980022.html. Accessed September 15, 2000. 2. Altman DG, Bland JM. Measurement is medicine: the analysis of method comparison studies. The Statistician 1983;32:307–317. 3. American Diabetes Association. Gestational diabetes mellitus. Diabetes Care 2000;23(suppl 1):S77–S79.
Address reprint requests to: Todd M. Gross, Ph.D. MiniMed Inc. 18000 Devonshire Street Northridge, CA 91325 E-mail:
[email protected]
This article has been cited by: 1. Teri L. Hernandez, Linda A. Barbour. 2013. A Standard Approach to Continuous Glucose Monitor Data in Pregnancy for the Study of Fetal Growth and Infant Outcomes. Diabetes Technology & Therapeutics 15:2, 172-179. [Abstract] [Full Text HTML] [Full Text PDF] [Full Text PDF with Links] 2. Young Ha Baek, Heung Yong Jin, Kyung Ae Lee, Seon Mee Kang, Woong Ji Kim, Min Gul Kim, Ji Hyun Park, Soo Wan Chae, Hong Sun Baek, Tae Sun Park. 2010. The Correlation and Accuracy of Glucose Levels between Interstitial Fluid and Venous Plasma by Continuous Glucose Monitoring System. Korean Diabetes Journal 34:6, 350. [CrossRef] 3. Giuseppe Derosa, Sibilla A.T. Salvadeo, Roberto Mereu, Angela D'Angelo, Leonardina Ciccarelli, Mario N. Piccinni, Ilaria Ferrari, Alessia Gravina, Pamela Maffioli, Carmine Tinelli. 2009. Continuous Glucose Monitoring System in Free-Living Healthy Subjects: Results from a Pilot Study. Diabetes Technology & Therapeutics 11:3, 159-169. [Abstract] [Full Text PDF] [Full Text PDF with Links] 4. Ralph Dutt-Ballerstadt, Colton Evans, Ashok Gowda, Roger McNichols. 2008. Preclinical In Vivo Study of a Fluorescence Affinity Sensor for Short-Term Continuous Glucose Monitoring in a Small and Large Animal Model. Diabetes Technology & Therapeutics 10:6, 453-460. [Abstract] [Full Text HTML] [Full Text PDF] [Full Text PDF with Links] 5. Wentholt I.M.E., Hart A.A.M., Hoekstra J.B.L., DeVries J.H.. 2008. How to Assess and Compare the Accuracy of Continuous Glucose Monitors?. Diabetes Technology & Therapeutics 10:2, 57-68. [Abstract] [Full Text PDF] [Full Text PDF with Links] 6. A NYBACKNAKELL, M VONHEIJNE, U ADAMSON, P LINS, L LANDSTEDTHALLIN. 2004. Accuracy of continuous nocturnal glucose monitoring after 48 and 72 hours in type 2 diabetes patients on combined oral and insulin therapy. Diabetes & Metabolism 30:6, 517-521. [CrossRef] 7. J. Hans DeVries, Iris M. E. Wentholt, Nathalie Masurel, Itske Mantel, Alessandro Poscia, Alberto Maran, Robert J. Heine. 2004. Nocturnal hypoglycaemia in type 1 diabetes?consequences and assessment. Diabetes/ Metabolism Research and Reviews 20:S2, S43-S46. [CrossRef] 8. Anneloes Kerssen, Harold W. De Valk, Gerard H.A. Visser. 2004. The Continuous Glucose Monitoring System During Pregnancy of Women with Type 1 Diabetes Mellitus: Accuracy Assessment. Diabetes Technology & Therapeutics 6:5, 645-651. [Abstract] [Full Text PDF] [Full Text PDF with Links] 9. Dale R. Tavris, Azadeh Shoaibi. 2004. The Public Health Impact of the MiniMed Continuous Glucose Monitoring System (CGMS®)—An Assessment of the Literature. Diabetes Technology & Therapeutics 6:4, 518-522. [Abstract] [Full Text PDF] [Full Text PDF with Links] 10. Philip A. Goldberg, Mark D. Siegel, Raymond R. Russell, Robert S. Sherwin, Joshua I. Halickman, Dawn A. Cooper, James D. Dziura, Silvio E. Inzucchi. 2004. Experience with the Continuous Glucose Monitoring System® in a Medical Intensive Care Unit. Diabetes Technology & Therapeutics 6:3, 339-347. [Abstract] [Full Text PDF] [Full Text PDF with Links] 11. Chee W. Chia, Christopher D. Saudek. 2004. Glucose sensors: toward closed loop insulin delivery. Endocrinology and Metabolism Clinics of North America 33:1, 175-195. [CrossRef] 12. N. Sachedina, J. C. Pickup. 2003. Performance assessment of the Medtronic-MiniMed Continuous Glucose Monitoring System and its use for measurement of glycaemic control in Type 1 diabetic subjects. Diabetic Medicine 20:12, 1012-1015. [CrossRef] 13. 2003. The Accuracy of the CGMS™ in Children with Type 1 Diabetes: Results of the Diabetes Research in Children Network (DirecNet) Accuracy Study. Diabetes Technology & Therapeutics 5:5, 781-789. [Abstract] [Full Text PDF] [Full Text PDF with Links] 14. C DJAKOUREPLATONOFF, R RADERMERCKER, G REACH, G SLAMA, J SELAM. 2003. Accuracy of the continuous glucose monitoring system in inpatient and outpatient conditions. Diabetes & Metabolism 29:2, 159-162. [CrossRef] 15. P.U Abel, T von Woedtke. 2002. Biosensors for in vivo glucose measurement: can we cross the experimental stage. Biosensors and Bioelectronics 17:11-12, 1059-1070. [CrossRef]
16. Reza Jamali, Johnny Ludvigsson, Simin Mohseni. 2002. Continuous Monitoring of the Subcutaneous Glucose Level in Freely Moving Normal and Diabetic Rats and in Humans with Type 1 Diabetes. Diabetes Technology & Therapeutics 4:3, 305-312. [Abstract] [Full Text PDF] [Full Text PDF with Links] 17. Margaret T. Lawlor, Lori M. B. Laffel. 2001. New technologies and therapeutic approaches for the management of pediatric diabetes. Current Diabetes Reports 1:1, 56-66. [CrossRef]
