OBJECTIVE

Cleared blood glucose monitors (BGMs) for personal use may not always deliver levels of accuracy currently specified by international and U.S. regulatory bodies. This study’s objective was to assess the accuracy of 18 such systems cleared by the U.S. Food and Drug Administration representing approximately 90% of commercially available systems used from 2013 to 2015.

RESEARCH DESIGN AND METHODS

A total of 1,035 subjects were recruited to have a capillary blood glucose (BG) level measured on six different systems and a reference capillary sample prepared for plasma testing at a reference laboratory. Products were obtained from consumer outlets and tested in three triple-blinded studies. Each of the three participating clinical sites tested a different set of six systems for each of the three studies in a round-robin. In each study, on average, a BGM was tested on 115 subjects. A compliant BG result was defined as within 15% of a reference plasma value (for BG ≥100 mg/dL [5.55 mmol/L]) or within 15 mg/dL (0.83 mmol/L) (for BG <100 mg/dL [5.55 mmol/L]). The proportion of compliant readings in each study was compared against a predetermined accuracy standard similar to, but more lenient than, current regulatory standards. Other metrics of accuracy included the overall compliance proportion; the proportion of extreme outlier readings differing from the reference value by >20%; modified Bland-Altman analysis including average bias, coefficient of variation, and 95% limits of agreement; and proportion of readings with no clinical risk as determined by the Surveillance Error Grid.

RESULTS

The different accuracy metrics produced almost identical BGM rankings. Six of the 18 systems met the predetermined accuracy standard in all three studies, 5 systems met it in two studies, and 3 met it in one study. Four BGMs did not meet the accuracy standard in any of the three studies.

CONCLUSIONS

Cleared BGMs do not always meet the level of analytical accuracy currently required for regulatory clearance. This information could assist patients, professionals, and payers in choosing products and regulators in evaluating postclearance performance.

Self-testing of blood glucose (BG) using a personal blood glucose monitor (BGM) is a cornerstone of diabetes treatment (1). BGMs are used for 1) measuring BG to determine therapeutic decisions, 2) calibrating continuous glucose monitoring systems, and 3) detection or confirmation of hypoglycemia. To be both safe and of clinical value, BGM systems should measure BG levels accurately (2).

International Organization for Standardization (ISO) 15197:2013 is an international standard for defining the accuracy of BGMs (3). However, it is not used by the U.S. Food and Drug Administration (FDA) as part of the clearance process for these devices. In 2016, the FDA developed a standard for BGMs for over-the-counter use (4) that was similar to ISO 15197:2013. In defining an acceptable level of accuracy, these two standards both require 95% of data pairs (defined as a BGM measurement and a reference measurement) to be within 15% for BG values >100 mg/dL (5.55 mmol/L). However, for BG values <100 mg/dL (5.55 mmol/L), ISO 15197:2013 requires data pairs to be within 15 mg/dL (0.83 mmol/L), whereas the FDA 2016 over-the-counter standard requires data pairs to be within 15%. These two standards also differ in the number of data pairs required for testing and the acceptable number of extreme outlier data pairs.

In recent years, personal BGMs have been reported to perform below international standards or FDA standards (57). Furthermore, adverse clinical and economic outcomes due to analytical inaccuracy of BGMs have been reported through empirical and modeling studies (5). Inaccurate BGMs could potentially put users at significant personal risk. The Diabetes Technology Society (DTS)-BGM Surveillance Program for marketed BGMs was developed to provide an independent assessment of the analytical performance of BGMs after clearance by the FDA and also to provide information that can assist the diabetes community, health care professionals, and payers to make informed decisions when selecting a BGM (8). The consensus protocol that was used in this study was developed by a panel of experts in clinical chemistry, clinical diabetes, and regulatory science with representation from the FDA, Centers for Disease Control and Prevention, National Institutes of Health, U.S. Army, professional organizations, academia, and industry.

This study was conducted during 2016–2017. The study was approved by the Schulman Institutional Review Board, and all subjects gave informed consent prior to participating in the study. The 18 selected BGMs (Table 1) represented the best-selling BGMs in the U.S., which comprised approximately 90% of products obtained from consumer outlets between 2013 and 2015 as measured by IMS Rx Xponent data (9), Office of Inspector General’s Medicare mail-order survey (10), and private label products (11). These BGMs were tested at three clinical sites (Rainier Clinical Research Center, Inc.; Diablo Clinical Research, Inc.; and AMCR [Advanced Metabolic Care + Research] Institute, Inc.). Every subject had a capillary BG level measured on six different BGMs and a simultaneous reference capillary sample prepared for comparator plasma testing at a reference laboratory. Each of three separate studies tested all 18 BGMs. Each clinical site assessed all 18 systems by testing a different set of six BGMs for each of the three studies in a round-robin. The reference laboratory was William Sansum Diabetes Center.

Subjects

Enrolled subjects were aged 18 years and older and had type 1 diabetes, type 2 diabetes, prediabetes, or no diabetes. Exclusion criteria included: 1) hemophilia or any other bleeding disorder; 2) pregnancy; and 3) a condition, which in the opinion of the investigator or designee, would put the person or study at risk. Subjects completed initial screening to assess eligibility. Limited demographic and medical information about the subjects was collected including age, sex, race, ethnicity, presence/type of diabetes, fasting or not fasting state, and medications, although this information was not used for inclusion or exclusion. A sample size of approximately 100 subjects in each study of each BGM was recommended by the surveillance protocol as large enough to adequately assess accuracy.

Sources of BGMs and Strips

The BGMs and test strips were obtained from various parts of the U.S. both from large retail pharmacies and from online retailers to mimic the experience of people with diabetes. There was no requirement regarding how many test strip lots were to be used per study or in the overall project.

Testing Procedure

Recruitment was designed to obtain a wide range of BG values. For enhancement of the percentage of low BG values, approximately one-third of recruited subjects did not have diabetes and some were asked to come to the clinic fasting. Conversely, for obtainment of values in the high glucose range, some subjects with diabetes were tested 60–120 min after a meal.

In each study at each site, every subject had finger-stick capillary blood obtained and measured on six BGMs. The sequence of testing six BGMs was randomized for each subject by a predetermined schedule that assigned all BGMs to be equally frequently in all six positions at each site. The BGM display readings, including any error messages, were recorded and photographed. Reference plasma samples were obtained via deep finger puncture and collected into a tube containing lithium heparin. This blood was centrifuged within 5 min of collection and the plasma was transferred to a tube without additive, frozen, and later shipped to the reference laboratory for analysis.

The deep stick to obtain a blood sample for reference testing was performed in the middle of the sequence of six finger-sticks for BGM testing. All six finger-sticks were done within 12 min of the deep stick to minimize in vivo changes in BG concentration during the testing period. All tests were performed by trained staff who were health care professionals. Subjects did not perform any self-testing.

This study was not designed or powered to look at glycemic bin subsets, such as in the range of <70 mg/dL (3.9 mmol/L) or >300 mg/dL (16.6 mmol/L). We also tested glycolized specimens on a subset of the 18 BGMs as part of this protocol and will report those results separately.

Capillary plasma was shipped to the reference laboratory for testing on five comparator instruments (YSI Life Sciences 2300 Stat Plus Glucose Lactate Analyzer). The comparator instruments performed autocalibration every 20 min and underwent regular stringent quality control testing, including testing with four glucose levels of National Institute of Standards and Technology (NIST) Standard Reference Material 965b Standards (Glucose in Frozen Serum) to assure accuracy traceable to mass spectrometry measurement.

Blinding

This study was triple blinded. Those reading the BGMs could not know the plasma reference measurements, since they were performed later and at a different location; the reference laboratory did not have the BGM readings, and during the collation and analysis of the results, the BGMs were designated by a code number. All analyses were performed while blinded to the identity of the meters. The results and conclusions were posted prior to unbinding. The sponsor was not aware of the data and was not involved in the writing of the manuscript.

Analyses

The protocol specified that to be compliant a BGM’s reported value must be within 15% of a reference plasma value ≥100 mg/dL (5.55 mmol/L) and within 15 mg/dL (0.83 mmol/L) of a reference value <100 mg/dL (5.55 mmol/L). This definition of data point compliance is the same as that used by ISO 15197:2013, which requires that 95% of a study’s data pairs be compliant for a BGM to pass (3). The FDA’s over-the-counter 2016 standard had not been released when the surveillance protocol was developed. This FDA guidance requires 95% of all BG results to be within 15% of the comparator results across the entire claimed measuring range of the device and that 99% of all BGM results be within 20% of the comparator results across the entire claimed measuring range of the device (4), which is a more stringent requirement than that specified by ISO 15197:2013 (3).

We applied a predetermined accuracy standard to each BGM in each of the three studies. A BGM was considered to have met the standard unless the proportion compliant was in the “clear rejection zone” (7). The clear rejection zone is defined by taking as the null hypothesis that the meter meets the ISO 15197:2013 level of performance, which is 95% compliant readings. For rejection of this hypothesis, the measured number of compliant readings must be low enough such that chance variation would account for this poor outcome <5% of the time. With 100 readings, the number compliant to reject the null hypothesis must be <91 (91% compliant) or the BGM meets the accuracy standard. With 125 readings, the number compliant to reject the null hypothesis must be <115 (92% compliant) or the BGM meets the accuracy standard. This means that a device with a compliant proportion as low as 91 of 100 (91%) or 115 of 125 (92%) would meet the accuracy standard, making it more lenient than the ISO standard of 95% compliance.

Four other metrics of accuracy were also assessed. These metrics included 1) overall compliance in the three studies (total compliant readings/total readings); 2) number and percent of values within specified error limits, including within 5%, 10%, 15%, and 20% of the reference value (or within 5 mg/dL [0.28 mmol/L], 10 mg/dL [0.56 mmol/L], 15 mg/dL [0.83 mmol/L], and 20 mg/dL [1.12 mmol/L] of the reference value when the reference value was <100 mg/dL [5.55 mmol/L]), and we defined data pair differences exceeding 20% or 20 mg/dL (1.11 mmol/L) as extreme outliers, 3) modified Bland-Altman analysis comparing the difference between the BGM reading and the reference value as a percentage of the reference value, including average bias, coefficient of variation, 95% limits of agreement, the larger absolute limit boundary, and a modified Bland-Altman plot; and 4) clinical risk using eight absolute levels in the Surveillance Error Grid (SEG) (12). The SEG is a modern metric for clinical accuracy of BGMs based on risk assessments of BGM errors by diabetes clinicians that assigns a unique risk score to each system-measured data point when compared with a reference value. The SEG specifies the clinical accuracy of a BGM to be portrayed as the percentages of data points falling into prespecified risk zones. This tool can be used to assist regulators and manufacturers to monitor and evaluate BGM performance in their surveillance programs.

Subjects

A total of 1,035 subjects were recruited and enrolled in 2016, of whom 1,032 subjects completed the study (Rainier Clinical Research Center, Inc., 352 subjects; Diablo Clinical Research, Inc., 335 subjects; and AMCR Institute, Inc., 345 subjects). In each study, on average, a BGM was tested on 115 subjects. One enrolled subject dropped out prior to completing the finger-sticks; two subjects’ plasma specimens were lost in shipping to the reference laboratory. The sexes, types of diabetes, age races, and ethnicities of subjects are presented in Table 2; however, the study was not intended or powered to study outcomes in any demographic subset. No adverse events occurred. The average number of strip lots per study was 2.1 (SD 1.1 and range 1–4) where evaluable strips could be located. One BGM (TRUEtrack) was excluded from one of the three studies because a recall made strips unavailable.

Reference Instrument Bias

Relative to NIST standards, the mean (SD) bias for each of the five YSI instruments used (in decreasing order of frequency of use) was −0.63% (1.88), −0.24% (1.68), −0.08% (1.66), −0.24% (1.16), and 0.69% (1.68). For the four NIST specimens that were each run on five different YSI instruments, 20 mean biases (one per glucose concentration per instrument) ranged from −1.67% to 1.78%.

Performance Relative to Accuracy Standard

Six of the 18 BGMs met the predetermined accuracy standard in all three studies; 5 BGMs met it in two studies; and 3 met it in one study. Four BGMs did not meet the accuracy standard in any of the three studies (Table 3). As mentioned in Research Design and Methods, the protocol defined a compliant data pair as within 15% for BG >100 mg/dL (5.55 mmol/L) or 15 mg/dL (0.83 mmol/L) for BG <100 mg/dL (5.55 mmol/L). Rankings by overall compliant proportion across the three studies (total compliant readings/total readings) coincided with the rankings by number of studies in which the BGM met the accuracy standard. The overall percentage of data pairs that were compliant for every BGM that met the accuracy standard in all three studies was 95% or higher. This overall percentage of compliance is consistent with the minimum necessary percentage for satisfactory performance according to ISO 15197:2013 and FDA 2016 over-the-counter standards. Furthermore, every BGM meeting the accuracy standard on two or fewer of the three studies had an overall compliant proportion of ≤92% (Table 3). By site, the number of BGMs meeting the accuracy standard were 10 of 18, 12 of 18, and 9 of 17 (with one BGM not tested). In terms of overall compliance percentages, the sites were also similar: 88%, 93%, and 88%.

Error Limits and Extreme Outliers

Rankings varied slightly with different tolerances for compliance (±5% or 5 mg/dL [0.28 mmol/L] of the reference value, ±10% [0.56 mmol/L], and ±20% (1.11 mmol/L) (see Supplementary Table 1). Regarding extreme outlier data points, for each of the six BGMs that met the accuracy standard on all three of studies, <2% of readings were >20% from the reference value. For the other BGMs, >2% of readings were >20% from the reference value, with the exception of LifeScan OneTouch Verio, which had 1.3% of readings >20% from the reference value.

Modified Bland-Altman Analysis

The results of the modified Bland-Altman analysis comparing the difference (BGM reading minus reference value) with the reference value were similar to the overall compliance results. The bias of each of the six top-performing BGMs according to the accuracy standard and of the 12 other BGMs ranged, respectively, from −6.0% to 2.4% (with five of these six showing negative bias) and from −10.1% to 5.9% (with 6 of 12 showing negative bias) (Table 4). The larger absolute 95% limit of agreement combines bias with the coefficient of variation. The larger absolute limit boundaries for the six top-performing BGMs and for the 12 other BGMs were, respectively, 11–19% and 19–43%. Although it did not meet the accuracy standard on all three studies, the LifeScan OneTouch Ultra 2 had a larger absolute limit boundary of 19%, which tied the highest value of among the six top-performing BGMs according to the other metrics. The modified Bland-Altman plots are available upon request to the corresponding author.

Clinical Accuracy

The SEG divides BGM reference pairs into eight risk levels, the lowest of which is “no risk.” The six top-performing BGMs on other metrics had >97% of readings in the “no risk” category, whereas none of the other BGMs had >97% of readings in this “no risk” category (see Supplementary Table 2).

Excluded Data

Of 1,032 plasma samples, 81 (7.8%) were excluded from analysis—63 for >4% variability between duplicate analyses on the comparator instrument runs, 6 for hemolysis, and 12 for other sample problems (including low sample volume, bubbles in the sample, and autocalibration failure). The remaining 951 reference plasma specimens corresponded with 5,584 BGM readings or 5.9 capillary BGM readings per reference specimen. There were fewer than six BGM-measured specimens per reference reading because 122 capillary readings were not performed or were not evaluable; 114 subjects were to be tested with a TRUEtrack BGM at one site, but the system’s strips were recalled just before the third study was scheduled, and no replacement strips could be located. Capillary BG testing was not performed with the recalled strip lot. The 114 subjects were tested with five other BGMs. Furthermore, eight BGM readings of capillary BG levels generated error codes. The proportion of reference values excluded did not differ significantly between the 6 top-performing BGMs and the 12 other BGMs.

The data analyses included samples with hematocrit ranges that were outside of the product labeling of certain BGMs. However, in an additional analysis, exclusion of 57 specimens with hematocrit outside the narrowest range for any of the 18 BGMs and 19 specimens for which no hematocrit was recorded had no effect on the BGM performance rankings (data not shown).

Sequence Effect

The position in the sequence did not significantly affect the difference from the reference value (P = 0.87). Furthermore, analysis after completion of the study showed that each BGM was equally likely to be tested in each available sequence position.

We found that of 18 commercially available BGMs, 6 met a predefined accuracy standard on three out of three studies. This accuracy standard was similar to, but more lenient than, those currently used by the FDA. The other metrics of accuracy confirmed the rankings based on meeting the accuracy standard. Therefore, based on our findings it appears that cleared BGMs do not always perform to the level of analytical accuracy that is currently required for clearance.

The six top-performing BGMs according to the accuracy standard also performed the best according to four additional metrics: 1) overall compliant proportion, 2) the proportion of extreme outliers (although the LifeScan OneTouch Verio also performed well on this metric), 3) the greater 95% limit of agreement, and 4) the proportion in the lowest clinical risk category according to the SEG. Among the 12 lower-ranking BGMs, there was a wide spectrum of overall performance, ranging from meeting the accuracy standard on two, one, or zero out of three trials and demonstrating an overall compliant proportion ranging from 71 to 92%.

In terms of clinical consensus accuracy, the six top-performing BGMs had at least 97% of their data points in the SEG no-risk zone and the other 12 BGMs had <97% of their data points in the SEG no-risk zone. Kovatchev et al. (13) used modeling to calculate that a device with ≤3% errors outside of the SEG no-risk “green” zone would meet the ISO requirements of ≤5% data pairs outside the 15 mg/dL (0.83 mmol/L)/15% standard limits, while higher percentages outside the SEG no-risk zone would indicate noncompliance with the standard. No empirical series to our knowledge has specified a target for clinical accuracy using the SEG, but based on risk zone results of 18 BGMs from this study and a post hoc analysis of these results, we propose that a cutoff for excellent clinical accuracy can be defined as ≥97% of data points in the no-risk zone of the SEG, as Kovatchev et al. had predicted.

Strips were purchased based on availability irrespective of lot number. A defective strip lot could not be ruled out as the cause of poor performance for a given product. The purpose of the study was to ascertain whether there was poor performance of the tested products. The study was not intended to seek out three different lots of any product. The study was also not intended to identify any specific strip lots associated with poor performance of a product.

Many factors affect the accuracy of a BGM, including those related to the test strip and the meter (14,15). Differences in accuracy were not unexpected because technological factors vary among the BGMs in this study. Although BGMs must now meet criteria similar to the ones we used in this study in order to receive clearance from the FDA to market in the U.S., some currently marketed older BGMs were cleared when accuracy standards were 20% (15 mg/dL [0.83 mmol/L]) per ISO 15197:2003 rather than the current ±15% requirements per the FDA 2016 over-the-counter standards.

The performance of BGMs may diminish over time (i.e., postmarket performance may deteriorate). This decline may be due to scale-up issues, manufacturing errors, changes in components between strip lots, other production issues, or improper shipping. Over time, the measured analytical accuracy might no longer represent the sponsor’s initial accuracy data that were submitted to the FDA. Such factors might account for our findings.

To assess whether there was significant year-to-year turnover in market share of the most widely purchased BGMs, we compared the Medicare mail-order shares distribution of the top BGMs purchased between quarter 4 of 2013 (9), when this surveillance program was first planned (16), and quarter 2 of 2016 (17) when we began our study. According to that database, the mail-order shares for the 18 BGMs that we tested changed from 90.1% to 84.3% over that 2.5-year period, which indicated only a small annual turnover.

The performance levels in this surveillance protocol represent how each BGM product functioned in our research study carried out by trained medical professionals. This performance cannot necessarily be extrapolated to use by patients. The total number of times that a BGM met the protocol’s accuracy standard as tested by health care professionals on a particular set of strips and meters at a specific time does not mean that a patient or other user can expect any particular performance from the product other than what the manufacturer claims. Product performance can change over time. The authors make no claims, endorsements, or predictions for future performance of the tested products.

Strengths of this study include the large number of subjects tested (1,032), the large number of data pairs evaluated for agreement (5,584), the large number of FDA-cleared BGMs tested (18 systems tested three times each), and the consistency of the outcomes achieved by several evaluation methods (e.g., number of studies meeting the accuracy standard, overall compliant proportion, frequency of extreme outliers, modified Bland-Altman analysis, and clinical accuracy using the SEG). To our knowledge, this is the largest accuracy study of FDA-cleared BGMs using a consensus protocol created with input from the FDA ever reported in the literature. All strips and monitors were purchased from commercial suppliers without the manufacturers’ knowledge to avoid positive bias that could occur if a manufacturer were to have an opportunity to submit their best performing strips or monitors for testing. Also, the protocol was developed by an impartial expert panel. Testing performed by health care professionals tends to lead to more accurate results than when subjects test themselves (18), which could lead to a higher level of accuracy in this study compared with other studies where subjects self-test. Finally, this study was triple blinded, which eliminated the possibility of systematic bias based on BGM brand.

A limitation of this study is the exclusion of 81 out of 1,032 (7.8%) of the reference samples. Another limitation is a mean downward drift of 0.12% between the time points of the capillary tests, 0.52% from the first to the sixth BGM tested. However, this decrease was not statistically significant and could not have biased findings in favor or against a specific BGM, since the testing sequence was randomized. Yet another limitation is that products are frequently replaced by newer models and some of the products tested may not remain on the market for a prolonged period of time in the future. The BGMs tested in this study are all intended only for outpatient self-monitoring. A similar study of prescription point-of-care BGMs used in hospitals and nursing homes could be performed in the future.

In conclusion, 6 of the 18 best-selling personal BGMs met a protocol-specified accuracy standard similar to current ISO and FDA standards on three of three studies. These same six meters ranked highest according to four other metrics. Since patients depend on their BGMs for day-to-day management, lack of accuracy may put patients at risk for both hypoglycemia and hyperglycemia. We believe that this study points out the varying degrees to which commonly used BGMs do or do not give accurate information. We hope that this study will provide objective and validated information for patients, health care professionals, and payers to make informed product selection. We also hope that this study will provide important information that will lead regulators to consider introducing a mechanism to evaluate postmarket performance of these types of analytical products.

Clinical trial reg. no. NCT02789319, clinicaltrials.gov.

This article is featured in a podcast available at http://www.diabetesjournals.org/content/diabetes-core-update-podcasts.

Acknowledgments. The authors thank the following persons for their contributions to conducting this study: Laura Bedolla (AMCR Institute, Inc.), Pam Martin (Rainier Clinical Research Center, Inc.), and Catherine Morimoto (Diablo Clinical Research) for recruiting subjects and performing capillary BG measurements; Dr. Kristin Castorino (William Sansum Diabetes Center) for overseeing performance of reference glucose measurements; and Megha Shah (clinical research professional) for oversight of the clinical sites. The authors also thank Mike Jarrett (QuesGen Systems, Inc., Burlingame, CA) for assistance with data analysis, Dan Shilstone (Diabetes Technology Society, Burlingame, CA) for assistance with data presentation, Brett McGreevy (Diabetes Technology Society) for managerial oversight of the project, and Annamarie Sucher (Diabetes Technology Society) for expert editorial assistance.

Duality of Interest. This study was supported by a grant from Abbott Diabetes Care. D.C.K. is a consultant for Ascensia, AstraZeneca, EOFlow, Intarcia, Lifecare, Novo Nordisk, and Voluntis; has received research funding from Diasome, Lexicon, and Novo Nordisk; and is an employee of DTS. J.L.P. is a Bayer retiree and a consultant for DTS. B.P.K. has received grant/research support from Dexcom, Roche Diabetes Care, Sanofi, Senseonics, and Tandem Diabetes Care; is on the advisory board, is a consultant, and is on the speaker’s bureau for Dexcom, Sanofi, and Senseonics; is a stock shareholder for TypeZero Technologies; and has patent royalties managed by the University of Virginia Licensing and Ventures group from LifeScan, Animas, and Sanofi. D.K. is a medical advisor to Glooko and Vicentra and is creator of www.diabetestravel.org and www.excarbs.com. William Sansum Diabetes Center has received research funding from Abbott Diabetes Care, Dexcom, Sanofi, Novo Nordisk, and Lilly. R.L.B. received research grant support from Abbott Diabetes Care, Roche, Bayer, Senseonics, Dexcom, and Medtronic. M.C. has received research funding from Abbott Diabetes Care, Bayer, Dexcom, Insulet, Medtronic, and Senseonics. T.S.B. has received research support from Abbott Diabetes Care, Ambra, Ascensia, BD, Boehringer Ingelheim, Calibra, Companion Medical, Dexcom, Elcelyx, GlySens, Janssen, Lexicon, Lilly, Medtronic, Novo Nordisk, Sanofi, Senseonics, Versartis, and Xeris; consulting honoraria from AstraZeneca, Ascensia, BD, Calibra, Lilly, Medtronic, Novo Nordisk, and Sanofi; and speaking honoraria from Abbott Diabetes Care, Insulet, Medtronic, Lilly, Novo Nordisk, and Sanofi. J.H.N. has accepted honoraria and travel expenses for professional speaking, consulting, and participation in scientific advisory boards for Abbott Laboratories and Roche Diagnostics. No other potential conflicts of interest relevant to this article were reported.

Author Contributions. D.C.K. wrote the manuscript and organized the study. J.L.P. wrote the manuscript and contributed to the discussion. B.P.K. reviewed the manuscript and advised about the statistics. D.K. reviewed and edited the manuscript. W.C.B. researched data at the reference laboratory. R.L.B. researched data at a clinical site. M.C. researched data at a clinical site. T.S.B. researched data at a clinical site. J.H.N. contributed to the discussion. M.A.K. wrote the manuscript and advised about the statistics. D.C.K. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Prior Presentation. The summary results were first reported on the DTS website in preparation for a presentation at the U.S. Congressional Diabetes Caucus, Washington, DC, 23 August 2017 (https://www.diabetestechnology.org/surveillance.shtml).

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2010
;
12
:
847
857
[PubMed]
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