Clinical application of emerging sensor technologies in diabetes management: consensus guidelines for continuous glucose monitoring (CGM).

Continuous glucose monitoring (CGM) is an evolving technology poised to redefine current concepts of glycemic control and optimal diabetes management. To date, there are few randomized studies examining how to most effectively use this new tool. Therefore, a group of eight diabetes specialists heard presentations on continuous glucose sensor technology and then discussed their experience with CGM in order to identify fundamental considerations, objectives, and methods for applying this technology in clinical practice. The group concluded that routine use of CGM, with real-time data showing the rate and direction of glucose change, could revolutionize current approaches to evaluating and managing glycemia. The need for such progress is indicated by the growing prevalence of inadequately treated hyperglycemia. Coordinating financial and educational resources and developing clear protocols for using glucose sensor technology are urgent priorities in promoting wide adoption of CGM by patients and health care providers. Finally, researchers, manufacturers, payers, and advocacy groups must join forces on the policy level to create an environment conducive to managing continuous data, measuring outcomes, and formalizing best practices.

Evaluating the clinical accuracy of two continuous glucose sensors using continuous glucose-error grid analysis.

OBJECTIVE: To compare the clinical accuracy of two different continuous glucose sensors (CGS) during euglycemia and hypoglycemia using continuous glucose-error grid analysis (CG-EGA). RESEARCH DESIGN AND METHODS: FreeStyle Navigator (Abbott Laboratories, Alameda, CA) and MiniMed CGMS (Medtronic, Northridge, CA) CGSs were applied to the abdomens of 16 type 1 diabetic subjects (age 42 +/- 3 years) 12 h before the initiation of the study. Each system was calibrated according to the manufacturer's recommendations. Each subject underwent a hyperinsulinemic-euglycemic clamp (blood glucose goal 110 mg/dl) for 70-210 min followed by a 1-mg.dl(-1).min(-1) controlled reduction in blood glucose toward a nadir of 40 mg/dl. Arterialized blood glucose was determined every 5 min using a Beckman Glucose Analyzer (Fullerton, CA). CGS glucose recordings were matched to the reference blood glucose with 30-s precision, and rates of glucose change were calculated for 5-min intervals. CG-EGA was used to quantify the clinical accuracy of both systems by estimating combined point and rate accuracy of each system in the euglycemic (70-180 mg/dl) and hypoglycemic (<70 mg/dl) ranges. RESULTS: A total of 1,104 data pairs were recorded in the euglycemic range and 250 data pairs in the hypoglycemic range. Overall correlation between CGS and reference glucose was similar for both systems (Navigator, r = 0.84; CGMS, r = 0.79, NS). During euglycemia, both CGS systems had similar clinical accuracy (Navigator zones A + B, 88.8%; CGMS zones A + B, 89.3%, NS). However, during hypoglycemia, the Navigator was significantly more clinically accurate than the CGMS (zones A + B = 82.4 vs. 61.6%, Navigator and CGMS, respectively, P < 0.0005). CONCLUSIONS: CG-EGA is a helpful tool for evaluating and comparing the clinical accuracy of CGS systems in different blood glucose ranges. CG-EGA provides accuracy details beyond other methods of evaluation, including correlational analysis and the original EGA.

Relationships between hyperglycemia and cognitive performance among adults with type 1 and type 2 diabetes.

OBJECTIVE: Hyperglycemia is a common event among patients with type 1 and type 2 diabetes. While the cognitive-motor slowing associated with hypoglycemia is well documented, the acute effects of hyperglycemia have not been studied extensively, despite patients' reports of negative effects. This study prospectively and objectively assessed the effects of hyperglycemia on cognitive-motor functioning in subjects' natural environment. RESEARCH DESIGN AND METHODS: Study 1 investigated 105 adults with type 1 diabetes (mean age 37 years and mean duration of diabetes 20 years), study 2 investigated 36 adults with type 2 diabetes (mean age 50 years and mean duration of diabetes 10 years), and study 3 investigated 91 adults with type 1 diabetes (mean age 39 years and mean duration of diabetes 20 years). Subjects used a hand-held computer for 70 trials over 4 weeks, which required them to complete various cognitive-motor tasks and then measure and enter their current blood glucose reading. RESULTS: Hyperglycemia (blood glucose >15 mmol/l) was associated with slowing of all cognitive performance tests (P < 0.02) and an increased number of mental subtraction errors for both type 1 and type 2 diabetic subjects. The effects of hyperglycemia were highly individualized, impacting approximately 50% of the subjects. CONCLUSIONS: Acute hyperglycemia is not a benign event for many individuals with diabetes, but it is associated with mild cognitive dysfunction.

Quantifying temporal glucose variability in diabetes via continuous glucose monitoring: mathematical methods and clinical application.

BACKGROUND: Continuous glucose monitors (CGMs) collect detailed blood glucose (BG) time series, which carry significant information about the dynamics of BG fluctuations. In contrast, the methods for analysis of CGM data remain those developed for infrequent BG self-monitoring. As a result, important information about the temporal structure of the data is lost during the translation of raw sensor readings into clinically interpretable statistics and images. METHODS: The following mathematical methods are introduced into the field of CGM data interpretation: (1) analysis of BG rate of change; (2) risk analysis using previously reported Low/High BG Indices and Poincare (lag) plot of risk associated with temporal BG variability; and (3) spatial aggregation of the process of BG fluctuations and its Markov chain visualization. The clinical application of these methods is illustrated by analysis of data of a patient with Type 1 diabetes mellitus who underwent islet transplantation and with data from clinical trials. RESULTS: Normative data [12,025 reference (YSI device, Yellow Springs Instruments, Yellow Springs, OH) BG determinations] in patients with Type 1 diabetes mellitus who underwent insulin and glucose challenges suggest that the 90%, 95%, and 99% confidence intervals of BG rate of change that could be maximally sustained over 15-30 min are [-2,2], [-3,3], and [-4,4] mg/dL/min, respectively. BG dynamics and risk parameters clearly differentiated the stages of transplantation and the effects of medication. Aspects of treatment were clearly visualized by graphs of BG rate of change and Low/High BG Indices, by a Poincare plot of risk for rapid BG fluctuations, and by a plot of the aggregated Markov process. CONCLUSIONS: Advanced analysis and visualization of CGM data allow for evaluation of dynamical characteristics of diabetes and reveal clinical information that is inaccessible via standard statistics, which do not take into account the temporal structure of the data. The use of such methods improves the assessment of patients' glycemic control.

Evaluating the accuracy of continuous glucose-monitoring sensors: continuous glucose-error grid analysis illustrated by TheraSense Freestyle Navigator data.

OBJECTIVE: The objective of this study was to introduce continuous glucose-error grid analysis (CG-EGA) as a method of evaluating the accuracy of continuous glucose-monitoring sensors in terms of both accurate blood glucose (BG) values and accurate direction and rate of BG fluctuations and to illustrate the application of CG-EGA with data from the TheraSense Freestyle Navigator. RESEARCH DESIGN AND METHODS: We approach the design of CG-EGA from the understanding that continuous glucose sensors (CGSs) allow the observation of BG fluctuations as a process in time. We account for specifics of process characterization (location, speed, and direction) and for biological limitations of the observed processes (time lags associated with interstitial sensors). CG-EGA includes two interacting components: 1) point-error grid analysis (P-EGA) evaluates the sensor's accuracy in terms of correct presentation of BG values and 2) rate-error grid analysis (R-EGA) assesses the sensor's ability to capture the direction and rate of BG fluctuations. RESULTS: CG-EGA revealed that the accuracy of the Navigator, measured as a percentage of accurate readings plus benign errors, was significantly different at hypoglycemia (73.5%), euglycemia (99%), and hyperglycemia (95.4%). Failure to detect hypoglycemia was the most common error. The point accuracy of the Navigator was relatively stable over a wide range of BG rates of change, and its rate accuracy decreased significantly at high BG levels. CONCLUSIONS: Traditional self-monitoring of BG device evaluation methods fail to capture the important temporal characteristics of the continuous glucose-monitoring process. CG-EGA addresses this problem, thus providing a comprehensive assessment of sensor accuracy that appears to be a useful adjunct to other CGS performance measures.

Methods for quantifying self-monitoring blood glucose profiles exemplified by an examination of blood glucose patterns in patients with type 1 and type 2 diabetes.

The maintenance of glycemic control in patients with type 1 or type 2 diabetes mellitus (T1DM and T2DM, respectively) is commonly assisted by devices for self-monitoring of blood glucose (SMBG) that store multiple BG determinations. However, besides average BG, no other SMBG characteristics are routinely computed. We describe several SMBG-based measures that quantify the extent and rate of patients' BG excursions into hypoglycemia and hyperglycemia and can be used as markers for patients' vulnerability to hypoglycemia and BG irregularity. These markers are applied to analyze data from patients with T1DM (n = 277) and T2DM (n = 323), all of whom used insulin. T1DM and T2DM patients were matched by HbA(1c), gender, and number of SMBG readings/day. On average, 230 SMBG readings and three HbA(1c) assays were collected for each subject over 3 months. Compared with T2DM, patients with T1DM diabetes had (1) more extreme low and high BGs, (2) greater risk for severe hypoglycemia as quantified by the Low BG Index, (3) faster descent into hypoglycemia as quantified by the risk rate of change/hour, and (4) greater BG irregularity as computed by BG rate of change/hour and BG SD (all p levels < 0.0001). SMBG data allow for computing and frequent updating of various idiosyncratic diabetes characteristics and risk factors. The use of such computations may assist in optimizing patients' glycemic control.

Algorithmic evaluation of metabolic control and risk of severe hypoglycemia in type 1 and type 2 diabetes using self-monitoring blood glucose data.

The optimization of metabolic control in Type 1 and Type 2 diabetes mellitus (T1DM and T2DM, respectively) [i.e., the maintenance of near-normal hemoglobin A(1c) (HbA(1c)) without increasing the risk of hypoglycemia] could be enhanced by analysis of self-monitoring blood glucose (SMBG) data assessing complementary processes: exposure to hyperglycemia and hypoglycemia. We present algorithms that simultaneously estimate HbA(1)c and risk for significant hypoglycemia using 45-60 days of SMBG. The algorithms were developed using a primary data for 96 subjects with T1DM (n = 48) and T2DM, and were validated in an external data for 520 subjects with T1DM (n = 231) and T2DM. All subjects were on insulin. In the primary (external) data the estimation of HbA(1c) had absolute error of 0.5 (0.7) units of HbA(1c) and percent error of 6.8% (8.1%); 96% (96%) of all estimates were within 20% from reference HbA(1c). The SMBG-estimated value of HbA(1c) was closer to current reference HbA(1c) than a reference HbA(1c) value taken only 2-3 months ago. The results in T1DM and T2DM were similar. Linear model predicted future significant hypoglycemia (R(2) = 62%, p < 0.0001). The leading predictor was a previously introduced Low Blood Glucose Index, which alone had R(2) = 55%. Probability model assessed accurately the odds for future moderate/severe hypoglycemia (coefficients of determination 92%/94%). Four risk categories were identified; within moderate- and high-risk category, there was no difference between T1DM and T2DM in the occurrence of prospective significant hypoglycemia. SMBG data allow for accurate estimation of the two most important markers of metabolic control in T1DM and T2DM - HbA(1c) and risk for hypoglycemia.

Clinical requirements for closed-loop control systems.

Closed-loop (CL) therapy systems should be safe, efficacious, and easily manageable for type 1 diabetes mellitus patient use. For the first two clinical requirements, noninferiority and superiority criteria must be determined based on current conventional and intensive therapy outcomes. Current frequencies of hypoglycemia and diabetic ketoacidosis are reviewed and safety expectations for CL therapy systems are proposed. Glycosylated hemoglobin levels lower than current American Diabetes Association recommendations for different age groups are proposed as superiority criteria. Measures of glycemic variability are described and the recording of blood glucose levels as percentages within, above, and below a target range are suggested as reasonable alternatives to sophisticated statistical analyses. It is also suggested that Diabetes Quality of Life and Fear of Hypoglycemia surveys should be used to track psychobehavioral outcomes. Manageability requirements for safe and effective clinical management of CL systems are worth being underscored. The weakest part of the infusion system remains the catheter, which is exposed to variable and under-delivery incidents. Detection methods are needed to warn both the system and the patient about altered insulin delivery, including internal pressure and flow alarms. Glucose monitor sensor accuracy is another requirement; it includes the definition of conditions that lead to capillary glucose measurement, eventually followed by sensor recalibration or replacement. The crucial clinical requirement will be a thorough definition of the situations when the patient needs to move from CL to manual management of insulin delivery, or inversely can switch back to CL after a requested interruption. Instructions about these actions will constitute a major part of the education process of the patients before using CL systems and contribute to the manageability of these systems.

Behavioral challenges in the management of childhood diabetes.

For many individuals, the diagnosis of diabetes is accompanied by the need for significant lifestyle changes, many of which seem difficult or impossible to implement. When diabetes is diagnosed in a child, those lifestyle changes may involve radical alterations in family life and significantly impact the child's normal growth and development as well as the family's social and economic status. This article describes some of the behavioral challenges associated with childhood diabetes and the importance of identifying strong, developmentally appropriate family support. Specific emphases are given to the complexity of the treatment regimens, the physiologic and emotional challenges associated with normal growth and development, and the family's role in ensuring successful diabetes management. Challenges inherent in both type 1 and type 2 diabetes mellitus are discussed as are factors important to ensuring adherence to the treatment plan.

Analytical and clinical performance of blood glucose monitors.

BACKGROUND: The objective of this study was to understand the level of performance of blood glucose monitors as assessed in the published literature. METHODS: Medline from January 2000 to October 2009 and reference lists of included articles were searched to identify eligible studies. Key information was abstracted from eligible studies: blood glucose meters tested, blood sample, meter operators, setting, sample of people (number, diabetes type, age, sex, and race), duration of diabetes, years using a glucose meter, insulin use, recommendations followed, performance evaluation measures, and specific factors affecting the accuracy evaluation of blood glucose monitors. RESULTS: Thirty-one articles were included in this review. Articles were categorized as review articles of blood glucose accuracy (6 articles), original studies that reported the performance of blood glucose meters in laboratory settings (14 articles) or clinical settings (9 articles), and simulation studies (2 articles). A variety of performance evaluation measures were used in the studies. The authors did not identify any studies that demonstrated a difference in clinical outcomes. Examples of analytical tools used in the description of accuracy (e.g., correlation coefficient, linear regression equations, and International Organization for Standardization standards) and how these traditional measures can complicate the achievement of target blood glucose levels for the patient were presented. The benefits of using error grid analysis to quantify the clinical accuracy of patient-determined blood glucose values were discussed. CONCLUSIONS: When examining blood glucose monitor performance in the real world, it is important to consider if an improvement in analytical accuracy would lead to improved clinical outcomes for patients. There are several examples of how analytical tools used in the description of self-monitoring of blood glucose accuracy could be irrelevant to treatment decisions.

Comparison of the clinical information provided by the FreeStyle Navigator continuous interstitial glucose monitor versus traditional blood glucose readings.

BACKGROUND: The purpose of the analysis was to compare the clinical utility of data from traditional self-monitoring of blood glucose (SMBG) to that of continuous glucose monitoring (CGM). METHODS: A clinical study of the clinical accuracy of the FreeStyle Navigator CGM System (Abbott Diabetes Care, Alameda, CA), which includes SMBG capabilities, was conducted by comparison to the YSI blood glucose analyzer (YSI Inc., Yellow Springs, OH) using 58 subjects with type 1 diabetes. The Continuous Glucose-Error Grid Analysis (CG-EGA) was used as the analytical tool. RESULTS: Using CG-EGA, the "clinically accurate," "benign errors," and "clinical errors" were 86.8%, 8.7%, and 4.5% for SMBG and 92.7%, 3.7%, and 3.6% for CGM, respectively. If blood glucose is viewed as a process in time, SMBG would provide accurate information about this process 86.8% of the time, whereas CGM would provide accurate information about this process 92.7% of the time (P < 0.0001). In the hypoglycemic range, however, SMBG is more accurate as the "clinically accurate," "benign errors," and "clinical errors" were 83.5%, 6.4%, and 10.1% for SMBG and 57.1%, 8.4%, and 34.5% (P < 0.0001) for CGM, respectively. CONCLUSIONS: While SMBG produces more accurate instantaneous glucose values than CGM, control of blood glucose involves a system in flux, and CGM provides more detailed insight into the dynamics of that system. In the normal and elevated glucose ranges, the additional information about the direction and rate of glucose change provided by the FreeStyle Navigator CGM System increases the ability to make correct clinical decisions when compared to episodic SMBG tests.

Type 1 diabetic drivers with and without a history of recurrent hypoglycemia-related driving mishaps: physiological and performance differences during euglycemia and the induction of hypoglycemia.

OBJECTIVE: Collisions are more common among drivers with type 1 diabetes than among their nondiabetic spouses. This increased risk appears to be attributable to a subgroup of drivers with type 1 diabetes. The hypothesis tested is that this vulnerable subgroup is more at risk for hypoglycemia and its disruptive effects on driving. RESEARCH DESIGN AND METHODS: Thirty-eight drivers with type 1 diabetes, 16 with (+history) and 22 without (-history) a recent history of recurrent hypoglycemia-related driving mishaps, drove a virtual reality driving simulator and watched a videotape of someone driving a simulator for 30-min periods. Driving and video testing occurred in a double-blind, randomized, crossover manner during euglycemia (5.5 mmol/l) and progressive hypoglycemia (3.9-2.5 mmol/l). Examiners were blind to which subjects were +/-history, whereas subjects were blind to their blood glucose levels and targets. RESULTS: During euglycemia, +history participants reported more autonomic and neuroglycopenic symptoms (P≤0.01) and tended to require more dextrose infusion to maintain euglycemia with the same insulin infusion (P<0.09). During progressive hypoglycemia, these subjects demonstrated less epinephrine release (P=0.02) and greater driving impairments (P=0.03). CONCLUSIONS: Findings support the speculation that there is a subgroup of type 1 diabetic drivers more vulnerable to experiencing hypoglycemia-related driving mishaps. This increased vulnerability may be due to more symptom "noise" (more symptoms during euglycemia), making it harder to detect hypoglycemia while driving; possibly greater carbohydrate utilization, rendering them more vulnerable to experiencing hypoglycemia; less hormonal counterregulation, leading to more profound hypoglycemia; and more neuroglycopenia, rendering them more vulnerable to impaired driving.

Metabolic Demand of Driving Among Adults with Type 1 Diabetes Mellitus (T1DM).

Recent research suggests that the frequency of driving mishaps is increased in people with Type 1 diabetes (T1DM) as compared to those with Type 2 diabetes or their non-diabetic spouses. This study involved a sample of T1DM drivers and was designed to investigate the metabolic and physiologic demands of driving compared to sitting passively. Participants (N=38) were divided into two groups: the -History group included those reporting no driving mishaps in the past two years, and the +History group included participants reporting at least two such mishaps in the past two years. Glucose utilization rates were determined in participants while: (a) they were driving a virtual reality driving simulator for 30 minutes, and (b) watching a 30-minute video. Blood glucose (BG) levels were maintained at similar levels during both procedures. Other biological variables including heart rate (HR) were monitored. Participants rated their hypoglycemia (low BG) symptoms before and after each of the two procedures. . Participants could self-treat if they perceived they were experiencing hypoglycemia. There were no differences between the two groups. However, glucose utilization rates were significantly higher during the driving scenario (3.83mg/kg/min + 1.7 vs. 3.37 mg/kg/min + 1.6, p=0.047). HR was significantly higher during the driving scenario. Drivers reported more autonomic symptoms during driving and 32% treated perceived hypoglycemia during driving. Driving a virtual reality simulator is associated with increased glucose utilization rates suggesting that driving per se has a metabolic cost and that BG should be measured prior to driving and periodically during long drives.

Neurocognitive Differences Between Drivers with Type 1 Diabetes with and without a Recent History of Recurrent Driving Mishaps.

OBJECTIVE: A subset of drivers with type 1 diabetes mellitus (T1DM) may be at significant risk of hypoglycemia-related driving collisions and moving vehicle violations due to acute and chronic neurocognitive impairment. The present study compared drivers with T1DM with and without a recent history of multiple driving mishaps on a neurocognitive battery during euglycemia, progressive mild hypoglycemia, and recovery from hypoglycemia, to determine whether neurocognitive measures differentiate the two risk groups. We hypothesized that drivers with a history of multiple recent hypoglycemia-related driving mishaps would demonstrate greater psychomotor slowing, both during hypoglycemia and euglycemia. STUDY DESIGN: Partcipants were 42 adults with T1DM and were assigned to one of two groups: those reporting no driving mishaps in the last year (-History) and those reporting two or more (+History).Neurocognitive testing was conducted before and repeated during a hyper-insulinemic clamping procedure. RESULTS: Not surprisingly, all drivers demonstrated a decrease in functioning across all neurocognitive tasks during hypoglycemia. However, in contrast to the common belief that neurocognitive functions return slowly and gradually following hypoglycemia, baseline neurocognitive functioning immediately recovered upon return of BG to euglycemia for all subjects. Between-group analyses revealed that subjects with a recent history of driving mishaps consistently demonstrated poorer performance on tasks measuring working memory. CONCLUSION: Working memory is a potential neurocognitive indicator that may help differentiate adults with T1DM with and without a history of driving mishaps, predict future risk for driving mishaps, and provide targeted intervention programs to address this critical public health issue.

Closed-loop artificial pancreas using subcutaneous glucose sensing and insulin delivery and a model predictive control algorithm: the Virginia experience.

BACKGROUND: Recent progress in the development of clinically accurate continuous glucose monitors (CGMs), automated continuous insulin infusion pumps, and control algorithms for calculating insulin doses from CGM data have enabled the development of prototypes of subcutaneous closed-loop systems for controlling blood glucose (BG) levels in type 1 diabetes. The use of a new personalized model predictive control (MPC) algorithm to determine insulin doses to achieve and maintain BG levels between 70 and 140 mg/dl overnight and to control postprandial BG levels is presented. METHODS: Eight adults with type 1 diabetes were studied twice, once using their personal open-loop systems to control BG overnight and for 4 h following a standardized meal and once using a closed-loop system that utilizes the MPC algorithm to control BG overnight and for 4 h following a standardized meal. Average BG levels, percentage of time within BG target of 70-140 mg/dl, number of hypoglycemia episodes, and postprandial BG excursions during both study periods were compared. RESULTS: With closed-loop control, once BG levels achieved the target range (70-140 mg/dl), they remained within that range throughout the night in seven of the eight subjects. One subject developed a BG level of 65 mg/dl, which was signaled by the CGM trend analysis, and the MPC algorithm directed the discontinuance of the insulin infusion. The number of overnight hypoglycemic events was significantly reduced (p = .011) with closed-loop control. Postprandial BG excursions were similar during closed-loop and open-loop control. CONCLUSION: Model predictive closed-loop control of BG levels can be achieved overnight and following a standardized breakfast meal. This "artificial pancreas" controls BG levels as effectively as patient-directed open-loop control following a morning meal but is significantly superior to open-loop control in preventing overnight hypoglycemia.

Cognitive function is disrupted by both hypo- and hyperglycemia in school-aged children with type 1 diabetes: a field study.

OBJECTIVE: We developed a field procedure using personal digital assistant (PDA) technology to test the hypothesis that naturally occurring episodes of hypo- and hyperglycemia are associated with deterioration in cognitive function in children with type 1 diabetes. RESEARCH DESIGN AND METHODS: A total of 61 children aged 6-11 years with type 1 diabetes received a PDA programmed with two brief cognitive tests (mental math and choice reaction time), which they completed just before home glucose readings. The computer recorded time to complete each test and number of correct responses. Children completed several trials per day over 4-6 weeks for a total of 70 trials. Performance variables were compared across glucose ranges. Individual impairment scores (IISs) were also computed for each child by calculating the SD between performance during euglycemia and that during glucose extremes. RESULTS: Time to complete both mental math and reaction time was significantly longer during hypoglycemia. During hyperglycemia, time to complete math was significantly longer and reaction time was marginally significant (P = 0.053). There were no differences on task accuracy. Decline in mental math performance was equivalent at glucose levels <3.0 and >22.2 mmol/l. IISs varied greatly across children, with no age or sex differences. CONCLUSIONS: A decrease in mental efficiency occurs with naturally occurring hypo- and hyperglycemic glucose fluctuations in children with type 1 diabetes, and this effect can be detected with a field procedure using PDA technology. With blood glucose levels >22.2 mmol/l, cognitive deterioration equals that associated with significant hypoglycemia.

Results of a second year of therapy with the 12-month histrelin implant for the treatment of central precocious puberty.

Background. Gonadotropin releasing hormone analogs (GnRHas) are standard of care for central precocious puberty (CPP). The histrelin subcutaneous implant is safe and effective in the treatment of CPP for one year. Objective. The study evaluates a second year of therapy in children with CPP who received a new implant after one year of treatment. Methods. A prospective one-year study following an initial 12-month treatment period was conducted. Results. Thirty-one patients (29 girls) aged 7.7 +/- 1.5 years received a second implant. Eighteen were naïve to GnRHa therapy at first implantation. Peak LH declined from 0.92 +/- 0.58 mIU/mL at 12 months to 0.51 +/- 0.33 mIU/mL at 24 months (P < .0001) in naïve subjects, and from 0.74 +/- 0.50 mIU/mL at 12 months to 0.45 +/- 0.35 mIU/mL at 24 months (P = .0081) in previously treated subjects. Predicted adult height increased by 5.1 cm at 24 months (P = .0001). Minor implant site reactions occurred in 61%, while minor difficulties with explantation occurred in 32.2% of subjects. Conclusion. The histrelin implant demonstrates profound hypothalamic-pituitary-gonadal axis suppression when a new implant is placed for a second year of treatment. Prospective follow-up of this therapeutic modality for the treatment of CPP is needed.