Hypoglycemic Accuracy and Improved Low Glucose Alerts of the Latest Dexcom G4 Platinum Continuous Glucose Monitoring System

A Critical Discussion of Alert Evaluations in the Context of Continuous Glucose Monitoring System Performance

Many continuous glucose monitoring (CGM) systems provide functionality which alerts users of potentially unwanted glycemic conditions. These alerts can include glucose threshold alerts to call the user's attention to hypoglycemia or hyperglycemia, predictive alerts warning about impeding hypoglycemia or hyperglycemia, and rate-of-change alerts. A recent review identified 129 articles about CGM performance studies, of which approximately 25% contained alert evaluations. In some studies, real alerts were assessed; however, most of these studies retrospectively determined the timing of CGM alerts because not all CGM systems record alerts which necessitates manual documentation. In contrast to assessment of real alerts, retrospective determination allows assessment of a variety of alert settings for all three types of glycemic condition alerts. Based on the literature and the Clinical and Laboratory Standards Institute's POCT05 guideline, two common approaches to threshold alert evaluation were identified, one value-based and one episode-based approach. In this review, a critical discussion of the two approaches, including a post hoc analysis of clinical study data, indicates that the episode-based approach should be preferred over the value-based approach. For predictive alerts, fewer results were found in the literature, and retrospective determination of CGM alert timing is complicated by the prediction algorithms being proprietary information. Rate-of-change alert evaluations were not reported in the identified literature, and POCT05 does not contain recommendations for assessment. A possible approach is discussed including post hoc analysis of clinical study data. To conclude, CGM systems should record alerts, and the episode-based approach to alert evaluation should be preferred.

Clinical Performance Evaluation of Continuous Glucose Monitoring Systems: A Scoping Review and Recommendations for Reporting

The use of different approaches for design and results presentation of studies for the clinical performance evaluation of continuous glucose monitoring (CGM) systems has long been recognized as a major challenge in comparing their results. However, a comprehensive characterization of the variability in study designs is currently unavailable. This article presents a scoping review of clinical CGM performance evaluations published between 2002 and 2022. Specifically, this review quantifies the prevalence of numerous options associated with various aspects of study design, including subject population, comparator (reference) method selection, testing procedures, and statistical accuracy evaluation. We found that there is a large variability in nearly all of those aspects and, in particular, in the characteristics of the comparator measurements. Furthermore, these characteristics as well as other crucial aspects of study design are often not reported in sufficient detail to allow an informed interpretation of study results. We therefore provide recommendations for reporting the general study design, CGM system use, comparator measurement approach, testing procedures, and data analysis/statistical performance evaluation. Additionally, this review aims to serve as a foundation for the development of a standardized CGM performance evaluation procedure, thereby supporting the goals and objectives of the Working Group on CGM established by the Scientific Division of the International Federation of Clinical Chemistry and Laboratory Medicine.

Point-of-care testing technologies for the home in chronic kidney disease: a narrative review

Point-of-care testing (POCT) performed by the patient at home, paired with eHealth technologies, offers a wealth of opportunities to develop individualized, empowering clinical pathways. The non-dialysis-dependent chronic kidney disease (CKD) patient who is at risk of or may already be suffering from a number of the associated complications of CKD represents an ideal patient group for the development of such initiatives. The current coronavirus disease 2019 pandemic and drive towards shielding vulnerable individuals have further highlighted the need for home testing pathways. In this narrative review we outline the evidence supporting remote patient management and the various technologies in use in the POCT setting. We then review the devices currently available for use in the home by patients in five key areas of renal medicine: anaemia, biochemical, blood pressure (BP), anticoagulation and diabetes monitoring. Currently there are few devices and little evidence to support the use of home POCT in CKD. While home testing in BP, anticoagulation and diabetes monitoring is relatively well developed, the fields of anaemia and biochemical POCT are still in their infancy. However, patients' attitudes towards eHealth and home POCT are consistently positive and physicians also find this care highly acceptable. The regulatory and translational challenges involved in the development of new home-based care pathways are significant. Pragmatic and adaptable trials of a hybrid effectiveness-implementation design, as well as continued technological POCT device advancement, are required to deliver these innovative new pathways that our patients desire and deserve.

Use of Continuous Glucose Monitor in Critically Ill COVID-19 Patients Requiring Insulin Infusion: An Observational Study

CONTEXT

The coronavirus disease 2019 (COVID-19) pandemic has created a need for remote blood glucose (BG) monitoring in the intensive care unit (ICU).

OBJECTIVE

To evaluate feasibility and patient safety of a hybrid monitoring strategy of point-of-care (POC) BG plus continuous glucose monitor (CGM) in the ICU.

DESIGN

Retrospective analysis.

SETTING

ICU of an academic medical center.

PATIENTS

Patients with COVID-19 on IV insulin.

INTERVENTION

After meeting initial validation criteria, CGM was used for IV insulin titration and POC BG was performed every 6 hours or as needed.

MAIN OUTCOME MEASURES

Outcomes included frequency of POC BG, workflow, safety, and accuracy measures.

RESULTS

The study included 19 patients, 18 with CGM data, mean age 58 years, 89% on mechanical ventilation, 37% on vasopressors, and 42% on dialysis. The median time to CGM validation was 137 minutes (interquartile range [IQR] 114-206). During IV insulin, the median number of POC values was 7 (IQR 6-16) on day 1, and declined slightly thereafter (71% reduction compared with standard of 24/day). The median number of CGM values used nonadjunctively to titrate IV insulin was 11.5 (IQR 0, 15) on day 1 and increased thereafter. Time in range 70 to 180 mg/dL was 64 ± 23% on day 1 and 72 ± 16% on days 2 through 7, whereas time <70 mg/dL was 1.5 ± 4.1% on day 1 and <1% on days 2 through 7.

CONCLUSIONS

This study provides data to support that CGM using a hybrid protocol is feasible, accurate, safe, and has potential to reduce nursing and staff workload.

A retrospective in-depth analysis of continuous glucose monitoring datasets for patients with hepatic glycogen storage disease: Recommended outcome parameters for glucose management

Continuous glucose monitoring (CGM) systems have great potential for real-time assessment of glycemic variation in patients with hepatic glycogen storage disease (GSD). However, detailed descriptions and in-depth analysis of CGM data from hepatic GSD patients during interventions are scarce. This is a retrospective in-depth analysis of CGM parameters, acquired in a continuous, real-time fashion describing glucose management in 15 individual GSD patients. CGM subsets are obtained both in-hospital and at home, upon nocturnal dietary intervention (n = 1), starch loads (n = 11) and treatment of GSD Ib patients with empagliflozin (n = 3). Descriptive CGM parameters, and parameters reflecting glycemic variation and glycemic control are considered useful CGM outcome parameters. Furthermore, the combination of first and second order derivatives, cumulative sum and Fourier analysis identified both subtle and sudden changes in glucose management; hence, aiding assessment of dietary and medical interventions. CGM data interpolation for nocturnal intervals reduced confounding by physical activity and diet. Based on these analyses, we conclude that in-depth CGM analysis can be a powerful tool to assess glucose management and optimize treatment in individual hepatic GSD patients.

Continuous Glucose Monitoring Use in Clinical Trials for On-Market Diabetes Drugs

To the best of our knowledge, there are no published data on the historical and recent use of CGM in clinical trials of pharmacological agents used in the treatment of diabetes. We analyzed 2,032 clinical trials of 40 antihyperglycemic therapies currently on the market with a study start date between 1 January 2000 and 31 December 2019. According to ClinicalTrials.gov, 119 (5.9%) of these trials used CGM. CGM usage in clinical trials has increased over time, rising from <5% before 2005 to 12.5% in 2019. However, it is still low given its inclusion in the American Diabetes Association's latest guidelines and known limitations of A1C for assessing ongoing diabetes care.

Roux-en-Y Gastric Bypass Increases Glycemic Variability and Time in Hypoglycemia in Patients With Obesity and Prediabetes or Type 2 Diabetes: A Prospective Cohort Study

OBJECTIVE

Roux-en-Y gastric bypass (RYGB) is an established treatment for type 2 diabetes and obesity. The study objective was to establish RYGB's effects on glycemic variability (GV) and hypoglycemia.

RESEARCH DESIGN AND METHODS

This was a prospective observational study of 10 participants with obesity and prediabetes or type 2 diabetes who underwent RYGB. Patients were studied before RYGB (Pre) and 1 month, 1 year, and 2 years postsurgery with continuous glucose measurement (CGM). A mixed-meal test (MMT) was conducted at Pre, 1 month, and 1 year.

RESULTS

After RYGB, mean CGM decreased (at 1 month, 1 year, and 2 years), and GV increased (at 1 year and 2 years). Five of the 10 participants had a percent time in range (%TIR) <3.0 mmol/L (54 mg/dL) greater than the international consensus target of 1% at 1 or 2 years. Peak glucagon-like peptide-1 (GLP-1) and glucagon area under the curve during MMT were positively and negatively associated, respectively, with contemporaneous %TIR <3.0 mmol/L.

CONCLUSIONS

Patients undergoing RYGB are at risk for development of postbariatric hypoglycemia due to a combination of reduced mean glucose, increased GV, and increased GLP-1 response.

Facilitators and Barriers to Nursing Implementation of Continuous Glucose Monitoring (CGM) in Critically Ill Patients With COVID-19

OBJECTIVE

We describe our implementation of a continuous glucose monitoring (CGM) guideline to support intravenous insulin administration and reduce point of care (POC) glucose monitoring frequency in the coronavirus disease 2019 medical intensive care unit (MICU) and evaluate nurses' experience with implementation of CGM and hybrid POC + CGM protocol using the Promoting Action on Research in Health Services framework.

METHODS

A multidisciplinary team created a guideline providing criteria for establishing initial sensor-meter agreement within each individual patient followed by hybrid use of CGM and POC. POC measures were obtained hourly during initial validation, then every 6 hours. We conducted a focus group among MICU nurses to evaluate initial implementation efforts with content areas focused on initial assessment of evidence, context, and facilitation to identify barriers and facilitators. The focus group was analyzed using a qualitative descriptive approach.

RESULTS

The protocol was integrated through a rapid cycle review process and ultimately disseminated nationally. The Diabetes Consult Service performed device set-up and nurses received just-in-time training. The majority of barriers centered on contextual factors, including limitations of the physical environment, complex device set-up, hospital firewalls, need for training, and CGM documentation. Nurses' perceived device accuracy and utility were exceptionally high. Solutions were devised to maximize facilitation and sustainability for nurses while maintaining patient safety.

CONCLUSION

Outpatient CGM systems can be implemented in the MICU using a hybrid protocol implementation science approach. These efforts hold tremendous potential to reduce healthcare worker exposure while maintaining glucose control during the COVID-19 pandemic.

Hypoglycemic symptoms in the absence of diabetes: Pilot evidence of clinical hypoglycemia in young women

Aims

Clinical visits of non-diabetic patients reporting hypoglycemic symptoms are common in endocrinology practices, but remain understudied and lack clinical definition and evidence-based recommendations for diagnosis or treatment. Our goal was to pilot test the concordance of hypoglycemic symptoms with low glucose values in young non-diabetic individuals.

Methods

We recruited eight individuals who reported regularly experiencing symptoms consistent with hypoglycemia to wear a blinded Dexcom continuous glucose monitor and report symptoms for seven days. We excluded individuals with diabetes or other known causes of hypoglycemia or similar symptoms.

Results

Participants were all women with an average age of 29 years. 25% were African American and 25% had obesity. All participants experienced glucose values ≤ 70 mg/dL and half (4/8) experienced glucose ≤ 54 mg/dL for at least 15 min or 3 consecutive readings. Average time between last meal and reported symptoms was 4.4 h. Lower glucose values were significantly associated with higher odds of experiencing hypoglycemic symptoms 1.15 (CI: 1.07-1.24) for every -5mg/dL, (p < 0.001) from mixed effects models for repeated measures adjusted for age, race, and body mass index. All participants also reported engaging in potentially obesogenic behaviors in order to avoid symptoms.

Conclusions

Individuals with hypoglycemic symptoms in the absence of diabetes experience clinical hypoglycemia, indicating the need to understand the etiology, behavioral responses, and other health risks that might be associated with this understudied condition.

Real-World Hypoglycemia Avoidance with a Continuous Glucose Monitoring System's Predictive Low Glucose Alert

BACKGROUND

Programmable and fixed auditory and/or vibratory threshold alerts are essential features of real-time continuous glucose monitoring (rtCGM) systems that provide users time to intervene before the onset of clinical hypoglycemia or hyperglycemia. A sixth-generation rtCGM system from Dexcom, Inc. (G6) includes a new alert that is triggered when an algorithm predicts that an estimated glucose value ≤55 mg/dL will occur within 20 min, allowing users more time to act to avoid hypoglycemia. We examined whether this predictive low glucose alert provided added benefit to traditional low threshold alerts.

METHODS

We analyzed glucose values from an anonymized sample of 1424 patients who transitioned to G6 from the preceding fifth-generation system (G5) with no predictive alert. Users with the low threshold alert setting of 70 or 80 mg/dL were evaluated separately. Receiver users, those who disabled the predictive low glucose alert, or those with <30 days of data immediately before or after the transition to G6 were excluded.

RESULTS

Percent time <54, ≤55, <70, and >250 mg/dL fell significantly after the transition to G6, independent of low threshold alert setting. Time in range improved for G6 users with a low threshold alert setting of 70 mg/dL.

CONCLUSIONS

Advance warning provided by predictive low glucose alerts may further reduce hypoglycemia among rtCGM-experienced users.

Automated closed-loop control of diabetes: the artificial pancreas

The incidence of Diabetes Mellitus is on the rise worldwide, which exerts enormous health toll on the population and enormous pressure on the healthcare systems. Now, almost hundred years after the discovery of insulin in 1921, the optimization problem of diabetes is well formulated as maintenance of strict glycemic control without increasing the risk for hypoglycemia. External insulin administration is mandatory for people with type 1 diabetes; various medications, as well as basal and prandial insulin, are included in the daily treatment of type 2 diabetes. This review follows the development of the Diabetes Technology field which, since the 1970s, progressed remarkably through continuous subcutaneous insulin infusion (CSII), mathematical models and computer simulation of the human metabolic system, real-time continuous glucose monitoring (CGM), and control algorithms driving closed-loop control systems known as the "artificial pancreas" (AP). All of these developments included significant engineering advances and substantial bioelectronics progress in the sensing of blood glucose levels, insulin delivery, and control design. The key technologies that enabled contemporary AP systems are CSII and CGM, both of which became available and sufficiently portable in the beginning of this century. This powered the quest for wearable home-use AP, which is now under way with prototypes tested in outpatient studies during the past 6 years. Pivotal trials of new AP technologies are ongoing, and the first hybrid closed-loop system has been approved by the FDA for clinical use. Thus, the closed-loop AP is well on its way to become the digital-age treatment of diabetes.

Selecting the Appropriate Continuous Glucose Monitoring System - a Practical Approach

Two types of continuous glucose monitoring (CGM) systems are currently available for daily diabetes self-management: real-time CGM and intermittently scanned CGM. Both approaches provide continuous measurement of glucose concentrations in the interstitial fluid; however, each has its own unique features that can impact their usefulness and acceptability within specific patient groups. This article explores the strengths and limitations of each approach and provides guidance to healthcare professionals in selecting the CGM type that is most appropriate to the individual needs of their patients.

Plasma Insulin Levels and Hypoglycemia Affect Subcutaneous Interstitial Glucose Concentration

BACKGROUND

Continuous glucose monitoring (CGM) accuracy during hypoglycemia is suboptimal. This might be partly explained by insulin or hypoglycemia-induced changes in the plasma interstitial subcutaneous (SC) fluid glucose gradient. The aim of the present study was to assess the role of plasma insulin (PI) and hypoglycemia itself in the plasma and interstitial SC fluid glucose concentration in patients with type 1 diabetes mellitus.

METHODS

Eleven subjects with type 1 diabetes (age 36.5 ± 9.1 years, HbA_1c 7.9 ± 0.4% [62.8 ± 2.02 mmol/mol]; mean ± standard deviation) were evaluated under hyperinsulinemic euglycemia and hypoglycemia. Each subject underwent two randomized crossover clamps with either a primed 0.3 (low insulin) or 1 mU/(kg·min) (high insulin) insulin infusion. The raw CGM signal was normalized with median preclamp values to obtain a standardized measure of the interstitial glucose (IG) concentration before statistical analysis.

RESULTS

The mean PI concentration was greater in high insulin studies (HISs) versus low insulin studies (LISs) (412.89 ± 13.63 vs. 177.22 ± 10.05 pmol/L). During hypoglycemia, glucagon, adrenaline, free fatty acids, glycerol, and beta-OH-butyrate were higher in the LIS (P < 0.0001). Likewise, the IG concentration was significantly different (P < 0.0001). This was due to lower IG concentration than plasma glucose (PG) concentration during the euglycemic hyperinsulinemic phases in the HIS. In contrast, no difference was observed during hypoglycemia. This was the result of an unchanged PG/IG gradient during the entire LIS, while in the HIS, this gradient increased during the hyperinsulinemic euglycemia phase.

CONCLUSION

Both PI levels and hypoglycemia affect the relationship between IG and PG concentration. ClinicalTrials.gov Identifier: NCT01714895.

Future of Automated Insulin Delivery Systems

Advances in continuous glucose monitoring (CGM) have brought on a paradigm shift in the management of type 1 diabetes. These advances have enabled the automation of insulin delivery, where an algorithm determines the insulin delivery rate in response to the CGM values. There are multiple automated insulin delivery (AID) systems in development. A system that automates basal insulin delivery has already received Food and Drug Administration approval, and more systems are likely to follow. As the field of AID matures, future systems may incorporate additional hormones and/or multiple inputs, such as activity level. All AID systems are impacted by CGM accuracy and future CGM devices must be shown to be sufficiently accurate to be safely incorporated into AID. In this article, we summarize recent achievements in AID development, with a special emphasis on CGM sensor performance, and discuss the future of AID systems from the point of view of their input-output characteristics, form factor, and adaptability.

Analysis of the Accuracy and Performance of a Continuous Glucose Monitoring Sensor Prototype: An In-Silico Study Using the UVA/PADOVA Type 1 Diabetes Simulator

BACKGROUND

Computer simulation has been shown over the past decade to be a powerful tool to study the impact of medical devices characteristics on clinical outcomes. Specifically, in type 1 diabetes (T1D), computer simulation platforms have all but replaced preclinical studies and are commonly used to study the impact of measurement errors on glycemia.

METHOD

We use complex mathematical models to represent the characteristics of 3 continuous glucose monitoring systems using previously acquired data. Leveraging these models within the framework of the UVa/Padova T1D simulator, we study the impact of CGM errors in 6 simulation scenarios designed to generate a wide variety of glycemic conditions. Assessment of the simulated accuracy of each different CGM systems is performed using mean absolute relative deviation (MARD) and precision absolute relative deviation (PARD). We also quantify the capacity of each system to detect hypoglycemic events.

RESULTS

The simulated Roche CGM sensor prototype (RCGM) outperformed the 2 alternate systems (CGM-1 & CGM-2) in accuracy (MARD = 8% vs 11.4% vs 18%) and precision (PARD = 6.4% vs 9.4% vs 14.1%). These results held for all studied glucose and rate of change ranges. Moreover, it detected more than 90% of hypoglycemia, with a mean time lag less than 4 minutes (CGM-1: 86%/15 min, CGM-2: 57%/24 min).

CONCLUSION

The RCGM system model led to strong performances in these simulation studies, with higher accuracy and precision than alternate systems. Its characteristics placed it firmly as a strong candidate for CGM based therapy, and should be confirmed in large clinical studies.

Predicting Insulin Treatment Scenarios with the Net Effect Method: Domain of Validity

BACKGROUND

A simulation methodology based on the net effect, a signal estimated from continuous glucose monitoring (CGM) and insulin data accounting for sources of glucose variability, for example, meals and exercise, has been proposed. This method has been recently used to "replay" real-life treatment scenarios and determine the minimal level of CGM sensor accuracy required for nonadjunctive use. Given the potential of the net effect method, it is important to assess its domain of validity.

METHODS

The UVA/Padova type 1 diabetes simulator is used to generate glucose and insulin data. The net effect signal is estimated and used to predict the glucose profiles resulting from the following therapy modifications: (1) basal insulin increase/decrease, (2) bolus reduction to prevent hypoglycemia, (3) bolus addition after CGM hyperalarms, (4) hypotreatment addition after CGM hypoalarms. Results of the net effect method are compared with the reference provided by the UVA/Padova simulator.

RESULTS

The net effect method (1) well predicts the effect of small basal insulin adjustments (±10%), but overestimates time in hypo/hyperglycemia for larger adjustments (±50%); (2) underestimates the bolus reduction required to prevent hypoglycemia; (3) underestimates time in hyperglycemia when introducing correction boluses; and (4) overestimates time in hypoglycemia when introducing hypotreatments.

CONCLUSIONS

The net effect method is reliable for small adjustments of basal insulin, while outside this domain of validity it can provide inaccurate results.

Outcome Measures for Artificial Pancreas Clinical Trials: A Consensus Report

Research on and commercial development of the artificial pancreas (AP) continue to progress rapidly, and the AP promises to become a part of clinical care. In this report, members of the JDRF Artificial Pancreas Project Consortium in collaboration with the wider AP community 1) advocate for the use of continuous glucose monitoring glucose metrics as outcome measures in AP trials, in addition to HbA1c, and 2) identify a short set of basic, easily interpreted outcome measures to be reported in AP studies whenever feasible. Consensus on a broader range of measures remains challenging; therefore, reporting of additional metrics is encouraged as appropriate for individual AP studies or study groups. Greater consistency in reporting of basic outcome measures may facilitate the interpretation of study results by investigators, regulatory bodies, health care providers, payers, and patients themselves, thereby accelerating the widespread adoption of AP technology to improve the lives of people with type 1 diabetes.

The Rationale for Continuous Glucose Monitoring-based Diabetes Treatment Decisions and Non-adjunctive Continuous Glucose Monitoring Use

Self-monitoring of blood glucose (SMBG) is now recognised as a core component of diabetes self-management. However, there are many limitations to SMBG use in individuals with diabetes who are treated with intensive insulin regimens. Many individuals do not test at the recommended frequencies. Additionally, because SMBG only provides a blood glucose reading at a single point in time, hypoglycaemia and hyperglycaemia can easily go undetected, limiting the user's ability to take corrective action. Inaccuracies due to user error, environmental factors and weaknesses in SMBG system integrity further limit the utility of SMBG. Real-time continuous glucose monitoring (CGM) displays the current glucose, direction and velocity of glucose change and provides programmable alarms. This trending information and 'around-the-clock' vigilance provides a significant safety advantage relative to SMBG. No published clinical studies have evaluated outcomes when CGM is used as a replacement for SMBG; however, recent in silico studies support this indication. This article reviews the limitations of SMBG and discusses recent evidence that supports CGM-based decisions as an effective approach to managing insulin-treated diabetes.

AMERICAN ASSOCIATION OF CLINICAL ENDOCRINOLOGISTS AND AMERICAN COLLEGE OF ENDOCRINOLOGY 2016 OUTPATIENT GLUCOSE MONITORING CONSENSUS STATEMENT

This document represents the official position of the American Association of Clinical Endocrinologists and American College of Endocrinology. Where there were no randomized controlled trials or specific U.S. FDA labeling for issues in clinical practice, the participating clinical experts utilized their judgment and experience. Every effort was made to achieve consensus among the committee members. Position statements are meant to provide guidance, but they are not to be considered prescriptive for any individual patient and cannot replace the judgment of a clinician.

Assessing Insulin Delivery Device Satisfaction in Patients with Type 1 and Type 2 Diabetes

Over the past several decades, insulin treatment has changed drastically, not only with the development of further insulin analogs but also with the introduction of novel insulin delivery devices such as pumps and pens. In addition, adjunct devices such as continuous glucose monitors and sensor-augmented pumps have become increasingly used in clinical care, increasing the volume of information available to patients and providers. However, with the development of new devices it has become clear that along with the many benefits of these advances, the use of these devices can also present a burden to people with diabetes. For example, some patients report being overwhelmed by too much data when using continuous glucose monitors. Furthermore, there are concerns regarding the accuracy of some of these new devices, particularly for glucose monitoring. As a result, some patients may choose not to use available devices, despite the recognized benefits. Therefore, it is critical to understand how the various insulin delivery devices available currently and in the future affect patients in terms of their diabetes management and perceived burdens and to understand which patient characteristics may predict a lack of satisfaction with these devices. This critical gap in our knowledge is addressed in an article in this issue of the journal through the development of a questionnaire that allows for a better understanding of the impact of insulin delivery devices on quality of life and diabetes management among both type 1 diabetes and insulin-dependent type 2 diabetes patients. The novelty, as well as limitations, of this new instrument for the assessment of insulin delivery device satisfaction are discussed.