An exploratory trial of basal and prandial insulin initiation and titration for type 2 diabetes in primary care with adjunct retrospective continuous glucose monitoring: INITIATION study

Effects of Continuous Glucose Monitoring on Glycemic Control in Type 2 Diabetes: A Systematic Review and Meta-Analysis

As the prevalence of diabetes is rapidly increasing, the use of continuous glucose monitoring, which is effective in improving glycemic control in type 2 diabetes, is increasing.

METHODS

Systematic review was performed according to PRISMA criteria. The search was conducted for articles published until 31 May 2023 in PubMed, CINAHL, Cochrane Library, EMBASE, ClinicalKey, etc. The meta-analysis involved the synthesis of effect size; tests of homogeneity and heterogeneity; trim and fill plot; Egger's regression test; and Begg's test for assessing publication bias.

RESULTS

491 studies were searched, of which 17 studies that met the selection criteria were analyzed. The overall effect on HbA1c was -0.37 (95% CI, -0.63~-0.11, p < 0.001), with HbA1c decreasing significantly after CGM interventions. Sub-analyses showed that the study was statistically significant in those aged 60 years or older, when rt-CGM was used and when the study was performed in multiple centers.

CONCLUSION

The results of this study showed that intervention using CGM was effective in reducing HbA1c in type 2 diabetes. The factors identified in this study can be used as guidelines for developing future CGM intervention programs.

Professional continuous glucose monitoring in patients with diabetes mellitus: A systematic review and meta-analysis

AIM

To evaluate the effect on glucose control of professional continuous glucose monitoring (p-CGM)-based care as compared with standard care in the management of patients with type 1 and type 2 diabetes.

MATERIALS AND METHODS

The PubMed database was searched comprehensively to identify prospective or retrospective studies evaluating p-CGM as a diagnostic tool for subsequent implementation of lifestyle and/or medication changes and reporting glycated haemoglobin (HbA1c) as an outcome measure.

RESULTS

We found 872 articles, 22 of which were included in the meta-analysis. Overall, the use of p-CGM was associated with greater HbA1c reduction from baseline (-0.28%, 95% confidence interval [CI] -0.36% to -0.21%, I²  = 0%, P < 0.00001) than usual care, irrespective of type of diabetes, length of follow-up, frequency of continuous glucose monitoring (CGM) use and duration of CGM recording. In the few studies describing CGM-derived glucose metrics, p-CGM showed a beneficial effect on change in time in range from baseline (5.59%, 95% CI 0.12 to 11.06, I²  = 0%, P = 0.05) and a neutral effect on change in time below the target range from baseline (-0.11%, 95% CI -1.76% to 1.55%, I²  = 33%, P = 0.90).

CONCLUSIONS

In patients with type 1 and type 2 diabetes, p-CGM-driven care is superior to usual care in improving glucose control without increasing hypoglycaemia.

The Benefits of Utilizing Continuous Glucose Monitoring of Diabetes Mellitus in Primary Care: A Systematic Review

BACKGROUND

Continuous glucose monitoring (CGM) and intermittently scanned CGM (is-CGM) have shown to effectively manage diabetes in the specialty setting, but their efficacy in the primary care setting remains unknown. Does CGM/is-CGM improve glycemic control, decrease rates of hypoglycemia, and improve staff/physician satisfaction in primary care? If so, what subgroups of patients with diabetes are most likely to benefit?

METHODS

A comprehensive search in seven databases was performed in June 2021 for primary studies examining any continuous glucose monitoring system in primary care. We excluded studies with fewer than 20 participants, specialty care only, or hospitalized participants. The National Heart, Lung and Blood Institute and Grading of Recommendations Assessment, Development and Evaluation were used for the quality assessment. The weighted mean difference (WMD) of HbA1c between CGM/is-CGM and usual care with 95% confidence interval was calculated. A narrative synthesis was conducted for change of time in, above, or below range (TIR, TAR, and TBR) hypoglycemic events and staff/patient satisfaction.

RESULTS

From ten studies and 4006 participants reviewed, CGM was more effective at reducing HbA1c compared with usual care (WMD -0.43%). There is low certainty of evidence that CGM/is-CGM improves TIR, TAR, or TBR over usual care. The CGM can reduce hypoglycemic events and staff/patient satisfaction is high. Patients with intensive insulin therapy may benefit more from CGM/is-CGM.

CONCLUSIONS

Compared with usual care, CGM/is-CGM can reduce HbA1c, but most studies had notable biases, were short duration, unmasked, and were sponsored by industry. Further research needs to confirm the long-term benefits of CGM/is-CGM in primary care.

Where to Initiate Basal Insulin Therapy: Inpatient or Outpatient Department? Real-World Observation in China

BACKGROUND

This study aims to compare the effectiveness of initiating insulin therapy in inpatient and outpatient settings during a 6-month follow-up period among patients with type 2 diabetes mellitus (T2DM) in real-world settings.

MATERIALS AND METHODS

The study was based on the ORBIT study, a real-world observational study which recruited patients with inadequate glycemic control by oral antidiabetic drugs (OAD) and initiated basal insulin (BI). We compare difference in initiation and evolution of insulin therapy and glycemic control after six months were compared between patients initiating basal insulin in the inpatient department (inpatient initiators) and those starting in outpatient (outpatient initiators) among participants without rehospitalization during the six months follow-up.

RESULTS

Among all 18,995 participants in the ORBIT study, 56.0% were inpatient initiators and 44.0% outpatient. We conducted in-depth analysis among 14,860 patients without rehospitalization, 8129 inpatient initiators and 6731 outpatient initiators. (1) Inpatient initiators had lower insulin therapy persistence during six months (64.2%) than outpatient ones (78.6%) (p<0.001), which was mainly explained by more therapy switches from basal-bolus regimen to other therapies among inpatient initiators (50.1%) than that among outpatient initiators (37.5%) (p<0.001). (2) Inpatient initiation had a higher proportion of people achieving glucose targets (HbA1c <7%) than outpatient initiation. However, the benefit of inpatient initiation versus outpatient initiation was mainly observed among patients persisting with the initial insulin therapies (46.3% vs 39.5% p<0.001), rather than those nonpersistent (37.3% vs 36.2%, p=0.723). (3) Among patients with HbA1c <9%, taking only one OAD and without complications at baseline, inpatient insulin initiation did not show a higher proportion of people achieving glucose target than outpatient initiation (adjusted odds ratio=0.96, 95% CI: 0.76-1.21).

CONCLUSION

For patients with HbA1c ≥9%, who were taking more than one OAD and had complications at baseline, initiating insulin treatment during hospitalization has a higher proportion of people achieving glucose target than that in the outpatient department, but the premise is that the initial therapy is acceptable and can be maintained after discharge. Patient-centered approach with co-agreed decision-making to select a suitable insulin regimen should be strengthened.

American Association of Clinical Endocrinology Clinical Practice Guideline: The Use of Advanced Technology in the Management of Persons With Diabetes Mellitus

OBJECTIVE

To provide evidence-based recommendations regarding the use of advanced technology in the management of persons with diabetes mellitus to clinicians, diabetes-care teams, health care professionals, and other stakeholders.

METHODS

The American Association of Clinical Endocrinology (AACE) conducted literature searches for relevant articles published from 2012 to 2021. A task force of medical experts developed evidence-based guideline recommendations based on a review of clinical evidence, expertise, and informal consensus, according to established AACE protocol for guideline development.

MAIN OUTCOME MEASURES

Primary outcomes of interest included hemoglobin A1C, rates and severity of hypoglycemia, time in range, time above range, and time below range.

RESULTS

This guideline includes 37 evidence-based clinical practice recommendations for advanced diabetes technology and contains 357 citations that inform the evidence base.

RECOMMENDATIONS

Evidence-based recommendations were developed regarding the efficacy and safety of devices for the management of persons with diabetes mellitus, metrics used to aide with the assessment of advanced diabetes technology, and standards for the implementation of this technology.

CONCLUSIONS

Advanced diabetes technology can assist persons with diabetes to safely and effectively achieve glycemic targets, improve quality of life, add greater convenience, potentially reduce burden of care, and offer a personalized approach to self-management. Furthermore, diabetes technology can improve the efficiency and effectiveness of clinical decision-making. Successful integration of these technologies into care requires knowledge about the functionality of devices in this rapidly changing field. This information will allow health care professionals to provide necessary education and training to persons accessing these treatments and have the required expertise to interpret data and make appropriate treatment adjustments.

Use of professional-mode flash glucose monitoring, at 3-month intervals, in adults with type 2 diabetes in general practice (GP-OSMOTIC): a pragmatic, open-label, 12-month, randomised controlled trial

BACKGROUND

Continuous glucose monitoring, either real-time (personal) or retrospective (professional mode), can identify day-to-day glucose profiles to guide management decisions for people with type 2 diabetes. We aimed to examine the effects of professional-mode flash glucose monitoring, done at 3-month intervals, in adults with type 2 diabetes in general practice.

METHODS

We did a pragmatic, two-arm, open label, 12-month, individually randomised controlled trial (GP-OSMOTIC) in 25 general practices in Victoria, Australia. Eligible participants were adults aged 18-80 years, with type 2 diabetes diagnosed for at least 1 year and HbA_1c at least 5·5 mmol/mol (0·5%) above their target in the past month despite being prescribed at least two non-insulin glucose-lowering drugs, insulin, or both (with therapy stable for at least 4 months). We randomly assigned participants (1:1) to either use of a professional-mode flash glucose monitoring system or usual clinical care (control). All participants wore the flash glucose monitoring sensor at baseline, and electronic randomisation (using permuted block sizes of four and six, and stratified by clinic) was done after the sensor was attached. Masking of participants and treating clinicians to group allocation was not possible, but the study statistician was masked to allocation when analysing the data. At baseline, and 3, 6, 9, and 12 months, participants in the flash glucose monitoring group wore the professional-mode flash glucose monitoring sensor for 5-14 days before their general practice visit. The sensor recorded interstitial glucose concentrations every 15 min, but the glucose data were not available to the participant until their general practice visit, where the sensor output would be uploaded to a computer by the health professional and discussed. Control group participants wore the sensor at baseline and at 12 months for data analysis only, and had usual care visits every 3 months. The primary outcome was the between-group difference in mean HbA_1c at 12 months. Secondary outcomes were the between-group differences in: mean percentage time in target glucose range (4-10 mmol/L), based on ambulatory glucose profile data at 12 months; mean diabetes-specific distress (assessed with the Problem Areas In Diabetes [PAID] scale) at 12 months; and mean HbA_1c at 6 months. Analysis was done by intention to treat. This trial is registered at the Australian and New Zealand Clinical Trials Registry, ACTRN12616001372471.

FINDINGS

Between Oct 4, 2016, and Nov 17, 2017, we randomly assigned 299 adults: 149 to flash glucose monitoring and 150 to usual care. At 6 months, HbA_1c was lower in the flash glucose monitoring group than in the usual care group (difference -0·5%, 95% CI -0·8% to -0·3%; p=0·0001). However, at 12 months (primary outcome), there was no significant between-group difference in estimated mean HbA_1c (8·2% [95% CI 8·0 to 8·4] for flash glucose monitoring vs 8·5% [8·3 to 8·7] for usual care; between-group difference -0·3%, 95% CI -0·5 to 0·01; [66 mmol/mol, 95% CI 64 to 68 vs 69 mmol/mol, 67 to 72; between-group difference -3·0, 95% CI -5·0 to 0·1]; p=0·059). Mean percentage time spent in target glucose range at 12 months was 7·9% (95% CI 2·3 to 13·5) higher in the flash glucose monitoring group than in the usual care group (p=0·0060). Diabetes-specific distress PAID scores were unchanged at 12 months (between-group difference -0·7, 95% CI -3·3 to 1·9; p=0·61). No episodes of severe hypoglycaemia or treatment-related deaths were reported. One participant died during the study from causes unrelated to the intervention (following complications post-myocardial infarction with multiple comorbidities).

INTERPRETATION

Professional-mode flash glucose monitoring in adults with type 2 diabetes in general practice did not improve the primary outcome of HbA_1c at 12 months or diabetes-specific distress compared with usual care, but did improve time in target glucose range at 12 months and HbA_1c at 6 months. Our findings suggest that professional-mode flash glucose monitoring can be implemented in a pragmatic primary care environment. Although there was no change in HbA_1c at 12 months, the improved time in target range might reflect the potential of the technology to support personalised clinical care by providing insights into glycaemic profiles for some people with type 2 diabetes.

FUNDING

National Health and Medical Research Council of Australia, Sanofi Australia, and Abbott Diabetes Care.

Hypoglycemia in People with Type 2 Diabetes and CKD

BACKGROUND AND OBJECTIVES

Among people with diabetes mellitus, CKD may promote hypoglycemia through altered clearance of glucose-lowering medications, decreased kidney gluconeogenesis, and blunted counter-regulatory response. We conducted a prospective observational study of hypoglycemia among 105 individuals with type 2 diabetes treated with insulin or a sulfonylurea using continuous glucose monitors.

DESIGN, SETTING, PARTICIPANTS & MEASUREMENTS

We enrolled 81 participants with CKD, defined as eGFR<60 ml/min per 1.73 m², and 24 control participants with eGFR≥60 ml/min per 1.73 m² frequency-matched on age, duration of diabetes, hemoglobin A1c, and glucose-lowering medications. Each participant wore a continuous glucose monitor for two 6-day periods. We examined rates of sustained level 1 hypoglycemia (<70 mg/dl) and level 2 hypoglycemia (<54 mg/dl) among participants with CKD. We then tested differences compared with control participants as well as a second control population (n=73) using Poisson and linear regression, adjusting for age, sex, and race.

RESULTS

Over 890 total days of continuous glucose monitoring, participants with CKD were observed to have 255 episodes of level 1 hypoglycemia, of which 68 episodes reached level 2 hypoglycemia. Median rate of hypoglycemic episodes was 5.3 (interquartile range, 0.0-11.7) per 30 days and mean time spent in hypoglycemia was 28 (SD 37) minutes per day. Hemoglobin A1c and the glucose management indicator were the main clinical correlates of time in hypoglycemia (adjusted differences 6 [95% confidence interval, 2 to 10] and 13 [95% confidence interval, 7 to 20] fewer minutes per day per 1% higher hemoglobin A1c or glucose management indicator, respectively). Compared with control populations, participants with CKD were not observed to have significant differences in time in hypoglycemia (adjusted differences 4 [95% confidence interval, -12 to 20] and -12 [95% confidence interval, -29 to 5] minutes per day).

CONCLUSIONS

Among people with type 2 diabetes and moderate to severe CKD, hypoglycemia was common, particularly with tighter glycemic control, but not significantly different from groups with similar clinical characteristics and preserved eGFR.

Continuous glucose monitoring: a review of the evidence, opportunities for future use and ongoing challenges

The advent of devices that can track interstitial glucose levels, which are closely related to blood glucose levels, on a near continuous basis, has facilitated better insights into patterns of glycaemia. Continuous glucose monitoring (CGM) therefore allows for more intensive monitoring of blood glucose levels and potentially improved glycaemic control. In the context of the announcement on 1 April 2017 that the Australian Government will fund CGM monitoring for people with type 1 diabetes under the age of 21 years, this paper provides a review of the evidence for CGM and some of the ongoing challenges. There is evidence that real-time CGM in type 1 diabetes improves HbA1c and hypoglycaemia, while in type 2 diabetes, the evidence is less robust. Initial barriers to widespread implementation of CGM included issues with accuracy and user friendliness; however, as the technology has evolved, these issues have largely improved. Ongoing barriers include cost, and weaker evidence for their benefit in certain populations such as those with type 2 diabetes and less glycaemic variability. CGM has the potential to reduce healthcare costs, although real-world studies, including cost-effectiveness analyses, are needed in this area.

GP-OSMOTIC trial protocol: an individually randomised controlled trial to determine the effect of retrospective continuous glucose monitoring (r-CGM) on HbA1c in adults with type 2 diabetes in general practice

INTRODUCTION

Optimal glycaemia can reduce type 2 diabetes (T2D) complications. Observing retrospective continuous glucose monitoring (r-CGM) patterns may prompt therapeutic changes but evidence for r-CGM use in T2D is limited. We describe the protocol for a randomised controlled trial (RCT) examining intermittent r-CGM use (up to 14 days every three months) in T2D in general practice (GP).

METHODS AND ANALYSIS

General Practice Optimising Structured MOnitoring To achieve Improved Clinical Outcomes is a two-arm RCT asking 'does intermittent r-CGM in adults with T2D in primary care improve HbA1c?'

PRIMARY OUTCOME

Absolute difference in mean HbA1c at 12 months follow-up between intervention and control arms.

SECONDARY OUTCOMES

(a) r-CGM per cent time in target (4-10 mmol/L) range, at baseline and 12 months; (b) diabetes-specific distress (Problem Areas in Diabetes).

ELIGIBILITY

Aged 18-80 years, T2D for ≥1 year, a (past month) HbA1c>5.5 mmol/mol (0.5%) above their individualised target while prescribed at least two non-insulin hypoglycaemic therapies and/or insulin (therapy stable for the last four months). Our general glycaemic target is 53 mmol/mol (7%) (patients with a history of severe hypoglycaemia or a recorded diagnosis of hypoglycaemia unawareness will have a target of 64 mmol/mol (8%)).Our trial compares r-CGM use and usual care. The r-CGM report summarising daily glucose patterns will be reviewed by GP and patient and inform treatment decisions. Participants in both arms are provided with 1 hour education by a specialist diabetes nurse.The sample (n=150/arm) has 80% power to detect a mean HbA1c difference of 5.5 mmol/mol (0.5%) with an SD of 14.2 (1.3%) and alpha of 0.05 (allowing for 10% clinic and 20% patient attrition).

ETHICS AND DISSEMINATION

University of Melbourne Human Ethics Sub-Committee (ID 1647151.1). Dissemination will be in peer-reviewed journals, conferences and a plain-language summary for participants.

TRIAL REGISTRATION NUMBER

>ACTRN12616001372471; Pre-results.

Impact of improving postprandial glycemic control with intensifying insulin therapy in type 2 diabetes

Worldwide, many people with type 2 diabetes are not at recommended glycemic targets and remain at increased risk of microvascular and macrovascular complications. Reaching recommended glycemic targets requires normalizing both fasting and postprandial glucose (PPG). For some patients, this will require addition of a prandial insulin delivered by injection to control PPG excursions. Evidence from epidemiological studies suggests an association between postprandial hyperglycemia and cardiovascular disease, and thus, expert guidelines recommend that treatment for elevated PPG not be delayed. Indeed, studies have demonstrated that PPG makes the greatest contribution to HbA_1c in patients who are approaching, but have not yet reached HbA_1c <7.0%. Appropriately timed exposure of the liver to insulin is critical in suppressing hepatic glucose output (and therefore PPG levels) after a meal. Rapid-acting insulin analogs, with their faster onset and shorter duration of action, offer advantages over regular human insulin. Unfortunately, even with improved pharmacokinetic/pharmacodynamic characteristics, rapid-acting insulin analogs are still unable to fully reproduce the rapid release of insulin into the portal circulation and suppression of hepatic glucose output that occurs in the individual without diabetes after starting a meal. The next generation of rapid-acting insulin analogs will have an even more favorable pharmacokinetic profile that should allow patients to further improve glycemic control. Continuous subcutaneous insulin infusion (CSII) represents another option for intensifying therapy and improving postprandial control in some patients, and studies have shown that the benefits are sustainable long-term. However, it is currently unclear which patients stand to benefit the most from the extra expense and complexity of a CSII regimen, and further studies are needed.

Assessing the Therapeutic Utility of Professional Continuous Glucose Monitoring in Type 2 Diabetes Across Various Therapies: A Retrospective Evaluation

BACKGROUND

There have been few large studies that have analyzed the effect of professional (masked) continuous glucose monitoring (P-CGM) on glycemic control in patients with type 2 diabetes (T2DM) who were on a broad spectrum of baseline therapies.

METHODS

We performed a retrospective, blinded evaluation of glycemic control in 296 T2DM adults for 6 months following a 6- to 7-day study of their glycemic profile using masked P-CGM. At baseline, 91% of the patients were on some form of insulin treatment with oral hypoglycemic agents (OHA), while 7% were on one or more OHAs without insulin, and the remaining 2% were on GLP-1RAs. On the basis of the masked CGM profile, patients were counselled on diet and exercise change(s) in their baseline diabetes therapy by our professionally trained diabetes team. They also continued to receive regular treatment advice and dose titrations through our Diabetes Tele-Management System (DTMS^®). The baseline changes in hemoglobin A1C (A1C) observed in these patients after 6 months of undergoing P-CGM was compared to a matched control group.

RESULTS

P-CGM revealed that the predominant pattern of hyperglycemia was postprandial while previously unknown hypoglycemia was found in 38% of the patients; over half of the cases of hypoglycemia were nocturnal. The mean A1C of the P-CGM group dropped from 7.5 ± 1.4% at baseline vs. 7.0 ± 0.9% at 6 months (p < 0.0001). The frequency of performing self-monitoring of blood glucose (SMBG) was also found to be significantly increased in these patients from the baseline. Meanwhile, no significant improvement in A1C was noted in the control group during the same time frame (7.7 ± 1.1% at baseline vs. 7.4 ± 1.1% at 6 months; p = 0.0663) and frequency of SMBG remained almost unchanged.

CONCLUSIONS

P-CGM can provide actionable data and motivate patients for diabetes self-care practices, resulting in an improvement in glycemic control over a wide range of baseline therapies.

Supporting insulin initiation in type 2 diabetes in primary care: results of the Stepping Up pragmatic cluster randomised controlled clinical trial

Objective To compare the effectiveness of a novel model of care ("Stepping Up") with usual primary care in normalising insulin initiation for type 2 diabetes, leading to improved glycated haemoglobin (HbA1c) levels.Design Cluster randomised controlled trial.Setting Primary care practices in Victoria, Australia, with a practice nurse and at least one consenting eligible patient (HbA1c ≥7.5% with maximal oral treatment).Participants 266 patients with type 2 diabetes and 74 practices (mean cluster size 4 (range 1-8) patients), followed up for 12 months.Intervention The Stepping Up model of care intervention involved theory based change in practice systems and reorientation of the roles of health professionals in the primary care diabetes team. The core components were an enhanced role for the practice nurse in leading insulin initiation and mentoring by a registered nurse with diabetes educator credentials.Main outcome measures The primary endpoint was change in HbA1c. Secondary endpoints included the proportion of participants who transitioned to insulin, proportion who achieved target HbA1c, and a change in depressive symptoms (patient health questionnaire, PHQ-9), diabetes specific distress (problem areas in diabetes scale, PAID), and generic health status (assessment of quality of life instrument, AQoL-8D).Results HbA1c improved in both arms, with a clinically significant between arm difference (mean difference -0.6%, 95% confidence interval -0.9% to -0.3%), favouring the intervention. At 12 months, in intervention practices, 105/151 (70%) of participants had started insulin, compared with 25/115 (22%) in control practices (odds ratio 8.3, 95% confidence interval 4.5 to 15.4, P<0.001). Target HbA1c (≤7% (53 mmol/mol)) was achieved by 54 (36%) intervention participants and 22 (19%) control participants (odds ratio 2.2, 1.2 to 4.3, P=0.02). Depressive symptoms did not worsen at 12 months (PHQ-9: -1.1 (3.5) v -0.1 (2.9), P=0.05). A statistically significant difference was found between arms in the mean change in mental health (AQoL mental component summary: 0.04 (SD 0.16) v -0.002 (0.13), mean difference 0.04 (95% confidence interval 0.002 to 0.08), P=0.04), favouring the intervention, but no significant difference in physical health (AQoL physical component summary: 0.03 (0.15) v 0.02 (0.13)) nor diabetes specific distress (5.6 (15.5) v -2.4 (15.4)). No severe hypoglycaemia events were reported.Conclusions The Stepping Up model of care was associated with increased insulin initiation rates in primary care, and improvements in glycated haemoglobin without worsening emotional wellbeing.Trial registration Australian and New Zealand Clinical Trials Registry ACTRN12612001028897.

Role of continuous glucose monitoring for type 2 in diabetes management and research

The advent of continuous glucose monitoring (CGM) is a significant stride forward in our ability to better understand the glycemic status of our patients. Current clinical practice employs two forms of CGM: professional (retrospective or "masked") and personal (real-time) to evaluate and/or monitor glycemic control. Most studies using professional and personal CGM have been done in those with type 1 diabetes (T1D). However, this technology is agnostic to the type of diabetes and can also be used in those with type 2 diabetes (T2D). The value of professional CGM in T2D for physicians, patients, and researchers is derived from its ability to: (1) to discover previously unknown hyper- and hypoglycemia (silent and symptomatic); (2) measure glycemic control directly rather than through the surrogate metric of hemoglobin A1C (HbA1C) permitting the observation of a wide variety of metrics that include glycemic variability, the percent of time within, below and above target glucose levels, the severity of hypo- and hyperglycemia throughout the day and night; (3) provide actionable information for healthcare providers derived by the CGM report; (4) better manage patients on hemodialysis; and (5) effectively and efficiently analyze glycemic effects of new interventions whether they be pharmaceuticals (duration of action, pharmacodynamics, safety, and efficacy), devices, or psycho-educational. Personal CGM has also been successfully used in a small number of studies as a behavior modification tool in those with T2D. This comprehensive review describes the differences between professional and personal CGM and the evidence for the use of each form of CGM in T2D. Finally, the opinions of key professional societies on the use of CGM in T2D are presented.

Non-medical prescribing versus medical prescribing for acute and chronic disease management in primary and secondary care

BACKGROUND

A range of health workforce strategies are needed to address health service demands in low-, middle- and high-income countries. Non-medical prescribing involves nurses, pharmacists, allied health professionals, and physician assistants substituting for doctors in a prescribing role, and this is one approach to improve access to medicines.

OBJECTIVES

To assess clinical, patient-reported, and resource use outcomes of non-medical prescribing for managing acute and chronic health conditions in primary and secondary care settings compared with medical prescribing (usual care).

SEARCH METHODS

We searched databases including CENTRAL, MEDLINE, Embase, and five other databases on 19 July 2016. We also searched the grey literature and handsearched bibliographies of relevant papers and publications.

SELECTION CRITERIA

Randomised controlled trials (RCTs), cluster-RCTs, controlled before-and-after (CBA) studies (with at least two intervention and two control sites) and interrupted time series analysis (with at least three observations before and after the intervention) comparing: 1. non-medical prescribing versus medical prescribing in acute care; 2. non-medical prescribing versus medical prescribing in chronic care; 3. non-medical prescribing versus medical prescribing in secondary care; 4 non-medical prescribing versus medical prescribing in primary care; 5. comparisons between different non-medical prescriber groups; and 6. non-medical healthcare providers with formal prescribing training versus those without formal prescribing training.

DATA COLLECTION AND ANALYSIS

We used standard methodological procedures expected by Cochrane. Two review authors independently reviewed studies for inclusion, extracted data, and assessed study quality with discrepancies resolved by discussion. Two review authors independently assessed risk of bias for the included studies according to EPOC criteria. We undertook meta-analyses using the fixed-effect model where studies were examining the same treatment effect and to account for small sample sizes. We compared outcomes to a random-effects model where clinical or statistical heterogeneity existed.

MAIN RESULTS

We included 46 studies (37,337 participants); non-medical prescribing was undertaken by nurses in 26 studies and pharmacists in 20 studies. In 45 studies non-medical prescribing as a component of care was compared with usual care medical prescribing. A further study compared nurse prescribing supported by guidelines with usual nurse prescribing care. No studies were found with non-medical prescribing being undertaken by other health professionals. The education requirement for non-medical prescribing varied with country and location.A meta-analysis of surrogate markers of chronic disease (systolic blood pressure, glycated haemoglobin, and low-density lipoprotein) showed positive intervention group effects. There was a moderate-certainty of evidence for studies of blood pressure at 12 months (mean difference (MD) -5.31 mmHg, 95% confidence interval (CI) -6.46 to -4.16; 12 studies, 4229 participants) and low-density lipoprotein (MD -0.21, 95% CI -0.29 to -0.14; 7 studies, 1469 participants); we downgraded the certainty of evidence from high due to considerations of serious inconsistency (considerable heterogeneity), multifaceted interventions, and variable prescribing autonomy. A high-certainty of evidence existed for comparative studies of glycated haemoglobin management at 12 months (MD -0.62, 95% CI -0.85 to -0.38; 6 studies, 775 participants). While there appeared little difference in medication adherence across studies, a meta-analysis of continuous outcome data from four studies showed an effect favouring patient adherence in the non-medical prescribing group (MD 0.15, 95% CI 0.00 to 0.30; 4 studies, 700 participants). We downgraded the certainty of evidence for adherence to moderate due to the serious risk of performance bias. While little difference was seen in patient-related adverse events between treatment groups, we downgraded the certainty of evidence to low due to indirectness, as the range of adverse events may not be related to the intervention and selective reporting failed to adequately report adverse events in many studies.Patients were generally satisfied with non-medical prescriber care (14 studies, 7514 participants). We downgraded the certainty of evidence from high to moderate due to indirectness, in that satisfaction with the prescribing component of care was only addressed in one study, and there was variability of satisfaction measures with little use of validated tools. A meta-analysis of health-related quality of life scores (SF-12 and SF-36) found a difference favouring usual care for the physical component score (MD 1.17, 95% CI 0.16 to 2.17), but not the mental component score (MD 0.58, 95% CI -0.40 to 1.55). However, the quality of life measurement may more appropriately reflect composite care rather than the prescribing component of care, and for this reason we downgraded the certainty of evidence to moderate due to indirectness of the measure of effect. A wide variety of resource use measures were reported across studies with little difference between groups for hospitalisations, emergency department visits, and outpatient visits. In the majority of studies reporting medication use, non-medical prescribers prescribed more drugs, intensified drug doses, and used a greater variety of drugs compared to usual care medical prescribers.The risk of bias across studies was generally low for selection bias (random sequence generation), detection bias (blinding of outcome assessment), attrition bias (incomplete outcome data), and reporting bias (selective reporting). There was an unclear risk of selection bias (allocation concealment) and for other biases. A high risk of performance bias (blinding of participants and personnel) existed.

AUTHORS' CONCLUSIONS

The findings suggest that non-medical prescribers, practising with varying but high levels of prescribing autonomy, in a range of settings, were as effective as usual care medical prescribers. Non-medical prescribers can deliver comparable outcomes for systolic blood pressure, glycated haemoglobin, low-density lipoprotein, medication adherence, patient satisfaction, and health-related quality of life. It was difficult to determine the impact of non-medical prescribing compared to medical prescribing for adverse events and resource use outcomes due to the inconsistency and variability in reporting across studies. Future efforts should be directed towards more rigorous studies that can clearly identify the clinical, patient-reported, resource use, and economic outcomes of non-medical prescribing, in both high-income and low-income countries.

Factors associated with duration of inpatient hospital stay for patients with diabetes mellitus admitted to a medical unit in a community public hospital

The aim was to examine predictors of duration of inpatient hospital stay in people with diabetes mellitus to assist implementation of strategies to reduce hospital stay. This audit prospectively studied patients with diabetes mellitus admitted to a medical unit of an Australian community public hospital. Other outcome measures included glucose treatment optimisation and access to GP and diabetes-specific healthcare professionals. Comparison was made to patients without diabetes mellitus who were admitted concomitantly. Diabetes patients represented 26% of admissions over a 2-month period. In total, 73% had seen a GP within the prior 6 months. Patients with diabetes mellitus (n=79) had a median age of 69 years; 53% were male and median HbA_1c was 65mmolmol^-1 (8.1%). Diabetes mellitus was associated with a longer inpatient stay (P=0.03), particularly among patients admitted with vascular disease. Age >65 years and seeing <3 members of the community-based diabetes mellitus multidisciplinary team (MDT) in the 2-years pre-admission were independently associated with a longer stay (P=0.02). In total, 10% were referred to an endocrinologist on discharge. Involvement of more of the diabetes-specific MDT, with a skilled GP, in primary care is recommended as it may shorten inpatient hospital stay, improve glycaemia and reduce demand for limited specialist endocrinologists.

Impact of insulin initiation on glycaemic variability and glucose profiles in a primary healthcare Type 2 diabetes cohort: analysis of continuous glucose monitoring data from the INITIATION study

AIM

To use continuous glucose monitoring to examine the effects of insulin initiation with glargine, with or without glulisine, on glycaemic variability and glycaemia in a cohort of people with Type 2 diabetes receiving maximum oral hypoglycaemic agents in primary healthcare.

METHODS

We conducted a post hoc analysis of continuous glucose monitoring data from 89 participants at baseline and at 24 weeks after insulin commencement. Indicators of glycaemic variability (standard deviation, J-index and mean amplitude of glycaemic excursion) and glycaemia (HbA1c , mean glucose, area under the glucose-time curve) were assessed. Multi-level regression analysis was used to identify the predictors of change.

RESULTS

Complete glycaemic variability data were available for 78 participants. Of these participants, 41% were women, their mean (sd) age was 59.2 (10.4) years, the median (interquartile range) diabetes duration was 10.4 (6.5, 13.3) years and the median (interquartile range) baseline HbA1c was 82.5 (71.6, 96.7) mmol/mol [9.7 (8.7, 11.0)%]. At baseline, BMI correlated negatively with standard deviation (r = -0.30) and mean amplitude of glycaemic excursion (r = -0.26), but not with J-index; HbA1c correlated with J-index (r = 0.61) but not with mean amplitude of glycaemic excursion and standard deviation. After insulin initiation the mean (sd) glucose level decreased [from 12.0 (3.0) to 8.5 (1.6) mmol/l; P < 0.001], as did the median (interquartile range) J-index [from 66.9 (47.7, 95.1) to 36.9 (27.6, 49.8) mmol/l; P < 0.001]. Baseline HbA1c correlated with a greater J-index reduction (r = -0.45; P < 0.001). The mean amplitude of glycaemic excursion and standard deviation values were unchanged. The baseline temporal profile, showing elevated postprandial morning glucose levels, was unchanged after insulin initiation, despite an overall reduction in glycaemia.

CONCLUSION

Insulin initiation reduced hyperglycaemia but did not alter glycaemic variability in adults with Type 2 diabetes receiving maximum oral hypoglycaemic agents. The most significant postprandial excursions were seen in the morning, which identifies prebreakfast as the most effective target for short-acting insulin therapy.

Optimizing insulin initiation in primary care: the Diabetes CoStars patient support program

PURPOSE

The purpose of this study was to evaluate the optimization of fasting blood glucose (FBG) levels in patients with type 2 diabetes mellitus newly initiated on insulin glargine who were enrolled in the Australian Diabetes CoStars Patient Support Program (PSP).

PATIENTS AND METHODS

A retrospective analysis of data from 514 patients with type 2 diabetes mellitus who completed the 12-week Diabetes CoStars PSP was performed. All patients were initiated on insulin glargine in primary care and enrolled by their general practitioner, who selected a predefined titration plan and support from a local Credentialled Diabetes Educator. The data collected included initial and final insulin dose, self-reported FBG, and glycated hemoglobin (A1c) levels.

RESULTS

The insulin dose increased in 81% of patients. Mean FBG was reduced from 208.8 mg/dL (11.6 mmol/L) to 136.8 mg/dL (7.6 mmol/L) after 12 weeks. Initial and final A1c values were available for 99 patients; mean A1c was reduced from 9.5% (80 mmol/mol) to 8.1% (65 mmol/mol). The reductions in mean FBG and A1c were similar irrespective of titration plan. Overall, 27.2% of patients achieved FBG levels within the titration plan target range of 72-108 mg/dL (4-6 mmol/L) and an additional 43.4% of patients achieved FBG within the range recommended by current Australian guidelines (110-144 mg/dL [6.1-8.0 mmol/L]). Overall, 23.3% of patients achieved the A1c target of ≤7%.

CONCLUSION

These data demonstrate that the majority of patients enrolled in the Diabetes CoStars PSP achieved acceptable FBG levels 12 weeks after starting insulin therapy irrespective of titration plan.

Optimizing care and outcomes for people with type 2 diabetes - lessons from a translational research program on insulin initiation in general practice

BACKGROUND

Clinical inertia, failure to intensify treatment according to evidence-based guidelines, leads to prolonged, avoidable hyperglycemia in people with type 2 diabetes (T2D). This is a challenge for General Practice and Primary Care, where most people with T2D receive most of their care. Sustained, integrated translational research programs are needed to embed effective treatments in routine practice, yet many challenges exist for developing such programs.

OBJECTIVES

To explore challenges and facilitators to implementing a translational research program focused on insulin initiation and titration among people with T2D in general practice and to identify key factors important to support and sustain such translation research in primary care. Operationalizing a program of translational work in primary care: We describe a series of studies on insulin initiation and titration in general practice including theory and qualitative work (Phase 1), a small feasibility and acceptability pilot (Phase 2), a large scale pilot (Phase 3), and a pragmatic cluster randomized trial currently under way (Phase 4). We used mixed methods to explore practice level implementation issues, and reflective investigator discussions to explore broader research program sustainability. Challenges for translational research in primary care: Key facilitators and barriers at practice and research program levels, include: Appropriate funding structures to secure long-term capacity building and people support; Building and maintaining linkages between communities of practice, primary and secondary/tertiary care researchers, institutions, and industry partners; Strategies for engagement and support for practitioners and participants.

CONCLUSION

Building effective and sustainable translational research programs are critical for developing evidence-based policy that drives improved outcomes at a population level. Diverse sources of funding that support extensive and sustained trans-mural collaboration as well as engagement with practitioners, patients, and policymakers in the field are crucial.