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Meta-Analysis
. 2011 Jul 7:343:d3805.
doi: 10.1136/bmj.d3805.

Glycaemic control in type 1 diabetes during real time continuous glucose monitoring compared with self monitoring of blood glucose: meta-analysis of randomised controlled trials using individual patient data

John C Pickup  1 , Suzanne C Freeman, Alex J Sutton
Affiliations
Meta-Analysis

Glycaemic control in type 1 diabetes during real time continuous glucose monitoring compared with self monitoring of blood glucose: meta-analysis of randomised controlled trials using individual patient data

John C Pickup et al. BMJ. .

Abstract

Objective: To determine the clinical effectiveness of real time continuous glucose monitoring compared with self monitoring of blood glucose in type 1 diabetes.

Design: Meta-analysis of randomised controlled trials.

Data sources: Cochrane database for randomised controlled trials, Ovid Medline, Embase, Google Scholar, lists of papers supplied by manufacturers of continuous glucose monitors, and cited literature in retrieved articles. Studies reviewed Randomised controlled trials of two or more months' duration in men and non-pregnant women with type 1 diabetes that compared real time continuous glucose monitoring with self monitoring of blood glucose and where insulin delivery was the same in both arms. Analysis Two step meta-analysis of individual patient data with the primary outcome of final glycated haemoglobin (HbA(1c)) percentage and area under the curve of hypoglycaemia (glucose concentration <3.9 mmol/L) during either treatment, followed by one step metaregression exploring patient level determinants of HbA(1c) and hypoglycaemia.

Results: Six trials were identified, consisting of 449 patients randomised to continuous glucose monitoring and 443 to self monitoring of blood glucose. The overall mean difference in HbA(1c) for continuous glucose monitoring versus self monitoring of blood glucose was -0.30% (95% confidence interval -0.43% to -0.17%) (-3.0, -4.3 to -1.7 mmol/mol). A best fit regression model of determinants of final HbA(1c) showed that for every one day increase of sensor usage per week the effect of continuous glucose monitoring versus self monitoring of blood glucose increased by 0.150% (95% credibility interval -0.194% to -0.106%) (1.5, -1.9 to -1.1 mmol/mol) and every 1% (10 mmol/mol) increase in baseline HbA(1c) increased the effect by 0.126% (-0.257% to 0.0007%) (1.3, -2.6 to 0.0 mmol/mol). The model estimates that, for example, a patient using the sensor continuously would experience a reduction in HbA(1c) of about 0.9% (9 mmol/mol) when the baseline HbA(1c) is 10% (86 mmol/mol). The overall reduction in area under the curve of hypoglycaemia was -0.28 (-0.46 to -0.09), corresponding to a reduction in median exposure to hypoglycaemia of 23% for continuous glucose monitoring compared with self monitoring of blood glucose. In a best fit regression model, baseline area under the curve of hypoglycaemia was only weakly related to the effect of continuous glucose monitoring compared with self monitoring of blood glucose on hypoglycaemia outcome, and sensor usage was unrelated to hypoglycaemia at outcome.

Conclusions: Continuous glucose monitoring was associated with a significant reduction in HbA(1c) percentage, which was greatest in those with the highest HbA(1c) at baseline and who most frequently used the sensors. Exposure to hypoglycaemia was also reduced during continuous glucose monitoring. The most cost effective or appropriate use of continuous glucose monitoring is likely to be when targeted at people with type 1 diabetes who have continued poor control during intensified insulin therapy and who frequently use continuous glucose monitoring.

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Conflict of interest statement

Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/coi_disclosure.pdf (available on request from the corresponding author) and declare that: no authors had support from companies for the submitted work; JCP has received speaker and advisory board honorariums from Medtronic, a manufacturer of continuous glucose monitoring devices that might have an interest in the submitted work in the previous 3 years; no other relationships or activities that could appear to have influenced the submitted work.

Figures

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Fig 1 Flow of papers through study
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Fig 2 Two step approach meta-analysis using individual patient data for difference in HbA1c percentage between continuous glucose monitoring and self monitoring of blood glucose using random effects model. Overall mean difference for all six trials was 0.30% (3 mmol/mol), favouring continuous glucose monitoring
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Fig 3 Model estimated difference in HbA1c using continuous glucose monitoring compared with self monitoring of blood glucose according to baseline HbA1c percentage and sensor usage in an example 40 year old participant with type 1 diabetes. An individual, for example, with an initial HbA1c value of 8% (64 mmol/mol) would be expected to have a reduction in HbA1c of 0.35% (4 mmol/mol) with use of a sensor for five days a week, increasing to 0.65% (7 mmol/mol) with usage for seven days a week
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Fig 4 Individual patient data two step meta-analysis of difference in ln area under the curve of hypoglycaemia during continuous glucose monitoring compared with self monitoring of blood glucose
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Fig 5 Aggregate data meta-analysis of severe hypoglycaemia incidence rate ratio on self monitoring of blood glucose compared with continuous glucose monitoring
See this image and copyright information in PMC

Comment in

References

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