Perioperative glycaemic control for people with diabetes undergoing surgery.
People with diabetes mellitus are at increased risk of postoperative complications. Data from randomised clinical trials and meta-analyses point to a potential benefit of intensive glycaemic control, targeting near-normal blood glucose, in people with hyperglycaemia (with and without diabetes mellitus) being submitted for surgical procedures. However, there is limited evidence concerning this question in people with diabetes mellitus undergoing surgery.
To assess the effects of perioperative glycaemic control for people with diabetes undergoing surgery.
For this update, we searched the databases CENTRAL, MEDLINE, LILACS, WHO ICTRP and ClinicalTrials.gov. The date of last search for all databases was 25 July 2022. We applied no language restrictions.
We included randomised controlled clinical trials (RCTs) that prespecified different targets of perioperative glycaemic control for participants with diabetes (intensive versus conventional or standard care).
Two authors independently extracted data and assessed the risk of bias. Our primary outcomes were all-cause mortality, hypoglycaemic events and infectious complications. Secondary outcomes were cardiovascular events, renal failure, length of hospital and intensive care unit (ICU) stay, health-related quality of life, socioeconomic effects, weight gain and mean blood glucose during the intervention. We summarised studies using meta-analysis with a random-effects model and calculated the risk ratio (RR) for dichotomous outcomes and the mean difference (MD) for continuous outcomes, using a 95% confidence interval (CI), or summarised outcomes with descriptive methods. We used the GRADE approach to evaluate the certainty of the evidence (CoE).
A total of eight additional studies were added to the 12 included studies in the previous review leading to 20 RCTs included in this update. A total of 2670 participants were randomised, of which 1320 were allocated to the intensive treatment group and 1350 to the comparison group. The duration of the intervention varied from during surgery to five days postoperative. No included trial had an overall low risk of bias. Intensive glycaemic control resulted in little or no difference in all-cause mortality compared to conventional glycaemic control (130/1263 (10.3%) and 117/1288 (9.1%) events, RR 1.08, 95% CI 0.88 to 1.33; I2 = 0%; 2551 participants, 18 studies; high CoE). Hypoglycaemic events, both severe and non-severe, were mainly experienced in the intensive glycaemic control group. Intensive glycaemic control may slightly increase hypoglycaemic events compared to conventional glycaemic control (141/1184 (11.9%) and 41/1226 (3.3%) events, RR 3.36, 95% CI 1.69 to 6.67; I2 = 64%; 2410 participants, 17 studies; low CoE), as well as those considered severe events (37/927 (4.0%) and 6/969 (0.6%), RR 4.73, 95% CI 2.12 to 10.55; I2 = 0%; 1896 participants, 11 studies; low CoE). Intensive glycaemic control, compared to conventional glycaemic control, may result in little to no difference in the rate of infectious complications (160/1228 (13.0%) versus 224/1225 (18.2%) events, RR 0.75, 95% CI 0.55 to 1.04; P = 0.09; I2 = 55%; 2453 participants, 18 studies; low CoE). Analysis of the predefined secondary outcomes revealed that intensive glycaemic control may result in a decrease in cardiovascular events compared to conventional glycaemic control (107/955 (11.2%) versus 125/978 (12.7%) events, RR 0.73, 95% CI 0.55 to 0.97; P = 0.03; I2 = 44%; 1454 participants, 12 studies; low CoE). Further, intensive glycaemic control resulted in little or no difference in renal failure events compared to conventional glycaemic control (137/1029 (13.3%) and 158/1057 (14.9%), RR 0.92, 95% CI 0.69 to 1.22; P = 0.56; I2 = 38%; 2086 participants, 14 studies; low CoE). We found little to no difference between intensive glycaemic control and conventional glycaemic control in length of ICU stay (MD -0.10 days, 95% CI -0.57 to 0.38; P = 0.69; I2 = 69%; 1687 participants, 11 studies; low CoE), and length of hospital stay (MD -0.79 days, 95% CI -1.79 to 0.21; P = 0.12; I2 = 77%; 1520 participants, 12 studies; very low CoE). Due to the differences within included studies, we did not pool data for the reduction of mean blood glucose. Intensive glycaemic control resulted in a mean lowering of blood glucose, ranging from 13.42 mg/dL to 91.30 mg/dL. One trial assessed health-related quality of life in 12/37 participants in the intensive glycaemic control group, and 13/44 participants in the conventional glycaemic control group; no important difference was shown in the measured physical health composite score of the short-form 12-item health survey (SF-12). One substudy reported a cost analysis of the population of an included study showing a higher total hospital cost in the conventional glycaemic control group, USD 42,052 (32,858 to 56,421) compared to the intensive glycaemic control group, USD 40,884 (31.216 to 49,992). It is important to point out that there is relevant heterogeneity between studies for several outcomes. We identified two ongoing trials. The results of these studies could add new information in future updates on this topic.
High-certainty evidence indicates that perioperative intensive glycaemic control in people with diabetes undergoing surgery does not reduce all-cause mortality compared to conventional glycaemic control. There is low-certainty evidence that intensive glycaemic control may reduce the risk of cardiovascular events, but cause little to no difference to the risk of infectious complications after the intervention, while it may increase the risk of hypoglycaemia. There are no clear differences between the groups for the other outcomes. There are uncertainties among the intensive and conventional groups regarding the optimal glycaemic algorithm and target blood glucose concentrations. In addition, we found poor data on health-related quality of life, socio-economic effects and weight gain. It is also relevant to underline the heterogeneity among studies regarding clinical outcomes and methodological approaches. More studies are needed that consider these factors and provide a higher quality of evidence, especially for outcomes such as hypoglycaemia and infectious complications.
Bellon F
，Solà I
，Gimenez-Perez G
，Hernández M
，Metzendorf MI
，Rubinat E
，Mauricio D
... -
《Cochrane Database of Systematic Reviews》
The efficacy of using continuous glucose monitoring as a behaviour change tool in populations with and without diabetes: a systematic review and meta-analysis of randomised controlled trials.
Continuous glucose monitoring (CGM) holds potential as a precision public health intervention, offering personalised insights into how diet and physical activity affect glucose levels. Nevertheless, the efficacy of using CGM in populations with and without diabetes to support behaviour change and behaviour-driven outcomes remains unclear. This systematic review and meta-analysis examines whether using CGM-based feedback to support behaviour change affects glycaemic, anthropometric, and behavioural outcomes in adults with and without diabetes.
Ovid MEDLINE, Cochrane Central Register of Controlled Trials, Elsevier Embase, EBSCOhost PsycINFO, and ProQuest Dissertations & Theses Global were searched through January 2024. Eligible studies were randomised controlled trials in adults that implemented CGM-based feedback in at least one study arm compared to a control without CGM feedback. Dual screening, data extraction, and bias assessment were conducted independently. Mean differences in outcomes between intervention and comparison groups were analysed using generic inverse variance models and random effects. Robustness of pooled estimates from random-effects models was considered with sensitivity and subgroup analyses.
Twenty-five clinical trials with 2996 participants were included. Most studies were conducted in adults with type 2 diabetes (n = 17/25; 68%), followed by type 1 diabetes (n = 3/25, 12%), gestational diabetes (n = 3/25, 12%), and obesity (n = 3/25, 12%). Eleven (44%) studies reported CGM-affiliated conflicts of interest. Interventions incorporating CGM-based feedback reduced HbA1c by 0.28% (95% CI 0.15, 0.42, p < 0.001; I2 = 88%), and increased time in range by 7.4% (95% CI 2.0, 12.8, p < 0.008; I2 = 80.5%) compared to arms without CGM, with non-significant effects on time above range, BMI, and weight. Sensitivity analyses showed consistent mean differences in HbA1c across different conditions, and differences between subgroups were non-significant. Only 4/25 studies evaluated the effect of CGM on dietary changes; 5/25 evaluated physical activity.
This evidence synthesis found favourable, though modest, effects of CGM-based feedback on glycaemic control in adults with and without diabetes. Further research is needed to establish the behaviours and behavioural mechanisms driving the observed effects across diverse populations.
CRD42024514135.
Richardson KM
，Jospe MR
，Bohlen LC
，Crawshaw J
，Saleh AA
，Schembre SM
... -
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Continuous glucose monitoring with FreeStyle Libre PRO sensor in patients with type 2 diabetes and end-stage renal failure on haemoDIALysis (FSLPRO-DIAL pilot study).
For end-stage renal disease (ESRD) patients with diabetes on haemodialysis, diabetes control is difficult to achieve. Hypoglycaemia is a major problem in these frailty subjects. Continuous glucose monitoring (CGM) devices appear therefore to be a good tool to help patients monitor their glycaemic control and to help practitioners optimize treatment. We aimed to compare the laboratory value of Hba1c with the sensor-estimated value of Hba1c (= glucose management indicator, GMI) in ESRD patients with type 2 diabetes (T2D) (with or without insulin treatment) on haemodialysis. Secondly, we aimed to identify CGM-derived monitoring parameters [time in range, time in hypo/hyperglycaemia, glycaemic variability (coefficient of variation, CV)] to identify patients at risk of frequent hypo- or hyperglycaemia.
The FSLPRO-DIAL pilot study (NCT04641650) was a prospective monocentric cohort study including 29 subjects with T2D who achieve the protocol. Inclusion criteria were: age ≥ 18 years, haemodialysis duration for at least 3 months, type 2 diabetes with no change in treatment for at least 3 months. Demographic data and blood sample were collected at the day of inclusion. Freestyle Libre pro IQ sensor (blinded CGM) was inserted for 14 days. After this period, all CGMs data were collected and analysed.
Data were available for 27 patients. Mean age was 73 ± 10, mean BMI 27.2 kg/m2, mean duration of diabetes 16.9 years and mean dialysis duration 2.9 years. Twenty-four subjects were treated with insulin. Mean HbA1c was 6.6% (SD 1.2), and mean GMI was 6.7% (SD 0.9) (no significant difference, p = 0.3). Twelve subjects (44.4%) had a discordance between HbA1c and GMI of < 0.5%, 11 (40.8%) had a discordance between 0.5 and 1%, and only 4 (14.8%) had a discordance of > 1%. Mean time in range (70-180 mg/dl) was 71.9%, mean time below range (< 70 mg/dl) was 5.6%, and mean time above range (> 180 mg/dl) was 22.1%. Mean CV was 31.8%. For 13 out of 27 patients, we reduced antidiabetic treatment by stopping treatments or reducing insulin doses.
In this pilot study, there was no global significant difference between HbA1c and GMI in this particular cohort with very well-controlled diabetes. However, the use of the sensor enabled us to identify an excessive time in hypoglycemia in this fragile population and to adapt their treatment.
Henry Z
，Villar E
，Chauvet C
，Belloi A
，Prunescu I
，Doroszewski F
，Luyton C
，Marchand L
... -
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ResearchGate
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