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Our New BMJ website does not support IE6 please upgrade your browser to the latest version or use alternative browsers suggested below. Most high risk and precious pregnancies may require frequentmonitoring of blood glucose to find out the fluctuations in levelsand adjust the dose of insulin accordingly. For the eating out scenario, closed loop delivery increased the time plasma glucose levels were in target by a median 28% (2-39%), P=0.01. Combining continuous glucose monitoring with pump therapy can improve the control of glucose,11 but responding to overnight real time data detected by sensor is impractical. The studies mimicked the two common scenarios of “eating in” and “eating out” in preparation for testing as an outpatient. In an initial study we evaluated closed loop delivery after a medium sized evening meal, mimicking an evening at home when closed loop delivery could be conveniently started with the meal. In a second, more challenging, study mimicking an evening out, the meal was larger, was consumed later, and was accompanied by wine.
Inclusion criteria were adults with type 1 diabetes (World Health Organization criteria or confirmed negative for C peptide), aged 18-65, and using insulin pump therapy for at least three months. Exclusion criteria were awareness of reduced hypoglycaemia and clinically significant nephropathy, neuropathy, or retinopathy. Figure 2? shows the flow of participants through the study.Fig 2?Flow of participants through study comparing closed loop delivery of insulin with conventional insulin pump therapy after a medium sized evening meal (eating in scenario), and study comparing closed loop delivery with insulin pump therapy after a large evening meal accompanied by alcohol (eating out scenario)Eating in scenarioOf 32 adults invited to take part in the study, 13 agreed and were randomly assigned to be treated overnight with either closed loop delivery of insulin or conventional insulin pump therapy during two study nights, separated by an interval of one to three weeks.
Before the first study visit we analysed data from continuous glucose monitoring for up to 120 hours to optimise the delivery of insulin by conventional pump therapy. On both visits participants ate an identical evening meal, comprising 60 g of carbohydrate, at 1900, accompanied by prandial insulin. We calculated the prandial boluses according to the participants’ own insulin to carbohydrate ratio and glucose values from finger stick testing.
During the intervention visit, closed loop insulin delivery was applied from 1900 until 0800 the next day. During the intervention visit, insulin was delivered by closed loop from 2200 until 1200 the next day. During the control visit, participants applied their usual insulin pump settings over the same timeframe.Continuous glucose monitoring and insulin deliveryTo measure subcutaneous glucose in the eating in scenario we used the continuous glucose monitoring system FreeStyle Navigator (Abbott Diabetes Care, Alameda, CA) with a 10 hour run-in calibration period. In the eating out scenario we used FreeStyle Navigator with a one hour run-in calibration period. The systems were calibrated using capillary finger stick measurements as per manufacturer’s instructions.
The calculations utilised a compartment model of glucose kinetics,20 describing the effect of rapid acting insulin and the carbohydrate content of meals on glucose excursions detected by the sensor. The algorithm was initialised using participant’s weight, total daily insulin dose, and basal insulin requirements. Additionally, the algorithm was provided with glucose levels measured by the sensor during a 30 minute period preceding the start of closed loop delivery, the carbohydrate content of the evening meal, and the prandial insulin bolus.


The algorithm adapted itself to participants by updating two model variables: an endogenous glucose flux correcting for errors in model based predictions, and carbohydrate bioavailability. In the eating in scenario we collected samples between 1830 and 0800 and in the eating out scenario between 1930 and 1200. These data were not used to alter insulin infusion rates during the visits for closed loop delivery or control. We calculated that 12 participants would provide 80% power at the 5% level of significance to detect this difference between conventional and closed loop insulin delivery systems.Randomisation and maskingFor each study we placed a computer generated allocation sequence with permuted block four randomisation in sealed envelopes.
For safety reasons, during the eating out scenario investigators had access to plasma glucose levels. During both studies, participants did not have access to data on plasma or sensor glucose levels.Statistical analysisSenior investigators and study statisticians agreed on the analysis plan in advance. We estimated glycaemic variability by mean amplitude of glycaemic excursions22 and standard deviation of glucose concentration. Figure 3? shows the profiles of plasma glucose and plasma insulin concentrations and insulin infusion. Insulin infusion was more variable during closed loop delivery, but the average insulin infusion rate and plasma insulin concentrations were similar during closed loop delivery and control visits (fig 3).Fig 3?Profiles (medians and interquartile ranges) of plasma glucose and insulin concentrations and insulin infusion in eating in scenario (12 participants). As for the eating in scenario, the average overnight infusion of insulin and plasma insulin concentration during closed loop delivery and pump therapy were comparable. The profiles for glucose and insulin levels showed lower variability of plasma glucose during closed loop delivery from 0200 onwards (fig 4?).Fig 4?Profiles (medians and interquartile ranges) of plasma glucose and insulin concentrations and insulin infusion in the eating out scenario (12 participants).
Three were attributable to the preceding prandial insulin dose and could not be prevented despite suspended insulin delivery immediately after the start of closed loop. Three episodes were treated with 15-36 g of oral carbohydrates in drink (Lucozade; GlaxoSmithKline, Brentford, UK) and the fourth lasted 30 minutes with spontaneous recovery.
The episode was not associated with symptoms but was treated with 30 g of oral carbohydrates in drink.
The remaining four events were not associated with symptoms; all but one event occurred after 0100 and were not treated.
Closed loop delivery significantly reduced the variability in plasma glucose levels and reduced time when the levels were above target (table 5?).
Closed loop delivery consistently outperformed conventional insulin pump therapy at both high and low glucose values (fig 5?).Fig 5?Distribution of plasma glucose values during closed loop insulin delivery and conventional insulin pump therapy (continuous subcutaneous insulin infusion) combining data collected from midnight until end of the eating in scenario and the eating out scenario.
Percentages represent total time plasma glucose level was below, at, and above target from midnight until end of closed loop deliveryTable 5 ?Comparisons between closed loop delivery of insulin and conventional insulin pump therapy based on plasma glucose concentrations after pooling results for two meal scenarios: eating in and eating out. The average dose of insulin delivered during closed loop and conventional pump therapy was comparable, illustrating the key role of glucose responsive insulin delivery to achieve target glucose levels. The large evening meal and alcohol intake provided a greater challenge to closed loop delivery than did a medium sized evening meal, reflected by a slightly reduced but still superior performance over insulin pump therapy.Strengths and limitations of the studyAn important feature of our closed loop algorithm is avoidance of hypoglycaemia, which if replicated in the home setting would revolutionise patient safety.
The algorithm suspends insulin delivery when the sensor detects a low or rapid fall in glucose levels and does so without increased risk of hyperglycaemia.24 Initiation of the algorithm is straightforward, requiring users to input their weight, total daily insulin dose, and basal infusion rates.


The system updates advice on insulin dose using a robust, computationally efficient approach suitable for use with insulin pumps and hand held devices to facilitate practical use. It can discriminate between slowly or rapidly absorbed meals, thus coping with considerable variability in gut absorption. Similarly, variability in insulin absorption is assessed through model based analysis of insulin delivery and the effects on sensor glucose levels.
These adaptive capabilities set our algorithm apart from most existing non-adaptive approaches.25 26 27Additional strengths of our study are that we used the closed loop system in circumstances near to those in real life, without artificially improving performance. Firstly, we used a commercially available continuous glucose monitor calibrated according to manufacturer’s instructions. Secondly, only one sensor was inserted and it was not recalibrated in the event of suboptimal accuracy. Our studies assessed closed loop delivery of insulin in preparation for home testing, when recalibration of a sensor is impractical (particularly overnight), and multiple sensors, although improving accuracy, are inconvenient and likely to affect users’ satisfaction.Limitations are that participants were compliant and predominantly of white ethnicity, hence applicability to less compliant people and those with poor glycaemic control is unknown. Also, we did not adjust for multiple statistical comparisons in secondary outcomes; hence these should be considered hypothesis generating rather than conclusive. Another limitation is that the research nurse manually entered sensor glucose values into the algorithm and manually adjusted the insulin pump.
Presumably the lack of automation did not to affect the performance of the closed loop system but will need to be dealt with before proceeding to home testing. This may be attributed to differences in protocol as we opted to omit breakfast and its meal related bolus to mimic sleeping until lunchtime (not uncommon in young adults). Another difference was that in our study alcohol was consumed within two hours of the evening meal compared with three hours later in the other study. As continuous glucose monitors and control algorithms are perfected, devices miniaturised, and performance limiting aspects identified, it is anticipated that early closed loop approaches such as overnight closed loop delivery will be followed by more advanced solutions for control of glucose during both the day and at night. Closed loop systems may serve as a bridge until type 1 diabetes is cured by, for example, stem cell therapy or islet transplantation.In conclusion, closed loop delivery of insulin can significantly improve the control of glucose levels overnight and reduce the risk of nocturnal hypoglycaemia in adults with type 1 diabetes. JH, KK, JMA, and DE provided patient care, collected the clinical and laboratory data, and contributed to biochemical analysis. MEW, DX, MN, RH, KK, and CK carried out or supported the data analysis, including the statistical analyses. RH, KK, MLE, SAA, SRH, HRM, DBD, and MEW contributed to the interpretation of the results and the writing and critical review of the report. Smiths Medical supplied the study pumps and Abbott Diabetes Care supplied the Freestyle Navigator devices and sensors for the eating in scenario.



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