MiniMed 780G TM in children with type 1 diabetes under seven years of age: Prospective open-label,single-arm,double-center,follow-up study

Abstract

BACKGROUND:

OBJECTIVE:

To evaluate glycaemic control in children below the age of seven with type 1 diabetes (T1D) and assess the safety of the advanced hybrid closed loop (AHCL) system in comparison to the previous treatment method, a sensor-augmented pump with predictive low-glucose suspend (SAP-PLGS).

METHOD:

Data from 10 children (aged 2.60–6.98 years) with T1D who transitioned to the AHCL system from SAP-PLGS were analysed. SAP-PLGS records from two weeks prior to the initiation of AHCL were compared with records from the initial four weeks post-switch (excluding the training period). These data were examined at two 2-week intervals and compared with records from two weeks post six-month usage of the AHCL.

RESULTS:

A significant decrease in the average nighttime glucose concentration was observed compared to pre-AHCL values ( $p=$ 0.001, concordance W $=$ 0.53). The Glucose Management Indicator (GMI) value significantly decreased from 6.88 $\pm$ 0.37% to 6.52 $\pm$ 0.32% ( $p=$ 0.018, rbc $=$ 0.93) immediately following the device switch and stabilized at 6.50 $\pm$ 0.28% ( $p=$ 0.001, W $=$ 0.53) and 6.55 $\pm$ 0.41% ( $p=$ 0.001, W $=$ 0.53) at subsequent stages of the study. An improvement was also observed in mean glucose values for time spent $<$ 54 mg/dl, while the proportion of time within this range was maintained, both during the day ( $p<$ 0.001, W $=$ 0.58) and at night ( $p=$ 0.002, W $=$ 0.83).

CONCLUSION:

The AHCL MiniMed 780G^TM system improved glycaemic control in the studied group of children under seven years of age with T1D compared to previous SAP-PLGS therapy. It proved to be safe for delivering insulin in this age group.

Keywords

Type 1 diabetes advanced hybrid closed-loop system young children hybrid closed-loop

1. Introduction

Given the steadily rising incidence of type 1 diabetes (T1D), particularly among the youngest preschool children, coupled with well-documented challenges of achieving and maintaining optimal metabolic control in this age group, there is a growing need for advanced technological devices. Such devices should help to overcome these barriers and meet the international and national guidelines (such as those issued by the International Society for Pediatric and Adolescent Diabetes, American Diabetes Association, or Diabetes Poland) without compromising the patient’s and family’s quality of life [1, 2, 3, 4, 5, 6, 7]. The Automated Insulin Delivery (AID) pumps, such as the MiniMed 780G^TM – an advanced hybrid closed-loop (AHCL) system, could offer potential solutions. The AHCL system maintains blood glucose levels at a predetermined level by combining information from continuous glucose monitoring with an algorithm that automatically estimates the insulin dose and correction boluses. The manual initiation of meal boluses is also incorporated [8]. AHCL use has been demonstrated to benefit children, adolescents, and adults by lowering HbA1c and increasing the time spent in the 70 $=$ 180 mg/dl range without increasing hypoglycaemia [9, 10, 11, 12]. However, a limitation of the MiniMed 780G system is its registration for use in children above the age of 7 years. The caregivers’ demand for new, more effective diabetes solutions has prompted its use where alternative devices may not be available in all countries [13, 14].

This study aimed to analyse the glycaemic control parameters in children with T1D under seven years of age, and asses the safety of the AHCL system, compared to the previously used sensor-augmented pump with predictive low-glucose suspend (SAP-PLGS).

2. Materials and methods

The study involved ten children with T1D treated at the regional paediatric diabetes centres of the University Clinical Hospital, Medical University of Silesia (Upper Silesia region) and University Clinical Hospital, University of Opole (Opolskie region), both of which are certified SWEET Centers of Reference [15]. These were all children within this age group whose caregivers purchased the MiniMed 780G^TM system (unrefunded in Poland) for their children after being informed about its limitations and age restriction. The patients’ characteristics are presented in Table 1. Apart from an age under 7 yearsT1D diagnosis, and switch to the AHCL system, the inclusion criterion was sensor use exceeding 70%. All patients used the SAP-PLGS (MiniMed 640G) before initiating the AHCL system. A 2-hour training preceded the MiniMed 780G system usage. Children and their guardians received education at each therapy modification, covering equipment use and hyper- and hypoglycaemia prophylaxis (a standard and age-adjusted procedure at any change of treatment or device). According to ISPAD recommendations, children under the age of 7 do not make therapeutic decisions independently [16]. According to the manufacturer’s recommendation, the auto mode was activated after 7 days in manual mode. Carbohydrate-to-insulin ratios remained unchanged, and a temporary target of 150 mg/dl was advised for physical activity. The alarms were set for high glucose at 180 mg/dl and low glucose at 60 mg/dl. Data were collected using the CareLink^® Professional (Medtronic, USA) software for the SAP-PLGS, and records were automatically transferred to the CareLink server for the MiniMed 780G^TM. The data analysis included two weeks of SAP-PLGS use before switching to the AHCL system, the first four weeks after the switch (excluding the initial training period), and two weeks following six months of AHCL use.

Table 1
Characteristics of the participants (10 children)

Age [years]	5.76 $\pm$ 1.37
Male [ $n$ , %]	3, 30
Age of onset of diabetes [years]	2.72 $\pm$ 1.23
Duration of diabetes [years]	3.04 $\pm$ 1.49

A very precise data curation was performed prior to the statistical analysis. Four two-week time series (one for SAP-PLGS and three for the MiniMed 780G^TM system) were summarized using the following parameters: average sensor glucose (the average glucose concentration measured by the sensor) (Avg SG), standard deviation (SD) of the glycemia, coefficient of variation (CV) and robust coefficient of variation (CVR) for the blood glucose and glucose management index (approximate A1C level calculated from average sensor glucose) (GMI). Compared to the classical CV, which is a relation between the SD and the average, the CVR represents the ratio between the interquartile range and the median. Additionally, the analysis considered the ratio and difference between the analysed parameters for two consecutive time points. We also calculated the percentages of time spent in blood glucose below 54 mg/dl, ranges of 54 to 70 mg/dl, 70 to 140 mg/dl, 140 to 180 mg/dl, 180 to 250 mg/dl, and above 250 mg/dl as well as mean glucose values during the time spent below 54 mg/dl, below 70 mg/dL, above 180 mg/dl, and above 250 mg/dl. These parameters, describing the dynamics of the patient’s blood glucose concentration, were supported by information from, respectively, the SAP-PLGS or AHCL systems, such as the total daily dose (sum of basal and bolus expressed in units per kilogram body weight), basal dose (background insulin), bolus (mealtime and correction insulin), sensor wear ( percentage of time during which the sensor was used), autocorrection amount (correction boluses delivered automatically in auto mode) and SmartGuard (percentage of time during which auto mode was used) (the last two parameters only for AHCL). Routine clinical parameters (patient’s age, disease duration, sex) were also used to characterize the study group.

Descriptive statistics (mean, standard deviation, median, interquartile range, minimum and maximum values, coefficient of variation and robust coefficient of variation) and their 95% confidence intervals were calculated for each parameter. Statistics describing each two-week time series with results broken down into daytime (from 6:00 a.m. to 10:00 p.m.) and nighttime were estimated. The Lilliefors test was used to verify the hypothesis about the normal distribution of parameters. Friedman’s repeated measures ANOVA was used for each parameter with Kendall’s coefficient of agreement as a measure of effect size. The post-hoc analysis used the Wilcoxon test with Bonferroni correction for multiple comparisons. Adjusted $p$ -values were supported with rank biserial correlation coefficient (rbc) as a measure of effect size. Adjusted $p$ -values less than 0.05 were considered statistically significant. Kendall’s coefficient of concordance (W) was used to assess the consistency in response time among patients: W less than 0.2 was interpreted as slight agreement, 0.2 to 0.4 as fair agreement, 0.4 to 0.6 as moderate, 0.6 to 0.8 as substantial, and 0.8 and above as an almost perfect consistency in response time among patients. For rbc, a value below 0.1 was interpreted as a neglectable effect, 0.1 to 0.3 as small, 0.3 to 0.5 as moderate/medium, and over 0.5 as a large effect.

Table 2

Glycemic outcomes, insulin therapy and using the sensors: during two weeks of SAP-PLGS, the first two weeks after AHCL system training, the second two weeks after AHCL system training and the two weeks after six months after AHCL system training

	SAP-PLGS			AHCL first two weeks after system training			AHCL second two weeks after system training			AHCL two weeks after 6 months after system training			Friedman ANOVA $p$ -value & Kendalls’ concordance coefficient W
	All	Day	Night	All	Day	Night	All	Day	Night	All	Day	Night	All	Day	Night
Avg SG [mg/dl]	149.31 $\pm$ 15.56	148.76 $\pm$ 21.75	150.62 $\pm$ 16.20	134.17 $\pm$ 13.30	126.81 $\pm$ 16.95	128.85 $\pm$ 9.41	133.34 $\pm$ 11.90	135.17 $\pm$ 15.87	129.78 $\pm$ 10.56	135.44 $\pm$ 17.24	139.02 $\pm$ 20.16	128.41 $\pm$ 14.85	$p=$ 0.010891 W $=$ 0.532000	$p=$ 0.026264 W $=$ 0.308000	$p=$ 0.001156 W $=$ 0.532000
SD SG [mg/dl]	57.55 $\pm$ 10.29	56.39 $\pm$ 10.04	56.15 $\pm$ 11.70	55.21 $\pm$ 8.37	55.56 $\pm$ 9.74	52.34 $\pm$ 9.82	53.46 $\pm$ 6.18	54.45 $\pm$ 6.87	49.32 $\pm$ 7.76	52.68 $\pm$ 12.39	54.2 $\pm$ 12.47	47.78 $\pm$ 11.92	$p=$ 0.177750 W $=$ 0.164000	$p=$ 0.217705 W $=$ 0.148000	$p=$ 0.045192 W $=$ 0.268000
CV [%]	38.50 $\pm$ 5.13	38.14 $\pm$ 5.98	37.03 $\pm$ 4.38	41.17 $\pm$ 5.13	40.64 $\pm$ 5.71	40.66 $\pm$ 7.29	40.14 $\pm$ 3.61	40.36 $\pm$ 3.36	38.14 $\pm$ 6.25	38.63 $\pm$ 6.10	38.77 $\pm$ 6.27	36.97 $\pm$ 6.80	$p=$ 0.144744 W $=$ 0.180000	$p=$ 0.168891 W $=$ 0.168000	$p=$ 0.391625 W $=$ 0.100000
CVR [%]	58.48 $\pm$ 11.12	56.96 $\pm$ 11.36	56.65 $\pm$ 11.25	59.34 $\pm$ 8.25	59.78 $\pm$ 8.63	58.15 $\pm$ 17.48	58.36 $\pm$ 7.03	58.30 $\pm$ 6.11	55.04 $\pm$ 12.38	57.58 $\pm$ 11.73	56.38 $\pm$ 9.42	56.70 $\pm$ 17.05	$p=$ 0.771956 W $=$ 0.037374	$p=$ 0.781904 W $=$ 0.036000	$p=$ 0.839878 W $=$ 0.028000
GMI [%]	6.88 $\pm$ 0.37			6.52 $\pm$ 0.32			6.50 $\pm$ 0.28			6.55 $\pm$ 0.41			$p=$ 0.001156, W $=$ 0.532000
TDI [u.]	15.42 $\pm$ 4.43			14.86 $\pm$ 4.26			15.44 $\pm$ 492			16.90 $\pm$ 5.80			$p=$ 0.163134, W $=$ 0.170707
Basal insulin [u.]	6.74 $\pm$ 3.27			4.93 $\pm$ 1.57			5.27 $\pm$ 1.88			5.70 $\pm$ 2.20			$p=$ 0.098949, W $=$ 0.209184
Bolus insulin [u.]	8.78 $\pm$ 2.41			9.93 $\pm$ 2.88			10.17 $\pm$ 3.10			11.20 $\pm$ 3.65			$p=$ 0.132431, W $=$ 0.186869
Autocorrect [u.]				2.49 $\pm$ 1.43			2.63 $\pm$ 1.63			2.92			$p=$ 0.284674, W $=$ 0.125641
Using the sensor [%]	86.80 $\pm$ 7.86			96.00 $\pm$ 2.16			95.7 $\pm$ 1.89			95.00 $\pm$ 2.40			$p=$ 0.000164, W $=$ 0.669072
Smartguard [%]				90.90 $\pm$ 8.29			98.80 $\pm$ 1.23			98.30 $\pm$ 1.57			$p=$ 0.078208, W $=$ 0.254839
	Percentage of sensor glucose values in range
	All	Day	Night	All	Day	Night	All	Day	Night	All	Day	Nighy	All	Day	Night
$>$ 250 mg/dl [%]	6.09 $\pm$ 4.68	6.40 $\pm$ 5.92	5.59 $\pm$ 7.14	4.29 $\pm$ 2.97	4.73 $\pm$ 4.14	3.39 $\pm$ 2.33	3.42 $\pm$ 1.95	4.08 $\pm$ 2.84	2.14 $\pm$ 1.39	4.49 $\pm$ 4.55	5.41 $\pm$ 5.44	2.69 $\pm$ 3.12	$p=$ 0.056037 W $=$ 0.172000	$p=$ 0.160449 W $=$ 0.172000	$p=$ 0.147026 W $=$ 0.178788
180–250 mg/dl [%]	22.06 $\pm$ 5.68	21.01 $\pm$ 6.83	24.17 $\pm$ 6.90	14.67 $\pm$ 4.37	15.68 $\pm$ 5.70	12.65 $\pm$ 4.55	15.06 $\pm$ 4.26	15.50 $\pm$ 5.28	14.24 $\pm$ 4.82	14.70 $\pm$ 6.72	15.62 $\pm$ 7.70	12.88 $\pm$ 7.25	$p=$ 0.001032 W $=$ 0.540000	$p=$ 0.008724 W $=$ 0.388000	$p=$ 0.004724 W $=$ 0.432000
140–180 mg/dl [%]	21.70 $\pm$ 4.35	22.05 $\pm$ 4.99	20.96 $\pm$ 5.96	19.09 $\pm$ 3.18	19.53 $\pm$ 3.23	18.21 $\pm$ 4.70	18.89 $\pm$ 2.38	19.08 $\pm$ 3.62	18.51 $\pm$ 3.42	18.81 $\pm$ 3.15	19.48 $\pm$ 3.76	17.52 $\pm$ 3.15	$p=$ 0.056037 W $=$ 0.252000	$p=$ 0.062368 W $=$ 0.244000	$p=$ 0.356081 W $=$ 0.108000
70–140 mg/dl [%]	43.93 $\pm$ 7.84	43.93 $\pm$ 9.96	43.82 $\pm$ 9.63	53.25 $\pm$ 6.40	51.92 $\pm$ 8.20	55.93 $\pm$ 8.00	54.76 $\pm$ 5.53	53.68 $\pm$ 7.30	56.84 $\pm$ 8.46	54.90 $\pm$ 10.38	53.01 $\pm$ 11.98	58.61 $\pm$ 9.93	$p=$ 0.000263 W $=$ 0.636000	$p=$ 0.000209 W $=$ 0.652000	$p=$ 0.008724 W $=$ 0.388000
54–70 mg/dl [%]	4.12 $\pm$ 2.60	4.25 $\pm$ 3.47	3.88 $\pm$ 2.07	6.23 $\pm$ 3.65	6.00 $\pm$ 3.90	6.68 $\pm$ 4.46	5.62 $\pm$ 3.60	5.62 $\pm$ 3.68	5.63 $\pm$ 4.85	5.09 $\pm$ 3.02	4.64 $\pm$ 2.88	5.96 $\pm$ 4.54	$p=$ 0.095350 W $=$ 0.212000	$p=$ 0.032658 W $=$ 0.292000	$p=$ 0.839878 W $=$ 0.028000
$<$ 54 mg/dl [%]	2.09 $\pm$ 2.63	2.35 $\pm$ 3.45	1.60 $\pm$ 1.26	2.47 $\pm$ 2.40	2.14 $\pm$ 2.45	3.14 $\pm$ 3.19	2.25 $\pm$ 2.06	2.05 $\pm$ 2.28	2.65 $\pm$ 2.26	2.02 $\pm$ 2.30	1.85 $\pm$ 2.44	2.34 $\pm$ 2.42	$p=$ 0. 614935 W $=$ 0.060000	$p=$ 0.839878 W $=$ 0.028000	$p=$ 0.815912 W $=$ 0.031313
Avg Sg $<$ 54 mg/dl	47.36 $\pm$ 1.94	47.22 $\pm$ 2.16	47.67 $\pm$ 2.07	44.55 $\pm$ 1.92	44.48 $\pm$ 2.17	44.82 $\pm$ 2.84	45.49 $\pm$ 3.70	45.84 $\pm$ 3.59	43.63 $\pm$ 2.46	50.62 $\pm$ 0.43	50.62 $\pm$ 0.43	50.98 $\pm$ 9.2	$p=$ 0.000655 W $=$ 0.572000	$p=$ 0.000585 W $=$ 0.580000	$p=$ 0.001817 W $=$ 0.833333
Avg Sg $<$ 70 mg/dl	58.61 $\pm$ 2.95	58.92 $\pm$ 3.24	59.37 $\pm$ 4.37	58.65 $\pm$ 2.97	59.40 $\pm$ 2.76	57.84 $\pm$ 3.52	58.50 $\pm$ 2.76	59.08 $\pm$ 2.62	57.69 $\pm$ 4.35	60.43 $\pm$ 2.40	60.54 $\pm$ 2.19	60.87 $\pm$ 3.93	$p=$ 0. 040566 W $=$ 0.276000	$p=$ 0. 177750 W $=$ 0.164000	$p=$ 0. 117597 W $=$ 0.196000
Avg Sg $>$ 180 mg/dl	223.14 $\pm$ 11.41	223.31 $\pm$ 12.41	220.29 $\pm$ 12.65	224.3 $\pm$ 10.99	224.54 $\pm$ 12.90	222.61 $\pm$ 10.33	221.68 $\pm$ 7.10	223.54 $\pm$ 8.61	215.52 $\pm$ 8.71	221.24 $\pm$ 13.76	223.70 $\pm$ 15.36	214.51 $\pm$ 12.71	$p=$ 0. 265816 W $=$ 0.132000	$p=$ 0. 450520 W $=$ 0.088000	$p=$ 0. 265816 W $=$ 0.196000
Avg Sg $>$ 250 mg/dl	282.19 $\pm$ 9.94	280.68 $\pm$ 11.74	275.93 $\pm$ 21.11	284.79 $\pm$ 11.19	285.33 $\pm$ 15.13	282.01 $\pm$ 12.48	283.07 $\pm$ 12.79	283.66 $\pm$ 13.65	277.70 $\pm$ 16.40	277.66 $\pm$ 12.39	280.31 $\pm$ 14.20	271.06 $\pm$ 10.36	$p=$ 0. 144744 W $=$ 0.180000	$p=$ 0. 539870 W $=$ 0.072000	$p=$ 0. 572407 W $=$ 0.111111

Avg SG – Average sensor glucose, CV – Coefficient of variation, CVR – robust coefficient of variation, GMI – glucose management indicator, TDI – Total Daily Insulin, SD SG – standard deviation sensor glucose.

The study protocol was approved by the local Bioethical Committee of the Medical University of Silesia (decision PCN/0022/KBI/83/2 issued on March 30, 2021).

3. Results

Data of 10 children (3 boys) aged 5.76 $\pm$ 1.37 years (median 6.17, range: 2.60–6.98) with T1D diagnosed at the age of 1.05 to 5.00 years and T1D duration from 0.94 to 5.31 years were analysed (Table 2).

We observed a significant and moderately consistent decrease in the average glucose at nighttime compared to SAP-PLGS ( $p=$ 0.001, W $=$ 0.53). The decline occurred during the first two weeks after completing the AHCL training ( $p=$ 0.018, rbc $=$ 0.96) and persisted after the six months of observation (Fig. 1).

Figure 1.

Average sensor glucose value at night.

Along with the decrease in the average SG value at night, a significant decrease in its variability expressed using the standard deviation was also observed ( $p=$ 0.045, W $=$ 0.27) (Fig. 2).

Figure 2.

Standard deviation sensor glucose value at night.

There was additionally an initial decrease in GMI from 6.88 $\pm$ 0.37% ( $p=$ 0.018, rbc $=$ 0.93) to 6.52 $\pm$ 0.32%, which was sustained in the successive time points of observation (6.50 $\pm$ 0.28% and 6.55 $\pm$ 0.41% respectively) (Fig. 3).

Figure 3.

Glucose management indicator.

The significant increase in sensor usage, as a result of the initiation of the AHCL system, from 86.80 $\pm$ 7.86% to 96.00 $\pm$ 2.16% ( $p=$ 0.006, rbc $=$ 1) remained at a similar level and in a substantial agreement throughout the whole observation (respectively 95.7 $\pm$ 1.89% and 95.00 $\pm$ 2.40% in the last two observed 2-week periods, $p<$ 0.001, W $=$ 0.67).

There was a significant decrease in the percentage of measurements in the range of 180–250 mg/dl, both during the day ( $p=$ 0.009, W $=$ 0.39) and at night ( $p=$ 0.005, W $=$ 0.43), with a simultaneous increase in the time spent in the range of 70–140 mg/dl ( $p<$ 0.001, W $=$ 0.65 and $p=$ 0.008, W $=$ 0.39 respectively). A statistically significant decrease for the 180–250 mg/dl range was observed already in the first two weeks of using the AHCL system ( $p=$ 0.029, rbc $=$ 0.89 for daytime measurements and $p=$ 0.012, rbc $=$ 0.96 for nighttime measurements), and this improvement was maintained in the following periods of observation. A similar dynamic was noted for the increase in the percentage of time spent in the range of 70–140 mg/dl. A significant rise was also observed for both 2-week periods after switching to the AHCL system ( $p=$ 0.006, rbc $=$ 1 and $p=$ 0.018, rbc $=$ 0.93 respectively), and the percentage of time spent within this range remained at a similar level also after six months.

Moreover, the mean glycaemic values for times spent below 54 mg/dl also improved while maintaining their percentage of time spent in this range, both during the day ( $p<$ 0.001, W $=$ 0.58) and at night ( $p=$ 0.002, W $=$ 0.83) (Fig. 4).

Figure 4.

Mean glycaemic values for time spent below 54 mg/dl A) daytime and B) nighttime.

4. Discussion

We describe the impact of the MiniMed 780G system working in auto mode on the glycaemic control parameters in children with T1D under the age of 7. The observation period of six months is the first such long one reported. One study has been published so far on the use of the AHCL system in children younger than seven years of age, but the follow-up was only 12 weeks [17]. Compared to it, the children comprising our study group had better glycaemic control when switching to the AHCL system (time in a range of 70–180 mg/dl 65.63% vs 58.3%). They also had a higher time in the target of 70–180 mg/dl at the end of the study (73.71% vs 66.6%). Additionally, our analysis also included separately daytime (from 6:00 to 22:00) and nighttime [17].

Our study observed a decrease in the Avg SG at night using the MiniMed 780G^TM system in auto mode compared to the values when using SAP-PLGS. This improvement, by over 20 mg/dl, occurred within the first two weeks after finishing the AHCL training. Also, in our previous study (mean patient age 11.00 $\pm$ 3.4), we observed better nighttime glycaemic control – the increase in the percentage of time spent in the target range was more pronounced at night than during the day (7.84% vs 3.8%) [10]. These findings confirm several authors. The results of an analysis of real-world data from 4,120 MiniMed 780G^TM system users showed that the percentage of time spent in the range of 70–180 mg/dl at night was higher than during daytime (83% vs 73.9%) [18]. Another observation of 12,870 users of the AHCL system is consistent with ours, where they also noticed a significant increase of nighttime spent in the range of 70–180 mg/dl (81.9% vs 73.7%) [19]. Weinzimer et al. showed that improved glycaemic control resulted from better control at night and during the later postprandial period (after 3 hours) [20].

Another interesting finding was the significant reduction in the variability of the mean senor glucose at night as measured by standard deviation, which is consistent with the observation from our previous study [10]. Better glucose control during the night and less variability may result from fewer factors affecting glucose values during these hours. Daytime glucose control continues to be a greater challenge for AHCL systems. However, in the paediatric population, especially in the youngest children, glycaemic control (both day and night) is enormously challenging due to high insulin sensitivity, the reverse dawn phenomenon and lack of collaboration with a small child [20, 21]. Therefore, improving nocturnal glycaemic control is essential to the AHCL system in auto mode. The use of the AHCL system in the auto mode in children with T1D younger than seven years, previously treated with SAP-PLGS, was associated with a decrease in GMI, which lasted up to 6 months of therapy. These results confirm the same benefits observed in other, older paediatric study groups – such as the reduction in GMI [10, 12, 22]. Aiming safely for normoglycemia while maintaining an optimal quality of life is very important when caring for small children with T1D [23, 24]. Unfortunately, the time spent living with diabetes for these patients significantly influences the risk of late complications and well-life expectancy [25].

Also, as in previous studies, our data showed a significant increase in sensor use when on the AHCL system [10, 11, 12]. This result can probably be explained by how the system works – the CGM is a must for the auto mode to run. Additionally, the high sensor use was maintained in our observation also after six months.

The additional calculations of average glucose concentrations in the ranges $<$ 54 mg/dl, $<$ 70 mg/dl, $>$ 180 and $>$ 250 mg/dl revealed an increase in the mean glycaemic values for time spent in the lowest glycaemic range ( $<$ 54 mg/dl) during the day and nighttime. The decrease in the average glycemia during time spent $<$ 54 mg/dl after initiating the AHCL system can be considered an effect of a relatively strong initial response of the control system. However, over time (after six months), we noticed the system’s adaptation to the individual profile, resulting in an increase of mean glucose values during the time spent in the lowest range suitable for day and night measurements. Decreasing the time below range, increasing the average glucose during this time, and decreasing the time above range are significant steps in caring for a small child with T1D. It is well-known that multiple hypo- and hyperglycaemia episodes negatively influence the development of the central nervous system [26].

Although we recognize that the small sample size limits this study, its main strength is its novelty. It is the most extended, 6-month observation of using the AHCL system in auto mode in children with T1D below seven years of age. It presents results divided into day and night, and an assessment of average glycaemic concentrations in the time spent in low and high blood glucose ranges. Good glycaemic control before changing to the AHCL system and switching from the same device can be considered strengths and limitations.

5. Conclusion

The AHCL MiniMed 780G system safely improved the glycaemic control parameters in the studied children with T1D under seven years compared to using the SAP-PLGS, and its use was not associated with an increased time in hypoglycaemia and glycaemic variability.

Data availability statement

The datasets generated and analysed for this study are available from the corresponding or first author upon reasonable request.

Ethics statement

The studies involving human participants were reviewed and approved by the Local Bioethics Committee of the Medical University of Silesia in Katowice (Decision no. PCN/0022/KBI/83/2 of March 30, 2021). Written informed consent to participate in this study was provided by the participant’s legal guardian/next of kin.

Author contributions

SS designed the study, researched data, participated in data interpretation and drafting the manuscript. PJC reviewed and edited the manuscript. AO, ER, and AB participated in researching data and contributed to the drafting of the manuscript. JP supervised the statistical analysis and participated in data interpretation. AC reviewed the study design, participated in data interpretation, reviewed and edited the manuscript. All authors approved the final version of the manuscript.

Funding

JP was financed by the SUT grant.

Supplementary data

The supplementary files are available to download from https://dx-doi-org.web.bisu.edu.cn/10.3233/THC-230490.

Footnotes

Conflict of interest

PJC received speaker honoraria from Medtronic, Dexcom, Abbott, Ypsomed, and Roche, was a member of the advisory boards for Medtronic and Abbott and received research support from Medtronic. SS received speaker honoraria from Medtronic and Ypsomed. The other authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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MiniMed 780G TM in children with type 1 diabetes under seven years of age: Prospective open-label,single-arm,double-center,follow-up study

Abstract

BACKGROUND:

OBJECTIVE:

METHOD:

RESULTS:

CONCLUSION:

Keywords

1. Introduction

2. Materials and methods

Table 1 Characteristics of the participants (10 children)

5. Conclusion

Data availability statement

Ethics statement

Author contributions

Funding

Supplementary data

Footnotes

Conflict of interest

References

Table 1
Characteristics of the participants (10 children)