Home Liver DiseasesLiver Cirrhosis Clinical Performance of Flash Glucose Monitoring System in Patients with Liver Cirrhosis and Diabetes Mellitus

Clinical Performance of Flash Glucose Monitoring System in Patients with Liver Cirrhosis and Diabetes Mellitus

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Clinical Performance of Flash Glucose Monitoring System in Patients with Liver Cirrhosis and Diabetes Mellitus

Patients and data collection

A prospective, case-control and single-center study was performed in ambulatory patients with clinical and/or histological diagnosis of LC and analytical diagnosis of DM, evaluated between January and October 2018 at the Gastroenterology Department of Braga Hospital.

Patients meeting any of the following criteria were excluded: active alcohol abuse, oncological disease (particularly hepatocellular carcinoma), portal vein thrombosis, chronic pancreatitis, congestive heart failure, chronic kidney disease, Human Immunodeficiency Virus infection, Mycobacterium tuberculosis and Hepatitis C virus (HCV) infection under pharmacological treatment, corticosteroids therapy, extended hospitalization during the study period and psychic/cognitive state alteration.

A sample size of 79 was sought, considering the criteria of 95% Confidence Level and 5% Confidence Interval applied by the Creative Research Systems software, available online. Since this sample could not be achieved, in order to minimize the loss of representativeness, a control group was created with patients with DM evaluated at the Endocrinology Department of Braga Hospital, without evidence of CLD (excluded based on imaging and biochemical evaluation). Patients were paired in a ratio 1:1, using the tool “propensity score matching” from Statistical Package for the Social Sciences (SPSS Inc., Chicago, Illinois, USA), according to predefined baseline characteristics (gender, age, BMI, insulin use and HbA1c), selected to exclude any confounding factors. The exclusion criteria previously described for the LC group were also applied.

Medical records were reviewed. The following parameters were considered for analysis: sociodemographic (age, gender), clinical (Cirrhosis etiology, Child-Pugh, Model for End-Stage Liver Disease the serum sodium incorporated (MELD-Na) score, edematoascitic decompensation, Body Mass Index (BMI), comorbidities and medication) and recently collected biochemical data (hemogram, liver and kidney biochemistry, ionogram).

Written informed consent was obtained from all patients. The study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of Braga Hospital (Approval number: 116/2018).

Glycemic control monitoring tools

Subcutaneous interstitial glucose levels were monitored using the FGMS FreeStyle Libre system (Abbott Diabetes Care, Alameda, CA). This tool includes a reader device and a small disposable sensor, applied on the back of the upper arm for up to 14 days, according to the manufacturer’s direction. It is factory-calibrated and has no automatic alarms. Data are transferred to the reader when it is brought into close proximity to the sensor, displaying current sensor glucose level. For a complete 24-hour glycemic profile, a scan once every 8 hours was required. Data are automatically stored on the sensor and can be uploaded from the reader, using the device software to generate summary glucose reports. Four sensor lots were used throughout the study, which is sufficient to demonstrate the performance of reagent systems across multiple production lots25. As recommended by Messer et al.26, in order to enhance sensor skin adhesion throughout device lifespan, kinesiology tape was applied over each participant’s sensor. It protects against water, increased perspiration and movement.

SMBG was achieved by finger-prick testing of capillary blood using the same reader of FGMS (Abbott Diabetes Care, Alameda, CA), in order to reduce the variability between devices. Glucose test strips (FreeStyle Precision) and lancets (Abbott Single Fire) were provided to the participants.

Study design

There were two scheduled in-clinic visits. At the first visit (day 1), baseline HbA1c (HbA1cD1) was collected and 1 FGMS sensor per participant was inserted. Throughout the next 14 days, participants were asked to perform at least 4 assessments per day of SMBG, each followed 5 minutes later27 by FGMS sensor scanning. For each patient, measurements were scheduled upon waking, 2 hours after the beginning of lunch, 2 hours after the beginning of dinner and at bedtime. Though results were automatically stored and uploaded from the reader at the end of the study, individuals were also asked to register the data on a given table. Patients were advised to maintain their established diabetes management plan.

A second visit (day 15) was programmed to deliver all the materials and upload the results. Between in-clinic visits, patients were contacted to clarify any doubt. Sensors that were accidentally dislodged within the first week of use were replaced. Sensors that were dislodged after that time were not replaced.

Statistical analysis

Statistical analysis was performed using SPSS software (Chicago, Illinois, USA), and MATLAB software (Mathworks Inc.).

Descriptive data were summarized using the appropriate statistical tools, given the nature of the variables involved. Normal distribution was assessed by Kolmogorov-Smirnov test. Student t or Mann-Whitney tests were performed to compare the distribution of independent continuous variables. Spearman correlation was used to evaluate correlation between continuous variables. Chi-Square test was assessed to test the association between categorical variables. The level of significance was set at p < 0.05.

To evaluate the analytical accuracy of FGMS, Mean Absolute Relative Difference (MARD) was calculated using SMBG as a reference, according to the formula: (|SMBG − FMGS|)/SMBG × 10025. Ancona et al.28 suggested that a value below 14% represents acceptable accuracy, a value between 14% and 18% represents intermediate accuracy and a value above 18% represents poor accuracy. Linear Regression was performed to identify any predictors of MARD.

The Consensus and Clarke Error Grid Analysis (EGA) were used to assess the magnitude of clinical risk from inaccurate flash glucose readings (clinical accuracy). EGA, widely accepted tool for defining glucose meter’s accuracy, subdivides plotted results into 5 zones: A, less than 20% difference from reference values; B, difference greater than 20% but the resulting clinical decisions are uncritical; C, could cause an overcorrection of glycemia; D, represents a dangerous failure to detect and treat; E, leads to erroneous treatment29.

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