Diabetes is a chronic metabolic disease that causes blood glucose (BG) concentration to make dangerous excursions outside its physiological range. Measuring the fraction of time spent by BG outside this range, and, specifically, the time-below-range (TBR), is a clinically common way to quantify the effectiveness of therapies. TBR is estimated from data recorded by continuous glucose monitoring (CGM) sensors, but the duration of CGM recording guaranteeing a reliable indicator is under debate in the literature. Here we framed the problem as random variable estimation problem and studied the convergence of the estimator, deriving a formula that links the TBR estimation error variance with the CGM recording length. Validation is performed on CGM data of 148 subjects with type-1-diabetes. First, we show the ability of the formula to predict the uncertainty of the TBR estimate in a single patient, using patient-specific parameters; then, we prove its applicability on population data, without the need of parameters individualization. The approach can be straightforwardly extended to other similar metrics, such as time-in-range and time-above-range, widely adopted by clinicians. This strengthens its potential utility in diabetes research, e.g., in the design of those clinical trials where minimal CGM monitoring duration is crucial in cost-effectiveness terms.

Aim To compute the uncertainty of time‐in‐ranges, such as time in range (TIR), time in tight range (TITR), time below range (TBR) and time above range (TAR), to evaluate glucose control and to determine the minimum duration of a trial to achieve the desired precision. Materials and Methods Four formulas for the aforementioned time‐in‐ranges were obtained by estimating the equation's parameters on a training set extracted from study A (226 subjects, ~180 days, 5‐minute Dexcom G4 Platinum sensor). The formulas were then validated on the remaining data. We also illustrate how to adjust the parameters for sensors with different sampling rates. Finally, we used study B (45 subjects, ~365 days, 15‐minute Abbott Freestyle Libre sensor) to further validate our results. Results Our approach was effective in predicting the uncertainty when time‐in‐ranges are estimated using n days of continuous glucose monitoring (CGM), matching the variability observed in the data. As an example, monitoring a population with TIR = 70%, TITR = 50%, TBR = 5% and TAR = 25% for 30 days warrants a precision of ±3.50%, ±3.68%, ±1.33% and ±3.66%, respectively. Conclusions The presented approach can be used to both compute the uncertainty of time‐in‐ranges and determine the minimum duration of a trial to achieve the desired precision. An online tool to facilitate its implementation is made freely available to the clinical investigator.

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Background: In type 1 diabetes (T1D) research, in-silico clinical trials (ISCTs) have proven effective in accelerating the development of new therapies. However, published simulators lack a realistic description of some aspects of patient lifestyle which can remarkably affect glucose control. In this paper, we develop a mathematical description of meal carbohydrates (CHO) amount and timing, with the aim to improve the meal generation module in the T1D Patient Decision Simulator (T1D-PDS) published in Vettoretti et al. Methods: Data of 32 T1D subjects under free-living conditions for 4874 days were used. Univariate probability density function (PDF) parametric models with different candidate shapes were fitted, individually, against sample distributions of: CHO amounts of breakfast (CHOB), lunch (CHOL), dinner (CHOD), and snack (CHOS); breakfast timing (TB); and time between breakfast-lunch (TBL) and between lunch-dinner (TLD). Furthermore, a support vector machine (SVM) classifier was developed to predict the occurrence of a snack in future fixed-length time windows. Once embedded inside the T1D-PDS, an ISCT was performed. Results: Resulting PDF models were: gamma (CHOB, CHOS), lognormal (CHOL, TB), loglogistic (CHOD), and generalized-extreme-values (TBL, TLD). The SVM showed a classification accuracy of 0.8 over the test set. The distributions of simulated meal data were not statistically different from the distributions of the real data used to develop the models (α = 0.05). Conclusions: The models of meal amount and timing variability developed are suitable for describing real data. Their inclusion in modules that describe patient behavior in the T1D-PDS can permit investigators to perform more realistic, reliable, and insightful ISCTs.

To design a clinical trial it is important to know for how long CGM data should be collected to accurately assess time in different glucose ranges (time in ranges). Several studies approached this problem through the computation of the correlation coefficient (R2) between a metric computed in a month-long trial and in several shorter windows of increasing duration. The minimal duration (MD) granting R2>threshold (e.g., 0.9) is then used to estimate the long-term metric. Here, we focus on time below range (TBR), defined as time <70 mg/dl [%]. We first implemented the R2-based analysis on trials of different duration: A1, A2, A3, lasting 100, 200, 300 days (d), respectively, obtained selecting portions of the same Study A (n=45, duration=360 d, sensor=Abbott Freestyle Libre). Table 1 shows that the longer the trial duration, the larger the resulting MD. Notably, all the obtained trial duration fractions are similar. Then, the analysis was repeated for other two trials of equal duration: Study B (n=85, duration=240 d, sensor=Dexcom G5) and A4, obtained selecting 240 d from Study A. Although B and A4 refer to different T1D populations and different sensors, the resulting trial duration fraction is the same. In conclusion, the R2-based analysis yields different results based on the duration of the considered dataset, and seems to identify only the fraction of data needed to match the reference TBR. Disclosure N. Camerlingo: None. M. Vettoretti: None. M. Cigler: None. A. Facchinetti: None. G. Sparacino: None. J.K. Mader: Advisory Panel; Self; Becton, Dickinson and Company, Eli Lilly and Company, Medtronic, Prediktor Medical, Sanofi-Aventis. Speaker’s Bureau; Self; Abbott, Eli Lilly and Company, Medtronic, Novo Nordisk A/S, Roche Diabetes Care, Sanofi-Aventis. P. Choudhary: Advisory Panel; Self; Abbott, Eli Lilly and Company, Insulet Corporation, Medtronic. Research Support; Self; European Union, JDRF. Speaker’s Bureau; Self; Dexcom, Inc., Novartis AG, Novo Nordisk A/S, Sanofi-Aventis. S. Del Favero: Research Support; Self; Dexcom, Inc. Funding Innovative Medicines Initiative 2 Joint Undertaking (777460); European Union; European Federation of Pharmaceutical Industries and Associations; T1D Exchange; JDRF; International Diabetes Federation; The Leona M. and Harry B. Helmsley Charitable Trust