Combining continuous glucose monitoring and insulin pumps to automatically tune the basal insulin infusion in diabetes therapy: a review

For individuals affected by Type 1 diabetes (T1D), a chronic disease in which the pancreas does not produce any insulin, maintaining the blood glucose (BG) concentration as much as possible within the safety range (70-180\ua0mg/dl) allows avoiding short- and long-term complications. The tuning of exogenous insulin infusion can be difficult, especially because of the inter- and intra-day variability of physiological and behavioral factors. Continuous glucose monitoring (CGM) sensors, which monitor glucose concentration in the subcutaneous tissue almost continuously, allowed improving the detection of critical hypo- and hyper-glycemic episodes. Moreover, their integration with insulin pumps for continuous subcutaneous insulin infusion allowed developing algorithms that automatically tune insulin dosing based on CGM measurements in order to mitigate the incidence of critical episodes. In this work, we aim at reviewing the literature on methods for CGM-based automatic attenuation or suspension of basal insulin with a focus on algorithms, their implementation in commercial devices and clinical evidence of their effectiveness and safety

Type 1 diabetes patient decision-making modeling for the in silico assessment of insulin treatment scenarios

In type 1 diabetes (T1D) exogenous insulin is administered to compensate for the absence of endogenous insulin production by pancreas beta-cells. T1D subjects must finely tune insulin doses to maintain blood glucose (BG) concentration within the normal range (70-180 mg/dl). For such a purpose, every day, T1D subjects need to frequently monitor their BG concentration and make several treatment decisions, e.g. the calculation of insulin and carbohydrate (CHO) doses to counterbalance, respectively, high and low BG values. The safety and effectiveness of T1D insulin therapies are normally assessed by clinical trials, which unfortunately are usually time-demanding, expensive and often present constraints of low numerosity and short duration, with consequently low probability of observing rare but risky situations, like severe hypoglycemia. These limitations can be overcome by the use of in silico clinical trials, based on computer simulations, that allow to test medical device-based treatments in a large number of subjects, over a long period, under reproducible conditions, at limited costs, and without implicating any risk for real subjects. A popular powerful tool to perform in silico clinical trials in T1D is the UVA/Padova T1D simulator, i.e. a model of glucose, insulin and glucagon dynamics in T1D subjects. However, to test insulin therapies in a real-life scenario, the UVA/Padova T1D simulator alone is not sufficient because a mathematical description of other fundamental components, like the device used for glucose monitoring and the patient's behavior in making treatment decisions, is required. The aim of this thesis is to design a mathematical model of T1D patients making treatment decisions fully usable for the comprehensive in silico assessment of insulin treatment scenarios. In particular, in the first part of the thesis we develop three submodels that the UVA/Padova T1D simulator requires (as complement) to pursue this scope. Specifically, we design a model of self-monitoring of blood glucose (SMBG) device, a model of minimally-invasive sensor for continuous glucose monitoring (CGM), and a model of the patient’s behavior in tuning CHO intakes and insulin doses according to SMBG and/or CGM measurements. The parameters of these models are either fitted on real data or derived from literature studies. The overall model, in the following called T1D decision-making (T1D-DM) model, can be used for several in silico experiments. To demonstrate its usefulness, in the second part of this thesis we apply the T1D-DM model to assess safety and effectiveness of nonadjunctive CGM use, i.e. the use of CGM measurements to make treatment decisions without requiring confirmatory SMBG measurements collected by fingerstick. This specific application is currently of great scientific and industrial interest for the diabetes technology research community because, until clinical evidence of its safety is provided, nonadjunctive CGM use cannot be approved by U.S. regulatory agencies, like the Food and Drug Administration. The thesis is organized in six chapters. In Chapter 1, after introducing T1D therapy, the importance of in silico clinical trials is discussed, both in general and specifically for the assessment of nonadjunctive CGM use. Then, some state-of-art simulation techniques are briefly introduced discussing their open problems. The aim of the thesis is illustrated at the end of the chapter. In Chapter 2, we analyse more in depth the limitations of the approaches currently available in the literature for the assessment of insulin treatments. In particular, we demonstrate that a recently proposed simulation method to "replay" in silico real-life treatment scenarios has domain of validity limited to small adjustments of basal insulin, calling for the development of more sophisticated techniques like that proposed in this thesis. In Chapter 3, our simulation method based on the T1D-DM model is presented. This model allows to simulate, in a real-life scenario, the glucose profiles of T1D subjects using SMBG and/or CGM to make treatment decisions. The T1D-DM model is composed of four components: A) the UVA/Padova T1D simulator, B) a model of glucose monitoring devices, C) a model of patient's behavior and treatment decisions and D) a model of the insulin pump. In particular, as far as B) is concerned, two different SMBG error models are derived by data collected with two popular SMBG devices (One Touch Ultra 2 and Bayer Contour Next USB). Using a recently published methodology which takes into account the main sensor error components, a CGM model is derived from data collected by a state-of-art CGM sensor (Dexcom G5 Mobile). Regarding C), a model of the patient's behavior in making treatment decisions based on SMBG and/or CGM, such as administration of insulin boluses and hypotreatments, is designed to simulate treatments based on i) SMBG, ii) adjunctive CGM, or iii) nonadjunctive CGM. In order to reproduce a real-life scenario, the model includes components describing the mistakes real subjects commonly make, such as miscalculation of meal CHO content and early/delayed insulin administrations. In Chapter 4 and Chapter 5, two in silico trials based on the T1D-DM model are designed to assess nonadjunctive CGM use. In the first trial, nonadjunctive CGM is compared to SMBG and adjunctive CGM over a two-week period in 100 virtual subjects. Results show that the use of CGM (both adjunctive and nonadjunctive) significantly improves glycemic control compared to SMBG, while no significant change is observed between adjunctive CGM and nonadjunctive CGM. This suggests that CGM is ready to substitute SMBG for T1D treatment. In the second trial, the impact of thresholds used for CGM hypo/hyperglycemic alerts on the performance of nonadjunctive CGM use is assessed. Results show that time in hypoglycemia is reduced by nonadjunctive CGM use with any alert setting, while time in hyperglycemia is significantly worsen by nonadjunctive CGM use, compared to SMBG, when the high alert threshold is set to 350 mg/dl or higher. Finally, the major findings of the work carried out in this thesis, its possible applications and margin of improvements are summarized in Chapter 6

Wearable continuous glucose monitoring sensors: A revolution in diabetes treatment

Worldwide, the number of people affected by diabetes is rapidly increasing due to aging populations and sedentary lifestyles, with the prospect of exceeding 500 million cases in 2030, resulting in one of the most challenging socio-health emergencies of the third millennium. Daily management of diabetes by patients relies on the capability of correctly measuring glucose concentration levels in the blood by using suitable sensors. In recent years, glucose monitoring has been revolutionized by the development of Continuous Glucose Monitoring (CGM) sensors, wearable non/minimally-invasive devices that measure glucose concentration by exploiting different physical principles, e.g., glucose-oxidase, fluorescence, or skin dielectric properties, and provide real-time measurements every 1–5 min. CGM opened new challenges in different disciplines, e.g., medicine, physics, electronics, chemistry, ergonomics, data/signal processing, and software development to mention but a few. This paper first makes an overview of wearable CGM sensor technologies, covering both commercial devices and research prototypes. Then, the role of CGM in the actual evolution of decision support systems for diabetes therapy is discussed. Finally, the paper presents new possible horizons for wearable CGM sensor applications and perspectives in terms of big data analytics for personalized and proactive medicine

Bayesian denoising algorithm dealing with colored, non-stationary noise in continuous glucose monitoring timeseries

Introduction: The retrospective analysis of continuous glucose monitoring (CGM) timeseries can be hampered by colored and non-stationary measurement noise. Here, we introduce a Bayesian denoising (BD) algorithm to address both autocorrelation of measurement noise and temporal variability of its variance.Methods: BD utilizes adaptive, a-priori models of signal and noise, whose unknown variances are derived on partially-overlapped CGM windows, via smoothing approach based on linear mean square estimation. The CGM signal and noise variability profiles are then reconstructed using a kernel smoother. BD is first assessed on two simulated datasets, DS1 and DS2. On DS1, the effectiveness of accounting for colored noise is evaluated by comparison against a literature algorithm; on DS2, the effectiveness of accounting for the noise variance temporal variability is evaluated by comparison against a Butterworth filter. BD is then evaluated on 15 CGM timeseries measured by the Dexcom G6 (DR).Results: On DS1, BD allows reducing the root-mean-square-error (RMSE) from 8.10 [6.79–9.24] mg/dL to 6.28 [5.47–7.27] mg/dL (median [IQR]); on DS2, RMSE decreases from 6.85 [5.50–8.72] mg/dL to 5.35 [4.48–6.49] mg/dL. On DR, BD performs a reasonable tracking of noise variance variability and a satisfactory denoising.Discussion: The new algorithm effectively addresses the nature of CGM measurement error, outperforming existing denoising algorithms

Un modello a parametri distribuiti degli scambi sangue-tessuto

Nel presente elaborato viene analizzato l’impiego di un modello a parametri distribuiti per la descrizione del sistema fisiologico coinvolto negli scambi sangue-tessuto. Si daranno prima delle nozioni sulla fisiologia della rete capillare, ove avvengono i suddetti processi di scambio. In seguito verrà presentato il modello a quattro regioni della singola unità capillare-tessuto proposto da Bassingthwaighte e colleghi. Di questo modello verranno discussi vari metodi di risoluzione. Successivamente si parlerà del modello multicapillare per la descrizione del sistema di perfusione dell’intero organo. A tal proposito verranno descritti due approcci: quello proposto da Rose e Goresky e quello proposto da Bassingthwaigthe, noto come il modello dei capillari paralleli. Infine verrà discussa un’applicazione del modello semplificato a tre regioni, condotta da Kuikka e colleghi, per lo studio del trasporto e del metabolismo del glucosio nel cuor

Type 1 diabetes patient decision-making modeling for the in silico assessment of insulin treatment scenarios

In type 1 diabetes (T1D) exogenous insulin is administered to compensate for the absence of endogenous insulin production by pancreas beta-cells. T1D subjects must finely tune insulin doses to maintain blood glucose (BG) concentration within the normal range (70-180 mg/dl). For such a purpose, every day, T1D subjects need to frequently monitor their BG concentration and make several treatment decisions, e.g. the calculation of insulin and carbohydrate (CHO) doses to counterbalance, respectively, high and low BG values. The safety and effectiveness of T1D insulin therapies are normally assessed by clinical trials, which unfortunately are usually time-demanding, expensive and often present constraints of low numerosity and short duration, with consequently low probability of observing rare but risky situations, like severe hypoglycemia. These limitations can be overcome by the use of in silico clinical trials, based on computer simulations, that allow to test medical device-based treatments in a large number of subjects, over a long period, under reproducible conditions, at limited costs, and without implicating any risk for real subjects. A popular powerful tool to perform in silico clinical trials in T1D is the UVA/Padova T1D simulator, i.e. a model of glucose, insulin and glucagon dynamics in T1D subjects. However, to test insulin therapies in a real-life scenario, the UVA/Padova T1D simulator alone is not sufficient because a mathematical description of other fundamental components, like the device used for glucose monitoring and the patient's behavior in making treatment decisions, is required. The aim of this thesis is to design a mathematical model of T1D patients making treatment decisions fully usable for the comprehensive in silico assessment of insulin treatment scenarios. In particular, in the first part of the thesis we develop three submodels that the UVA/Padova T1D simulator requires (as complement) to pursue this scope. Specifically, we design a model of self-monitoring of blood glucose (SMBG) device, a model of minimally-invasive sensor for continuous glucose monitoring (CGM), and a model of the patient’s behavior in tuning CHO intakes and insulin doses according to SMBG and/or CGM measurements. The parameters of these models are either fitted on real data or derived from literature studies. The overall model, in the following called T1D decision-making (T1D-DM) model, can be used for several in silico experiments. To demonstrate its usefulness, in the second part of this thesis we apply the T1D-DM model to assess safety and effectiveness of nonadjunctive CGM use, i.e. the use of CGM measurements to make treatment decisions without requiring confirmatory SMBG measurements collected by fingerstick. This specific application is currently of great scientific and industrial interest for the diabetes technology research community because, until clinical evidence of its safety is provided, nonadjunctive CGM use cannot be approved by U.S. regulatory agencies, like the Food and Drug Administration. The thesis is organized in six chapters. In Chapter 1, after introducing T1D therapy, the importance of in silico clinical trials is discussed, both in general and specifically for the assessment of nonadjunctive CGM use. Then, some state-of-art simulation techniques are briefly introduced discussing their open problems. The aim of the thesis is illustrated at the end of the chapter. In Chapter 2, we analyse more in depth the limitations of the approaches currently available in the literature for the assessment of insulin treatments. In particular, we demonstrate that a recently proposed simulation method to "replay" in silico real-life treatment scenarios has domain of validity limited to small adjustments of basal insulin, calling for the development of more sophisticated techniques like that proposed in this thesis. In Chapter 3, our simulation method based on the T1D-DM model is presented. This model allows to simulate, in a real-life scenario, the glucose profiles of T1D subjects using SMBG and/or CGM to make treatment decisions. The T1D-DM model is composed of four components: A) the UVA/Padova T1D simulator, B) a model of glucose monitoring devices, C) a model of patient's behavior and treatment decisions and D) a model of the insulin pump. In particular, as far as B) is concerned, two different SMBG error models are derived by data collected with two popular SMBG devices (One Touch Ultra 2 and Bayer Contour Next USB). Using a recently published methodology which takes into account the main sensor error components, a CGM model is derived from data collected by a state-of-art CGM sensor (Dexcom G5 Mobile). Regarding C), a model of the patient's behavior in making treatment decisions based on SMBG and/or CGM, such as administration of insulin boluses and hypotreatments, is designed to simulate treatments based on i) SMBG, ii) adjunctive CGM, or iii) nonadjunctive CGM. In order to reproduce a real-life scenario, the model includes components describing the mistakes real subjects commonly make, such as miscalculation of meal CHO content and early/delayed insulin administrations. In Chapter 4 and Chapter 5, two in silico trials based on the T1D-DM model are designed to assess nonadjunctive CGM use. In the first trial, nonadjunctive CGM is compared to SMBG and adjunctive CGM over a two-week period in 100 virtual subjects. Results show that the use of CGM (both adjunctive and nonadjunctive) significantly improves glycemic control compared to SMBG, while no significant change is observed between adjunctive CGM and nonadjunctive CGM. This suggests that CGM is ready to substitute SMBG for T1D treatment. In the second trial, the impact of thresholds used for CGM hypo/hyperglycemic alerts on the performance of nonadjunctive CGM use is assessed. Results show that time in hypoglycemia is reduced by nonadjunctive CGM use with any alert setting, while time in hyperglycemia is significantly worsen by nonadjunctive CGM use, compared to SMBG, when the high alert threshold is set to 350 mg/dl or higher. Finally, the major findings of the work carried out in this thesis, its possible applications and margin of improvements are summarized in Chapter 6.Nella terapia del diabete di tipo 1 viene somministrata insulina esogena per compensare l'assenza di secrezione di insulina da parte della beta cellule del pancreas. Per mantenere la glicemia ad un livello normale (70-180 mg/dl), i soggetti diabetici di tipo 1 devono accuratamente regolare le proprie dosi di insulina. A questo scopo, essi necessitano ogni giorno di misurare frequentemente la loro glicemia e prendere numerose decisioni terapeutiche, per esempio per calcolare le dosi di insulina e carboidrati necessarie a controbilanciare livelli glicemici rispettivamente elevati e bassi. La sicurezza e l'efficacia di terapie insuliniche per il diabete di tipo 1 sono comunemente valutate in trial clinici, i quali solitamente necessitano di tempi e costi elevati e presentano limiti di bassa numerosità e breve durata, con conseguente ridotta probabilità di osservare situazioni rare seppur rischiose, come ad esempio l'ipoglicemia severa. Queste limitazioni posso essere superate mediante trial clinici simulati, cioè basati su simulazioni al computer, che permettono di testare terapie basate su dispositivi medici in un vasto numero di soggetti, per un lungo periodo, in condizioni riproducibili, a costi limitati e senza comportare alcun rischio per i pazienti reali. Un popolare strumento per svolgere trial clinici simulati nell'ambito del diabete di tipo 1 è il simulatore UVA/Padova-T1D, un modello che descrive le dinamiche di glucosio, insulina e glucagone nei soggetti diabetici di tipo 1. Tuttavia, al fine di testare terapie insuliniche in uno scenario realistico, il simulatore UVA/Padova-T1D non è da solo sufficiente in quanto necessaria la descrizione matematica di altre componenti fondamentali, come il dispositivo utilizzato per il monitoraggio del glucosio e il comportamento del paziente nel prendere le decisioni terapeutiche. Lo scopo di questa tesi è la progettazione di un modello matematico del paziente diabetico di tipo 1 e delle decisioni terapeutiche che esso prende, utilizzabile per una completa valutazione in simulazione di terapie insuliniche. In particolare, nella prima parte della tesi vengono sviluppati i tre sottomodelli che il simulatore UVA/Padova-T1D necessita (come complemento) per raggiungere tale scopo. Nello specifico, vengono sviluppati un modello del dispositivo pungidito per il monitoraggio della glicemia (SMBG), un modello del sensore minimamente invasivo per il monitoraggio a tempo continuo della glicemia (CGM) e un modello del comportamento del paziente nel regolare le somministrazioni di carboidrati e insulina a seconda delle misure SMBG e/o CGM. I parametri di questi modelli sono fittati su dati reali o derivati da studi di letteratura. Il modello complessivo, chiamato in seguito modello decisionale del diabete di tipo 1 (T1D-DM), può essere impiegato per molti esperimenti simulati. Per dimostrare la sua utilità, nella seconda parte della tesi il modello T1D-DM viene impiegato per valutare la sicurezza e l'efficacia dell'uso "nonadjunctive" del sensore CGM, cioè l'uso delle misure CGM per prendere decisioni terapeutiche senza la necessità di confermarne le letture mediante misure SMBG raccolte con dispositivi pungidito. Questa specifica applicazione è attualmente di grande interesse scientifico e industriale nella comunità della ricerca sulle tecnologie per il diabete poiché, finché non ne viene dimostrata la sicurezza, l'uso "nonadjunctive" del CGM non può essere approvato dalle agenzie regolatorie statunitensi, come la Food and Drug Administration. La tesi è organizzata in sei capitoli. Nel capitolo 1, dopo aver introdotto la terapia del diabete di tipo 1, viene discussa l'importanza dei trial clinici simulati, sia in generale sia in maniera specifica per la valutazione dell'uso "nonadjunctive" del sensore CGM. In seguito, vengono brevemente introdotte alcune tecniche di simulazione allo stato dell'arte discutendone i problemi aperti. Lo scopo della tesi è illustrato alla fine del capitolo. Nel capitolo 2 vengono analizzate nel dettaglio le limitazioni degli approcci allo stato dell'arte per la valutazione di terapie insuliniche. In particolare, viene dimostrato che un metodo di simulazione recentemente proposto per riprodurre in simulazione scenari terapeutici della vita reale presenta un dominio di validità limitato a piccole variazioni della dose basale di insulina, suggerendo la necessità di sviluppare tecniche più sofisticate come quella proposta in questa tesi. Nel capitolo 3 viene presentato il nostro metodo di simulazione basato sul modello T1D-DM. Questo modello consente di simulare, in uno scenario che riproduce la vita reale, i profili glicemici di soggetti diabetici di tipo 1 che utilizzano dispositivi SMBG e/o CGM a supporto delle decisioni terapeutiche. Il modello T1D-DM è composto da quattro componenti: A) il simulatore UVA/Padova-T1D, B) un modello dei dispositivi per il monitoraggio del glucosio, C) un modello del comportamento del paziente nel prendere le decisioni terapeutiche e D) un modello della pompa per l'infusione di insulina. Per quanto riguarda B), due modelli dell'errore delle misure SMBG sono derivati utilizzando misure raccolte con due popolari dispositivi SMBG (lo One Touch Ultra 2 e il Bayer Contour Next). Utilizzando un metodo recentemente pubblicato che prende in considerazione le componenti principali dell'errore del sensore, viene derivato un modello delle misure CGM sulla base di dati raccolti con un sensore CGM allo stato dell'arte (Dexcom G5 Mobile). Per quanto concerne C), viene progettato un modello del comportamento del paziente nel prendere le decisioni terapeutiche sulla base di misure SMBG e/o CGM, come la somministrazione di boli di insulina e trattamenti per l'ipoglicemia, al fine di simulare terapie basate su i) SMBG, ii) uso del CGM a supporto dell'SMBG (uso "adjunctive") o iii) uso "nonadjunctive" del CGM. Per riprodurre uno scenario realistico, il modello include componenti che descrivono gli errori comunemente commessi dai pazienti reali, come per esempio gli errori nella stima della quantità di carboidrati contenuti nel pasto e ritardi/anticipi nella somministrazione delle dosi di insulina. Nei capitoli 4 e 5 vengono progettati due trial clinici simulati basati sul modello T1D-DM per valutare l'uso "nonadjunctive" del sensore CGM. Nel primo trial, l'uso "nonadjunctive" del sensore CGM è confrontato con l'uso dell'SMBG e l'uso "adjunctive" del CGM in 100 soggetti virtuali per un periodo di due settimane. I risultati dimostrano che l'uso del CGM (sia "adjunctive", sia "nonadjunctive") migliora significativamente il controllo glicemico rispetto all'uso dell'SMBG, mentre non si osservano differenze significative tra l'uso "adjunctive" e "nonadjunctive" del sensore CGM. Questo risultato suggerisce che il CGM è pronto per sostituire l'SMBG nel trattamento del diabete di tipo 1. Nel secondo trial, viene valutato come le soglie impostabili per le allerte ipo/iperglicemiche del sensore CGM influenzano le performance dell'uso "nonadjunctive" del CGM. I risultati dimostrano che l'uso "nonadjunctive" del sensore CGM consente di ridurre il tempo in ipoglicemia per qualsiasi impostazione delle allerte, mentre il tempo in iperglicemia viene significativamente peggiorato dall'uso "nonadjunctive" del sensore CGM, rispetto all'SMBG, quando la soglia dell'allerta di iperglicemia è impostata ad un valore maggiore o uguale a 350 mg/dl. Infine i risultati principali del lavoro svolto in questa tesi, nonché le possibili applicazioni e i margini di miglioramento sono riassunti nel capitolo 6

Il problema della calibrazione di sensori per il monitoraggio in continua della glicemia: proposta di un nuovo approccio basato su deconvoluzione ed uso di prior bayesiani e confronto con lo stato dell'arte

In questa tesi di laurea viene proposto un nuovo metodo per la calibrazione dei sensori per il monitoraggio in continua della glicemia, basato sull'uso della deconvoluzione, di prior bayesiani e di una legge di calibrazione a parametri tempo varianti. Le prestazioni del metodo di calibrazione proposto vengono confrontate con quelle dell'algoritmo implementato dall'azienda Dexcom per la calibrazione del sensore Dexcom Gen 4 Platinu

Development of an Error Model for a Factory-Calibrated Continuous Glucose Monitoring Sensor with 10-Day Lifetime

Factory-calibrated continuous glucose monitoring (FC-CGM) sensors are new devices used in type 1 diabetes (T1D) therapy to measure the glucose concentration almost continuously for 10–14 days without requiring any in vivo calibration. Understanding and modelling CGM errors is important when designing new tools for T1D therapy. Available literature CGM error models are not suitable to describe the FC-CGM sensor error, since their domain of validity is limited to 12-h time windows, i.e., the time between two consecutive in vivo calibrations. The aim of this paper is to develop a model of the error of FC-CGM sensors. The dataset used contains 79 FC-CGM traces collected by the Dexcom G6 sensor. The model is designed to dissect the error into its three main components: effect of plasma-interstitium kinetics, calibration error, and random measurement noise. The main novelties are the model extension to cover the entire sensor lifetime and the use of a new single-step identification procedure. The final error model, which combines a first-order linear dynamic model to describe plasma-interstitium kinetics, a second-order polynomial model to describe calibration error, and an autoregressive model to describe measurement noise, proved to be suitable to describe FC-CGM sensor errors, in particular improving the estimation of the physiological time-delay