Toward an Optimal Definition of Hypoglycemia with Continuous Glucose Monitoring. (September 2021)

• Record Type: Journal Article
• Authors: Mahmoudi, Zeinab; Del Favero, Simone; Jacob, Peter; Choudhary, Pratik
• Is Part Of: Computer methods and programs in biomedicine. Volume 209(2021)
• Publication Date: 2021-09
• Subjects: Type 1 diabetes -- Hypoglycemia -- Optimal definition -- Continuous glucose monitoring -- Particle Markov chain Monte Carlo optimization -- Modeling and Simulation; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2021.106303
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Data show that CGM identify many episodes of low interstitial glucose (LIG) that are not experience by patients The aim is to design a parametric LIG definition that maximizes the detection of patient reported hypoglycemia (PRH) The LIG parameters are a glycemic threshold (h) and a minimum duration (d) below the threshold. Testing the algorithm using real CGM and simulated PRH events indicate the feasibility of the method to optimize LIG definition with respect to PRH detection Abstract: Background and Objective: As continuous glucose monitoring (CGM) becomes common in research and clinical practice, there is a need to understand how CGM-based hypoglycemia relates to hypoglycemia episodes defined conventionally as patient reported hypoglycemia (PRH). Data show that CGM identify many episodes of low interstitial glucose (LIG) that are not experienced by patients, and so the aim of this study is to use different PRH simulations to optimize CGM parameters of threshold ( h ) and duration ( d ) to provide the best PRH detection performance. Methods: The algorithm uses particle Markov chain Monte Carlo optimization to identify the optimal h and d which maximize an objective function for detecting PRH. We tested our algorithm by creating three different cases of PRH simulations. Results: We added three types of simulated PRH events to 10 weeks of anonymized CGM data from 96 type 1 diabetes people to see if the algorithm can detect the optimal parameters set out in the simulations . In simulation 1, we changed the locations of PRHs with respect to LIG episodes in the CGM signal to simulate random optimal LIG parameters for every individual. In simulation 2, the PRHs are CGM glucose <3.9 mmol/L followed by at least 20 min of rise > 0.11 mmol/L/min. Simulation 3 is like simulation 2 but with glucose threshold of 3.0 mmol/L. The median [interquartile range] of deviation between the optimized (found by the algorithm) and the optimal (known) h and d are −0.07% [−0.4, 1.9] and −1.3% [−5.9, 6.8], respectively across the subjects for simulation 1. The mean [min max] of the optimized LIG parameters are h = 3.8 [3.7, 3.8] mmol/L and d = 12 [10, 14] min for simulation 2 and they are h = 3.0 [2.9, 3] mmol/L and d = 10 [8, 14] min for simulation 3 across a 10-fold cross validation. Conclusions: This work demonstrates the feasibility of the algorithm to find the best-fit definition of CGM-based hypoglycemia for PRH detection. In a prospective clinical study collecting CGM and PRH, the current algorithm will be used to optimize the definition of hypoglycemia with respect to PRH with the ambition of using the resulted definition as a surrogate for PRH in clinical practice.