A strategy based on integer programming for optimal dosing and timing of preventive hypoglycemic treatments in type 1 diabetes management

• Published in: Computer methods and programs in biomedicine 2024-06, Vol.250, p.108179-108179, Article 108179
• Main Authors: Pavan, J., Noaro, G., Facchinetti, A., Salvagnin, D., Sparacino, G., Del Favero, S.
• Format: Article
• Language: English
• Subjects: Carbohydrates suggestions; Hypoglycemia; Hypoglycemic treatments; Integer programming; Optimization; Rescue carbohydrates; Type 1 diabetes
• ISSN: 0169-2607; 1872-7565
• DOI: 10.1016/j.cmpb.2024.108179

One of the major problems related to type 1 diabetes (T1D) management is hypoglycemia, a condition characterized by low blood glucose levels and responsible for reduced quality of life and increased mortality. Fast-acting carbohydrates, also known as hypoglycemic treatments (HT), can counteract this event. In the literature, dosage and timing of HT are usually based on heuristic rules. In the present work, we propose an algorithm for mitigating hypoglycemia by suggesting preventive HT consumption, with dosages and timing determined by solving an optimization problem. By leveraging integer programming and linear inequality constraints, the algorithm can bind the amount of suggested carbohydrates to standardized quantities (i.e., those available in “off-the-shelf” HT) and the minimal distance between consecutive suggestions (to reduce the nuisance for patients). The proposed method was tested in silico and compared with competitor algorithms using the UVa/Padova T1D simulator. At the cost of a slight increase of HT consumed per day, the proposed algorithm produces the lowest median and interquartile range of the time spent in hypoglycemia, with a statistically significant improvement over most competitor algorithms. Also, the average number of hypoglycemic events per day is reduced to 0 in median. Thanks to its positive performances and reduced computational burden, the proposed algorithm could be a candidate tool for integration in a DSS aimed at improving T1D management. •Hypoglycemia is a life-threatening condition for people with Type 1 Diabetes.•Fast acting carbohydrates can prevent hypoglycemia when consumed in time.•This algorithm optimizes carbohydrates intake suggestions to mitigate hypoglycemia.•Integer programming enables to quantize the suggestions and reduce patient burden.•In silico, the algorithm performed better than state-of-the-art comparators.