Background and Aims

Background and aims: Glycated hemoglobin A1c (HbA1c) has been recently shown as a weak indicator both short- and long-term blood glucose control. The popularization of continuous glucose monitoring (CGM) has fostered the upraise of the time-in-range (TIR) as a more robust and accurate metric for blood glucose control. Recent studies have shown a good correlation between HbA1c and TIR that may permit the transition to TIR as the preferred metric for determining the outcome of clinical.

This work aimed to examine HbA1c measures from a dynamic perspective by applying process mining tools, in order to obtain dynamic risk models of blood glucose control in general population.

Methods

Methods: We propose a method based on the use of process mining to discover and identify HbA1c changes during a period, and apply Clustering Algorithms to discover dynamic risk models for diabetes using HbA1c test results and other variables available in Health Electronic Records, such as Body Mass Index, age and co-morbidities.

We applied this methodology to real data from 50,169 patients followed-up for seven years (2012-2018) in the outpatient clinics of a tertiary hospital.

Results

Results: Results showed a population stratification and characterisation based on their dynamic evolution of HbA1c results over the given period, that let us inferring a Dynamic Diabetes Risk Model in an understandable way for health professionals.

Conclusions

Conclusion: This information will support health professionals to translate one-fits-all current approach of treatments and care, to a personalised one, fitting treatment strategy based on patients’ unique behaviour thanks to dynamic modelling.