Multi-State Markov Model for Analysing Blood Glucose Changes
Typ
Examensarbete för masterexamen
Program
Engineering mathematics and computational science (MPENM), MSc
Publicerad
2020
Författare
Ingemarsson, Viktor
Svensson, Marcus
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
A multi-state Markov model was adapted in order to model glycemic control, based
on continuous glucose measurements (CGM). A library was implemented, written
in Python, that allows for user-specific input in regards to modelling parameters
and analysis. The CGM-readings were collected during two previous clinical trials,
involving patients with type 1 diabetes and inadequate glycemic control. The clinical
trials involved the administration of dapagliflozin which together with insulin
improves glycemic control, compared with only administering insulin. The states of
the Markov model were defined based on blood glucose levels, where increased time
in the target range, normoglycemia, constituted better glycemic control. Based
on the CGM-readings collected, this Markov model was used to analyse how the
glycemic control of patients is affected by their kidney function as well as insulin
reduction. Results show that improvement in glycemic control due to dapagliflozin
is independent of kidney function in the range investigated. When modelling the insulin
reduction in patients, it was seen that an increased insulin usage corresponded
to increased glucose levels. There is a well established causal relationship between
insulin and decreased blood glucose levels. The opposite relation seen in the modelling
and data must mean that something is masking the effect. One explanation
would be that this is due to the fact that insulin is only a proxy for eating unevenly,
but the exact cause is unknown.
Beskrivning
Ämne/nyckelord
logistic regression, longitudinal data, Markov process, multi-state model