PMID- 32223551
OWN - NLM
STAT- MEDLINE
DCOM- 20211014
LR  - 20211014
IS  - 1557-8593 (Electronic)
IS  - 1520-9156 (Print)
IS  - 1520-9156 (Linking)
VI  - 22
IP  - 10
DP  - 2020 Oct
TI  - Modeling Carbohydrate Counting Error in Type 1 Diabetes Management.
PG  - 749-759
LID - 10.1089/dia.2019.0502 [doi]
AB  - Background: The error in estimating meal carbohydrates (CHO) amount is a critical 
      mistake committed by type 1 diabetes (T1D) subjects. The aim of this study is 
      both to investigate which factors, related to meals and subjects, affect the CHO 
      counting error most and to develop a mathematical model of CHO counting error 
      embeddable in T1D patient decision simulators to conduct in silico clinical 
      trials. Methods: A published dataset of 50 T1D adults is used, which includes a 
      patient's CHO count of 692 meals, dietitian's estimates of meal composition (used 
      as reference), and several potential explanatory factors. The CHO counting error 
      is modeled by multiple linear regression, with stepwise variable selection 
      starting from 10 candidate predictors, that is, education level, insulin 
      treatment duration, age, body weight, meal type, CHO, lipid, energy, protein, and 
      fiber content. Inclusion of quadratic and interaction terms is also evaluated. 
      Results: Larger errors correspond to larger meals, and most of the large meals 
      are underestimated. The linear model selects CHO (P < 0.00001), meal type 
      (P < 0.00001), and body weight (P = 0.047), whereas its extended version embeds a 
      quadratic term of CHO (P < 0.00001) and interaction terms of meal type with CHO 
      (P = 0.0001) and fiber amount (P = 0.001). The extended model explains 34.9% of 
      the CHO counting error variance. Comparison with the CHO counting error 
      description previously used in the T1D patient decision simulator shows that the 
      proposed models return more credible realizations. Conclusions: The most 
      important predictors of CHO counting errors are CHO and meal type. The 
      mathematical models proposed improve the description of patients' behavior in the 
      T1D patient decision simulator.
FAU - Roversi, Chiara
AU  - Roversi C
AD  - Department of Information Engineering, University of Padova, Padova, Italy.
FAU - Vettoretti, Martina
AU  - Vettoretti M
AD  - Department of Information Engineering, University of Padova, Padova, Italy.
FAU - Del Favero, Simone
AU  - Del Favero S
AD  - Department of Information Engineering, University of Padova, Padova, Italy.
FAU - Facchinetti, Andrea
AU  - Facchinetti A
AD  - Department of Information Engineering, University of Padova, Padova, Italy.
FAU - Sparacino, Giovanni
AU  - Sparacino G
AD  - Department of Information Engineering, University of Padova, Padova, Italy.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20200924
PL  - United States
TA  - Diabetes Technol Ther
JT  - Diabetes technology & therapeutics
JID - 100889084
RN  - 0 (Blood Glucose)
RN  - 0 (Dietary Carbohydrates)
RN  - 0 (Insulin)
SB  - IM
MH  - Adult
MH  - Blood Glucose
MH  - *Diabetes Mellitus, Type 1/diet therapy/drug therapy
MH  - *Diet, Diabetic
MH  - Dietary Carbohydrates/*analysis
MH  - Humans
MH  - Insulin
MH  - Meals
MH  - *Models, Theoretical
PMC - PMC7594710
OTO - NOTNLM
OT  - Carbohydrate counting error
OT  - Carbohydrates
OT  - Insulin therapy
OT  - Mathematical modeling
OT  - Simulation
OT  - Type 1 diabetes
COIS- No competing financial interests exist.
EDAT- 2020/04/01 06:00
MHDA- 2021/10/15 06:00
PMCR- 2020/10/06
CRDT- 2020/04/01 06:00
PHST- 2020/04/01 06:00 [pubmed]
PHST- 2021/10/15 06:00 [medline]
PHST- 2020/04/01 06:00 [entrez]
PHST- 2020/10/06 00:00 [pmc-release]
AID - 10.1089/dia.2019.0502 [pii]
AID - 10.1089/dia.2019.0502 [doi]
PST - ppublish
SO  - Diabetes Technol Ther. 2020 Oct;22(10):749-759. doi: 10.1089/dia.2019.0502. Epub 
      2020 Sep 24.