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Medientyp:
E-Artikel
Titel:
Prediction of Individual Basal Rate Profiles From Patient Characteristics in Type 1 Diabetes on Insulin Pump Therapy
Beteiligte:
Nauck, Michael A.;
Kahle-Stephan, Melanie;
Lindmeyer, Anna M.;
Wenzel, Sina;
Meier, Juris J.
Erschienen:
SAGE Publications, 2021
Erschienen in:
Journal of Diabetes Science and Technology, 15 (2021) 6, Seite 1273-1281
Sprache:
Englisch
DOI:
10.1177/1932296820972691
ISSN:
1932-2968
Entstehung:
Anmerkungen:
Beschreibung:
Background:Basal rate profiles in patients with type 1 diabetes on insulin pump therapy are subject to enormous inter-individual heterogeneity. Tools to predict basal rates based on clinical characteristics may facilitate insulin pump therapy.Methods:Data from 339 consecutive in-patients with adult type 1 diabetes on insulin pump therapy were collected. Basal rate tests were performed over 24 hours. A mathematical algorithm to predict individual basal rate profiles was generated by relating the individual insulin demand to selected clinical characteristics in an exploratory cohort of 170 patients. The predicted insulin pump profiles were validated in a confirmatory cohort of 169 patients.Findings:Basal rates (0.27 ± 0.01 IU.d−1.kg−1) showed circadian variations with peaks corresponding to the “dawn” and “dusk” phenomena. Age, gender, duration of pump treatment, body-mass-index, HbA1c, and triacylglycerol concentrations largely predicted the individual basal insulin demand per day (IU/d; exploratory vs prospective cohorts: r2 = 0.518, P < .0001). Model-predicted and actual basal insulin rates were not different (exploratory cohort: Δ 0.1 (95% CI −0.9; 1.0 U/d; P = .95; prospective cohort: Δ −0.5 (95% CI −1.5; 0.6 IU/d; P = .46). Similarly, precise predictions were possible for each hour of the day. Actual and predicted “dawn” index correlated significantly in the exploratory but not in the confirmatory cohort.Interpretation:Clinical characteristics predict 52% of the variation in individual basal rate profiles, including their diurnal fluctuations. The multivariate regression model can be used to initiate or optimize insulin pump treatment in patients with type 1 diabetes.