Calculating individualized glycaemic targets using an algorithm based on expert worldwide diabetologists: Implications in real-life clinical practice.

Fernando Álvarez-GuisasolaAna M Cebrián-CuencaXavier CosManuel Ruiz-QuinteroJose M MillarueloAvivit CahnItamar Raz Domingo Orozco-Beltrannull null

Published in: Diabetes/metabolism research and reviews (2018)

In a real-life clinical setting, applying individualized targets did not change the overall rate of patients with good glycaemic control yet led to reclassification of 7.1% (29 of 408) of the patients. More studies are needed to validate these results in different populations.

Keyphrases

clinical practice
type diabetes
end stage renal disease
ejection fraction
newly diagnosed
chronic kidney disease
machine learning
prognostic factors
deep learning
peritoneal dialysis
case control