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Updated: Feb 16, 2026

Improving IV Insulin Administration in a Community Hospital
Published on: June 11, 2012
Calculating individualized glycaemic targets using an algorithm based on expert worldwide diabetologists:
Fernando Alvarez-Guisasola1, Ana M Cebrián-Cuenca2, Xavier Cos3
1Ribera del Órbigo Primary Care Centre, Leon, Spain.
Background:
The aim of this study was to assess the clinical implications of calculating an individualized HbA1c target using a recently published algorithm in a real-life clinical setting.
Methods:
General practitioners (GPs) from the Spanish Society of Family Medicine Diabetes Expert Group were invited to participate in the study. Each GP selected a random sample of patients with diabetes from his or her practice and submitted their demographic and clinical data for analysis. Individualized glycaemic targets were calculated according to the algorithm. Predictors of good glycaemic control were studied. The rate of patients attaining their individualized glycaemic target or the uniform target of HbA1c < 7.0% was calculated.
Results:
Forty GPs included 408 patients in the study. Of the 8 parameters included in the algorithm, "comorbidities," "risk of hypoglycaemia from treatment," and "diabetes duration" had the greatest impact on determining the individualized glycaemic target. Number of glucose-lowering agents and adherence were independently associated with glycaemic control. Overall, 60.5% of patients had good glycaemic control per individualized target, and 56.1% were well controlled per the uniform target of HbA1c < 7.0% (P = .20). However, 12.8% (23 of 246) of the patients with HbA1c ≥ 7.0% were adequately controlled per individualized target, and 2.6% (6 of 162) of the patients with HbA1c < 7.0% were uncontrolled since their individualized target was lower.
Conclusions:
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.
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