Individualizing HbA1c targets for patients with diabetes: impact of an automated algorithm within a primary care network

Aims To develop glycaemic goal individualization algorithms and assess potential impact on a healthcare system and different segments of the population with diabetes. Methods A cross-sectional observational study of patients with diabetes in a primary care network age >18years with an HbA1c measured between 1 January and 31 December 2011. We applied diabetes guidelines to create targeted algorithms 1 and 2, which assigned HbA1c goals based on age, duration of diabetes (<15years or <10years), diabetes complications and Charlson co-morbidity score (<6 or <4) [targeted algorithm2 was designed to assign more patients a goal <64mmol/mol (8.0%) than targeted algorithm1]. Each patient's HbA1c was compared with these targeted goals and to the standard' goal <53mmol/mol (7.0%). Agreement was tested using McNemar's test. Results Overall, 55.7% of 12199 patients would be considered controlled under the standard' approach, 61.2% under targeted algorithm1 and 67.5% under targeted algorithm2. Targeted algorithm1 reclassified 1213 (23.6%) patients considered uncontrolled under the standard approach to controlled, P<0.001. Targeted algorithm2 reclassified 1844 (35.2%) patients, P<0.001. Compared with those controlled under the standard goal, there was no significant difference in the proportion of those controlled using targeted goals who had Medicaid, had less than a high school diploma or received primary care in a federally qualified health centre. Conclusions Two automated targeted algorithms would reclassify one quarter to one third of patients from uncontrolled to controlled within a primary care network without differentially affecting vulnerable patient subgroups.