
AbstractAimsTo develop glycaemic goal individualization algorithms and assess potential impact on a healthcare system and different segments of the population with diabetes.MethodsA cross‐sectional observational study of patients with diabetes in a primary care network age > 18 years 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 (< 15 years or < 10 years), diabetes complications and Charlson co‐morbidity score (< 6 or < 4) [targeted algorithm 2 was designed to assign more patients a goal < 64 mmol/mol (8.0%) than targeted algorithm 1]. Each patient's HbA1c was compared with these targeted goals and to the ‘standard’ goal < 53 mmol/mol (7.0%). Agreement was tested using McNemar's test.ResultsOverall, 55.7% of 12 199 patients would be considered controlled under the ‘standard’ approach, 61.2% under targeted algorithm 1 and 67.5% under targeted algorithm 2. Targeted algorithm 1 reclassified 1213 (23.6%) patients considered uncontrolled under the standard approach to controlled, P < 0.001. Targeted algorithm 2 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.ConclusionsTwo 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.
Blood Glucose, Glycated Hemoglobin, Male, Primary Health Care, Medicaid, Comorbidity, Middle Aged, United States, Cross-Sectional Studies, Diabetes Mellitus, Type 1, Diabetes Mellitus, Type 2, Glycemic Index, Educational Status, Humans, Female, Precision Medicine, Algorithms, Aged
Blood Glucose, Glycated Hemoglobin, Male, Primary Health Care, Medicaid, Comorbidity, Middle Aged, United States, Cross-Sectional Studies, Diabetes Mellitus, Type 1, Diabetes Mellitus, Type 2, Glycemic Index, Educational Status, Humans, Female, Precision Medicine, Algorithms, Aged
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