title: Individualizing HbA1c targets for patients with diabetes: impact of an automated algorithm within a primary care network.
abstract: OBJECTIVE
  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 > 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.
  
  
  RESULTS
  Overall, 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.
  
  
  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.
authors.affiliation: General Medicine Division, Department of Medicine, Massachusetts General Hospital, Boston, MA; Department of Medicine, Harvard Medical School, Boston, MA.
authors.forename: S A
authors.initials: SA
authors.lastname: Berkowitz
authors.forename: S J
authors.initials: SJ
authors.lastname: Atlas
authors.forename: R W
authors.initials: RW
authors.lastname: Grant
authors.forename: D J
authors.initials: DJ
authors.lastname: Wexler
mesh_terms: D000368:Aged; D000465:Algorithms; D001786:Blood Glucose / Q000378:metabolism*; D015897:Comorbidity; D003430:Cross-Sectional Studies; D003922:Diabetes Mellitus, Type 1 / Q000097:blood / Q000188:drug therapy / Q000453:epidemiology*; D003924:Diabetes Mellitus, Type 2 / Q000097:blood / Q000188:drug therapy / Q000453:epidemiology*; D004522:Educational Status; D005260:Female; D006442:Glycated Hemoglobin / Q000378:metabolism*; D038321:Glycemic Index; D006801:Humans; D008297:Male; D008484:Medicaid; D008875:Middle Aged; D057285:Precision Medicine / Q000639:trends; D011320:Primary Health Care; D014481:United States / Q000453:epidemiology
chemical_list: D001786:Blood Glucose; D006442:Glycated Hemoglobin; C517652:hemoglobin A1c protein, human