title: Monitoring modifiable cardiovascular risk in type 2 diabetes care in general practice: the use of an aggregated z-score.
abstract: BACKGROUND
  Because many patients in usual care reach the diabetes treatment goals, it may be more efficacious to focus quality improvement efforts on those general practice populations requiring additional support. We therefore developed a tool based on a composite end point considering blood pressure, lipids, and glycaemia.
  
  
  METHODS
  We created an aggregated z(A)-score, calculated as the average of 3 z-scores testing whether the mean practice values of hemoglobin A1c, low density lipoprotein cholesterol, and systolic blood pressure are significantly higher than the corresponding ADA-target (respectively 7%, 100 mg/dL, and 130 mm Hg). This score was used with 100 general practitioners who participated in a Quality Improvement Program. We defined the cut-off value (COV) to determine "Practices Requiring Support" (z(A) <COV) using a receiver's operating characteristics curve with the mean practice CHD risk as gold standard. To further test the z-score validity, we calculated the correlation coefficient between the z-score and the mean practice CHD risk and the improvement in the z-score after the Quality Improvement Program.
  
  
  RESULTS
  The COV was -1.22 and was valid to discriminate between practices at higher risk from practices at lower CHD risk (24% +/- 4% vs. 19% +/- 4%). The correlation coefficient was -0.515 (P = 0.001). The average z-score increased from -1.21 +/- 0.97 at baseline to 0.49 +/- 1.01 after the intervention (P < 0.001).
  
  
  CONCLUSIONS
  This scoring system is useful to picture practice populations with diabetes who are at high cardiovascular risk because of modifiable risk factors. Although the unadjusted z-score cannot be used to compare physicians, this technique can be used to evaluate improvement efforts over time.
authors.affiliation: Department of General Practice, Katholieke Universiteit Leuven, Leuven, Belgium; double daggerDepartment of Endocrinology, University Hospitals Leuven, Leuven, Belgium. geert.goderis@skynet.be
authors.forename: Geert
authors.initials: G
authors.lastname: Goderis
authors.forename: Liesbeth
authors.initials: L
authors.lastname: Borgermans
authors.forename: Jan
authors.initials: J
authors.lastname: Heyrman
authors.forename: Carine
authors.initials: C
authors.lastname: Van Den Broeke
authors.forename: An
authors.initials: A
authors.lastname: Carbonez
authors.forename: Chantal
authors.initials: C
authors.lastname: Mathieu
authors.forename: Geert
authors.initials: G
authors.lastname: Verbeke
authors.forename: Richard
authors.initials: R
authors.lastname: Grol
mesh_terms: D000368:Aged; D001794:Blood Pressure; D002318:Cardiovascular Diseases / Q000209:etiology* / Q000517:prevention & control; D008078:Cholesterol, LDL / Q000097:blood; D003924:Diabetes Mellitus, Type 2 / Q000150:complications*; D005194:Family Practice / Q000379:methods / Q000592:standards; D005260:Female; D006442:Glycated Hemoglobin / Q000032:analysis; D006801:Humans; D008297:Male; D008875:Middle Aged; D011446:Prospective Studies; D019984:Quality Indicators, Health Care; D011787:Quality of Health Care / Q000592:standards; D018570:Risk Assessment; D012307:Risk Factors; D012680:Sensitivity and Specificity
chemical_list: D008078:Cholesterol, LDL; D006442:Glycated Hemoglobin