title: COSEHC global vascular risk management quality improvement program: rationale and design.
abstract: BACKGROUND
  The Consortium for Southeastern Hypertension Control (COSEHC) promotes global risk factor management in patients with metabolic syndrome. The COSEHC Global Vascular Risk Management Study (GVRM) intends to quantify these efforts on long-term patient outcomes. The objectives of this study were to present baseline demographics of patients enrolled in the GVRM, calculate a modified COSEHC risk score using 11 variables (COSEHC-11), and compare it with the original COSEHC-17 and Framingham, Prospective Cardiovascular Münster (PROCAM), and Systemic Coronary Risk Evaluation (SCORE) risk scores.
  
  
  METHODS
  Deidentified electronic medical records of enrolled patients were used to calculate the risk scores. The ability of the COSEHC-11 score to predict the COSEHC-17 score was assessed by regression analysis. Raw risk scores were converted to probability estimates of fatal coronary heart disease (CHD) and compared with predicted risks from other algorithms.
  
  
  RESULTS
  Of the 177,404 patients enrolled, 43,676 had data for all 11 variables. The COSEHC-11 score (mean ± standard deviation) of these 43,676 patients was 31.75 ± 11.66, implying a five-year fatal CHD risk of 1.4%. The COSEHC-11 score was highly predictive of the COSEHC-17 score (R(2) = 0.93; P < 0.0001) and correlated well with the SCORE algorithm.
  
  
  CONCLUSIONS
  The COSEHC-11 risk score is statistically similar to the COSEHC-17 risk score and should be a viable tool for evaluating its ability to predict five-year cardiovascular mortality in the coming years.
authors.affiliation: Department of General Surgery, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA. cferrari@wfubmc.edu
authors.forename: Carlos M
authors.initials: CM
authors.lastname: Ferrario
authors.forename: Michael A
authors.initials: MA
authors.lastname: Moore
authors.forename: William
authors.initials: W
authors.lastname: Bestermann
authors.forename: Chris
authors.initials: C
authors.lastname: Colby
authors.forename: Alex
authors.initials: A
authors.lastname: Exuzides
authors.forename: Debra
authors.initials: D
authors.lastname: Simmons
authors.forename: Sumeet
authors.initials: S
authors.lastname: Panjabi
mesh_terms: D000368:Aged; D000465:Algorithms; D002318:Cardiovascular Diseases / Q000401:mortality / Q000517:prevention & control*; D015897:Comorbidity; D003661:Decision Support Techniques; D057286:Electronic Health Records; D005260:Female; D006305:Health Status Indicators; D006801:Humans; D006973:Hypertension / Q000175:diagnosis / Q000188:drug therapy* / Q000401:mortality; D016014:Linear Models; D008297:Male; D008390:Markov Chains; D008403:Mass Screening / Q000379:methods*; D024821:Metabolic Syndrome / Q000175:diagnosis / Q000453:epidemiology / Q000517:prevention & control*; D008875:Middle Aged; D009010:Monte Carlo Method; D015995:Prevalence; D011379:Prognosis; D016730:Program Development; D011446:Prospective Studies; D058996:Quality Improvement; D018570:Risk Assessment; D012307:Risk Factors; D015144:Southeastern United States / Q000453:epidemiology; D013997:Time Factors