title: Electronic health record-based cardiac risk assessment and identification of unmet preventive needs.
abstract: BACKGROUND
  Cardiac risk assessment may not be routinely performed. Electronic health records (EHRs) offer the potential to automate risk estimation. We compared EHR-based assessment with manual chart review to determine the accuracy of automated cardiac risk estimation and determination of candidates for antiplatelet or lipid-lowering interventions.
  
  
  METHODS
  We performed an observational retrospective study of 23,111 adults aged 20 to 79 years, seen in a large urban primary care group practice. Automated assessments classified patients into 4 cardiac risk groups or as unclassifiable and determined candidates for antiplatelet or lipid-lowering interventions based on current guidelines. A blinded physician manually reviewed 100 patients from each risk group and the unclassifiable group. We determined the agreement between full review and automated assessments for cardiac risk estimation and identification of which patients were candidates for interventions.
  
  
  RESULTS
  By automated methods, 9.2% of the population were candidates for lipid-lowering interventions, and 8.0% were candidates for antiplatelet medication. Agreement between automated risk classification and manual review was high (kappa = 0.91; 95% confidence interval [CI], 0.88-0.93). Automated methods accurately identified candidates for antiplatelet therapy [sensitivity, 0.81 (95% CI, 0.73-0.89); specificity, 0.98 (95% CI, 0.96-0.99); positive predictive value, 0.86 (95% CI, 0.78-0.94); and negative predictive value, 0.98 (95% CI, 0.97-0.99)] and lipid lowering [sensitivity, 0.92 (95% CI, 0.87-0.96); specificity, 0.98 (95% CI, 0.97-0.99); positive predictive value, 0.94 (95% CI, 0.89-0.99); and negative predictive value, 0.99 (95% CI, 0.98-> or =0.99)].
  
  
  CONCLUSIONS
  EHR data can be used to automatically perform cardiovascular risk stratification and identify patients in need of risk-lowering interventions. This could improve detection of high-risk patients whom physicians would otherwise be unaware.
authors.affiliation: Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA. spersell@nmff.org
authors.forename: Stephen D
authors.initials: SD
authors.lastname: Persell
authors.forename: Alexis P
authors.initials: AP
authors.lastname: Dunne
authors.forename: Donald M
authors.initials: DM
authors.lastname: Lloyd-Jones
authors.forename: David W
authors.initials: DW
authors.lastname: Baker
mesh_terms: D000328:Adult; D000368:Aged; D002318:Cardiovascular Diseases / Q000517:prevention & control*; D020000:Decision Support Systems, Clinical; D005260:Female; D006301:Health Services Needs and Demand; D006801:Humans; D008297:Male; D008485:Medical Audit; D016347:Medical Records Systems, Computerized; D008875:Middle Aged; D011315:Preventive Medicine; D012189:Retrospective Studies; D018570:Risk Assessment / Q000458:organization & administration*; D055815:Young Adult