Patient-Centered Research: Case Finding for Computerized Clinical Reminders: To Whom do the Guidelines Apply?

PURPOSE: An often overlooked problem with clinical guideline implementation is the identification of patients to whom the guidelines should be applied. This study developed and validated a computerized tool to identify patients with congestive heart failure (CHF) who would be candidates for management according to a computerized version of a clinical practice guideline. METHODS: A retrospective cohort study was conducted in the General Internal Medicine Clinic at the VA Puget Sound Health Care System. Subjects having at least one echocardiogram (Echo) or radionuclide ventriculogram during 1996–1998 were included. CHF was defined by left ventricular systolic dysfunction (LVSD) on either study. Subjects were randomly split into two samples; one for prediction model development and the second for validation. To determine the utility of clinical data in discriminating between patients with and without CHF, we examined demographic variables, pharmacy data, and inpatient and outpatient primary or secondary ICD-9 codes (428,398.91) that were available from VA administrative database. The medications of interest included ACE inhibitors, AII antagonists, beta-blockers, calcium channel blockers, digoxin, diuretics, and nitrates. Discriminant analysis was used to build predictive models to identify LVSD patients using pharmacy and demographic data. Accuracy of both the prediction model and diagnosis was compared to presence of LVSD as determined by cardiac imaging. RESULTS: 1314 subjects had at least one cardiac imaging study. 536 (40.8%) met criteria for LVSD. The mean age was 68, 84% were Caucasian, and 97% males. Subjects with LVSD were slightly older than those without LVSD (69 versus 67, p = 0.004), but were similar with regard to gender, race and marital status. Subjects with LVSD were more likely to have prescriptions for ACE inhibitors, aspirin, digoxin, loop diuretics, hydralazine and nitrates. (Carvedilol was not on formulary during the study period.) ACE inhibitors, aspirin, digoxin, loop diuretics, hydralazine, nitrates and age were included in the final predictive model. Use of ACE inhibitors, loop diuretics and digoxin exerted the greatest predictive power. The predictive model was 70% accurate (72% sensitivity, 69% specificity), while ICD-9 codes were 72% accurate (74% sensitivity, 71% specificity). CONCLUSION: A model using pharmacy data identified most subjects with CHF, and performed equally well compared to ICD-9 codes in the VA database. Although the model did not demonstrate improvement over ICD-9 codes, it was able to predict LVSD using a relatively small number of potential covariates