Mortality Reduction Benefits of a Comprehensive Cardiac Care Program for Patients with Occlusive Coronary Artery Disease

Using population-based automated pharmacy data, patterns of use of selected prescription medications during a 1 year time period identified by a consensus judgement process were used to construct a measure of chronic disease status (Chronic Disease Score). This score was evaluated in terms of its stability over time and its association with other health status measures. In a pilot test sample of high utilizers of ambulatory health care well known to their physicians (n = 219), Chronic Disease Score (CDS) was correlated with physician ratings of physical disease severity (r = 0.57). In a second random sample of patients (n = 722), its correlation with physician-rated disease severity was 0.46. In a total population analysis (n = 122,911), it was found to predict hospitalization and mortality in the following year after controlling for age, gender and health care visits. In a population sample (n = 790), CDS showed high year to year stability (r = 0.74). Based on health survey data, CDS showed a moderate association with self rated health status and self reported disability. Unlike self-rated health status and health care utilization, CDS was not associated with depression or anxiety. We conclude that scoring automated pharmacy data can provide a stable measure of chronic disease status that, after controlling for health care utilization, is associated with physician-rated disease severity, patient-rated health status, and predicts subsequent mortality and hospitalization rates. Specific methods of scoring automated pharmacy data to measure global chronic disease status may require adaptation to local prescribing practices. Scoring might be improved by empirical estimation of weighting factors to optimize prediction of mortality and other health status measures.

Different types of medication prescribed during a 6-month period for the treatment and management of chronic conditions were utilized in the refinement and validation of a chronic disease score. Prescription data, in addition to age and sex, were utilized to develop a chronic disease score based on empirically derived weights for each of three outcomes: total cost, outpatient cost, and primary care visits. The ability of the revised chronic disease score to predict health care utilization, costs, hospitalization, and mortality was compared to an earlier version of the chronic disease score (original) that was derived through clinical judgments of disease severity. The predictive validity of the chronic disease score is also compared to ambulatory care groups, which utilize outpatient diagnoses to form mutually exclusive diagnostic categories. Models based on a concurrent 6-month period and a 6-month prospective period (ie, the 6-month period after the chronic disease score or ambulatory care group derivation period) were estimated using a random one half sample of 250,000 managed-care enrollees aged 18 and older. The remaining one half of the enrollee population was used as a validation sample. The revised chronic disease score showed improved estimation and prediction over the original chronic disease score. The difference in variance explained prospectively by the revised chronic disease score versus the ambulatory care groups, conversely, was small. The revised chronic disease score was a better predictor of mortality than the ambulatory care groups. The combination of revised chronic disease score and ambulatory care groups showed only marginally greater predictive power than either one alone. These results suggest that the revised chronic disease score and ambulatory care groups with empirically derived weights provide improved prediction of health care utilization and costs, as well as hospitalization and mortality, over age and sex alone. We recommend the revised chronic disease score with total cost weights for general use as a severity measure because of its relative advantage in predicting mortality compared to the outpatient cost and primary care visit weights.