Determining compound comorbidities for heart failure from hospital discharge data.

The course of treatment and ultimate clinical outcome often depends on a holistic understanding of the patient status, which often requires cataloguing of concomitant conditions ("comorbidities"). A number of approaches have been developed to quantify the effect of comorbidities (e.g., the Charlson Comorbidity Index); however, reported metrics have been based on pair-wise analyses of co-occurring conditions. This study explored the potential to develop "compound co-morbidities" (CCMs) as a knowledge construct to represent multiple comorbidities, which accommodates for relative prevalence, statistical significance, and rate of increased cost. In the context of congestive heart failure, which is a leading cause for hospital admissions nationally (particularly for the elderly), CCMs were developed and analyzed based on hospital discharge data for an entire state population (Vermont). The results suggest that CCMs may be a valuable construct for characterizing complex co-morbidity relationships that may not be captured using conventional pair-wise approaches.