Using binary logistic regression coefficients for the dynamic quantification of comorbidities

Comorbidities are multiple co-occurring disorders related with a series of inpatient effects which affect the overall quality of care. We describe a methodology for the dynamic quantification of the effect of comorbidities on important health outcomes, such as the in-hospital mortality and patient complications. Using a comprehensive Medicare dataset, our algorithm utilizes the coefficients of binary logistic regression models to predict the impact of a comorbidity to a binary outcome and the effect of any new disease to this comorbidity profile. To demonstrate the functionality of the algorithm, we developed a pilot Java based web application. The system can be useful upon the first patient encounter as well as during the entire service episode of the care.