Preprint Article Version 1 This version is not peer-reviewed

A Bayesian Study of the Dynamic Effect of Comorbidities on Hospital Outcomes of Care for CHF Patients

Version 1 : Received: 7 July 2019 / Approved: 9 July 2019 / Online: 9 July 2019 (04:16:43 CEST)

How to cite: Zikos, D.; Zimeras, S.; Ragina, N. A Bayesian Study of the Dynamic Effect of Comorbidities on Hospital Outcomes of Care for CHF Patients. Preprints 2019, 2019070127 (doi: 10.20944/preprints201907.0127.v1). Zikos, D.; Zimeras, S.; Ragina, N. A Bayesian Study of the Dynamic Effect of Comorbidities on Hospital Outcomes of Care for CHF Patients. Preprints 2019, 2019070127 (doi: 10.20944/preprints201907.0127.v1).

Abstract

Comorbidities can have a cumulative effect on hospital outcomes of care, such as the length of stay (LOS), and hospital mortality. This study examines patients hospitalized with Congestive Heart Failure (CHF), a life-threatening condition, which, when it coexists with a burdened disease profile, the risk for negative hospital outcomes increases. Since coexisting conditions co-interact, with a variable effect on outcomes, clinicians should be able to recognize these joint effects. In order to study CHF comorbidities, we used medical claims data from CMS. After extracting the most frequent cluster of CHF comorbidities, we: (i) Calculated, step-by-step, the conditional probabilities for each disease combination inside this cluster (ii) Estimated the cumulative effect of each comorbidity combination on the LOS and hospital mortality (iii) Constructed (a) Bayesian, scenario-based graphs and (b) Bayes-networks to visualize results. Results show that, for CHF patients, different comorbidity constructs have variable effect on the LOS and hospital mortality. Therefore, dynamic comorbidity risk assessment methods should be implemented for informed clinical decision making in any ongoing effort for quality of care improvements.

Subject Areas

comorbidities; congestive heart failure; health informatics; Bayes networks; clustering; risk assessment; clinical decision making

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our diversity statement.

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.