Objective Length of stay (LOS) is a major driver of inpatient care costs. To date, few studies have investigated risk factors associated with increased LOS in patients with adult congenital heart disease (ACHD). In the present work, we sought to address this knowledge gap.
Methods We conducted an analysis of the State Inpatient Databases from Arkansas, California, Florida, Hawaii, Nebraska and New York. We analysed data on admissions in patients with ACHD and constructed a series of hierarchical regression models to identify the clinical factors having the greatest effects on LOS.
Results We identified 99 103 inpatient hospitalisations meeting criteria for inclusion. Diagnoses associated with the longest LOS were septicaemia (LOS=14.2 days in patients atrial septal defect, and 11.7 days among all other ACHD) and pericarditis, endocarditis and myocarditis (LOS=13.6 days and 10.0 days, respectively). When separated by underlying anatomy, the variables most consistently associated with longer LOS were bacterial infection, complications of surgeries or medical care, acute renal disease and anaemia.
Conclusions In the present study, we identified risk factors associated with longer LOS in ACHD. These data may be used to identify at-risk patients for targeted intervention to decrease LOS and thereby cost.
- congenital heart disease
- quality and outcomes of care
Statistics from Altmetric.com
If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.
Contributors AC: conceived the project, participated in statistical analysis, participated in drafting manuscript, responsible for all manuscript modifications and submission, obtained funding for study. LB: participated in data interpretation and in drafting the manuscript. SVB: participated in statistical analysis. EN: participated in statistical analysis. AA: participated in statistical analysis, interpretation of data and in providing funding for statistical analysis.
Funding Research reported in this publication was supported by the Washington University Institute of Clinical and Translational Sciences grant UL1 TR000448 from the National Center for Advancing Translational Sciences (NCATS) of the National Institutes of Health (NIH). The content is solely the responsibility of the authors and does not necessarily represent the official view of the NIH.
Competing interests None declared.
Provenance and peer review Not commissioned; externally peer reviewed.