Literature DB >> 28237970

Clinical predictors of length of stay in adults with congenital heart disease.

Ari Cedars1, Lawrence Benjamin2, Sara V Burns2, Eric Novak3, Amit Amin3.   

Abstract

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. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Entities:  

Keywords:  congenital heart disease; quality and outcomes of care

Mesh:

Year:  2017        PMID: 28237970      PMCID: PMC5594917          DOI: 10.1136/heartjnl-2016-310841

Source DB:  PubMed          Journal:  Heart        ISSN: 1355-6037            Impact factor:   5.994


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Authors:  Paul Khairy; Raluca Ionescu-Ittu; Andrew S Mackie; Michal Abrahamowicz; Louise Pilote; Ariane J Marelli
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4.  Accuracy of administrative data for detection and categorization of adult congenital heart disease patients from an electronic medical record.

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5.  Health care resource utilization in adults with congenital heart disease.

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Authors:  P Moons; K Siebens; S De Geest; I Abraham; W Budts; M Gewillig
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Authors:  Alexander R Opotowsky; Omar K Siddiqi; Gary D Webb
Journal:  J Am Coll Cardiol       Date:  2009-07-28       Impact factor: 24.094

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Review 9.  Common long-term complications of adult congenital heart disease: avoid falling in a H.E.A.P.

Authors:  M Ministeri; R Alonso-Gonzalez; L Swan; K Dimopoulos
Journal:  Expert Rev Cardiovasc Ther       Date:  2016-01-25

10.  Projected growth of the adult congenital heart disease population in the United States to 2050: an integrative systems modeling approach.

Authors:  Catherine P Benziger; Karen Stout; Elisa Zaragoza-Macias; Amelia Bertozzi-Villa; Abraham D Flaxman
Journal:  Popul Health Metr       Date:  2015-10-15
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2.  Health Service Utilization Patterns Among Adults With Congenital Heart Disease: A Population-Based Study.

Authors:  Michal Benderly; Jonathan Buber; Ofra Kalter-Leibovici; Leonard Blieden; Alexander Dadashev; Avraham Lorber; Amiram Nir; Sergei Yalonetsky; Gabriel Chodick; Dahlia Weitzman; Ran Balicer; Efrat Mazor Dray; Havi Murad; Yaron Razon; Rafael Hirsch
Journal:  J Am Heart Assoc       Date:  2021-01-12       Impact factor: 5.501

3.  Application of machine learning missing data imputation techniques in clinical decision making: taking the discharge assessment of patients with spontaneous supratentorial intracerebral hemorrhage as an example.

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Journal:  BMC Med Inform Decis Mak       Date:  2022-01-13       Impact factor: 2.796

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