Literature DB >> 10385767

Effect of clinical factors on length of stay after coronary artery bypass surgery: results of the cooperative cardiovascular project.

A B Rosen1, J O Humphries, L H Muhlbaier, C I Kiefe, T Kresowik, E D Peterson.   

Abstract

BACKGROUND: Rising health care costs have prompted careful review of comparative hospital resource use. Length of stay after bypass surgery has received particular attention. However, many providers assert that these variations are caused by differences in the clinical mix of patients treated. Our goals were to identify the major clinical predictors of postoperative length of stay (PLOS) after coronary artery bypass graft surgery (CABG), document variations in PLOS among 28 hospitals, and assess the degree to which patient characteristics account for hospital variations in PLOS.
METHODS: Detailed clinical data on 3605 Medicare patients undergoing CABG in 28 Alabama and Iowa hospitals were analyzed by stepwise linear regression to identify significant clinical predictors of PLOS. Analysis of variance was used to compare hospitals' PLOS while controlling for significant patient risk factors.
RESULTS: The mean age was 72.1 years, 34.7% were female, and the in-hospital mortality rate was 5.6%. The median and mean PLOS were 8 and 11.1 days, respectively. Significant predictors of longer PLOS included increasing age, female sex, history of chronic obstructive pulmonary disease, cerebrovascular disease, or mitral valve disease, elevated admission blood urea nitrogen, and preoperative placement of an intraaortic balloon pump. Hospitals varied significantly (P =.0001) in their unadjusted PLOS. These hospital-level variations persisted despite adjustment for both preoperative patient characteristics (P =.0001) and postoperative complications and death (P =.0001).
CONCLUSIONS: This study found significant between-hospital variations in PLOS that were not explained by patient factors. This finding suggests the potential for increased efficiency in the care of patients undergoing CABG at many institutions. Further research is needed to determine the practice patterns contributing to variations in length of stay after bypass surgery.

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Year:  1999        PMID: 10385767     DOI: 10.1016/s0002-8703(99)70249-8

Source DB:  PubMed          Journal:  Am Heart J        ISSN: 0002-8703            Impact factor:   4.749


  7 in total

1.  Alabama coronary artery bypass grafting project: results from phase II of a statewide quality improvement initiative.

Authors:  William L Holman; Monique Sansom; Catarina I Kiefe; Eric D Peterson; Steve G Hubbard; James F Delong; Richard M Allman
Journal:  Ann Surg       Date:  2004-01       Impact factor: 12.969

Review 2.  Advanced neurologic monitoring for cardiac surgery.

Authors:  Alexander Y Razumovsky; Lavern D Gugino; Jeffrey H Owen
Journal:  Curr Cardiol Rep       Date:  2006-02       Impact factor: 2.931

3.  Caregiver status: a simple marker to identify cardiac surgery patients at risk for longer postoperative length of stay, rehospitalization, or death.

Authors:  Heidi Mochari-Greenberger; Matthew Mosca; Brooke Aggarwal; Tianna M Umann; Lori Mosca
Journal:  J Cardiovasc Nurs       Date:  2014 Jan-Feb       Impact factor: 2.083

4.  Psychological risk factors for increased post-operative length of hospital stay following coronary artery bypass graft surgery.

Authors:  Melissa Oxlad; John Stubberfield; Robert Stuklis; James Edwards; Tracey D Wade
Journal:  J Behav Med       Date:  2006-02-22

5.  Relation of length of hospital stay in acute myocardial infarction to postdischarge mortality.

Authors:  Alan K Berger; Sue Duval; David R Jacobs; Cheryl Barber; Gabriela Vazquez; Seungmin Lee; Russell V Luepker
Journal:  Am J Cardiol       Date:  2008-02-15       Impact factor: 2.778

6.  The Determinants of Costs and Length of Stay for Hip Fracture Patients.

Authors:  Adriana Castelli; Silvio Daidone; Rowena Jacobs; Panagiotis Kasteridis; Andrew David Street
Journal:  PLoS One       Date:  2015-07-23       Impact factor: 3.240

7.  Length of Hospital Stay Prediction at the Admission Stage for Cardiology Patients Using Artificial Neural Network.

Authors:  Pei-Fang Jennifer Tsai; Po-Chia Chen; Yen-You Chen; Hao-Yuan Song; Hsiu-Mei Lin; Fu-Man Lin; Qiou-Pieng Huang
Journal:  J Healthc Eng       Date:  2016       Impact factor: 2.682

  7 in total

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