Literature DB >> 25583462

The impact of high-risk cases on hospitals' risk-adjusted coronary artery bypass grafting mortality rankings.

Brian R Englum1, Paramita Saha-Chaudhuri2, David M Shahian3, Sean M O'Brien2, J Matthew Brennan4, Fred H Edwards5, Eric D Peterson6.   

Abstract

BACKGROUND: Risk-adjusted mortality (RAM) models are increasingly used to evaluate hospital performance, but the validity of the RAM method has been questioned. Providers are concerned that these methods might not adequately account for the highest levels of risk and that treating high-risk cases will have a negative impact on RAM rankings.
METHODS: Using cases of isolated coronary artery bypass grafting (CABG) performed at 1002 sites in the United States participating in The Society of Thoracic Surgeons (STS) Adult Cardiac Surgery Database from 2008 to 2010 (N = 494,955), the STS CABG RAM model performance in high-risk patients was assessed. The ratios of observed to expected (O/E) perioperative mortality were compared among groups of hospitals with varying expected risks. Finally, RAM rates during the overall study period for each site were compared with its performance in a simulated "nightmare year" in which the site's highest risk cases over a 3-year period were concentrated into a 1-year period of exceptional risk.
RESULTS: The average predicted mortality for center risk groups ranged from 1.46% for the lowest risk quintile to 2.87% for the highest. The O/E ratios for center risk quintiles 1 to 5 during the overall period were 1.01 (95% confidence interval, 0.96% to 1.06%), 1.00 (0.95% to 1.04%), 0.98 (0.94% to 1.03%), 0.97 (0.93% to 1.01%), and 0.80 (0.77% to 0.84%), respectively. The sites' risk-adjusted mortality rates were not increased when the centers' highest risk cases were concentrated into a single "nightmare year."
CONCLUSIONS: Our results show that the current risk-adjusted models accurately estimate CABG mortality and that hospitals accepting more high-risk CABG patients have equal or better outcomes than do those with predominately lower-risk patients.
Copyright © 2015 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.

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Year:  2015        PMID: 25583462      PMCID: PMC5054747          DOI: 10.1016/j.athoracsur.2014.09.048

Source DB:  PubMed          Journal:  Ann Thorac Surg        ISSN: 0003-4975            Impact factor:   4.330


  25 in total

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Journal:  JAMA       Date:  1990-12-05       Impact factor: 56.272

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Authors:  Timothy G Ferris; David F Torchiana
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5.  Assessment of coronary artery bypass graft surgery performance in New York. Is there a bias against taking high-risk patients?

Authors:  E L Hannan; A L Siu; D Kumar; M Racz; D B Pryor; M R Chassin
Journal:  Med Care       Date:  1997-01       Impact factor: 2.983

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Journal:  Health Serv Res       Date:  1987-02       Impact factor: 3.402

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Authors:  J Green; N Wintfeld
Journal:  N Engl J Med       Date:  1995-05-04       Impact factor: 91.245

8.  The Society of Thoracic Surgeons National Cardiac Surgery Database: current risk assessment.

Authors:  F H Edwards; F L Grover; A L Shroyer; M Schwartz; J Bero
Journal:  Ann Thorac Surg       Date:  1997-03       Impact factor: 4.330

9.  Provider profiling and quality improvement efforts in coronary artery bypass graft surgery: the effect on short-term mortality among Medicare beneficiaries.

Authors:  Edward L Hannan; Mary S Vaughn Sarrazin; Donna R Doran; Gary E Rosenthal
Journal:  Med Care       Date:  2003-10       Impact factor: 2.983

10.  The unintended consequences of publicly reporting quality information.

Authors:  Rachel M Werner; David A Asch
Journal:  JAMA       Date:  2005-03-09       Impact factor: 56.272

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  5 in total

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Authors:  William Z Chancellor; J Hunter Mehaffey; Jared P Beller; Elizabeth D Krebs; Robert B Hawkins; Kenan Yount; Clifford E Fonner; Alan M Speir; Mohammed A Quader; Jeffrey B Rich; Leora T Yarboro; Nicholas R Teman; Gorav Ailawadi
Journal:  J Thorac Cardiovasc Surg       Date:  2019-01-26       Impact factor: 5.209

2.  Identifying early-measured variables associated with APACHE IVa providing incorrect in-hospital mortality predictions for critical care patients.

Authors:  Shuo Feng; Joel A Dubin
Journal:  Sci Rep       Date:  2021-11-12       Impact factor: 4.379

3.  Investigating Risk Adjustment Methods for Health Care Provider Profiling When Observations are Scarce or Events Rare.

Authors:  Timo B Brakenhoff; Karel Gm Moons; Jolanda Kluin; Rolf Hh Groenwold
Journal:  Health Serv Insights       Date:  2018-07-05

4.  Outlier classification performance of risk adjustment methods when profiling multiple providers.

Authors:  Timo B Brakenhoff; Kit C B Roes; Karel G M Moons; Rolf H H Groenwold
Journal:  BMC Med Res Methodol       Date:  2018-06-15       Impact factor: 4.615

5.  Mortality risk prediction in high-risk patients undergoing coronary artery bypass grafting: Are traditional risk scores accurate?

Authors:  Maxim Goncharov; Omar Asdrúbal Vilca Mejia; Camila Perez de Souza Arthur; Bianca Maria Maglia Orlandi; Alexandre Sousa; Marco Antônio Praça Oliveira; Fernando Antibas Atik; Rodrigo Coelho Segalote; Marcos Gradim Tiveron; Pedro Gabriel Melo de Barros E Silva; Marcelo Arruda Nakazone; Luiz Augusto Ferreira Lisboa; Luís Alberto Oliveira Dallan; Zhe Zheng; Shengshou Hu; Fabio Biscegli Jatene
Journal:  PLoS One       Date:  2021-08-03       Impact factor: 3.240

  5 in total

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