Literature DB >> 19854393

Predicted risk of mortality models: surgeons need to understand limitations of the University HealthSystem Consortium models.

Benjamin D Kozower1, Gorav Ailawadi, David R Jones, Robert D Pates, Christine L Lau, Irving L Kron, George J Stukenborg.   

Abstract

BACKGROUND: The University HealthSystem Consortium (UHC) mortality risk adjustment models are increasingly being used as benchmarks for quality assessment. But these administrative database models may include postoperative complications in their adjustments for preoperative risk. The purpose of this study was to compare the performance of the UHC with the Society of Thoracic Surgeons (STS) risk-adjusted mortality models for adult cardiac surgery and evaluate the contribution of postoperative complications on model performance. STUDY
DESIGN: We identified adult cardiac surgery patients with mortality risk estimates in both the UHC and Society of Thoracic Surgeons databases. We compared the predictive performance and calibration of estimates from both models. We then reestimated both models using only patients without any postoperative complications to determine the relative contribution of adjustments for postoperative events on model performance.
RESULTS: In the study population of 2,171 patients, the UHC model explained more variability (27% versus 13%, p < 0.001) and achieved better discrimination (C statistic = 0.88 versus 0.81, p < 0.001). But when applied in the population of patients without complications, the UHC model performance declined severely. The C statistic decreased from 0.88 to 0.49, a level of discrimination equivalent to random chance. The discrimination of the Society of Thoracic Surgeons model was unchanged (C statistic of 0.79 versus 0.81).
CONCLUSIONS: Although the UHC model demonstrated better performance in the total study population, this difference in performance reflects adjustments for conditions that are postoperative complications. The current UHC models should not be used for quality benchmarks.

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Year:  2009        PMID: 19854393      PMCID: PMC3627222          DOI: 10.1016/j.jamcollsurg.2009.08.008

Source DB:  PubMed          Journal:  J Am Coll Surg        ISSN: 1072-7515            Impact factor:   6.113


  13 in total

1.  Adult open heart surgery in New York State. An analysis of risk factors and hospital mortality rates.

Authors:  E L Hannan; H Kilburn; J F O'Donnell; G Lukacik; E P Shields
Journal:  JAMA       Date:  1990-12-05       Impact factor: 56.272

2.  Modifying DRG-PPS to include only diagnoses present on admission: financial implications and challenges.

Authors:  Chunliu Zhan; Anne Elixhauser; Bernard Friedman; Robert Houchens; Yen-Pin Chiang
Journal:  Med Care       Date:  2007-04       Impact factor: 2.983

3.  Using Medicare claims data to assess provider quality for CABG surgery: does it work well enough?

Authors:  E L Hannan; M J Racz; J G Jollis; E D Peterson
Journal:  Health Serv Res       Date:  1997-02       Impact factor: 3.402

4.  Risk-adjusting acute myocardial infarction mortality: are APR-DRGs the right tool?

Authors:  P S Romano; B K Chan
Journal:  Health Serv Res       Date:  2000-03       Impact factor: 3.402

5.  The need for accurate risk-adjusted measures of outcome in surgery. Lessons learned through coronary artery bypass.

Authors:  B P Griffith; B G Hattler; R L Hardesty; R L Kormos; S M Pham; H T Bahnson
Journal:  Ann Surg       Date:  1995-10       Impact factor: 12.969

6.  Benefits and hazards of reporting medical outcomes publicly.

Authors:  M R Chassin; E L Hannan; B A DeBuono
Journal:  N Engl J Med       Date:  1996-02-08       Impact factor: 91.245

7.  The meaning and use of the area under a receiver operating characteristic (ROC) curve.

Authors:  J A Hanley; B J McNeil
Journal:  Radiology       Date:  1982-04       Impact factor: 11.105

8.  Risk adjustment methods can affect perceptions of outcomes.

Authors:  L I Iezzoni; M Shwartz; A S Ash; Y Mackiernan; E K Hotchkin
Journal:  Am J Med Qual       Date:  1994       Impact factor: 1.852

9.  Predicting who dies depends on how severity is measured: implications for evaluating patient outcomes.

Authors:  L I Iezzoni; A S Ash; M Shwartz; J Daley; J S Hughes; Y D Mackiernan
Journal:  Ann Intern Med       Date:  1995-11-15       Impact factor: 25.391

10.  Comparison of risk adjustment methodologies in surgical quality improvement.

Authors:  Steven M Steinberg; Michael R Popa; Judith A Michalek; Matthew J Bethel; E Christopher Ellison
Journal:  Surgery       Date:  2008-10       Impact factor: 3.982

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  5 in total

1.  Comparison of cardiac surgery mortality reports using administrative and clinical data sources: a prospective cohort study.

Authors:  Cedric Manlhiot; Vivek Rao; Barry Rubin; Douglas S Lee
Journal:  CMAJ Open       Date:  2018-09-04

2.  Not just full of hot air: hyperbaric oxygen therapy increases survival in cases of necrotizing soft tissue infections.

Authors:  Joshua J Shaw; Charles Psoinos; Timothy A Emhoff; Shimul A Shah; Heena P Santry
Journal:  Surg Infect (Larchmt)       Date:  2014-05-01       Impact factor: 2.150

Review 3.  Risk assessment methods for cardiac surgery and intervention.

Authors:  Nassir M Thalji; Rakesh M Suri; Kevin L Greason; Hartzell V Schaff
Journal:  Nat Rev Cardiol       Date:  2014-09-23       Impact factor: 32.419

4.  A comparison of administrative and physiologic predictive models in determining risk adjusted mortality rates in critically ill patients.

Authors:  Kyle B Enfield; Katherine Schafer; Mike Zlupko; Vitaly Herasevich; Wendy M Novicoff; Ognjen Gajic; Tracey R Hoke; Jonathon D Truwit
Journal:  PLoS One       Date:  2012-02-24       Impact factor: 3.240

5.  Predicting mortality in the intensive care unit: a comparison of the University Health Consortium expected probability of mortality and the Mortality Prediction Model III.

Authors:  Angela K M Lipshutz; John R Feiner; Barbara Grimes; Michael A Gropper
Journal:  J Intensive Care       Date:  2016-05-23
  5 in total

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