Literature DB >> 25695571

The impact of anesthesiologists on coronary artery bypass graft surgery outcomes.

Laurent G Glance1, Arthur L Kellermann, Edward L Hannan, Lee A Fleisher, Michael P Eaton, Richard P Dutton, Stewart J Lustik, Yue Li, Andrew W Dick.   

Abstract

BACKGROUND: One of every 150 hospitalized patients experiences a lethal adverse event; nearly half of these events involves surgical patients. Although variations in surgeon performance and quality have been reported in the literature, less is known about the influence of anesthesiologists on outcomes after major surgery. Our goal of this study was to determine whether there is significant variation in outcomes between anesthesiologists after controlling for patient case mix and hospital quality.
METHODS: Using clinical data from the New York State Cardiac Surgery Reporting System, we conducted a retrospective observational study of 7920 patients undergoing isolated coronary artery bypass graft surgery. Multivariable logistic regression modeling was used to examine the variation in death or major complications (Q-wave myocardial infarction, renal failure, stroke) across anesthesiologists, controlling for patient demographics, severity of disease, comorbidities, and hospital quality.
RESULTS: Anesthesiologist performance was quantified using fixed-effects modeling. The variability across anesthesiologists was highly significant (P < 0.001). Patients managed by low-performance anesthesiologists (corresponding to the 25th percentile of the distribution of anesthesiologist risk-adjusted outcomes) experienced nearly twice the rate of death or serious complications (adjusted rate 3.33%; 95% confidence interval [CI], 3.09%-3.58%) as patients managed by high-performance anesthesiologists (corresponding to the 75th percentile) (adjusted rate 1.82%; 95% CI, 1.58%-2.10%). This performance gap was observed across all patient risk groups.
CONCLUSIONS: The rate of death or major complications among patients undergoing coronary artery bypass graft surgery varies markedly across anesthesiologists. These findings suggest that there may be opportunities to improve perioperative management to improve outcomes among high-risk surgical patients.

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Year:  2015        PMID: 25695571     DOI: 10.1213/ANE.0000000000000522

Source DB:  PubMed          Journal:  Anesth Analg        ISSN: 0003-2999            Impact factor:   5.108


  16 in total

1.  Delirium Prediction using Machine Learning Models on Preoperative Electronic Health Records Data.

Authors:  Anis Davoudi; Ashkan Ebadi; Parisa Rashidi; Tazcan Ozrazgat-Baslanti; Azra Bihorac; Alberto C Bursian
Journal:  Proc IEEE Int Symp Bioinformatics Bioeng       Date:  2018-01-11

2.  Improved survival in critically ill patients: are large RCTs more useful than personalized medicine? Yes.

Authors:  Rinaldo Bellomo; Giovanni Landoni; Paul Young
Journal:  Intensive Care Med       Date:  2016-09-12       Impact factor: 17.440

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Authors:  Shamsuddin Akhtar; Joseph Heng; Feng Dai; Robert B Schonberger; Mathew M Burg
Journal:  Drugs Aging       Date:  2016-10       Impact factor: 3.923

Review 4.  OR Management and Metrics: How It All Fits Together for the Healthcare System.

Authors:  Steven D Boggs; Derek W Tan; Caleb L Watkins; Mitchell H Tsai
Journal:  J Med Syst       Date:  2019-04-22       Impact factor: 4.460

5.  In Defense of Science.

Authors:  David Sidebotham
Journal:  J Extra Corpor Technol       Date:  2021-12

6.  Antiemetic Prophylaxis as a Marker of Health Care Disparities in the National Anesthesia Clinical Outcomes Registry.

Authors:  Michael H Andreae; Jonah S Gabry; Ben Goodrich; Robert S White; Charles Hall
Journal:  Anesth Analg       Date:  2018-02       Impact factor: 5.108

7.  Apophenia and anesthesia: how we sometimes change our practice prematurely.

Authors:  Neil A Hanson; Matthew B Lavallee; Robert H Thiele
Journal:  Can J Anaesth       Date:  2021-05-07       Impact factor: 6.713

8.  Does it matter who the anesthesiologist is for my heart surgery?

Authors:  Bryan G Maxwell; Charles W Hogue; Peter J Pronovost
Journal:  Anesth Analg       Date:  2015-03       Impact factor: 6.627

9.  Lung-Centered Open Heart Surgery: A Call for a Paradigm Change.

Authors:  Edward Gologorsky; Angela Gologorsky; Tomas Antonio Salerno
Journal:  Front Cardiovasc Med       Date:  2016-05-12

10.  Application of Machine Learning Techniques to High-Dimensional Clinical Data to Forecast Postoperative Complications.

Authors:  Paul Thottakkara; Tezcan Ozrazgat-Baslanti; Bradley B Hupf; Parisa Rashidi; Panos Pardalos; Petar Momcilovic; Azra Bihorac
Journal:  PLoS One       Date:  2016-05-27       Impact factor: 3.240

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