Literature DB >> 11728352

Development and validation of a clinical prediction rule for major adverse outcomes in coronary bypass grafting.

E B Fortescue1, K Kahn, D W Bates.   

Abstract

In this study, we develop and internally validate a clinical prediction rule for in-hospital major adverse outcomes, defined as death, renal failure, reinfarction, cardiac arrest, cerebrovascular accident, or coma, in patients who underwent coronary artery bypass grafting (CABG). All adult patients (n = 9,498) who underwent a CABG and no other concomitant surgery at 12 academic medical centers from August 1993 to October 1995 were included in the study. We assessed in-hospital major adverse outcomes and their predictors using information on admission, coronary angiography, and postoperative hospital course. Predictor variables were limited to information available before the procedure, and outcome variables were represented only by events that occurred postoperatively. We developed and internally validated a clinical prediction rule for any major adverse outcome after CABG. The rule's ability to discriminate outcomes and its calibration were assessed using receiver-operating characteristic analysis and the Hosmer-Lemeshow goodness-of-fit statistic, respectively. A major adverse outcome occurred in 6.5% of patients in the derivation set and 7.2% in the validation set. Death occurred in 2.5% of patients in the derivation set and 2.2% in the validation set. Sixteen variables were independently correlated with major adverse outcomes, with the risk score value attributed to each risk factor ranging from 2 to 12 points. The rule stratified patients into 6 levels of risk based on the total risk score. The spread in probability between the lowest and highest risk groups of having a major adverse outcome was 1.7% to 32.3% in the derivation set and 2.2% to 22.3% in the validation set. The prediction model performed well in both outcome discrimination and calibration. Thus, this clinical prediction rule allows accurate stratification of potential CABG candidates before surgery according to the risk of experiencing a major adverse outcome postoperatively.

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Year:  2001        PMID: 11728352     DOI: 10.1016/s0002-9149(01)02086-0

Source DB:  PubMed          Journal:  Am J Cardiol        ISSN: 0002-9149            Impact factor:   2.778


  15 in total

1.  Stratification of adverse outcomes by preoperative risk factors in coronary artery bypass graft patients: an artificial neural network prediction model.

Authors:  Chee-Fah Chong; Yu-Chuan Li; Tzong-Luen Wang; Hang Chang
Journal:  AMIA Annu Symp Proc       Date:  2003

2.  David Westfall Bates, MD: a conversation with the editor on improving patient safety, quality of care, and outcomes by using information technology. Interview by William Clifford Roberts.

Authors:  David Westfall Bates
Journal:  Proc (Bayl Univ Med Cent)       Date:  2005-04

3.  Impact of admission serum glucose level on in-hospital outcomes following coronary artery bypass grafting surgery.

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Journal:  Can J Cardiol       Date:  2010-03       Impact factor: 5.223

4.  Prayer and reverence in naturalistic, aesthetic, and socio-moral contexts predicted fewer complications following coronary artery bypass.

Authors:  Amy L Ai; Paul Wink; Terrence N Tice; Steven F Bolling; Marshall Shearer
Journal:  J Behav Med       Date:  2009-10-25

5.  Transient post-operative atrial fibrillation predicts short and long term adverse events following CABG.

Authors:  Femi Philip; Matthew Becker; John Galla; Eugene Blackstone; Samir R Kapadia
Journal:  Cardiovasc Diagn Ther       Date:  2014-10

6.  Predictive Ability of European Heart Surgery Risk Assessment System II (EuroSCORE II) and the Society of Thoracic Surgeons (STS) Score for in-Hospital and Medium-Term Mortality of Patients Undergoing Coronary Artery Bypass Grafting.

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Journal:  Int J Gen Med       Date:  2021-11-19

7.  Are coronary angiograms of value in the risk stratification of patients undergoing coronary artery bypass surgery?

Authors:  David R Lawrence; Rajael Somaskanthan; Matthew J Barnard; Miles Curtis; Bruce E Keogh
Journal:  Ann R Coll Surg Engl       Date:  2009-04-02       Impact factor: 1.891

8.  A multivariate Bayesian model for assessing morbidity after coronary artery surgery.

Authors:  Bonizella Biagioli; Sabino Scolletta; Gabriele Cevenini; Emanuela Barbini; Pierpaolo Giomarelli; Paolo Barbini
Journal:  Crit Care       Date:  2006-07-17       Impact factor: 9.097

9.  Predictors of total morbidity burden on days 3, 5 and 8 after cardiac surgery.

Authors:  Julie Sanders; Jackie Cooper; Michael G Mythen; Hugh E Montgomery
Journal:  Perioper Med (Lond)       Date:  2017-02-14

10.  A comparative analysis of predictive models of morbidity in intensive care unit after cardiac surgery - part II: an illustrative example.

Authors:  Gabriele Cevenini; Emanuela Barbini; Sabino Scolletta; Bonizella Biagioli; Pierpaolo Giomarelli; Paolo Barbini
Journal:  BMC Med Inform Decis Mak       Date:  2007-11-22       Impact factor: 2.796

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