Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 305

The DOI matches its Crossref record.

Identification of preoperative variables needed for risk adjustment of short-term mortality after coronary artery bypass graft surgery

Open access
Jones, Robert H., Hannan, Edward L., Hammermeister, Karl E., DeLong, Elizabeth R., O'Connor, Gerald T., Luepker, Russell V., Parsonnet, Victor, Pryor, David B.·1996·Journal of the American College of Cardiology

Abstract

OBJECTIVES: The purpose of this consensus effort was to define and prioritize the importance of a set of clinical variables useful for monitoring and improving the short-term mortality of patients undergoing coronary artery bypass graft surgery (CABG). BACKGROUND: Despite widespread use of data bases to monitor the outcome of patients undergoing CABG, no consistent set of clinical variables has been defined for risk adjustment of observed outcomes for baseline differences in disease severity among patients. METHODS: Experts with a background in epidemiology, biostatistics and clinical care with an interest in assessing outcomes of CABG derived from previous work with professional societies, government or academic institutions volunteered to participate in this unsponsored consensus process. Two meetings of this ad hoc working group were required to define and prioritize clinical variables into core, level 1 or level 2 groupings to reflect their importance for relating to short-term mortality after CABG. Definitions of these 44 variables were simple and specific to enhance objectivity of the 7 core, 13 level 1 and 24 level 2 variables. Core and level 1 variables were evaluated using data from five existing data bases, and core variables only were examined in an additional two data bases to confirm the consensus opinion of the relative prognostic power of each variable. RESULTS: Multivariable logistic regression models of the seven core variables showed all to be predictive of bypass surgery mortality in some of the seven existing data sets. Variables relating to acuteness, age and previous operation proved to be the most important in all data sets tested. Variables describing coronary anatomy appeared to be least significant. Models including both the 7 core and 13 level 1 variables in five of the seven data sets showed the core variables to reflect 45% to 83% of the predictive information. However, some level 1 variables were stronger than some core variables in some data sets. CONCLUSIONS: A relatively small number of clinical variables provide a large amount of prognostic information in patients undergoing CABG.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/s0735-1097(96)00359-2

Cites

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cited by

See all 305

Metadata

Authors
Jones, R. H., Hannan, E. L., Hammermeister, K. E., DeLong, E. R., O'Connor, G. T., Luepker, R. V., Parsonnet, V., & Pryor, D. B.
Venue
Journal of the American College of Cardiology
Publisher
Elsevier BV
Published
1996-11
Volume
28
Issue
6
Pages
1478-1487
ISSN
0735-1097 (ISSN-L)
OpenAlex
W1992190819
Cited by
305
References
0
FWCI
10.84
Type
journal article
Language
en
Open access
hybrid

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Identification of preoperative variables needed for risk adjustment of short-term mortality after coronary artery bypass graft surgery. https://app.cochat.ai/papers/10.1016/s0735-1097(96)00359-2
The paper
Jones, R. H., Hannan, E. L., Hammermeister, K. E., DeLong, E. R., O'Connor, G. T., Luepker, R. V., Parsonnet, V., & Pryor, D. B. (1996). Identification of preoperative variables needed for risk adjustment of short-term mortality after coronary artery bypass graft surgery. Journal of the American College of Cardiology. https://doi.org/10.1016/s0735-1097(96)00359-2

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.