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Record W2166929659 · doi:10.1016/j.ejcts.2011.06.015

Comparison of the EuroSCORE and Cardiac Anesthesia Risk Evaluation (CARE) score for risk-adjusted mortality analysis in cardiac surgery

2011· article· en· W2166929659 on OpenAlexafffund
Diem Tran, Jean‐Yves Dupuis, Thierry Mesana, Marc Ruel, H Nathan

Bibliographic record

VenueEuropean Journal of Cardio-Thoracic Surgery · 2011
Typearticle
Languageen
FieldMedicine
TopicCardiac, Anesthesia and Surgical Outcomes
Canadian institutionsOttawa HospitalUniversity of Ottawa
FundersUniversity of Ottawa
KeywordsEuroSCOREMedicineReceiver operating characteristicConfidence intervalLogistic regressionCardiac surgeryRisk assessmentFramingham Risk ScorePopulationArea under the curveInternal medicineEmergency medicineEnvironmental healthDisease

Abstract

fetched live from OpenAlex

OBJECTIVE: The European System for Cardiac Operative Risk Evaluation (EuroSCORE) and the Cardiac Anesthesia Risk Evaluation (CARE) score are risk indices designed in the mid-1990 s to predict mortality after cardiac surgery. This study assesses their ability to provide risk-adjusted mortality in a contemporary cardiac surgical population. METHODS: The mortality probability was estimated with the additive and logistic EuroSCORE, and CARE score, for 3818 patients undergoing cardiac surgery at one institution between 1 April 2006 and 31 March 2009. Model discrimination was obtained using the area under the receiver operating characteristics (ROC) curve and calibration using the appropriate chi-square goodness-of-fit test. Recalibration of risk models was obtained by logistic calibration, when needed. Calculation of risk-adjusted mortality was performed for the institution and eight surgeons, using each model before and when needed, after recalibration. RESULTS: The area under the ROC curve is 0.72 (95% confidence interval (CI): 0.71-0.74) with the additive EuroSCORE, 0.84 (95% CI: 0.83-0.85) with the logistic EuroSCORE, and 0.79 (95% CI: 0.78-0.81) with the CARE score. The additive and logistic EuroSCORE have poor calibration, predicting a hospital mortality of 6.24% and 7.72%, respectively, versus an observed mortality of 3.25% (P < 0.001). Consequently, the risk-adjusted mortality obtained with those models is significantly underestimated for the institution and all surgeons. The CARE score has good calibration, predicting a mortality of 3.38% (P = 0.50). The hospital risk-adjusted mortality with the recalibrated additive and logistic EuroSCORE and CARE score is 3.24% (95% CI: 3.05-3.43%), 3.25% (95% CI: 3.05-3.44%), and 3.12% (95% CI: 2.94-3.34%), respectively. The individual surgeons' risk-adjusted mortality is similar with the recalibrated EuroSCORE models and CARE score, identifying two surgeons with higher rates than the hospital average mortality. CONCLUSIONS: The original additive and logistic EuroSCORE models significantly overestimate the risk of mortality after cardiac surgery. However, after recalibration both models provide reliable risk-adjusted mortality results. Despite its lower discrimination as compared with the logistic EuroSCORE, the CARE score remains calibrated a decade after its development. It is as robust as the recalibrated additive and logistic EuroSCORE to perform risk-adjusted mortality analysis.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.029
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0290.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.006
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.120
GPT teacher head0.346
Teacher spread0.226 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations35
Published2011
Admission routes2
Has abstractyes

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