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Risk-Prediction Model for Transfusion of Erythrocyte Concentrate During Extracorporeal Circulation in Coronary Surgery.

Authors
  • Paiva, Patrícia Pinheiro1, 2
  • Leite, Filipe Miguel3
  • Antunes, Pedro E3
  • Antunes, Manuel J4
  • 1 Department of Clinical Pharmacology, Hospital and University Centre of Coimbra, Coimbra, Portugal. , (Portugal)
  • 2 Department of Clinical Pharmacology, University of Coimbra Faculty of Medicine, Coimbra, Portugal. , (Portugal)
  • 3 Department of Cardiothoracic Surgery, Hospital and University Centre of Coimbra, Coimbra, Portugal. , (Portugal)
  • 4 Department of Cardiothoracic Surgery, University of Coimbra Faculty of Medicine, Coimbra, Portugal. , (Portugal)
Type
Published Article
Journal
Brazilian journal of cardiovascular surgery
Publication Date
Jun 01, 2021
Volume
36
Issue
3
Pages
323–330
Identifiers
DOI: 10.21470/1678-9741-2020-0322
PMID: 33656832
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Our objective was to identify preoperative risk factors and to develop and validate a risk-prediction model for the need for blood (erythrocyte concentrate [EC]) transfusion during extracorporeal circulation (ECC) in patients undergoing coronary artery bypass grafting (CABG). This is a retrospective observational study including 530 consecutive patients who underwent isolated on-pump CABG at our Centre over a full two-year period. The risk model was developed and validated by logistic regression and bootstrap analysis. Discrimination and calibration were assessed using the area under the receiver operating characteristic curve (AUC) and the Hosmer-Lemeshow (H-L) test, respectively. EC transfusion during ECC was required in 91 patients (17.2%). Of these, the majority were transfused with one (54.9%) or two (41.8%) EC units. The final model covariates (reported as odds ratios; 95% confidence interval) were age (1.07; 1.02-1.13), glomerular filtration rate (0.98; 0.96-1.00), body surface area (0.95; 0.92-0.98), peripheral vascular disease (3.03; 1.01-9.05), cerebrovascular disease (4.58; 1.29-16.18), and hematocrit (0.55; 0.48-0.63). The risk model developed has an excellent discriminatory power (AUC: 0,963). The results of the H-L test showed that the model predicts accurately both on average and across the ranges of deciles of risk. A risk-prediction model for EC transfusion during ECC was developed, which performed adequately in terms of discrimination, calibration, and stability over a wide spectrum of risk. It can be used as an instrument to provide accurate information about the need for EC transfusion during ECC, and as a valuable adjunct for local improvement of clinical practice. Key Findings: Risk factors with the greatest prediction for EC transfusion. Take-Home Message: The implementation of this model would be an important step in optimizing and improving the quality of surgery.

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