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Validation of Three Postoperative Risk Prediction Models for Intensive Care Unit Mortality after Cardiac Surgery

Grant, Stuart; Venkateswaran, Rajamiyer; Malagon, Ignacio; Goldstein, Michael; McCollum, Charles; Caiado, Camila; Howitt, Samuel

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Authors

Stuart Grant

Rajamiyer Venkateswaran

Ignacio Malagon

Michael Goldstein

Charles McCollum

Samuel Howitt



Abstract

Background: Several cardiac surgery risk prediction models based on postoperative data have been developed. However, unlike preoperative cardiac surgery risk prediction models, postoperative models are rarely externally validated or utilized by clinicians. The objective of this study was to externally validate three postoperative risk prediction models for intensive care unit (ICU) mortality after cardiac surgery. Methods: The logistic Cardiac Surgery Scores (logCASUS), Rapid Clinical Evaluation (RACE), and Sequential Organ Failure Assessment (SOFA) scores were calculated over the first 7 postoperative days for consecutive adult cardiac surgery patients between January 2013 and May 2015. Model discrimination was assessed using receiver operating characteristic curve analyses. Calibration was assessed using the Hosmer–Lemeshow (HL) test, calibration plots, and observed to expected ratios. Recalibration of the models was performed. Results: A total of 2255 patients were included with an ICU mortality rate of 1.8%. Discrimination for all three models on each postoperative day was good with areas under the receiver operating characteristic curve of >0.8. Generally, RACE and logCASUS had better discrimination than SOFA. Calibration of the RACE score was better than logCASUS, but ratios of observed to expected mortality for both were generally <0.65. Locally recalibrated SOFA, logCASUS and RACE models all performed well. Conclusion: All three models demonstrated good discrimination for the first 7 days after cardiac surgery. After recalibration, logCASUS and RACE scores appear to be most useful for daily risk prediction after cardiac surgery. If appropriately calibrated, postoperative cardiac surgery risk prediction models have the potential to be useful tools after cardiac surgery.

Citation

Grant, S., Venkateswaran, R., Malagon, I., Goldstein, M., McCollum, C., Caiado, C., & Howitt, S. (2018). Validation of Three Postoperative Risk Prediction Models for Intensive Care Unit Mortality after Cardiac Surgery. Thoracic and Cardiovascular Surgeon, 66(8), 651-660. https://doi.org/10.1055/s-0037-1608897

Journal Article Type Article
Acceptance Date Oct 19, 2017
Online Publication Date Jan 9, 2018
Publication Date Jan 9, 2018
Deposit Date Aug 7, 2018
Publicly Available Date Mar 28, 2024
Journal Thoracic and Cardiovascular Surgeon
Print ISSN 0171-6425
Electronic ISSN 1439-1902
Publisher Thieme Gruppe
Peer Reviewed Peer Reviewed
Volume 66
Issue 8
Pages 651-660
DOI https://doi.org/10.1055/s-0037-1608897
Related Public URLs https://www.research.manchester.ac.uk/portal/en/publications/validation-of-three-postoperative-risk-prediction-models-for-intensive-care-unit-mortality-after-cardiac-surgery(17364f7b-a0bc-42f5-bb9d-064ca5b7ffc6).html

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