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Thorac Cardiovasc Surg 2018; 66(08): 651-660
DOI: 10.1055/s-0037-1608897
Original Cardiovascular
Georg Thieme Verlag KG Stuttgart · New York

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

Samuel Henry Howitt
1   Institute of Cardiovascular Sciences, University of Manchester, Manchester, England, United Kingdom of Great Britain and Northern Ireland
2   Department of Cardiothoracic Anaesthesia and Critical Care, University Hospital of South Manchester NHS Foundation Trust, Manchester, United Kingdom of Great Britain and Northern Ireland
,
Camila Caiado
3   Department of Statistics, Durham University, Durham, United Kingdom of Great Britain and Northern Ireland
,
Charles McCollum
1   Institute of Cardiovascular Sciences, University of Manchester, Manchester, England, United Kingdom of Great Britain and Northern Ireland
,
Michael Goldstein
3   Department of Statistics, Durham University, Durham, United Kingdom of Great Britain and Northern Ireland
,
Ignacio Malagon
2   Department of Cardiothoracic Anaesthesia and Critical Care, University Hospital of South Manchester NHS Foundation Trust, Manchester, United Kingdom of Great Britain and Northern Ireland
,
Rajamiyer Venkateswaran
4   Department of Cardiothoracic Surgery, University Hospital of South Manchester NHS Foundation Trust, Manchester, England, United Kingdom of Great Britain and Northern Ireland
,
Stuart William Grant
1   Institute of Cardiovascular Sciences, University of Manchester, Manchester, England, United Kingdom of Great Britain and Northern Ireland
› Author Affiliations Funding Statement Work performed producing this manuscript was funded by the British Heart Foundation (grant number PG/16/80/32411).
Further Information

Publication History

17 August 2017

19 October 2017

Publication Date:
09 January 2018 (online)

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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.

Keywords

postoperative care - statistics - outcomes

Letter to this article:

Developing a Risk Prediction Model for Intensive Care Unit Mortality after Cardiac SurgeryThorac Cardiovasc Surg 2018; 66(08): e1-e2
DOI: 10.1055/s-0038-1639336

Letter to this article:

Reply to “Developing a Risk Prediction Model for Intensive Care Unit Mortality after Cardiac Surgery”Thorac Cardiovasc Surg 2018; 66(08): e3-e4
DOI: 10.1055/s-0038-1639342

 

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