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Development and Validation of Elective and Nonelective Risk Prediction Models for In-Hospital Mortality in Proximal
Mohamad Bashir1, Matthew A Shaw2, Anthony D Grayson2
1Department of Health Economics, University of Liverpool, Liverpool, United Kingdom.
The Annals of Thoracic Surgery
|January 30, 2016
Summary
Developing reliable risk prediction models for proximal aortic operations is crucial for patient choice and outcome adjustment. These models identify key predictors of in-hospital mortality, improving risk assessment for complex aortic surgeries.
Area of Science:
- Cardiovascular Surgery
- Medical Informatics
- Public Health
Background:
- Reliable predictive tools are needed for patient choice and risk adjustment in aortic operations.
- The study aimed to develop a risk prediction model for in-hospital mortality after proximal aortic surgery.
Purpose of the Study:
- To develop and validate risk prediction models for in-hospital mortality in patients undergoing proximal aortic operations.
- To facilitate informed patient choice and improve risk adjustment of surgical outcomes.
Main Methods:
- Analysis of 8641 UK patients undergoing proximal aortic operations (April 2007-March 2013).
- Utilized multivariable logistic regression to identify independent predictors of in-hospital mortality.
- Assessed model calibration and discrimination using the Hosmer-Lemeshow test and AUROC curves.
Main Results:
- In-hospital mortality rates were 4.6% (elective) and 16.5% (nonelective).
- Key predictors for elective surgery included previous cardiac operation and ejection fraction >30%.
- For nonelective surgery, salvage operations and previous cardiac operation were significant predictors. AUROC values were 0.805 (elective) and 0.761 (nonelective).
Conclusions:
- Proposed risk models can enhance patient awareness of surgical risks.
- The models aim to improve risk adjustment for case-mix in aortic operations.
- These tools support better patient decision-making and outcome evaluation.