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Thorac Cardiovasc Surg
DOI: 10.1055/s-0043-1760747
DOI: 10.1055/s-0043-1760747
Review Article
Risk Prediction Models for Long-Term Survival after Cardiac Surgery: A Systematic Review
Funding None.
Abstract
Background The reporting of alternative postoperative measures of quality after cardiac surgery is becoming increasingly important as in-hospital mortality rates continue to decline. This study aims to systematically review and assess risk models designed to predict long-term outcomes after cardiac surgery.
Methods The MEDLINE and Embase databases were searched for articles published between 1990 and 2020. Studies developing or validating risk prediction models for long-term outcomes after cardiac surgery were included. Data were extracted using checklists for critical appraisal and systematic review of prediction modeling studies.
Results Eleven studies were identified for inclusion in the review, of which nine studies described the development of long-term risk prediction models after cardiac surgery and two were external validation studies. A total of 70 predictors were included across the nine models. The most frequently used predictors were age (n = 9), peripheral vascular disease (n = 8), renal disease (n = 8), and pulmonary disease (n = 8). Despite all models demonstrating acceptable performance on internal validation, only two models underwent external validation, both of which performed poorly.
Conclusion Nine risk prediction models predicting long-term mortality after cardiac surgery have been identified in this review. Statistical issues with model development, limited inclusion of outcomes beyond 5 years of follow-up, and a lack of external validation studies means that none of the models identified can be recommended for use in contemporary cardiac surgery. Further work is needed either to successfully externally validate existing models or to develop new models. Newly developed models should aim to use standardized long-term specific reproducible outcome measures.
Publication History
Received: 23 June 2022
Accepted: 24 November 2022
Article published online:
07 February 2023
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