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  • Review Article
  • Published:

Risk assessment methods for cardiac surgery and intervention

Nature Reviews Cardiology volume 11, pages 704–714 (2014)Cite this article

Subjects

Key Points

  • Risk models control for heterogeneous patient demographics, comorbidities, and disease-severity profiles within and between institutions, thereby allowing a fair comparison of operative outcomes

  • Risk-prediction models rely on the quality of the databases from which they are developed; incomplete and inaccurate source data might result in overestimation or underestimation of surgical risk

  • Results of trials to compare surgical aortic valve replacement and transcatheter aortic valve implantation (TAVI) suggest that 3-month mortality or survival might be an important end point to model

  • Performance of cardiac risk models might be suboptimal in patients at high surgical risk, and must, therefore, be used with caution when stratifying patients for TAVI

  • New variables, such as mediastinal radiation, liver failure, and frailty, might enhance prediction in high-risk patients, and optimize model utility in selecting patients for TAVI

  • Risk-prediction models and scores might be useful decision-making tools in cardiac surgery and intervention, but are not intended as substitutes for sound clinical judgement

Abstract

Surgical risk models estimate operative outcomes while controlling for heterogeneity in 'case mix' within and between institutions. In cardiac surgery, risk models are used for patient counselling, surgical decision-making, clinical research, quality assurance and improvement, and financial reimbursement. Importantly, risk models are only as good as the databases from which they are derived; physicians and investigators should, therefore, be aware of shortcomings of clinical and administrative databases used for modelling risk estimates. The most frequently modelled outcome in cardiac surgery is 30-day mortality. However, results of randomized trials to compare conventional surgery versus transcatheter aortic valve implantation (TAVI) indicate attrition of surgical patients at 2–4 months postoperatively, suggesting that 3-month survival or mortality might be an appropriate procedural end point worth modelling. Risk models are increasingly used to identify patients who might be better-suited for TAVI. However, the appropriateness of available statistical models in this application is controversial, particularly given the tendency of risk models to misestimate operative mortality in high-risk patient subsets. Incorporation of new risk factors (such as previous mediastinal radiation, liver failure, and frailty) in future surgical or interventional risk-prediction tools might enhance model performance, and thereby optimize patient selection for TAVI.

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Figure 1: The effect of a congenital cardiac surgery database with errors in operative outcome coding (survival versus death) on estimates of operative mortality.
Figure 2: Assessment of STS score, logistic EuroSCORE, and EuroSCORE II model calibration for predicting in-hospital mortality.

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Authors and Affiliations

  1. Division of Cardiovascular Surgery, Mayo Clinic College of Medicine, 200 First Street SW, Rochester, 55905, MN, USA

    Nassir M. Thalji, Rakesh M. Suri, Kevin L. Greason & Hartzell V. Schaff

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  1. Nassir M. Thalji
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  2. Rakesh M. Suri
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N.M.T. and H.V.S. researched data for the article and wrote the manuscript. All the authors made substantial contributions to discussion of the content, reviewed, and edited the manuscript before submission.

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Correspondence to Hartzell V. Schaff.

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Competing interests

The Mayo Clinic Division of Cardiovascular Surgery has research relationships with Edwards Lifesciences, Medtronic, and St. Jude Medical. R.M.S. is principal investigator for the FDA trial of the Sorin PERCEVAL valve, a member of the steering committee for the St. Jude PORTICO trial, a member of the clinical selection committee for the Abbott COAPT trial, and Mayo Clinic principal investigator for the Edwards Lifesciences PARTNER II trial. N.M.T., K.L.G., and H.V.S. declare no competing interests.

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Thalji, N., Suri, R., Greason, K. et al. Risk assessment methods for cardiac surgery and intervention. Nat Rev Cardiol 11, 704–714 (2014). https://doi.org/10.1038/nrcardio.2014.136

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