Cardiovascular Risk Assessment in Cardiac Surgery

Purpose of Risk Assessment Before Cardiac Surgery

In 1986, publication of poorly adjusted institutional mortality data for coronary bypass surgery by the federal government spurred the creation of the Society of Thoracic Surgeons (STS) database to create a fair and unbiased registry for public reporting. Risk modeling has become an integral instrument in the current health-care environment driven by value-based care. Furthermore, risk adjustment allows cardiac surgery programs, hospitals, and regions to be benchmarked in mortality and morbidity performance reflecting variations in the patient population better than unadjusted comparisons. Several studies have shown that dissemination of risk-adjusted outcomes has resulted in improved mortality and morbidity within a health-care system. Appropriate risk adjustment has become increasingly emphasized, given pay-for-performance reimbursement programs. Finally, appropriate patient counseling is reliant on the integrity of preoperative risk modeling. Nonetheless, risk calculation mandates the recognition of strengths and limitations of a designated model in order to prevent misinterpretation of derived endpoints.

Outcome Measurement in Cardiac Surgery

With major improvements in myocardial protection and rapid dissemination of cardiac surgery in the late 1970s, institutional, regional, and national databases facilitated outcome reporting and risk prediction. Though some efforts were motivated in response to government reports thought to inaccurately depict operative outcomes, the majority of efforts were scholarly and voluntary. Although many efforts used administrative data, prospectively collected clinical data gathered by objective clinicians has become the mainstay of national databases and quality outcome initiatives.

Development of Perioperative Risk Assessment for Cardiac Surgery

Regardless of collection method, accuracy of data elements is critical to the validity of any prediction model. Moreover, responsible stewardship of statistical methodology and choice of sound statistical techniques enhance the ultimate utility of risk models. Among the various perioperative risk calculators, patient characteristics, operation types (all cardiac surgery, coronary artery bypass grafting [CABG], valve, CABG + valve), institutional structure and location, and statistical methods impact the predictive capability. Inclusion of emergent and urgent operations will further bias the results of risk assessment tools. Models are often forced to lump heterogeneous operations (such as valve repair/replacement) in order to achieve improved discriminatory power of relatively rare events such as death. Regardless of these considerations, risk scores based on retrospective observational data are inherently biased because of the inevitable impact of the surgeon’s selection bias. Furthermore, model development requires balanced inclusion of patient and hospital characteristics.

Features of a risk assessment scale include discrimination and ability for calibration. Discrimination can be defined as a model’s ability to distinguish between patients suffering from a specific adverse event such as mortality and/or major morbidity and those who do not. Most models measure discriminatory power using the C-statistic obtained from the area under the receiver operating characteristic curve (AUC). Calibration of models has also been described as a crucial part of accurate model development. Without calibration, a risk calculator cannot be expected to provide accurate predictions of patient risk. Historic techniques for assessing model calibration have included the Hosmer-Lemeshow goodness of fit. More recently, many have proposed replacement of Hosmer-Lemeshow model calibration with risk-adjusted mortality using observed/predicted ratios. Bhatti and colleagues have also suggested performing chi-square tests to compare the observed to expected mortality as a means to better fit the model to actual data. While availability of a validation cohort to perform model discrimination and calibration is critical, several currently available risk assessment scores, including the European System for Cardiac Operative Risk Evaluation (EuroSCORE), were calibrated using only the derivation dataset.

Currently, most risk algorithms are based on logistic regression analysis with a priori assumptions of linear relationships. Current risk prediction can be improved by using complex techniques such as an artificial neural network, which has the advantage of the capacity to model complex, nonlinear relationships and is relatively robust and tolerant of missing data.

Established Risk Assessment Models in Cardiac Surgery

Over the last 30 years, over 20 cardiac surgery risk stratification models have been devised ( Table 6.1 ). The characteristics included vary for each unique patient population; the most commonly used models are compared in Table 6.2 . Discussed in more detail in this section are the Parsonnet; EuroSCORE; age, creatinine, and ejection fraction (ACEF); and STS mortality and morbidity scores.