Cardiovascular Risk Assessment in Cardiac Surgery


Despite significant advancements in surgical and perioperative technology, cardiac surgery remains associated with significant risk of morbidity and mortality. Over the last 30 years, at least 20 risk models have been developed to account for variations in patient comorbidities, operations subtypes, and statistical techniques. The utility of risk models is intimately related to the characteristics of the population used to generate them. Therefore, currently accepted and prominent risk calculators based on data from the 1990s and early 2000s require further attention to ensure accurately informed patient and provider decision-making. Furthermore, risk assessment in cardiac surgery has evolved from a primary focus on mortality to other measures of perioperative morbidity, approaching the currently unattainable standard of assessment quality-of-life measures and patient satisfaction with high-risk surgical interventions.

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.

Table 6.1
A summary of cardiac surgery risk stratification models.
With permission from Mayer EK, Sevdalis N, Rout S, et al: Surgical checklist implementation project. Ann Surg 2016;263(1):58-63. doi:10.1097/SLA.0000000000001185; and Afilalo J, Eisenberg MJ, Morin JF, et al: Gait speed as an incremental predictor of mortality and major morbidity in elderly patients undergoing cardiac surgery. J Am Coll Cardiol 2010;56(20):1668-1676. doi:10.1016/J.JACC.2010.06.039.
Model Region Years of data collection Year of publication Number of patients (centers) Risk variables
Amphiascore The Netherlands 1997–2001 2003 7282 (1) 8
Cabdeal Finland 1990–1991 1996 386 (1) 7
Cleveland Clinic USA 1986–1988 1992 5051 (1) 13
EuroSCORE (additive) Europe 1995 1999 13,302 (128) 17
EuroSCORE (logistic) Europe 1995 1999 13,302 (128) 17
French score France 1993 1995 7181 (42) 13
Magovern USA 1991–1992 1996 1567 (1) 18
NYS USA 1998 2001 18,814 (33) 14
NNE USA 1996–1998 1999 7290 (N/A) 8
Ontario Canada 1991–1993 1995 6213 (9) 6
Parsonnet USA 1982–1987 1989 3500 (1) 16
Parsonnet (modified) France 1992–1993 1997 6649 (42) 41
Pons Spain 1994 1997 1309 (7) 11
STS risk calculator a

  • isolated CABG

  • valve procedures

  • CABG and valve

USA 2002–2006 2007 774,881 (819)
109,759
101,661
49
50
50
Toronto Canada 1993–1996 1999 7491 (2) 9
Toronto (modified) Canada 1996–1997 2000 1904 (1) 9
Tremblay Canada 1989–1990 1993 2029 (1) 8
Tuman USA N/A 1992 3156 (1) 10
UK national score UK 1995–1996 1998 1774 (2) 19
Veterans Affairs USA 1987–1990 1993 12,715 (43) 10
CABG, Coronary artery bypass grafting surgery; EuroSCORE, European System for Cardiac Operative Risk Evaluation; NNE, Northern New England; NYS, New York State; STS, Society of Thoracic Surgeons.

a The STS risk calculator of seven risk prediction models in three main categories, namely isolated CABG, valve procedures, and combined CABG and valve procedures. Data represented for the STS risk calculator reflect the number of patients and risk variable captured in the database used for the latest models developed (version 2.61).

Table 6.2
A comparison of common cardiac surgery risk calculators.
Preoperative risk factor EuroSCORE STS Initial parsonnet Cleveland clinic NNE (CABG only) Complex bayes (CABG only)
Age X X X X X X
Sex X X X X
Race X
Weight/BSA X X X X X
IABP/inotropes X X X
LV function X X X X X X
Renal disease X X X X X X
Lung disease X X X X X
PVD X X X X
Diabetes X X X X X
Neurologic dysfunction X X X X
Active endocarditis X
Unstable angina or recent MI X X X
Previous cardiac surgery X X X X X X
Combined surgery X X X NA NA
Aortic involvement X X NA NA
Valve surgery X X X X NA NA
Emergency surgery X X X X X X
BSA, Body surface area; CABG, coronary artery bypass grafting; EuroSCORE, European System for Cardiac Operative Risk Evaluation; IABP, intra-aortic balloon pump; LV, left ventricular; MI, myocardial infarction; NA, not applicable; NNE, Northern New England; PVD, peripheral vascular disease; STS, Society of Thoracic Surgeons.

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