Mayo Clinic Risk Score for Percutaneous Coronary Intervention Predicts In-Hospital Mortality in Patients Undergoing Coronary Artery Bypass Graft Surgery
Background— Current risk models predict in-hospital mortality after either coronary artery bypass graft surgery or percutaneous coronary interventions separately, yet the overlap suggests that the same variables can define the risks of alternative coronary reperfusion therapies. Our goal was to seek a preprocedure risk model that can predict in-hospital mortality after either percutaneous coronary intervention or coronary artery bypass graft surgery.
Methods and Results— We tested the ability of the recently validated, integer-based Mayo Clinic Risk Score (MCRS) for percutaneous coronary intervention, which is based solely on preprocedure variables (age, creatinine, ejection fraction, myocardial infarction ≤24 hours, shock, congestive heart failure, and peripheral vascular disease), to predict in-hospital mortality among 370 793 patients in the Society of Thoracic Surgeons database undergoing isolated coronary artery bypass graft surgery from 2004 to 2006. For the Society of Thoracic Surgeons coronary artery bypass graft surgery population studied, the median age was 66 years (quartiles 1 to 3, 57 to 74 years), with 37.2% of patients ≥70 years old. A high prevalence of comorbid conditions, including diabetes mellitus (37.1%), hypertension (80.5%), peripheral vascular disease (15.3%), and renal disease (creatinine ≥1.4 mg/dL; 11.8%), was present. A strong association existed between the MCRS and the observed mortality in the Society of Thoracic Surgeons database. The in-hospital mortality ranged between 0.3% (95% confidence interval 0.3% to 0.4%) with a score of 0 on the MCRS and 33.8% (95% confidence interval 27.3% to 40.3%) with an MCRS score of 20 to 24. The discriminatory ability of the MCRS was moderate, as measured by the area under the receiver operating characteristic curve (C-statistic=0.715 to 0.784 among various subgroups); performance was inferior to the Society of Thoracic Surgeons model for most categories tested.
Conclusions— This model, which is based on 7 preprocedure risk variables, may be useful for providing patients with individualized, evidence-based estimates of procedural risk as part of the informed consent process before percutaneous or surgical revascularization.
Received April 29, 2007; accepted October 17, 2007.
Risk stratification is an essential step toward optimizing care for patients undergoing coronary revascularization. Risk-prediction models can provide healthcare providers, patients, and their families a better understanding of the attendant procedural risks and an objective basis for selecting treatment. Recent attempts toward simplification of risk-stratification tools have been helpful in decision making and accurately portraying patients’ periprocedural risk from coronary revascularization.1–3
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Although multiple models for predicting patients’ risks with coronary revascularization have been published, several issues remain. First, all current risk models predict outcomes after either coronary artery bypass graft (CABG) surgery or percutaneous coronary interventions (PCIs) separately, which limits their use for easy comparisons of procedural risks. Moreover, the complexity of most current models precludes their routine clinical use. A need therefore exists for a parsimonious risk model that can predict outcomes after either PCI or CABG. In addition, most existing models incorporate angiographic or procedural variables and thus preclude the evaluation of risk and the ability to inform decision making before the patient is already engaged in a procedure; for example, the original Mayo Clinic PCI risk model included 5 clinical and 3 angiographic variables.3 Finally, inclusion of covariates based on subjective judgment, such as “urgent” procedural status or “unstable hemodynamics,” may reduce the reliability of the model due to varying definitions and/or collinearity caused by the subjective variable’s association with other risk factors.
The risk factors in existing CABG risk scores are very similar to those used in validated PCI models.1,2 In the recent New York State CABG risk score, 7 of the 10 variables in the CABG risk score were also used in the PCI risk score, a fact that underscores the commonality in determinants of risk and the feasibility of a model that can predict outcome from either revascularization strategy.1 It thus appears possible to establish a dually applicable system by which information based on the same set of variables can be used to estimate the risk of alternative strategies for coronary reperfusion therapy.
To that end, we tested a newly constructed Mayo Clinic Risk Score (MCRS) for outcomes after coronary angioplasty4 to assess whether they would be applicable to bypass surgery. This new MCRS model is based solely on baseline clinical and noninvasive assessments and avoids variables that rely on subjective assessment. Thus, the MCRS can potentially serve as a risk-assessment aid to physicians and patients before they undergo coronary angiography for percutaneous or surgical coronary revascularization. The goal of the present study is to determine whether the MCRS for in-hospital mortality, which was developed in a PCI population, can also serve as a risk-assessment tool for in-hospital mortality among patients undergoing CABG surgery.
Development and Validation of the MCRS
The development of the new MCRS has been described previously (Figure).4 Briefly, 9 independent variables were selected and entered into a logistic regression model to determine their relationship with PCI mortality. Regression coefficients from the logistic regression model were then converted to integers to create a simple bedside risk score. The final risk score was validated with data collected between January 2000 and April 2005 from 7457 PCI patients. The MCRS demonstrated excellent ability to discriminate between high- and low-risk PCI patients (c-index=0.90) and good calibration. The probabilities estimated from the logistic model matched the observed data well, as indicated by a nonsignificant Hosmer-Lemeshow goodness-of-fit test (P=0.44).
The Society of Thoracic Surgeons (STS) National Cardiac Database includes the records of millions of patients who have undergone cardiac surgery at participating sites across the United States. For the present study, we only included patients who underwent isolated CABG surgery, and we excluded patients undergoing CABG after failed PCI (n=3601) or other concomitant procedures (eg, valve replacements [n=105 457]). The present analysis is thus confined to 370 793 patients undergoing CABG only from 2004 to 2006 who had their data collected under the most recent STS data collection form (version 2.52). Approval for the development and validation of the MCRS model was obtained from the Institutional Review Board of the Mayo Foundation.
The outcome for the present analysis was the incidence of in-hospital mortality after CABG surgery. Age, ejection fraction, and serum creatinine were entered as numerical values and then mapped with closest integer in the model. The definitions of myocardial infarction, congestive heart failure, and peripheral vascular disease were similar in the 2 original data sets. Shock was defined more stringently (Appendix 1) in the STS database than in the Mayo Clinic database.
Crude incidence rates of in-hospital mortality after CABG were compared across levels of demographic and clinical variables. Each patient was assigned a risk score by application of the MCRS model. The relationship between the MCRS and mortality risk was estimated by grouping together all patients who had the same value of the MCRS. Within each MCRS group, the observed in-hospital mortality rate was calculated and presented along with approximate 95% binomial confidence intervals (CIs). The discrimination of the MCRS as a predictor of CABG mortality was assessed by the C-statistic (also known as the area under the receiver operating characteristics curve). The C-statistic represents the probability that a randomly selected patient who died in the hospital had a higher predicted risk of mortality than a randomly selected patient who survived until discharge. The C-statistic generally ranges from 0.5 to 1.0, with 0.5 representing no discrimination (ie, a coin flip) and 1.0 representing perfect discrimination. This analysis was initially conducted in the overall study population and subsequently repeated in selected subgroups based on variables such as age and ejection fraction. To provide a context for interpreting the C-statistic, we also qualitatively compared the discrimination of the MCRS to the published STS CABG mortality model as applied to the present study population. The difference in the C-statistics for the MCRS versus STS models was tested with the method of DeLong et al.5 We anticipated that the C-statistic of the MCRS would be somewhat smaller because the STS model contains a larger number of covariates and was developed specifically for CABG patients.
The approach used to handle missing data was similar to that proposed in the original MCRS (Figure). Missing values were rare in the STS database for the variables included in the MCRS. The percentage of missing values was <1%, with the exception of ejection fraction, for which values were missing in 4.6% of cases.
The authors had full access to and take full responsibility for the integrity of the data. All authors have read and agree to the manuscript as written.
For the STS CABG population studied, the median age was 66 years (quartiles 1 to 3, 57 to 74 years), with 37.2% of patients ≥70 years old. This generally male population (72.8%) was noted to have a high prevalence of comorbid conditions, including diabetes mellitus (37.1%), hypertension (80.5%), peripheral vascular disease (15.3%), and renal disease (creatinine ≥1.4 mg/dL; 11.8%). In addition, 44.6% had previous myocardial infarction, 13.3% had congestive heart failure, and 1.8% presented with cardiogenic shock. Other relevant variables are listed in Table 1⇓. In the STS database, in-hospital mortality occurred in 6919 patients (1.9%).
Table 2 demonstrates a strong correlation between the MCRS and the observed mortality in the STS database. In general, the observed in-hospital mortality in the STS database increased with higher MCRS value. The in-hospital mortality rate ranged between 0.3% (95% CI 0.3% to 0.4%) with an MCRS score of 0 and 33.8% (95% CI 27.3% to 40.3%) in those with an MCRS score of 20 to 24.
Table 3 demonstrates the discriminatory ability of the MCRS to predict in-hospital mortality in patients undergoing isolated CABG. Overall, the MCRS model had modest discriminatory ability, with an area under the receiver operating characteristic curve of 0.773. The discriminatory ability of the MCRS prediction equation was also modest across most subgroups (0.715 to 0.784). The MCRS model fared well in patients in varying age groups and in those with and without heart failure, myocardial infarction, shock, renal failure, diabetes mellitus, or 3-vessel disease.
The present study established that the new MCRS, originally derived to predict outcomes after PCI with specific preprocedure variables, can also predict in-hospital mortality after CABG surgery. This is the first risk score that has been shown to demonstrate good discriminatory ability both in patients undergoing PCI and in those undergoing CABG. Favoring its widespread deployment is that it is simple, easy to use, and derived from preprocedural variables without reliance on subjective variables. Using the STS database, we found an overall acceptable discriminatory ability of the MCRS, with an increase in the observed in-hospital mortality rate with higher MCRS scores. Moreover, the model was robust across most low- and high-risk subgroups.
All previous attempts in the development of the model were predicated on the selection of either surgical or percutaneous revascularization therapy. Excellent models predicting not only mortality but also other major adverse cardiovascular complications are available.2,3,6–8 Several available CABG risk models are either derived from older data sets or derived from European data, such that they may have limited applicability to patients undergoing CABG in the United States.9–13 More recently, integer-based risk indices have facilitated quick estimation of in-hospital mortality and other adverse cardiovascular events.1 New York State models for both PCI and CABG reported surprisingly similar risk factors, such that 7 of the 10 variables in the CABG risk score were listed in both scores.1,2 Six of the 7 listed MCRS risk variables are similar to those in the New York State model, a fact that underscores the commonality of risk variables in prediction of outcome irrespective of the revascularization strategy. Besides demonstrating the utility of predicting outcome in patients undergoing PCI and CABG, the MCRS has several advantages over the current New York State CABG model. First, the MCRS does not use subjective covariates, thus minimizing inconsistencies in data collection and collinearity. For example, the new MCRS model does not refer to “urgent” interventions or an “extensively calcified” ascending aorta. Second, age, ejection fraction, and creatinine are entered as continuous variables, thus helping providers calculate risk gradients with minimal change in the value of these variables. For example, renal failure that requires dialysis is a risk variable in the New York State model; however, lesser elevation of creatinine is known to be associated with adverse outcome.14 The only variable from the MCRS that was not consistently included in the STS CABG mortality model was presentation with acute myocardial infarction, which likely represents the performance of urgent or emergent PCI in such situations.
Previous attempts at any risk model development have been geared either toward surgical or percutaneous coronary revascularization, which hindered objective outcome assessment by patients or healthcare providers for patients who were potentially suitable for either revascularization therapy. The present analyses create a window of opportunity to use a single risk score to assess the risk of coronary revascularization therapies based on easily obtainable demographic and laboratory parameters. There appears to be a high degree of overlap between risk factors that are predictive of outcome regardless of mode of coronary revascularization. For example, increasing age, renal disease, measures of left ventricular dysfunction (ejection fraction, congestive heart failure), cardiogenic shock, and peripheral vascular disease are a subset of risk factors that are predictive across several models developed for outcomes after CABG or PCI.15 This core set of variables is included in the current MCRS. Most of the available models for CABG include similar variables and demonstrate discriminatory ability similar to that of the MCRS.
We acknowledge that the existing STS mortality model, which was developed specifically for CABG, has better discrimination than the MCRS, and one could improve on the existing MCRS by choosing new weights based on the CABG data. The practical advantage of using a single, parsimonious model for both populations (PCI and CABG) is attractive; however, further studies are needed to demonstrate the applicability of such models in guiding the choice of revascularization strategy. We have demonstrated that the MCRS can be used by healthcare providers for bedside risk assessment during the first contact with patients. It conveys risks of in-hospital mortality from either CABG or PCI and has potential value to assist patients in selecting a mode of coronary revascularization. It remains to be seen whether assignment of risk by the model changes the selection of therapy and whether it impacts the prognosis of the patient. We realize, however, that personal or physician preferences and angiographic characteristics will likely determine the ultimate decision. We also want to underscore that the model is unlikely to convey the superiority of a specific revascularization therapy and that additional factors, including patients’ preferences, are required for decision making. Yet, given the opportunity to exaggerate or underestimate risks from procedures with which a given provider is less familiar, we thought that the MCRS could be a valuable tool in “fairly” benchmarking patients’ risks.
The MCRS was developed to predict mortality after PCI and may weight risk factors differently than a risk score that was developed specifically for CABG patients.16 Yet, the finding that the MCRS has modest discrimination for predicting mortality after CABG supports the generalizability of the original MCRS across procedures. Second, we did not address the additional prognostic value of several variables known to be associated with adverse outcomes after CABG, namely, left main or multivessel disease. We deliberately omitted these variables in an attempt to retain the simplicity of the model and to keep the perspective of using the preprocedure risk variables (ie, before coronary angiography, because for many patients, the decision about subsequent revascularization is made in the cardiac catheterization laboratory immediately after coronary angiography). We also excluded patients undergoing valve surgery or emergent CABG as a consequence of failed PCI, limiting its role only to patients undergoing isolated CABG; however, given that the need for concomitant valve surgery minimizes the utility of concurrent evaluation of the risks of PCI, we do not believe that this is a significant limitation. Because of differences in definitions and variations in reporting of periprocedural myocardial infarction among patients undergoing CABG or PCI, this outcome measure and other measures that increase morbidity or length of hospital stay were not included in the final analyses. Although this report was accurate in predicting in-hospital mortality, no predictive model can overcome the effect of chance and uncertainty, which are inherent in invasive treatments.
The present study reports, for the first time, the application of a preprocedural PCI risk model to prediction of in-hospital mortality among patients undergoing isolated CABG in the large STS database. All variables included in the model can be obtained at the time of first contact with the patient. Risk stratification with this risk score could help both the patient and the healthcare provider assess the risks of both PCI and CABG, facilitate the healthcare provider in obtaining informed consent, provide objectively risk-adjusted comparisons, and enable counseling of patients when coronary revascularization is contemplated.
In the STS database: Indicate whether the patient was, at the time of procedure, in a clinical state of hypoperfusion according to either of the following criteria: (1) systolic blood pressure <80 mm Hg and/or cardiac index <1.8 L · min−1 · m−2 despite maximal treatment; (2) intravenous inotropes and/or intra-aortic balloon pump necessary to maintain systolic blood pressure >80 mm Hg and/or cardiac index >1.8 L · min−1 · m−2.
In the Mayo Clinic database: Prolonged hypotension, with blood pressure <95 mm Hg without inotropes or intra-aortic balloon pump support or <110 mm Hg with inotropes or intra-aortic balloon pump support, which required treatment with inotropes or intra-aortic balloon pump.
Sources of Funding
Dr Peterson is the recipient of a research grant from the Society of Thoracic Surgeons.
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This study established that the new Mayo Clinic Risk Score, originally derived to predict outcomes after percutaneous coronary intervention with preprocedure variables, can also predict in-hospital mortality after coronary artery bypass graft surgery. This is the first risk score to demonstrate good discriminatory ability in both patients undergoing percutaneous coronary intervention and those undergoing coronary artery bypass grafting. Favoring its widespread deployment is that it is simple, easy to use, and derived from preprocedural variables without reliance on subjective variables. Using the Society of Thoracic Surgeons database, we found overall acceptable discriminatory ability of the Mayo Clinic Risk Score, with an increase in the observed in-hospital mortality rate with higher Mayo Clinic Risk Scores. Moreover, the model was robust across most low- and high-risk subgroups.