Predictors of Long-Term Survival After Coronary Artery Bypass Grafting SurgeryCLINICAL PERSPECTIVE
Results From the Society of Thoracic Surgeons Adult Cardiac Surgery Database (The ASCERT Study)
Background—Most survival prediction models for coronary artery bypass grafting surgery are limited to in-hospital or 30-day end points. We estimate a long-term survival model using data from the Society of Thoracic Surgeons Adult Cardiac Surgery Database and Centers for Medicare and Medicaid Services.
Methods and Results—The final study cohort included 348 341 isolated coronary artery bypass grafting patients aged ≥65 years, discharged between January 1, 2002, and December 31, 2007, from 917 Society of Thoracic Surgeons–participating hospitals, randomly divided into training (n=174 506) and validation (n=173 835) samples. Through linkage with Centers for Medicare and Medicaid Services claims data, we ascertained vital status from date of surgery through December 31, 2008 (1- to 6-year follow-up). Because the proportional hazards assumption was violated, we fit 4 Cox regression models conditional on being alive at the beginning of the following intervals: 0 to 30 days, 31 to 180 days, 181 days to 2 years, and >2 years. Kaplan-Meier–estimated mortality was 3.2% at 30 days, 6.4% at 180 days, 8.1% at 1 year, and 23.3% at 3 years of follow-up. Harrell's C statistic for predicting overall survival time was 0.732. Some risk factors (eg, emergency status, shock, reoperation) were strong predictors of short-term outcome but, for early survivors, became nonsignificant within 2 years. The adverse impact of some other risk factors (eg, dialysis-dependent renal failure, insulin-dependent diabetes mellitus) continued to increase.
Conclusions—Using clinical registry data and longitudinal claims data, we developed a long-term survival prediction model for isolated coronary artery bypass grafting. This provides valuable information for shared decision making, comparative effectiveness research, quality improvement, and provider profiling.
Risk-adjusted mortality after coronary artery bypass grafting (CABG) surgery has been the dominant cardiac surgery outcome metric for >2 decades. Ideally, these rates are based on audited clinical data registries such as those maintained by the Society of Thoracic Surgeons (STS), state and federal government agencies, and regional collaboratives. Clinical registries include important preoperative, intraoperative, and postoperative variables that are typically unavailable in administrative data sources. Analyses of risk-adjusted clinical outcomes data from these registries have been used for a variety of quality assessment and improvement activities as well as for clinical research.
Editorial see p 1475
Clinical Perspective on p 1500
Despite their many advantages, clinical registries also have an important limitation. Because of cost and other practical barriers, most clinical data registries collect only in-hospital or 30-day postoperative outcomes, including mortality. Ascertainment of longer-term vital status is especially problematic for referral centers whose patients are often returned to the care of their primary physicians in distant cities or states. Because many important events occur after the index hospitalization, this limited long-term follow-up is a significant barrier to the optimal utilization of registry data. Particularly as short-term procedural mortality has decreased, longer-term outcomes are of equal or greater relevance to patients, providers, and other stakeholders.
If robust, long-term follow-up data were available, it would enable investigators to study the association of these outcomes with relevant clinical factors (eg, patient characteristics and disease severity on admission). Longitudinal data would greatly enhance shared decision making, individualized patient management strategies, the study of long-term efficacy and safety, and comparative effectiveness research.
The American College of Cardiology Foundation, the STS, and the Duke Clinical Research Institute are collaborating on a comparative effectiveness study (American College of Cardiology Foundation–Society of Thoracic Surgeons Collaboration on the Comparative Effectiveness of Revascularization Strategies [ASCERT]) of CABG and percutaneous coronary interventions (PCI), funded by the National Heart, Lung, and Blood Institute of the National Institutes of Health.1 The first aim of the ASCERT study is to develop novel, long-term mortality risk prediction models for CABG and PCI. By linking the STS Adult Cardiac Surgery Database and the Center for Medicare and Medicaid Services (CMS) 100% denominator file,2 we developed long-term mortality models that estimate the time-dependent effect of preoperative patient factors on medium- and long-term mortality after CABG.
Institutional Review Board Approval
This analysis has been reviewed and approved by the Duke University Health System institutional review board under protocol No. Pro00019987.
The study population consisted of isolated CABG patients at STS-participating hospitals who were discharged between January 1, 2002, and December 31, 2007, and whose clinical data were collected with the use of STS Adult Cardiac Surgery Database version 2.41 and 2.52 data specifications.3 Data quality in the STS database has been shown to be high. In audits of STS data from 12 sites in Iowa conducted by the Iowa Foundation for Medical Care in 2001–2002 (corresponding to the earliest data used for the present study), the overall agreement rate for risk predictors was 96%.4 External audits of the entire STS national database currently include 5% of randomly selected participants annually, and the overall agreement rate for 2009 records (>70 data elements in each) was 96.1%.
Patients aged <65 years or having a history of coma were excluded, as were patients with missing data on age, sex, or status (elective, urgent, emergent, salvage). For patients with multiple operations in the data set, only the first operation was included. The final study population included 348 341 patients from 917 STS-participating hospitals (Figure 1).
Procedural records in the STS database were linked to CMS inpatient claims and denominator databases.2,5 STS and CMS claims records from 2003 to 2007 were considered to be a match if they agreed exactly on site, sex, admission date, discharge date, date of birth (if present), and age. For 2002, dates of birth, admission, and discharge were coarsened to protect confidentiality, and thus a more complicated matching criterion was required. Records were considered to be a match if they agreed exactly on site, sex, length of stay, procedure month and year, days from birth to admission (if present), age, and days from admission to surgery. Overall, 86.5% of records were collected during 2003 to 2007 and matched exactly on all available matching criteria. In a validation study of this methodology in which heart failure patients from Duke University were used,5 the estimated false match rate was 0% (0/109) when the most stringent matching criterion was used and 1% (1/109) when a less stringent matching rule was used.
Vital status and dates of death through December 31, 2008, were obtained by linking CMS claims records to the denominator file on the basis of an encrypted Medicare patient identifier. Follow-up was considered to be administratively censored on December 31, 2008, and was at least 1 year for all patients (median, 4 years; maximum, 7 years).
Predictor variables were summarized as percentages if categorical and as mean, median, SD, and quartiles (25th, 75th) if continuous. Predictors were chosen on the basis of published CABG short-term models6 and clinical experience. Variable definitions are available at http://www.sts.org. The variable for “number of diseased vessels” in the STS Adult Cardiac Surgery Database was designed to reflect the amount of myocardium at risk. Thus, although patients with significant left main coronary disease are specifically identified by a separate dichotomous variable, for the purposes of defining myocardium at risk, they are also classified as 2 diseased vessels.
Development and Validation Samples
Data were randomly divided into a 50% training sample (n=174 506) to determine the form of the model and estimate regression coefficients and a 50% validation sample (n=173 835) to assess model calibration and discrimination.
Form of Model
We estimated survival as a function of patient preoperative characteristics using the Cox proportional hazards model.7 The proportional hazards assumption was investigated by plotting and visually inspecting transformed (log-log) survival probabilities versus time after CABG. To allow for non–proportional hazards, we estimated separate hazard ratio parameters for all model variables for each of the following time intervals: 0 to 30 days, 31 to 180 days, 181 days to 2 years, and >2 years. Time intervals were chosen after we conducted a preliminary analysis that involved fitting Cox models with several relatively narrow categories, then collapsing adjacent categories on the basis of a combination of statistical and nonstatistical considerations. The first cut point (30 days) was chosen for consistency with many existing short-term CABG mortality models and quality metrics. As the ability to support even the most seriously ill postoperative patients has increased as a result of modern critical care, some have suggested that our definition of the “early” postoperative period should likewise be lengthened so as not to underestimate early risk.8 This was the basis for our relatively narrow second time interval, 31 to 180 days. The remaining intervals were chosen by collapsing adjacent categories for which the hazard ratios appeared most similar while retaining sufficient events in each to ensure precise estimation of category-specific hazard ratio parameters.
We fit 4 separate Cox regression models that were conditional on being alive at the beginning of each time interval. Mathematically, this was equivalent to fitting a single Cox model with piecewise constant, time-dependent hazard ratios for all model variables.
Functional Form of Predictors
Graphical exploratory analyses were used to determine the functional form of continuous variables and to decide whether categorical variables with several categories could be collapsed into fewer categories. In a preliminary Cox model in which flexible regression splines for continuous variables were used, plots of the variables age, height, and year of surgery revealed an approximately linear association with the log-hazard of mortality and were modeled as linear. The association between body mass index and mortality was determined to be nonmonotone (U shaped) and was modeled as a continuous polynomial regression function with linear and quadratic effects. We arbitrarily selected body mass indices of 20, 30, 35, and 40 kg/m2 to compare their hazard ratios relative to a “normal” reference body mass index of 25 kg/m2. For modeling renal function, patients on dialysis were adequately represented by an indicator variable for dialysis without further adjustment for the patient's last preoperative creatinine level. For patients not on dialysis, the relationship between last preoperative creatinine and mortality was modeled as a straight line with a change of slope at 1.5 mg/dL. Ejection fraction was modeled as linear <60% and constant >60%. Finally, aortic stenosis pressure gradient was modeled as linear <77 mm Hg and constant >77 mm Hg (the 99th percentile).
Interactions between predictors were examined by identifying 5 predictors with the highest global χ2 statistics and creating all possible pairwise interactions among them, in each case considering whether these were also clinically plausible. Although some interaction terms were statistically significant, they were not believed to be of major practical significance. Measures of model calibration and discrimination were not materially affected by their inclusion (ie, model fit was not substantially improved), and models without interactions were also considered to be substantially more interpretable and usable. Therefore, we retained only main effects in the final model.
Predictor data were highly complete, with most covariates having <1% missing data (Table I in the online-only Data Supplement). Missing values were imputed to the median of continuous variables (after stratifying on relevant variables to enhance prediction of the missing value) and the most common category of binary and polytomous variables. More computationally intensive missing data strategies, such as multiple imputations, were not used for this analysis because they have been documented to have minimal impact in previous STS risk models.9
Model performance was assessed in the 50% validation sample. Predicted survival curves were generated by applying estimated regression coefficients from the development sample to covariate data of patients in the validation sample. To assess calibration (fit), model-based predicted survival curves were averaged across patients in the validation sample and compared with nonparametric (Kaplan-Meier) survival curves. This was done in the overall validation population and in various subgroups. To further assess calibration, patients in the validation sample were ranked into 20 categories on the basis of their estimated risk of dying within 3 years. Average expected and observed (Kaplan-Meier) 3-year survival probabilities were then calculated within each category and plotted.
Discrimination was quantified by 2 methods. First, discrimination for predicting mortality status as a dichotomous end point (alive/dead) was assessed by the area under the receiver operating characteristic curve (C index) for 3 selected time points: 30 days, 1 year, and 3 years. All patients had at least 1 year of follow-up and were included in the estimation of discrimination for the 30-day and 1-year time points. For the 3-year time point, the 65% of patients with at least 3 years of potential follow-up (ie, those treated between January 1, 2004, and December 31, 2005) were included. Second, an analogous overall measure of discrimination for predicting survival time as a continuous variable was calculated with the use of Harrell's C index for censored survival data.10 To apply Harrell's method, patients were ranked according to their predicted 3-year mortality risk. We then calculated the proportion of pairs of patients for which the patient with the lower predicted probability of mortality survived longer than the patient with the higher predicted probability, accounting appropriately for censoring.
After model development and validation were completed, we reestimated the final model coefficients on the basis of the complete data set (development plus validation samples). Confidence intervals for hazard ratios were calculated with sandwich SE estimates to account for within-hospital clustering.11
Table II in the online-only Data Supplement compares the characteristics of STS CABG patients who were or were not matched to CMS. For most variables, these 2 groups were quite similar.
Table 1 depicts the characteristics of the final study population of 348 341 patients who underwent isolated CABG. Kaplan-Meier estimated mortality in the overall study cohort (development and validation samples) was 3.2% at 30 days, 6.4% at 180 days, 8.1% at 1 year, 11.3% at 2 years, and 23.3% at 3 years of follow-up. Table III in the online-only Data Supplement summarizes the univariable association between each candidate predictor variable and estimated mortality rates at 30 days, 1 year, and 3 years.
Table 2 shows hazard ratios derived by fitting multivariable Cox regression models to 4 time intervals (see Methods). In multivariable analyses, several distinct, temporal risk factor patterns are evident. For example, higher ejection fraction was protective over all time periods, and the magnitude of effect was stable. Conversely, past history of a stroke, transient ischemic attack, or reversible ischemic neurological deficit, moderate or severe chronic lung disease, or immunosuppressive treatment had a significant negative impact on survival at all end points. The magnitude of effect of some important early predictors of risk, including current smoking, insulin-dependent diabetes mellitus, and dialysis-dependent renal failure, increased over time, suggesting an accumulation of risk from these debilitating chronic behaviors and diseases. On the other hand, the effect of some important early predictors of increased mortality (eg, emergency status, cardiogenic shock, acute preoperative myocardial infarction, and reoperation) diminished rather quickly and became nonsignificant for those patients who survived the early postoperative and recovery periods.
Our results confirm the so-called obesity paradox reported in other short-term analyses12,13 and demonstrate that these effects persist for at least 2 years postoperatively. Low body mass index (20 versus 25 kg/m2) predicted higher mortality at all time periods postoperatively, whereas obesity (>25 kg/m2) was associated with decreased risk.
Model discrimination (C index) in the validation set was 0.762 for predicting 30-day status, 0.764 for predicting 1-year status, and 0.748 for predicting 3-year status. Harrell's C statistic for predicting overall survival time was 0.732. Thirty-day model discrimination differs from that observed in our most recent STS isolated CABG risk models,6 most likely because the present model is limited to patients aged >65 years. Model discrimination at longer time intervals is also lower than that in the early postoperative period. As the time interval from surgery increases, there is correspondingly greater probability that other factors not included in the risk models may affect survival.
Figure 2 depicts the expected and Kaplan-Meier observed survival curves for the overall validation cohort. Figure 3 compares observed and expected 3-year mortality risk across 20 categories of predicted risk. Within the typical range of expected mortalities, prediction is highly accurate. From 20% to 40% expected mortality, there is very slight underestimation of mortality risk, and at the highest expected mortality (>50%), there is slight overestimation. Figure 4 depicts Kaplan-Meier observed and expected survival curves for selected patient subgroups in the validation cohort. The expected (solid) and observed (dashed) lines are nearly superimposable on most of the plots.
Short-term duration of follow-up has prevented the full potential of clinical data registries from being realized. As average acute hospital length of stay has shortened, procedure-related deaths and complications are correspondingly more likely to occur after patients have been discharged from the hospital. The use of advanced mechanical and pharmacological support has increasingly prolonged the lives of many critically ill postoperative patients, and such patients may be transferred to long-term critical care facilities on ventilators or dialysis. Deaths among such patients may not occur for months after their index hospital discharge, and these delayed postoperative deaths would not be captured in most existing clinical registries.8 Short-term follow-up is also a major limitation of comparative effectiveness studies of various treatment strategies, such as CABG or PCI for coronary artery disease. Differences in efficacy of alternative treatments are often not apparent for months or years, much longer than the typical end points in most clinical registries. Finally, some preoperative risk factors may have little impact on short-term mortality but are major considerations in the longer term and vice versa.
Some previous studies have assessed the long-term impact of preoperative risk factors, operative and perioperative care processes, and postoperative complications.14–28 Our study focuses specifically on the former, and it addresses the major limitations of these earlier studies. Many are from single institutions, and their inferences may be confounded by idiosyncratic hospital practice patterns. Most prior studies include only a few hundred to a few thousand patients and lack the power to identify the full spectrum of factors associated with outcomes. Some studies of long-term CABG mortality have been based solely on large administrative databases. This strategy ensures adequate sample size and provides valuable information regarding vital status, readmissions, reinterventions, costs, aggregate resource utilization, and outpatient activities. However, administrative databases have a number of well-known deficiencies that limit their usefulness in clinical research, including misclassification of procedures and diagnoses; unavailability of important clinical variables; inability to distinguish comorbidities from complications (in the absence of Present on Admission indicators); and focus on narrow patient populations29–34 There are studies of long-term CABG outcome predictors based on clinical registries, but these are derived from data that are 10 to 20 years old and may not reflect current patient condition severity and surgical practice.35,36
Our study seeks to overcome the inherent limitations of both clinical and administrative data registries by linking the 2 together. This approach compensates for their individual deficiencies while harnessing their complementary strengths. The resulting linked data retain the granularity and clinical detail of clinical registries while adding long-term outcomes and cost data available in administrative data sources. These linked data are ideally suited to studies of long-term clinical outcomes, comparative effectiveness, resource utilization, and provider performance for particular types of patients.
Using predicted long-term outcomes tailored to their specific risk profiles, patients may more effectively participate in shared decision making with their providers. Awareness of both the short-term and long-term risks and benefits (eg, survival, complications, quality of life) might assist patients in deciding whether or not to proceed with surgery. Furthermore, just as short-term outcomes vary among providers, it is possible that long-term outcomes may also vary, and such information could be useful for all stakeholders.
The long-term CABG mortality model described in this report, based solely on preoperative patient characteristics, is only the first of many applications we envision to exploit the advantages of linked registries. In addition to mortality, it will also be possible to study other long-term end points such as readmissions, reinterventions, and cumulative costs and resource use. Other models will estimate the effect of intraoperative decisions, such as use of all-arterial grafting or off-pump procedures, on long-term outcomes. Combined with preoperative variables, such information could help to determine the specific procedures or perioperative strategies that are most useful for specific types of patients. The addition of early postoperative events (eg, stroke or mediastinitis) as predictor variables would permit more effective discussions with such patients regarding their long-term health expectations.
Linkages with CMS and other administrative data sources will also enhance the accuracy of outcomes data used to calculate performance metrics such as the STS CABG composite scores.37,38 For example, ongoing linkages with the Social Security Death Master File or National Death Index would permit continuous input and validation of vital status for patients of all ages, not just the Medicare population.39
Linked clinical and administrative data will facilitate the determination of risk-adjusted, long-term freedom from reoperation and readmission not only for surgical procedures but also for a variety of medical devices, such as cardiac valve prostheses. This ability to capture objective long-term patient status, coupled with extensive clinical data from the perioperative period, will be a marked improvement over existing methods for postmarket surveillance.
Linkages to other clinical registries will also be useful, and their combined utility will be further enhanced by linking to administrative data, as demonstrated by the ASCERT comparative effectiveness study.1 Furthermore, as payment strategies evolve from a focus on procedures or acute hospitalizations to episodes of care, the ability to link related clinical registries (eg, cardiology and cardiac surgery) will facilitate the study and implementation of these reimbursement policies. Finally, linkages between clinical and payer registries would provide unique information such as outpatient visits, compliance with medications, and cumulative resource use.
There are inherent limitations to any studies that use voluntarily collected data, but these are mitigated by the robust STS audit program described previously.
To obtain accurate long-term follow-up, it was necessary to link our data to CMS claims data. Because Medicare claims data are restricted to patients aged ≥65 years, the generalizability of our findings to younger populations is uncertain.
It was impossible to accurately determine cause of death (eg, cardiac versus noncardiac), and our analyses use all-cause mortality as the end point.
Our linkages are based on combinations of indirect identifiers. Previous analyses have demonstrated that nonunique identifiers can be combined to create high-quality links between a clinical registry and an administrative data set, allowing researchers to capitalize on the strengths of both types of data to answer important clinical questions.5,40 Although we believe that this strategy yielded highly accurate matches in our study, some errors may have been introduced through this process.
Finally, the more distant from the time of surgery, the more opportunity there is for non–surgery-related events to confound the apparent associations between preoperative factors and outcomes.
We linked broadly representative, real-world clinical data from the STS Adult Cardiac Surgery Database and vital status from Medicare claims data to construct a robust, long-term CABG survival prediction model. Because of the large study cohort, model performance is excellent.
As the time interval from surgery lengthens, the clinical outcomes of postoperative survivors are less affected by traditional predictors of early survival, such as emergency status, shock, and reoperation. Conversely, late mortality is increasingly associated with chronic diseases such as insulin-dependent diabetes mellitus and dialysis-dependent renal failure and behaviors such as smoking.
As short-term CABG mortality rates decline, the ability to estimate long-term outcomes for patients with particular risk factors will become increasingly important for shared decision making, comparative effectiveness research, optimal treatment planning, quality improvement initiatives, and provider profiling.
Sources of Funding
The ASCERT study is supported by award RC2HL101489 from the National Heart, Lung, and Blood Institute. This award has been issued under the American Recovery and Reinvestment Act of 2009 for a 2-year period. The funders played no role in the design, interpretation, or decision to publish the analysis presented herein. The content of this article is solely the responsibility of the authors and does not necessarily represent the official views of the National Heart, Lung, and Blood Institute or the National Institutes of Health.
The authors report the following disclosures: consultancy, expert testimony, grants/grants pending: Drs Dangas and Weintraub; other: Duke Clinical Research Institute serves as the data warehouse and analysis center for the STS and American College of Cardiology Foundation registries (Drs DeLong, Grau-Sepulveda, O'Brien, Peterson, Sheng).
Continuing medical education (CME) credit is available for this article. Go to http://cme.ahajournals.org to take the quiz.
Guest Editor for this article was Philippe Gabriel Steg, MD.
The online-only Data Supplement is available with this article at http://circ.ahajournals.org/lookup/suppl/doi:10.1161/CIRCULATIONAHA.111.066902/-/DC1.
- Received September 8, 2011.
- Accepted January 20, 2012.
- © 2012 American Heart Association, Inc.
- Klein LW,
- Edwards FH,
- DeLong ER,
- Ritzenthaler L,
- Dangas GD,
- Weintraub WS
- Jacobs JP,
- Edwards FH,
- Shahian DM,
- Haan CK,
- Puskas JD,
- Morales DL,
- Gammie JS,
- Sanchez JA,
- Brennan JM,
- O'Brien SM,
- Dokholyan RS,
- Hammill BG,
- Curtis LH,
- Peterson ED,
- Badhwar V,
- George KM,
- Mayer JE Jr.,
- Chitwood WR Jr.,
- Murray GF,
- Grover FL
Society of Thoracic Surgeons. http://www.sts.org/quality-research-patient-safety/national-database/database-managers/adult-cardiac-surgery-database/d. Accessed Aug 3, 2011.
- Shahian DM,
- O'Brien SM,
- Filardo G,
- Ferraris VA,
- Haan CK,
- Rich JB,
- Normand SL,
- Delong ER,
- Shewan CM,
- Dokholyan RS,
- Peterson ED,
- Edwards FH,
- Anderson RP
- Cox DR,
- Oakes D
- Osswald BR,
- Blackstone EH,
- Tochtermann U,
- Thomas G,
- Vahl CF,
- Hagl S
Single vs multiple imputation in STS risk models. Duke University Web site. http://www.duke.edu/∼obrie027/STS2008. Accessed March 31, 2011.
- Barsness GW,
- Peterson ED,
- Ohman EM,
- Nelson CL,
- DeLong ER,
- Reves JG,
- Smith PK,
- Anderson RD,
- Jones RH,
- Mark DB,
- Califf RM
- Birkmeyer JD,
- Quinton HB,
- O'Connor NJ,
- McDaniel MD,
- Leavitt BJ,
- Charlesworth DC,
- Hernandez F,
- Ricci MA,
- O'Connor GT
- Filardo G,
- Hamilton C,
- Hebeler RF Jr.,
- Hamman B,
- Grayburn P
- Shahian DM,
- Silverstein T,
- Lovett AF,
- Wolf RE,
- Normand S-L
- Best WR,
- Khuri SF,
- Phelan M,
- Hur K,
- Henderson WG,
- Demakis JG,
- Daley J
- Iezzoni LI
- O'Brien SM,
- Shahian DM,
- Delong ER,
- Normand SL,
- Edwards FH,
- Ferraris VA,
- Haan CK,
- Rich JB,
- Shewan CM,
- Dokholyan RS,
- Anderson RP,
- Peterson ED
- Pasquali SK,
- Jacobs JP,
- Shook GJ,
- O'Brien SM,
- Hall M,
- Jacobs ML,
- Welke KF,
- Gaynor JW,
- Peterson ED,
- Shah SS,
- Li JS
Most survival prediction models for coronary artery bypass grafting surgery are limited to in-hospital or 30-day end points. However, particularly as short-term mortality rates decrease, it is increasingly important for providers, patients, payers, and other stakeholders to better understand the likelihood of long-term survival. We linked broadly representative, real-world clinical data from the Society of Thoracic Surgeons Adult Cardiac Surgery Database and vital status from Medicare claims data to construct a robust, long-term coronary artery bypass grafting surgery survival prediction model. This study included 348 341 patients aged ≥65 years who underwent isolated coronary artery bypass grafting surgery between 2002 and 2007. Because of the large study cohort and clinical predictors, model performance is excellent. On the basis of the results of this study, late outcomes for patients who initially survive coronary artery bypass grafting surgery are less affected by traditional predictors of early mortality such as emergency status, shock, and reoperation. Conversely, late mortality is increasingly associated with chronic debilitating diseases such as insulin-dependent diabetes mellitus and dialysis-dependent renal failure and behaviors such as smoking. This is valuable information for shared decision making, comparative effectiveness research, quality improvement, patient counseling, and provider profiling.