David B. Pryor

A brief self-administered questionnaire to determine functional capacity (the Duke Activity Status Index).

To develop a brief, self-administered questionnaire that accurately measures functional capacity and assesses aspects of quality of life, 50 subjects undergoing exercise testing with measurement of peak oxygen uptake were studied. All subjects were questioned about their ability to perform a variety of common activities by an interviewer blinded to exercise test findings. A 12-item scale (the Duke Activity Status Index) was then developed that correlated well with peak oxygen uptake (Spearman correlation coefficient 0.80). To test this new index, an independent group of 50 subjects completed a self-administered questionnaire to determine functional capacity and underwent exercise testing with measurement of peak oxygen uptake. The Duke Activity Status Index correlated significantly (p less than 0.0001) with peak oxygen uptake (Spearman correlation coefficient 0.58) in this independent sample. The Duke Activity Status Index is a valid measure of functional capacity that can be obtained by self-administered questionnaire.

Prognostic Value of a Treadmill Exercise Score in Outpatients with Suspected Coronary Artery Disease

BACKGROUND The treadmill exercise test identifies patients with different degrees of risk of death from cardiovascular events. We devised a prognostic score, based on the results of treadmill exercise testing, that accurately predicts outcome among inpatients referred for cardiac catheterization. This study was designed to determine whether this score could also accurately predict prognosis in unselected outpatients. METHODS We prospectively studied 613 consecutive outpatients with suspected coronary disease who were referred for exercise testing between 1983 and 1985. Follow-up was 98 percent complete at four years. The treadmill score was calculated as follows: duration of exercise in minutes--(5 x the maximal ST-segment deviation during or after exercise, in millimeters)--(4 x the treadmill angina index). The numerical treadmill angina index was 0 for no angina, 1 for nonlimiting angina, and 2 for exercise-limiting angina. Treadmill scores ranged from -25 (indicating the highest risk) to +15 (indicating the lowest risk). RESULTS Predicted outcomes for the outpatients, based on their treadmill scores, agreed closely with the observed outcomes. The score accurately separated patients who subsequently died from those who lived for four years (area under the receiver-operating-characteristic curve = 0.849). The treadmill score was a better discriminator than the clinical data and was even more useful for outpatients than it had been for inpatients. Approximately two thirds of the outpatients had treadmill scores indicating low risk (greater than or equal to +5), reflecting longer exercise times and little or no ST-segment deviation, and their four-year survival rate was 99 percent (average annual mortality rate, 0.25 percent). Four percent of the outpatients had scores indicating high risk (less than -10), reflecting shorter exercise times and more severe ST-segment deviation; their four-year survival rate was 79 percent (average annual mortality rate, 5 percent). CONCLUSIONS The treadmill score is a useful and valid tool that can help clinicians determine prognosis and decide whether to refer outpatients with suspected coronary disease for cardiac catheterization. In this study, it was a better predictor of outcome than the clinical assessment.

Discordance of Databases Designed for Claims Payment versus Clinical Information Systems: Implications for Outcomes Research

Insurance claims data are being used increasingly to study clinical outcomes and quality of care [1-4]. Each year, hospital-specific mortality rates, adjusted by clinically modified International Classification of Diseases (ICD-9-CM) codes from Medicare bills, are released by the Health Care Financing Administration (HCFA) [1-3]. Using ICD-9-CM data to adjust for illness severity, threefold differences for surgeon-specific mortality in Philadelphia were found by Williams and colleagues [4]. Many of the Patient Outcomes Research Teams (PORTs), supported by the Agency for Health Care Policy Research, are using ICD-9-CM coded Medicare discharge abstracts to examine the process of medical care, including physician- and hospital-specific performance [5-7]. The potential advantages of using insurance claims data sets for clinical research have been described in many previous publications [8]. They include 1) large samples of geographically dispersed patients; 2) longitudinal records; 3) data already collected and available; and 4) defined sampling frames. The question remains: Are data collected to obtain insurance reimbursement a valid proxy for data collected for clinical care and research purposes? Such validity is essential to identify clinically relevant populations and to adjust for illness severity and differences in outcomes [9]. Six reabstracting studies have attempted to answer this question with respect to analysis of patients discharged after acute myocardial infarction [10-15]. These studies selected patients with the ICD-9-CM code 410, the code for acute myocardial infarction. By examining medical records, they found that clinical criteria for an acute myocardial infarction were met in 43% to 87% of records where the code was used at discharge. Errors resulted when the physician listed the acute myocardial infarction incorrectly, when a myocardial infarction occurred in a previous admission, or when myocardial infarction was ruled out (if it was the admitting diagnosis). A substantial limitation of five of these studies was that they selected patients based on claims data. Thus, the groups selected for review were only those patients with an ICD-9-CM code for myocardial infarction. Using this design, it was only possible to obtain estimates of disagreement in one direction; patients who had a condition coded in the clinical data set, but not in the claims data set, could not be examined. A second limitation of the previous studies is that their comparison gold standard was based on retrospective review of information recorded in the discharge summary or medical chart. Medical record data are limited by the unstructured way in which they are collected. Inaccuracies in these sources cannot be identified in such a study, and it is possible that in some disagreements with ICD-9-CM codes, the medical record is incorrect. Our study examined the suitability of billing data compared with clinical data (prospectively collected for cardiology research and patient care) for use in clinical outcomes research. The descriptors for coronary artery disease that we examined were those listed as important determinants of prognosis by an expert panel from the American College of Cardiology [16]. Methods Insurance Claims Data The administrative or insurance claims information comprised all discharge abstracts from Duke University Medical Center between July 1985 and May 1990 containing any procedure code for coronary arteriography. All discharged patients, regardless of insurance status or age, were routinely classified by ICD-9-CM codes recorded by trained medical record technicians based on the attending physicians listed discharge diagnoses, the discharge summary, and selected information from the progress notes and from the test result sections of the hospital chart [17]. These records contained up to 30 diagnostic codes and 9 procedure codes. After the technician had assembled the ICD-9-CM codes, the discharge abstract and the chart were returned to the attending physician for final approval by signature; ICD-9-CM codes were not generated for patients having outpatient cardiac catheterization unless they were subsequently admitted for further evaluation or treatment. The records for the subgroup of Medicare patients in this study were sent by Duke Hospital to the North Carolina Medicare intermediary and, thus, reflect the Duke Hospital data contained in the Health Care Financing Administration data sets. Clinical Database Data The clinical information consisted of important diagnostic and prognostic information about coronary artery disease routinely collected on standardized data forms by the cardiology fellow doing the cardiac catheterization for suspected ischemic heart disease. Information collected included details from the patient history, physical examination, laboratory studies, and cardiac catheterization, as previously described [18]. Each new fellow entering the catheterization laboratory was given a 3-hour training session on variable definitions and use of the data forms and was given an operations manual covering these details. In addition, all data were reviewed for accuracy by the attending angiographer associated with the case; additional consistency, range check, and other quality control measures were done during the data entry process by trained research technicians. This information was stored in the Duke Databank for Cardiovascular Disease, a completely separate and independent system from the hospital administrative records described above. Records Matching and Variable Definitions Records from the administrative and clinical files were matched by unique, patient hospital identification numbers and hospitalization dates. Only the first matching clinical record for each patient was included in the analysis. Twelve clinical variables were mapped to ICD-9-CM codes according to an algorithm developed by the Patient Outcomes Research Team for chronic ischemic heart disease (Table 1) (Romano PS, Roos LL. Unpublished observations). The variables studied were selected if they met two criteria: 1) They were considered to be determinants of prognosis for coronary artery disease according to an expert panel from the American College of Cardiology; 2) they could be mapped to diagnoses contained in the ICD-9-CM coding system [16, 17]. The definitions of the clinically identified conditions appear in the Appendix. Table 1. International Classification of Diseases-9-CM and Clinical Detail Map Appendix.Glossary of Terms Data Analysis Based on the clinical condition and the ICD-9-CM map described above, two-by-two tables were constructed to assess the agreement between the data sources. For the claims data, a condition was considered to be absent if it was not coded. For the clinical data, patients with missing data were excluded from the analysis for the specific missing condition. Kappa statistics were generated for each condition to measure agreement while controlling for chance agreement [19]. Confidence intervals and test statistics for proportions were calculated by the normal approximation. For the diagnoses of acute myocardial infarction, congestive heart failure, angina, and unstable angina, we reviewed a random sample of 15 clinical-positive and claims-negative charts as well as 15 claims-positive and clinical-negative charts for each diagnosis to illustrate the major reasons for disagreement. In addition to the comparisons made in the overall data sets, subsets defined by age, fiscal year, and sex were compared to determine if the coding accuracy varied according to these factors. Results The study group consisted of 12 937 consecutive patients having inpatient cardiac catheterization between July 1985 and May 1990. Although each record represented the first cardiac catheterization in the claims records, from the perspective of the clinical records, 89% involved the first catheterization, 8% involved the second catheterization, and the remaining 3% involved the third or subsequent catheterization. The patients had a mean age of 58.8 years, 34% were women, and the racial composition was 88% white, 10% black, and 2% other. At cardiac catheterization, the mean left ventricular ejection fraction was 52%. The distribution of the number of diseased major epicardial vessels (zero, one, two, or three) was 23%, 26%, 23%, and 28%, respectively. Overall, the study group characteristics were similar to those of other large angiographic registries except for the greater proportion of women and the higher mean age [20, 21]. Measures of Agreement Specific measures of agreement between clinical database and ICD-9-CM variables are listed in Table 2 in descending order of value (the agreement rate adjusted for chance agreement). Kappas ranged from 0.83 for diabetes mellitus to 0.09 for unstable angina. Of the 12 conditions, only 3 (diabetes, acute myocardial infarction, and hypertension) were identified by the claims data more than 50% of the time that they were identified by the clinical data. Table 2. Comparison of Agreement by Condition Ranked by Kappa Value In the clinical data set, two conditions were graded according to severity, congestive heart failure, and mitral regurgitation. With increasing severity levels, the claims data were more likely to identify the presence of these conditions. Claims data identified 31% of clinically identified congestive heart failure that was New York Heart Association class I and II and identified 45% of class III and IV heart failure (P < 0.0001) [22]. Similarly, claims data identified 40% of grades I and II mitral regurgitation and identified 69% of grades III and IV mitral regurgitation (P < 0.0001). When all diagnoses were considered together, the overall agreement of ICD-9-CM codes with clinical data was 0.75 (99% CI, 0.75 to 0.76). The proportion of conditions in the clinical data set identified by claims data was 0.39 (99% CI, 0.38 to 0.39) (Table 3). Stratified by fisc

Exercise Treadmill Score for Predicting Prognosis in Coronary Artery Disease

To determine the prognostic value of the treadmill exercise test, we evaluated 2842 consecutive patients with chest pain who had both treadmill testing cardiac catheterization. The population was randomly divided into two equal-sized groups and the Cox regression model was used in one to form a treadmill score that was then validated in the other group. The final treadmill score was calculated as follows: exercise time--(5 X ST deviation)--(4 X treadmill angina index). Using this treadmill score, 13% of the patients were found to be at high risk; 53%, at moderate risk; and 34%, at low risk. The treadmill score added independent prognostic information to that provided by clinical data, coronary anatomy, and left ventricular ejection fraction: patients with three-vessel disease with a score of -11 or less had a 5-year survival rate of 67%, and those with a score of +7 or more had a 5-year survival rate of 93%. The treadmill score was useful for stratifying prognosis in patients with suspected coronary artery disease who were referred to us for catheterization, and may provide a useful adjunct to clinical decision making in the larger population of patients being evaluated for chest pain.

Racial variation in the use of coronary-revascularization procedures. Are the differences real? Do they matter?

BACKGROUND Studies have reported that blacks undergo fewer coronary-revascularization procedures than whites, but it is not clear whether the clinical characteristics of the patients account for these differences or whether they indicate underuse of the procedures in blacks or overuse in whites. METHODS In a study at Duke University of 12,402 patients (10.3 percent of whom were black) with coronary disease, we calculated unadjusted and adjusted rates of angioplasty and bypass surgery in blacks and whites after cardiac catheterization. We also examined patterns of treatment after stratifying the patients according to the severity of disease, angina status, and estimated survival benefit due to revascularization. Finally, we compared five-year survival rates in blacks and whites. RESULTS After adjustment for the severity of disease and other characteristics, blacks were 13 percent less likely than whites to undergo angioplasty and 32 percent less likely to undergo bypass surgery. The adjusted black:white odds ratios for receiving these procedures were 0.87 (95 percent confidence interval, 0.73 to 1.03) and 0.68 (95 percent confidence interval, 0.56 to 0.82), respectively. The racial differences in rates of bypass surgery persisted among those with severe anginal symptoms (31 percent of blacks underwent surgery, vs. 45 percent of whites, P<0.001) and among those predicted to have the greatest survival benefit from revascularization (42 percent vs. 61 percent, P<0.001). Finally, unadjusted and adjusted rates of survival for five years were significantly lower in blacks than in whites. CONCLUSIONS Blacks with coronary disease were significantly less likely than whites to undergo coronary revascularization, particularly bypass surgery - a difference that could not be explained by the clinical features of their disease. The differences in treatment were most pronounced among those predicted to benefit the most from revascularization. Since these differences also correlated with a lower survival rate in blacks, we conclude that coronary revascularization appears to be underused in blacks.

Value of the History and Physical in Identifying Patients at Increased Risk for Coronary Artery Disease

Physicians frequently evaluate patients with symptoms that may represent angina. The initial assessment usually begins with a history, physical examination, electrocardiogram, and chest radiograph. On the basis of this initial assessment, the physician must decide whether to begin empiric therapy or to consider further evaluation with noninvasive testing, cardiac catheterization, or both. Additional testing is often justified on the grounds that much of the information collected in the initial assessment is soft data and not sufficiently precise to permit the accurate identification of patients at high or low risk. Further testing, although often justified, exposes the patient to additional risk and cost. Strategies for evaluating patients with suspected ischemic heart disease that maximize the quality of care while minimizing the use of unnecessary tests depend on the accurate identification of patients who need further evaluation. The accurate identification of high- and low-risk patients based on the physicians initial assessment would permit the development of cost-efficient strategies for evaluating patients with suspected ischemic heart disease. Stored in the Duke Database for Cardiovascular Disease is the accumulated experience at Duke of all patients with suspected coronary artery disease who were referred for cardiac catheterization [1-7]. At the time of cardiac catheterization, findings from the history, physical examination, electrocardiogram, chest radiograph, noninvasive tests, and catheterization are recorded. Patients are then prospectively followed at regular intervals. We have previously developed statistical models that use a subset of this informationthe history, physical examination, electrocardiogram, and chest radiographto estimate the anatomic severity of catheterization findings and to estimate long-term survival. Outpatients with chest pain who are evaluated in a physicians office might differ substantially from patients subsequently referred for cardiac catheterization [8]. Thus, we were not certain that models developed in the catheterization cohort would perform well when applied to outpatients. We describe the performance of models based on information from the physicians initial assessment when prospectively applied to a cohort of outpatients. We wished to determine whether a physicians office evaluation of a patient with nonacute chest pain could identify high-and low-risk patients and to evaluate the potential importance of this information in the delivery of cost-effective quality care. Methods Patients Our study sample included 1030 consecutive, symptomatic patients who had not had previous cardiac catheterization and who were referred for outpatient noninvasive testing at the Duke University Medical Center between 28 March 1983 and 31 January 1985. All patients had complete baseline evaluations that were done prospectively before testing. The sample included 602 patients referred by Duke cardiologists or fellows and 428 patients referred by other physicians at Duke or in the surrounding community. Our study sample comprised a consecutive series of patients with suspected coronary artery disease for whom the physician felt noninvasive testing was warranted. Baseline evaluations were done by a cardiology fellow or physician assistant who completed a standardized form containing all descriptors. The evaluation was facilitated by two other forms: a self-administered questionnaire completed by each patient and a referral form completed by the Duke staff cardiologist (for patients referred by cardiology staff) that together provided all descriptors. Chest pain histories were classified at the time of the patient interview by the examiner. Definitions and further descriptors have been previously described [6, 9-11]. The methods of data management and follow-up have been reported previously [6, 9-11]. In brief, baseline information was entered prospectively into the Duke Database for Cardiovascular Disease. Because missing information interrupts the clinical report process, descriptors were complete on all patients. Follow-up information was obtained at 1 and 3 years using a mailed, self-administered patient questionnaire. Patients not returning the questionnaire were contacted via telephone by trained interviewers. For patients who died, we obtained death certificates as well as physician and hospital records (including autopsy information when available), and we conducted telephone interviews with the next of kin to discuss the circumstances of the patients death. All deaths were classified by an independent events committee (blinded to baseline information). Analysis We examined three diagnostic outcomes and one prognostic outcome. The diagnostic outcomes (available only in the 168 patients subsequently referred for cardiac catheterization within 90 days) were the presence of significant coronary artery disease ( 75% luminal diameter narrowing of at least one major coronary artery); the presence of severe coronary artery disease (the presence of significant obstruction of all three major coronary arteries or of the left main coronary artery); and the presence of significant left main coronary artery obstruction. Survival at 3 years was the prognostic end point. In the survival model, patients who were referred for angioplasty or coronary artery bypass graft surgery or who were dying of noncardiovascular causes were censored (withdrawn alive) the first time one of these events occurred. The development of the predictive models evaluated in our study has been described previously [1-7], and model details are included in the Appendix. In brief, the models were developed in consecutive series of patients referred for cardiac catheterization between 1969 and 1983; none of these patients were included in the present study. The strategy used to develop the models required the division of patients into training and test samples to minimize spurious associations. Model development in each case was done entirely in the training sample. Logistic multiple regression [12] was used for diagnostic outcomes, and the Cox proportional-hazards regression model [13, 14] was used for survival. All candidate predictor variables were examined graphically to ensure that their relation with the outcome was modeled appropriately. When nonlinearities were present that would violate model assumptions, appropriate recoding or transformation of the variables was carried out so that model assumptions were satisfied in each case. To decrease the risk for spurious relations and overfitting the models, a series of clinical indexes were developed to reflect important areas of pathophysiology [4]. Forward stepwise variable selection was used to aid in identifying important baseline predictors. Selected interactions among predictor variables were also examined. When a final model had been developed, it was tested and validated in the independent test sample. Baseline variables important for estimating each of the diagnostic and prognostic outcomes are listed in Table 1. Baseline descriptors collected for each patient were entered into each model to generate a patient-specific estimate of the probability of each outcome. Model predictions of the likelihood of significant coronary artery disease, severe coronary artery disease, left main coronary artery disease, and survival at 3 years were generated for each outpatient in this study at the time of his or her initial evaluation based solely on information collected before noninvasive testing. Table 1. Characteristics Used To Estimate Outcomes* Assessing the quality of predictions requires the use of statistics unfamiliar to most clinicians. Two components of predictive quality were examined. Reliability, the concordance between predicted and observed outcomes, was assessed by grouping all patients into quantiles of predicted risk and graphically comparing the observed prevalence of the outcome as a function of the mean predicted risk for each quantile group. Discrimination, the ability to separate patients with and without the outcome of interest, was assessed in two ways. First, the distribution of predictions for patients with and for patients without each outcome was graphically compared. Second, a concordance probability or c-index was computed [5]. The c-index is calculated by pairing each patient who has the outcome with each patient who does not have the outcome and determining the proportion of patient pairs in which the patient with the outcome had a higher estimated probability. A c-index of 0.80, for example, can be interpreted as follows: Eighty percent of the time a patient with the outcome was given a higher predicted probability of the outcome than the patient without the outcome. The c-index ranges from 1 to 0, with 1 corresponding to perfect discrimination, 0.5 to random performance of a predictor, and 0 to perfectly incorrect discrimination. For a binary outcome, the c-index equals the area under the receiver-operating characteristic (ROC) curve [15]. To further show the discrimination of the survival model, the sample was divided into subgroups of equal size based on the risk for dying within 3 years, and Kaplan-Meier [16] empirical survival curves were calculated. Placing the Results in Perspective The two approaches to describing the discriminatory ability of the models (the distribution of predictions for patients with and without the outcome and the c-index) do not effectively communicate a perspective on the importance of information. A traditional approach to showing the discriminatory ability of two tests is to compare the ROC curves of each test. Receiver-operating characteristic curves show the tradeoff between sensitivity (among patients with the outcome, the proportion with a positive test) and specificity (among patients without the outcome, the proportion with a negative test), as the threshold value above which the test is conside

Prognostic value of a coronary artery jeopardy score

The prognostic value of a coronary artery jeopardy score was evaluated in 462 consecutive nonsurgically treated patients with significant coronary artery disease, but without significant left main coronary stenosis. The jeopardy score is a simple method for estimating the amount of myocardium at risk on the basis of the particular location of coronary artery stenoses. In patients with a previous myocardial infarction, higher jeopardy scores were associated with a lower left ventricular ejection fraction. When the jeopardy score and the number of diseased vessels were considered individually, each descriptor effectively stratified prognosis. Five year survival was 97% in patients with a jeopardy score of 2 and 95, 85, 78, 75 and 56%, respectively, for patients with a jeopardy score of 4, 6, 8, 10 and 12. In multivariable analysis when only jeopardy score and number of diseased vessels were considered, the jeopardy score contained all of the prognostic information. Thus, the number of diseased vessels added no prognostic information to the jeopardy score. The left ventricular ejection fraction was more closely related to prognosis than was the jeopardy score. When other anatomic factors were examined, the degree of stenosis of each vessel, particularly the left anterior descending coronary artery, was found to add prognostic information to the jeopardy score. Thus, the jeopardy score is a simple method for describing the coronary anatomy. It provides more prognostic information than the number of diseased coronary arteries, but it can be improved by including the degree of stenosis of each vessel and giving additional weight to disease of the left anterior descending coronary artery.(ABSTRACT TRUNCATED AT 250 WORDS)

Factors Affecting Sensitivity and Specificity of Exercise Electrocardiography Multivariable Analysis

Unlike the predictive value of a diagnostic test, which depends on the prevalence of disease in the population tested, its sensitivity and specificity have been assumed to be constants. This assumption was examined in patients who had both exercise electrocardiography and cardiac catheterization. The effects on sensitivity of factors from clinical history, catheterization, and exercise performance were defined by multivariable logistic regression analysis in 1,401 patients with coronary disease; effects on specificity were defined by a similar analysis in 868 patients without coronary disease. Five factors had significant, independent effects on exercise electrocardiographic sensitivity: maximal exercise heart rate, number of diseased coronary arteries, type of angina, and the patients age and sex. Only maximal exercise heart rate had a significant, independent effect on exercise electrocardiographic specificity. Thus, the sensitivity and specificity of exercise electrocardiography vary with clinical history, extent of disease, and treadmill performance; the sensitivity and specificity of other diagnostic tests may also vary.

Estimating the likelihood of significant coronary artery disease

Among 23 clinical characteristics examined in 3,627 consecutive, symptomatic patients referred for cardiac catheterization between 1969 and 1979, nine were found to be important for estimating the likelihood a patient had significant coronary artery disease. A model using these characteristics accurately estimated the likelihood of disease when applied prospectively to 1,811 patients referred since 1979 and when used to estimate the prevalence of disease in subgroups reported in the literature. Since accurate estimates of the likelihood of significant disease that are based on clinical characteristics are reproducible, they should be used in interpreting the results of additional noninvasive tests and in quantitating the added diagnostic value.

Identification of preoperative variables needed for risk adjustment of short-term mortality after coronary artery bypass graft surgery

OBJECTIVES The purpose of this consensus effort was to define and prioritize the importance of a set of clinical variables useful for monitoring and improving the short-term mortality of patients undergoing coronary artery bypass graft surgery (CABG). BACKGROUND Despite widespread use of data bases to monitor the outcome of patients undergoing CABG, no consistent set of clinical variables has been defined for risk adjustment of observed outcomes for baseline differences in disease severity among patients. METHODS Experts with a background in epidemiology, biostatistics and clinical care with an interest in assessing outcomes of CABG derived from previous work with professional societies, government or academic institutions volunteered to participate in this unsponsored consensus process. Two meetings of this ad hoc working group were required to define and prioritize clinical variables into core, level 1 or level 2 groupings to reflect their importance for relating to short-term mortality after CABG. Definitions of these 44 variables were simple and specific to enhance objectivity of the 7 core, 13 level 1 and 24 level 2 variables. Core and level 1 variables were evaluated using data from five existing data bases, and core variables only were examined in an additional two data bases to confirm the consensus opinion of the relative prognostic power of each variable. RESULTS Multivariable logistic regression models of the seven core variables showed all to be predictive of bypass surgery mortality in some of the seven existing data sets. Variables relating to acuteness, age and previous operation proved to be the most important in all data sets tested. Variables describing coronary anatomy appeared to be least significant. Models including both the 7 core and 13 level 1 variables in five of the seven data sets showed the core variables to reflect 45% to 83% of the predictive information. However, some level 1 variables were stronger than some core variables in some data sets. CONCLUSIONS A relatively small number of clinical variables provide a large amount of prognostic information in patients undergoing CABG.