Coronary artery bypass grafting (CABG) has become a

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1 Predictors of Stroke Risk in Coronary Artery Bypass Patients Guy M. McKhann, MD, Maura A. Goldsborough, MSN, Louis M. Borowicz, Jr, MS, E. David Mellits, ScD,* Ronald Brookmeyer, PhD, Shirley A. Quaskey, BS, William A. Baumgartner, MD, Duke E. Cameron, MD, R. Scott Stuart, MD, and Timothy J. Gardner, MD Zanvyl Krieger Mind/Brain Institute and Departments of Neurology, Surgery, and Biostatistics, Johns Hopkins University, Baltimore, Maryland Background. Stroke occurs after coronary artery bypass grafting with an incidence ranging between 0.8% and 5.2%. To identify factors associated with stroke, we prospectively examined a study cohort and tested findings in an independent validation sample. Methods. The study cohort comprised 456 patients undergoing coronary artery bypass grafting only, and the validation sample comprised 1,298 patients. Stroke was detected postoperatively by the study team and confirmed by neurologic consultation and computed tomographic scanning. Results. Five factors taken together were correlated with stroke: previous stroke, presence of carotid bruit, history of hypertension, increasing age, and history of Coronary artery bypass grafting (CABG) has become a routine surgical procedure for patients with coronary artery disease [1 3]; in the United States alone, 485,000 CABG procedures were performed in 1993 [4]. Although of great benefit, CABG is associated with adverse neurologic complications including stroke in a subgroup of patients. The reported incidence of stroke ranges widely from 0.8% to 3.2% in retrospective studies [5, 6] and from 1.5% to 5.2% in prospective studies [7, 8]. The mortality rate from stroke in CABG patients ranges For editorial comment, see page 322. from 0% to 38% [9, 10]. Thus, thousands of patients each year die of or are permanently disabled by stroke after CABG. As a result, much attention has been focused on the identification of factors associated with stroke. Previous studies have determined individual factors related to stroke, but these factors have not been examined in combination in a predictive model. This has led to conflicting reports in the literature. For example, some studies [11, 12] cite previous stroke as an important risk Accepted for publication Sep 26, Address reprint requests to Dr McKhann, Pathology 627-C, Johns Hopkins Hospital, 600 N Wolfe St, Baltimore, MD *This author is deceased. diabetes mellitus. The only significant intraoperative factor was cardiopulmonary bypass time. Probabilities were calculated, and patients were placed into low, medium, and high stroke-risk groups. In the validation sample, this model was able to rank the majority of patients with stroke into the high-risk group. Conclusions. These five factors taken together can identify the risk of stroke in patients having coronary artery bypass grafting. Recognition of the high-risk group will aid studies on the mechanism and prevention of stroke by modification of surgical procedures or pharmacologic intervention. (Ann Thorac Surg 1997;63:516 21) 1997 by The Society of Thoracic Surgeons factor, whereas another [13] has refuted its significance. Identification of these factors is of particular concern because the procedure is being performed in older patients as well as those with more complicated medical histories [14]. In light of the conflicting reports on the factors associated with stroke after CABG, the present study was designed to identify patients at high risk for stroke by examining possible predictive factors by use of a multiple logistic regression analysis. This analysis included factors known preoperatively as well as intraoperative factors. The predictions were then assessed in an independent validation sample. This strategy produced a method for identifying patients who have a higher stroke risk. Material and Methods Study Cohort patients. In the study cohort, we evaluated all patients undergoing first-time (n 412) or redo CABG (n 44) by four surgeons at our hospital from February 1992 through March 1993 (total 456). Each surgeon contributed approximately the same number of cases. Only patients undergoing CABG with no concomitant cardiac procedures, such as valve replacement, repair of a congenital heart defect, or carotid endarterectomy, were eligible. No exclusion criteria were used. operation. All 456 patients had cardiopulmonary bypass support with Sarns/3M roller-head pumps (Ann Arbor, 1997 by The Society of Thoracic Surgeons /97/$17.00 Published by Elsevier Science Inc PII S (96)

2 Ann Thorac Surg McKHANN ET AL 1997;63: PREDICTORS OF STROKE RISK 517 MI), Bentley 1040D heparin-coated arterial line filters, and either a Bentley Univox membrane oxygenator or a Bentley B10-Plus bubble oxygenator (Irvine, CA). Pulsatile flow perfusion (using the roller-head pump) was done according to surgeon preference. Cannulation of the aorta was performed in an area free from palpable plaque. Crystalloid cardioplegia was used in all patients, and anesthesia management consisted of a combination of benzodiazepines (midazolam hydrochloride, diazepam, and lorazepam), inhalation agents, and opiates (fentanyl citrate and sufentanil citrate). Intraoperative monitoring was accomplished with intraarterial and pulmonary artery catheters. Temperatures were monitored with rectal and esophageal probes. Arterial blood gas samples were obtained at 15-minute intervals while patients were on cardiopulmonary bypass. Alpha-stat blood gas management was used during hypothermic cardiopulmonary bypass. A cell-saving device (Haemonetics, Braintree, MA) was used for intraoperative blood conservation (cardiac surgical protocol). data collection. Twenty-six demographic and medical history variables believed to be relevant to an outcome of stroke were collected by the study investigators (M.A.G. and L.M.B.). Medical history variables were defined as patient report of a previous diagnosis by a physician. Physical examination was performed by either a physician s assistant or a surgical intern as well as the cardiac anesthesiologist. Patients were treated in a routine manner, and no additional invasive or noninvasive testing was performed for this analysis. Because data relative to cardiac function, such as ejection fraction, were not available on all patients, a cardiac sickness index was created to identify the severity of symptoms of coronary artery disease. This index considered the patient s preoperative location (home, hospital inpatient unit, or intensive care unit) and preoperative anticoagulation status and classified him or her into one of four groups. Intraoperative physiologic data were recorded by the perfusionist every 15 minutes during cardiopulmonary bypass. The primary outcome measure in this study was postoperative stroke. The presence of a stroke was detected by members of the study team who saw all patients postoperatively during daily visits to the cardiac surgical intensive care unit. In all instances, the diagnosis of stroke was made by a neurologist at our institution. Strokes were verified by head computed tomography in all but 1 patient in whom imaging was not performed. Secondary outcomes were death and length of hospital stay. statistical analysis. All statistical analyses were completed using the SAS Institute Inc statistical package (Cary, NC). A univariate analysis was done that analyzed the possible relationships between stroke and the variables collected. Contingency table analyses for categoric data and t tests or their nonparametric analogue (Mann- Whitney U test) for continuous variables were used. Some variables required log transformations to achieve approximate normality or constancy or additivity of scale. Any variable that achieved a p value of 0.1 or less in the univariate analysis was included in a multiple logistic regression analysis that resulted in a subset of variables related to stroke. Two separate multivariate analyses were completed. In the first, only variables known preoperatively were examined, and in the second, preoperative variables were combined with intraoperative variables. From the variables that were significant in these analyses, a formula was produced, and probabilities of stroke were calculated as a function of these variables. The sample size used was not predetermined but was adequate for analyzing the individual variables for 0.05 (two-tailed) and allowed for detection of a doubling of the stroke rate with and without the variable in question at a power of about 80%. When continuous variables were analyzed, greater power was achieved. Validation Sample patients. From January 1994 through October 1995, all 1,320 patients undergoing first-time CABG (n 1,221) and redo CABG (n 99) at this institution were followed prospectively in an attempt to validate the stroke risk model created from the cohort sample. All eight cardiac surgeons, three of whom were involved in the cohort study, participated in the validation sample. Only data on the six variables that were significant in the cohort study were collected for this sample. operation. Operative techniques in this validation sample were the same as previously described, with four exceptions, the first two of which were based on surgeon preference. First, the majority of patients received crystalloid cardioplegia and the remainder, blood cardioplegia. Second, there was more variability in the lowest temperature used during cardiopulmonary bypass. Third, six different commercially available membrane oxygenators were used. Fourth, bladder temperature monitoring replaced rectal temperature monitoring for all patients. statistical analysis. Using the formula derived from the study cohort, a probability for each patient in the validation sample was calculated. The average stroke rate and the 95% confidence intervals were used to define the parameters of a low-risk group ( 0.02), a medium-risk group ( ), and a high-risk group ( 0.05) for stroke. Patients were then stratified by probability into one of these three groups. The actual number of strokes was compared with the expected number of strokes in each group. Results Demographic data for the study cohort are presented in Table 1. Twenty-six patients (5.7%, 95% confidence interval 3.6% to 7.9%) in the study cohort had a stroke associated with CABG. Factors that were significantly associated with stroke at the univariate level (p 0.1) are shown in Table 1.

3 518 McKHANN ET AL Ann Thorac Surg PREDICTORS OF STROKE RISK 1997;63: Table 1. Univariate Analysis of Characteristics of Study Cohort a Variable No Stroke Stroke p Value b Preoperative Demographics No. of patients 430 (94.3) 26 (5.7)... Age (y) (53.3) 5 (19.2) 0.01 c (34.2) 15 (57.7) (12.6) 6 (23.1) Retired 150 (37.6) 15 (60.0) 0.05 c White 384 (89.3) 23 (88.5) 0.89 Male 322 (74.9) 17 (65.4) 0.28 Medical history Previous stroke 21 (4.9) 8 (30.8) c Hypertension 271 (63.0) 24 (92.3) 0.01 c Diabetes mellitus 115 (26.7) 15 (57.7) 0.01 c Transient ischemic 23 (5.5) 4 (15.4) 0.10 c attack Smoking ( 10 packyears) 287 (67.4) 16 (61.5) 0.54 Myocardial infarction 252 (58.9) 18 (69.2) 0.30 Hypercholesterolemia 232 (54.0) 12 (46.2) 0.44 Obesity at any time 128 (30.1) 10 (38.5) 0.37 Peripheral vascular 83 (19.6) 8 (30.8) 0.17 disease Angioplasty 83 (19.4) 2 (8.0) 0.16 Syncope 34 (8.2) 2 (7.7) 0.93 Seizures 11 (2.6) 0 (0) 0.41 Physical examination Carotid bruit 65 (15.3) 12 (46.2) c Mean systolic blood c pressure (mm Hg) Mean diastolic blood pressure (mm Hg) Admission Anticoagulated 185 (43.3) 12 (46.2) 0.78 (heparin) Transfer from another 155 (36.9) 11 (42.3) 0.34 hospital Surgical procedure 42 (9.8) 2 (7.7) 0.73 (redo CABG) Cardiac sickness index At home/elective 186 (45.6) 11 (42.3) 0.74 Hospitalized with 55 (13.5) 3 (11.5) 0.78 angina Hospitalized and 106 (26.0) 7 (26.9) 0.92 requiring IV heparin In ICU and 61 (15.0) 5 (19.2) 0.56 requiring IV heparin Mean blood urea nitrogen (mg/dl) Mean creatinine (mg/ dl) Triple-vessel disease 251 (60.1) 18 (69.2) 0.35 Intraoperative CPB time (min) c Use of intraaortic 21 (4.9) 5 (19.2) 0.01 c balloon pump Aortic cross-clamp time (min) c Table 1. Continued Variable No Stroke Stroke p Value b Lowest rectal c temperature ( C) Use of membrane 222 (51.6) 20 (76.9) 0.05 c oxygenator Highest peak flow c rate on CPB (L/min) Average MAP on CPB c (mm Hg) Lowest MAP on CPB c (mm Hg) Lowest esophageal c temperature ( C) Use of pulsatile pump 104 (24.2) 10 (38.5) 0.10 c Use of cell-saving 218 (50.7) 15 (57.7) 0.49 device Average peak flow rate on CPB (L/min) Lowest peak flow rate on CPB (L/min) Highest Po 2 level on CPB (mm Hg) Average Po 2 level on CPB (mm Hg) Highest MAP on CPB (mm Hg) a Numbers in parentheses are percentages. b The p values were calculated by 2 analysis for dichotomous variables and Student s t test for continuous variables. c These variables were used in the multiple logistic regression analysis. CABG coronary artery bypass grafting; CPB cardiopulmonary bypass; ICU intensive care unit; IV intravenous; MAP mean arterial blood pressure; Po 2 oxygen tension. Logistic Regression Analyses preoperative model. Preoperative factors significant in the logistic regression model were previous stroke, increasing age, history of diabetes mellitus, history of hypertension, and carotid bruit (Table 2). Variables were weighted according to levels of significance, and a probability of stroke risk was calculated for each combination of risk factors (Fig 1). For example, a 70-year-old patient with no other risk factors has a probability of 0.01 and would be placed in the low-risk group. In contrast, a patient of the same age without a previous stroke but with a carotid bruit and a history of hypertension and diabetes mellitus has a probability of 0.30 and would be placed in the high-risk group. Probabilities could not be calculated for 4 patients because carotid bruit data were missing. The distribution of patients by risk factor combinations is also shown in Figure 1. As can be seen in the right-hand columns in Figure 1, some of the probabilities are based on very few patients (n 29). preoperative and intraoperative model. In this analysis, preoperative and intraoperative variables were combined. Although use of a membrane oxygenator was significant in the univariate analysis, we did not include it in the multiple logistic regression analysis because of potential bias, as discussed later in the Comment section. Factors significant in this logistic regression analysis were previous stroke, time on cardiopulmonary bypass,

4 Ann Thorac Surg McKHANN ET AL 1997;63: PREDICTORS OF STROKE RISK 519 Table 2. Multiple Logistic Regression Analysis for Study Cohort Step Variable Odds Ratio p Value Preoperative risk factors 1 CVA Yes CB Yes HTN Yes Age DM Yes Intercept Preoperative and intraoperative risk factors 1 CVA Yes CPB time HTN Yes DM Yes Age (y) Intercept Age age of patient on admission; CB presence of carotid bruit; CPB time cardiopulmonary bypass time (used as a continuous variable; odds ratio shown is for 30 minutes; a patient with a CPB time of 120 minutes would be 6.6 times as likely to have a stroke); CVA previous stroke; DM history of diabetes mellitus; HTN history of hypertension. history of hypertension, increasing age, and history of diabetes mellitus (see Table 2). When intraoperative variables were combined with preoperative variables, cardiopulmonary bypass time was the only intraoperative variable to reach significance, and the presence of a carotid bruit as a risk factor was no longer significant. The probability of stroke for each combination of risk factors was calculated using the intercept variable (data not shown). Validation Sample A total of 1,320 patients were studied. Three patients died in the immediate postoperative period and could not be evaluated for the outcome of stroke. In this sample (n 1,317), 43 patients had postoperative strokes (3.3%, 95% confidence interval 2.2% to 4.2%). The incidences of the risk factors increasing age, history of hypertension, history of diabetes mellitus, and previous stroke in this sample were not significantly different from those in the study cohort. In the validation sample, the incidence of carotid bruit was lower (p 0.05), and patients were on cardiopulmonary bypass longer (p 0.001). There were 19 patients in this sample for whom data were incomplete, and therefore, stroke probabilities could not be calculated. For the remaining 1,298 patients, probabilities were calculated, and patients were classified into low-, medium-, and high-risk groups (Table 3). Comparison of the actual versus the predicted number of strokes for each risk group is shown. For example, in the high-risk group, the model predicted 67.5 strokes, and there were actually 30. Comparison of Study Cohort and Validation Sample During the validation sample period, the incidence of stroke was significantly lower (p 0.05) than in the study cohort (3.3% versus 5.7%). The reason for this difference is not clear. A logistic regression analysis found significant differences in stroke rate after accounting for the known risk factors. Carotid Duplex Results In the study cohort, complete carotid duplex ultrasound data were obtained for 80 patients as part of routine clinical care. This was done if a carotid bruit was heard or if there was a previous history of stroke or transient ischemic attack. Of the 80 patients, 65 had 0% to 60% stenosis reported in the right internal carotid artery, and 55 in the left. Fifteen patients had 60% to 100% stenosis of the right internal carotid artery, and 25 in the left. Because Doppler information was not available for all CABG patients in the study cohort, this variable was not analyzed. In addition, there did not appear to be any pattern of association between side of carotid bruit and anatomic location of postoperative stroke. Length of Stay and Mortality In the study cohort, an outcome of stroke dramatically increased mean length of hospital stay from 8.7 [ 6.3 (standard deviation)] days to 25.2 ( 21.9) days. The overall mortality rate for this cohort was 4.2% (19/456). The mortality rate for stroke patients was 19.2% (5/26). In the validation sample, the mean length of stay of the stroke population was 20.7 ( 27.6) days versus 8.2 ( 8.5) days for all other patients. The overall mortality rate in the validation sample was 2.9% (38/1,320). The mortality rate for stroke patients was 18.6% (8/43). Comment Prediction of Stroke Risk In a prospective study of 456 patients undergoing CABG, we identified five preoperative factors associated with stroke: increasing age, history of previous stroke, hypertension, diabetes mellitus, and presence of carotid bruit. When these factors are taken together in a risk model, we can place patients in low-, medium-, and high-risk

5 520 McKHANN ET AL Ann Thorac Surg PREDICTORS OF STROKE RISK 1997;63: Fig 1. Calculating probability of stroke using factors known preoperatively. To find the probability of stroke for any patient, respond yes ( ) or no ( ) to the following questions: Has the patient had a previous stroke (CVA)? If no, move to the left side of the figure. Does the patient have a carotid bruit (CB)? Does the patient have a history of hypertension (HTN)? Does the patient have diabetes mellitus (DM)? Match this column with the patient s age group (on admission), and you will identify the patient s estimate of stroke risk. Low stroke risks are shown in black, medium stroke risks in blue, and high stroke risks in red. The predicted probabilities were calculated using the multiple logistic regression results given in Table 2 with the intercept term of (CVAPOST/Total patients number of patients with postoperative stroke/ total number of patients with these risk factors.) Table 3. Comparison of Actual Versus Predicted Stroke Probabilities for Cohort and Validation Groups Probabilities No. of Patients No. of Actual Strokes Mean Probability a Expected No. of Strokes Cohort population Low ( 0.02) Medium ( ) High ( 0.05) Totals Validation population Low ( 0.02) Medium ( ) High ( 0.05) Total 1,298 b 43 a This is the calculated mean probability for all patients in these groups. b This total excludes 19 patients with incomplete data and 3 patients who died. groups. The major implication of this study compared with previous studies is that individual risk factors should not be viewed in isolation. Failure to do so has led to conflicting results regarding carotid bruit, previous stroke, and age [7, 15, 16]. For example, on the basis of our model, a 70-year-old patient without other risk factors is at low risk for stroke. Hence, age taken in isolation should not be used to determine stroke risk. When preoperative variables were combined with intraoperative variables, cardiopulmonary bypass time was the only intraoperative factor to be significant. The fact that other intraoperative variables did not reach significance further emphasizes the importance of the identified preoperative risk factors in this model. Use of a membrane oxygenator was significant in the univariate analysis but was excluded from the multivariate analysis. The choice of oxygenator was based on surgeon preference, expected length of cardiopulmonary bypass, and other patient characteristics. Because of selection biases, we were not able to determine the validity of this variable, and it was removed from the analysis. Although a general belief exists that membrane oxygenators are superior [17] and although there is a trend toward exclusive use of membrane oxygenators, we are not aware of any studies evaluating the interaction of oxygenator type and stroke. Validation Sample To test the efficacy of the model based on preoperative risk factors, we studied a subsequent sample of 1,298 patients undergoing CABG at our institution. There was a discrepancy between the expected and actual occurrence of strokes in the validation sample. As shown in Table 3, the model overpredicted the number of strokes in the high-risk group compared with the study cohort. This may be due to the fact that some of the higher probabilities in the study cohort were based on only a few patients. Therefore, this suggests that assigning a precise stroke risk to an individual patient is unreliable. To enhance the clinical usefulness of this stroke model, three stroke-risk groups were created (low, medium, and high). The average stroke rate and the 95% confidence intervals were used to define the parameters of these three groups. After placing patients into these groups, the model successfully ranked them into low, medium, or high stroke-risk groups. There were several differences between the study cohort and validation sample that may explain the discrepancy in the predicted and actual number of strokes. First,

6 Ann Thorac Surg McKHANN ET AL 1997;63: PREDICTORS OF STROKE RISK 521 we know that there were changes in operative procedures, and inclusion of more surgeons in the study might have added currently unidentified technical changes. Second, the populations were sampled during two time periods, and it was observed that the stroke rate fluctuated to some extent during different months. We have identified those differences of which we are aware, but it seems unlikely that these account for all the variation. We are continuing to seek an explanation for this change in incidence of stroke in the high-risk group of patients. Implications for Study Design We suggest that the prevention of stroke in CABG patients can be considered in stepwise fashion. First, individuals identified by the preoperative risk factors (see Fig 1) as having a higher probability of stroke should be evaluated more closely. The predictive factors identified in this study are all associated with or are consequences of atherosclerotic disease and potentially undiagnosed extracranial or intracranial disease. Atherosclerotic involvement of the carotid arteries as detected by carotid bruit can be further defined with respect to degree of stenosis by ultrasound techniques [16]. Atherosclerosis of the ascending aorta can be determined intraoperatively, and a calcified aorta may lead to more embolic phenomena during cross-clamping [18]. We suggest that highrisk patients be targeted for carotid duplex scanning, epicardial echocardiography, or both of these, neither of which are done routinely. Identification of this high-risk population makes this approach both logistically and economically feasible. Second, there are patients for whom modification of surgical management, such as perfusion flow rate and blood pressure, may be important. Studies correlating physiologic intraoperative changes with neurologic outcome have used methods such as quantitative electroencephalography [19] and transcranial Doppler study [20]. These methods show promise for evaluating the shortterm and long-term effects of intraoperative physiologic variables. Third, a number of pharmacologic agents, such as gangliosides [21], glutamate receptor antagonists, and antioxidants, have the potential to minimize neuronal damage [22] and decrease the occurrence of stroke. The design of any such pharmacologic intervention study should recognize the risk factors identified in this report. Finally, we suggest that the strategy used in this study for developing predictive models based on prospective data could be applied to other cardiac surgical procedures. In addition, other neurologic outcomes such as postoperative encephalopathy and cognitive change should be analyzed in a similar fashion. Use of this approach will allow the design and the evaluation of methods to protect the brain from injury. This research was supported by The Research Network on Successful Aging of The John D. and Catherine T. MacArthur Foundation; The Charles A. Dana Foundation; the Seaver Institute; and The AIREN Foundation. We thank Drs Pamela Talalay and Peter S. Greene for their assistance in the preparation of this manuscript. References 1. Jenkins CD, Stanton BA, Savageau JA, Denlinger P, Klein MD. Coronary artery bypass surgery: physical, psychological, social, and economic outcomes six months later. JAMA 1983;250: Califf RM, Harrell FE, Lee KL, et al. The evolution of medical and surgical therapy for coronary artery disease. A 15-year perspective. JAMA 1989;261: Brillman J. Central nervous system complications in coronary artery bypass surgery. Neurol Clin 1993;11: Summary national hospital discharge summary survey. 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