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J Thorac Cardiovasc Surg 2008;135:503-511
© 2008 The American Association for Thoracic Surgery

Surgery for Acquired Cardiovascular Disease

Optimal timing of coronary artery bypass after acute myocardial infarction: A review of California discharge data

^a Division of Cardiac Surgery, The Johns Hopkins Medical Institutions, Baltimore, Maryland
^b Department of Surgery, The Johns Hopkins Medical Institutions, Baltimore, Maryland

Received for publication June 30, 2007; revisions received October 6, 2007; accepted for publication October 19, 2007.

^_* Address for reprints: David D. Yuh, MD, Division of Cardiac Surgery, The Johns Hopkins Hospital, 600 North Wolfe Street, Blalock 618, Baltimore, MD 21287. (Email: dyuh{at}csurg.jhmi.jhu.edu).

	Abstract

Top Abstract Introduction Materials and Methods Results Discussion Conclusions Table E1 Table E2 Table E3 References

 
Objective: The optimal timing for coronary artery bypass grafting after acute myocardial infarction is not well established. The California Discharge Database facilitates the study of this issue by providing data from a large patient cohort free of institutional bias. We examine the timing of coronary artery bypass grafting after acute myocardial infarction on short-term outcomes.

Methods: We reviewed California Discharge Data to identify 40,159 patients who were hospitalized for acute myocardial infarction (day 0) and underwent subsequent coronary artery bypass grafting. Patients were stratified by the timing of coronary artery bypass grafting to "early" (days 0–2) and "late" groups (day 3 or later). The primary outcome variable was all-cause hospital mortality. Multiple logistic and linear regression and propensity analyses assessed the risk of adverse events, controlling for factors associated with preoperative clinical acuity, including the Charlson Comorbidity Index, shock, mechanical ventilation, and the use of intra-aortic balloon counterpulsation.

Results: Of 9476 patients identified, 4676 (49%) were in the early coronary artery bypass grafting group and 4800 (51%) were in the late coronary artery bypass grafting group. A total of 444 patients (4.7%) died during hospitalization, with a peak mortality rate of 8.2% among patients undergoing coronary artery bypass grafting on day 0, declining to a nadir of 3.0% among patients undergoing coronary artery bypass grafting on day 3. The mean time to coronary artery bypass grafting was 3.2 days. Patients undergoing early coronary artery bypass grafting experienced a higher mortality rate than those undergoing late coronary artery bypass grafting (5.6% vs 3.8%, P < .001). Early coronary artery bypass grafting was an independent predictor of mortality after controlling for clinical acuity and on propensity analysis (odds ratio 1.43, P = .003).

Conclusion: Patients undergoing coronary artery bypass grafting within 2 days of hospitalization for acute myocardial infarction experienced higher mortality rates than those undergoing coronary artery bypass grafting 3 or more days after acute myocardial infarction, independently of clinical acuity. This suggests that coronary artery bypass grafting may best be deferred for 3 or more days after admission for acute myocardial infarction in nonurgent cases.

	Introduction

Top Abstract Introduction Materials and Methods Results Discussion Conclusions Table E1 Table E2 Table E3 References

 
Much attention has focused on the outcomes of coronary artery bypass grafting (CABG) in the setting of acute myocardial infarction (AMI).^1-3 Although it seems clear that patients who undergo CABG after AMI possess a higher risk of short-term mortality compared with patients who undergo elective CABG, the optimal timing of surgical revascularization remains controversial.^1,4-6 Recent randomized controlled trials and meta-analyses performed in the setting of AMI have convincingly demonstrated that primary percutaneous coronary interventions (PCIs) yield superior results to thrombolytic therapy with decreased rates of death, reinfarction, and stroke.^7-9 Consequently, an increased number of patients are receiving coronary angiography in the setting of AMI.^10,11 This, in turn, has led to an increase in identification of candidates for surgical coronary revascularization (ie, in the setting of failed angioplasty or left main/multivessel disease). With this focus on CABG in the context of AMI and new trends in early management, the optimal timing of CABG after AMI should be scrutinized.

Although no definitive recommendation exists regarding the appropriate timing of CABG after recent AMI, the notion that these patients assume greater risk for short-term mortality is gaining consensus.^5,12-14 This is not surprising given that the majority of patients who undergo early CABG present with a higher degree of clinical acuity, which, in turn, translates to higher mortality rates. For the stable patient post-AMI for whom the culprit lesion has been effectively treated with PCI, it is common practice to discharge the patient to recover for some time before undergoing CABG electively. Less certain, however, is the optimal management of patients post-AMI who cannot be discharged from the hospital before CABG because of a tenuous PCI result, disease severity, unstable angina, or compromised ventricular function. The optimal timing for CABG in these patients post-AMI was the focus of this study in which we performed a retrospective review with multivariable and propensity-based adjustments. We hypothesize that by controlling for both clinical acuity and early surgical propensity, an optimal time interval between AMI and CABG can be identified such that early postoperative mortality is reduced.

	Materials and Methods

Top Abstract Introduction Materials and Methods Results Discussion Conclusions Table E1 Table E2 Table E3 References

 
Data Source
The State of California Office of Statewide Health Planning and Development (Healthcare Quality and Analysis Division) provided hospital discharge data for the years 1999 to 2005. The State of California requires all licensed hospitals to submit data on all discharged patients every 6 months. These records thus comprise a 100% sampling of California nonfederal hospital discharges. All data are de-identified and include demographic information, including age, gender, race, information on primary and concomitant diagnoses recorded and procedures performed, discharge information (eg, disposition to skilled nursing facility, death), and administrative information (eg, costs, payer information, length of stay, level of care). Unlike many other databases, the California discharge database discriminates between prior and new diagnoses with respect to each hospital admission and identifies the dates of procedures and operations performed. These features facilitated the examination of outcomes related to the timing of CABG after AMI. Because individual patients are not identified in this multicenter registry report, the need for consent and institutional review board approval is waived at The Johns Hopkins Medical Institutions.

Study Design
A retrospective review of California discharge data was performed for the years 1999 to 2005. We identified all adult patients (>17 years of age) who were admitted to a California hospital with the primary diagnosis of AMI as identified by International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis codes (410.x).¹⁵ We combined these patients with those who underwent CABG of any type during their hospitalization (also identified by ICD-9-CM procedure codes 36.x).

Patients were excluded if they had valvular pathology (ie, stenosis, insufficiency or active endocarditis of the mitral, aortic, pulmonary, or tricuspid valves) or underwent any concomitant cardiac surgical procedures such as mitral or aortic valve repair or replacement, ventricular remodeling, or ventricular assist device placement. In addition, patients were excluded if they presented with "do not resuscitate" status on admission or if they were transferred from another hospital (because we did not have information on the timing of AMIs in these patients) (Table E1).

Creation of Variables
Variables crucial to the analysis not present in the data set were derived using existing data set variables and known ICD-9-CM codes. The primary derived variable was the timing of CABG after AMI. The data set parameters yielded time intervals expressed in "days" rather than finer time intervals of hours or minutes. In this analysis, we designated day 0 as the initial day of hospital admission. Baseline independent variables describing medical status and outcome variables (other than death) were developed from ICD-9-CM coding and are subsequently listed in Table E2. We used the Charlson scoring system¹⁶ with Deyo adaptation¹⁷ as a standardized index reflecting the overall burden of comorbidities in the study population. Our primary outcome variable was all-cause in-hospital mortality.

Patient Groups
Because of the lack of standardized timing intervals for the performance of CABG after AMI, we grouped patients into "early" and "late" groups on the basis of the median time to CABG for our study population (day 3). Early CABG was thus defined as CABG performed on hospitalization day 0, 1, or 2, and late CABG was defined as CABG occurring on or after day 3. We also examined differences in outcomes dichotomized between other time points such as day 0 versus day 1 and after, day 0 and 1 versus day 2 and after, and so forth.

Statistical Analysis
Comparisons of baseline characteristics between study groups were performed using the Student t test for continuous variables and the chi-square test for categoric variables. Mortality was first assessed for all risk factors using a univariate model. Significant predictors of mortality (both chronic and acute) were incorporated into a multivariable logistic regression model in a stepwise fashion to assess the effect of timing of CABG on mortality. Operative timing and mortality were plotted for all time points present to determine whether an optimal time for the performance of CABG after AMI exists.

To assess selection bias not controlled for in our multivariate model and to counter the censoring that occurs from early mortality, we used a propensity-adjustment model. We developed propensity scores (based on the likelihood of receiving early CABG on days 0, 1, or 2) derived from a logistic regression model incorporating 35 potential predictors of operative timing (Table E3). We incorporated the resultant propensity scores into a logistic regression model to negate selection bias in examining the effect of propensity for early CABG on in-hospital mortality. We also used quintile stratification for both internal validation of our technique and examination of the effects of timing in different subsets of propensity.

All odds ratios (ORs) are presented with 95% confidence intervals. All statistical analysis was performed with the aid of STATA software (version 9.0, StataCorp LP, College Station, Tex).

	Results

Top Abstract Introduction Materials and Methods Results Discussion Conclusions Table E1 Table E2 Table E3 References

 
Between 1999 and 2005, California hospitals recorded a total of 443,069 patient discharges with the primary diagnosis of AMI. Of these patients, 40,159 underwent CABG during their hospitalization. After exclusion of children (<18 years of age), hospital transfers, patients with concomitant cardiac procedures, and patients with incomplete data, 9476 patients were included in the final study population.

We noted that 75% of CABG (n = 7086) procedures were performed during the first 5 days of hospitalization ( Figure 1). The median time to CABG was 3 days (mean 3.2 ± 3.0). By stratifying according to this median, 4676 patients (49%) were classified as the early CABG group (CABG performed on days 0, 1, or 2) and 4800 patients were classified as the late group (CABG performed on day 3 or later). The early group consisted of a lower percentage of female patients compared with the late group (28% vs 33%, P < .001). Both groups had similar baseline comorbidities with mean Charlson scores of 5.2 and 5.8, respectively (P < .001) ( Table 1). As expected, patients in the early CABG group were of higher acuity with greater proportions presenting with shock and requiring intra-aortic balloon pump (IABP) counterpulsation. Of note, the early group had a lower percentage of subendocardial infarcts (45% vs 61%, P < .001).

View larger version (33K): [in this window] [in a new window]   Figure 1. CABG volume and mortality over time. Distribution of CABG volume (left Y-axis, number of cases per day) and percent mortality (right Y-axis) per day of hospitalization. Dashed horizontal line represents total mortality over the study period (4.8%). Black arrow points to nadir of mortality occurring on day 3. CABG, Coronary artery bypass graft.

Univariate analysis revealed early CABG to be highly associated with an increased risk of mortality (5.6% vs 3.8%, P < .001). This positive association was confirmed with multivariable logistic regression analysis controlling for gender, baseline comorbidities as assessed by the Charlson index, and markers of clinical acuity, including IABP, shock, cardiac arrest on admission, and ventilation before CABG ( Table 2). The OR of 1.43 corresponds to a 43% increase in the risk of death among patients who underwent early CABG, controlling for the aforementioned markers of clinical acuity. Other strong predictors of death included IABP, shock on admission, cardiac arrest on admission, female gender, and Charlson index.

To determine optimal operative timing in patients with AMI presenting with high clinical acuity, we examined those patients with transmural infarctions (ie, acute infarctions not coded as "subendocardial") who were classified as having shock or who underwent IABP placement. Among these patients, we noted that the lowest mortality rates were observed when CABG was performed on hospital day 3 ( Figure 2). As for the entire study population, mortality rates increased after day 4 among patients in this high-acuity group. The overall in-hospital mortality rates for patients presenting with shock and IABP placement were 24% (90/480 patients) and 7% (65/881 patients), respectively.

View larger version (27K): [in this window] [in a new window]   Figure 2. Mortality in patients undergoing acute CABG. Percent mortality for those patients with transmural AMI (defined as acute infarctions not coded as "subendocardial") and shock (light grey bars) or IABP placement preoperatively (dark grey bars). Note that for both patient sets, the nadir of mortality occurs on hospital day 3. Total patient numbers for each day (n) are given below each bar. IABP, Intra-aortic balloon pump; CABG, coronary artery bypass graft.

	Discussion

Top Abstract Introduction Materials and Methods Results Discussion Conclusions Table E1 Table E2 Table E3 References

More recently, retrospective examinations have concluded that CABG should be deferred, when possible, for 3 or more days after AMI.^12,13,20 Lee and colleagues^13,20 have conducted several studies describing the appropriate timing of CABG after AMI. By using the state of New York database, the authors showed that the risk of early CABG is substantially higher before hospital day 3, with a doubling of mortality risk compared with patients who underwent later surgery. This positive association between early CABG and mortality was seen to be particularly important for transmural infarcts. Another study by Voisine and associates¹² concluded that CABG is best deferred for a period of 7 days after AMI. One should note, however, that this study examined only 77 subjects from a single institution (1991–2005) who underwent CABG within 24 hours of admission.

Although these prior studies are valuable in supporting the notion that delayed CABG is preferable under elective clinically stable circumstances, they have not specifically addressed patients with AMI presenting with higher clinical acuity. Kamohara and colleagues¹⁴ attempted to examine this issue by focusing on patients who underwent urgent CABG only. In their report, they conclude that patients who underwent early CABG (ie, within 6 hours of AMI) experienced higher mortality rates (9.1% vs 2.9%) than those who underwent CABG later in their course (ie, 6–24 hours after AMI). Although this study attempted to examine the effect of timing among acutely ill patients, it is a single-center report that did not include patients undergoing CABG 24 or more hours after AMI. It is possible that outcomes would have improved among these patients if CABG had been further delayed.

In our analysis, we attempted to bridge this gap in knowledge by focusing on hospitalized patients, identifying a modern cohort of patients among multiple centers who presented with higher clinical acuity than those reported in prior studies but did not require urgent CABG. We used this strategy to determine the optimal timing of surgery among patients with AMI who underwent CABG during their initial hospitalization.

Our study uniquely possesses an even distribution of post-AMI CABG timing intervals. With 1477 patients undergoing CABG on their initial hospital day, early CABG was well represented in this sample. The fact that we only examined patients who required hospital admission may partially explain why the in-hospital mortality rate of 4.8% may be higher than previously reported mortality rates, typically ranging from 2.3% to 3.3%.^12,20

This study is also distinguished by the robust statistical methods we used to control bias inherent in its retrospective design. We addressed 2 important statistical problems. First, patients who underwent early CABG inherently assumed a greater mortality risk associated with their increased level of acuity, constituting a selection bias. Although traditional multivariate models can attempt to limit this risk, an obvious flaw is the difficulty of accounting for all potential confounders. Second, patients who underwent early CABG and died are excluded from being a part of the late CABG group. This effect, termed censoring, leads to relative inequality between the 2 groups and violates a fundamental assumption of multivariate models. Because those patients who die cannot be part of the late group, a bias that inflates late group survival occurs. In the absence of a gold standard blinded, randomized controlled trial, these effects are difficult to control.

We attempted to address these statistical concerns by using a propensity-adjusted analysis. The use of propensity scores represents another tool to help eliminate bias in a retrospective study and has been used successfully in other landmark surgical studies.^21-23 The propensity score (ranging from 0–100) predicts which patients are likely to receive a treatment or, in this case, early CABG. Propensity scores are particularly useful when the primary outcome is rare. Unlike traditional multivariate analyses, the propensity-based approach addresses the issues of selection bias and censoring by adjusting for the likelihood of receiving early CABG, not simply by adjusting for known confounders. We think that the use of a propensity-based approach is an effective way to address the issue of timing in this analysis.

By using propensity-adjusted analysis, the findings of this study are consistent with previous reports showing that early CABG (before day 3) is associated with an increased risk of mortality. We found an approximate doubling of mortality among those patients who underwent CABG before day 3 (5.6% vs 2.8%). This effect was independent of factors associated with acuity and propensity-based analysis. It is noteworthy that on quintile assessment, the effect seemed to be restricted to the lowest propensity quintile. In other words, those patients with low likelihood for undergoing early CABG derived the worst effect from receiving early CABG. These results suggest that early elective CABG in the setting of AMI is associated with a heightened postoperative mortality risk that might otherwise be ameliorated by deferring the operation. Our analysis suggests that 3 to 5 days seems to be an optimal timing window for performing an elective CABG after AMI.

An unexpected observation from this study showed that postoperative mortality rates trended upward after day 5. In fact, patients who underwent CABG between days 14 and 28 experienced a 12.5% risk of mortality. In contrast, previous studies have noted a steady downward trend in mortality with increasing time after AMI. In our cohort, among patients who underwent CABG after day 14, 16% presented in shock (n = 7), 29% required mechanical ventilation (n = 13), and 20% required IABP placement (n = 9) preoperatively. Thus, it is likely that our findings are reflective of sicker patients who required extended hospitalization after an AMI.

We also examined a critically ill population by examining only those patients with a transmural infarction who presented in hemodynamic shock or who underwent IABP placement. It is noteworthy that these patients also derived mortality benefit from waiting until day 3 to perform CABG but experienced increased mortality if CABG was performed after day 4. These findings support that, even in the setting of high-acuity patients, survival benefit may be derived from deferring CABG 2 to 3 days after AMI.

Use of the California Database
We chose to use the California statewide database in this analysis because of 2 key features. The first was the ability to gauge procedural timing, which was crucial to the analysis. The second was the discrimination between diagnoses present on admission to the hospital versus new diagnoses made during the hospitalization. This allowed us to determine whether a patient characteristic was preexistent or developed subsequent to CABG. For the purposes of retrospective analyses, a large database provides a large sample size that is free of institutional bias. Because individual institutional practices with respect to performing CABG after AMI differ dramatically, we think that this freedom from institutional bias gives our study added power.

Study Limitations
We recognize that there are several limitations in our study. First, our study is retrospective and cannot account for inherent undocumented differences in patient characteristics. Although we attempted to control for selection bias with propensity and multivariate statistical methods, we concede that a fundamental problem of any retrospective study examining interventional timing is the inherent heightened acuity of patients who undergo early CABG. Second, we were reliant on the variables provided by the California discharge database. Several markers of clinical acuity (eg, left ventricular ejection fraction) were not available and thus could not be included. Finally, administrative databases, including the one we used, and studies based on them are reliant on accurate coding. We acknowledge that the data presented here were not necessarily entered by individuals with clinical expertise. Subtle differences in coding definitions may exist between different institutions; neither the coding nor procedural timing included in the California discharge data has been validated for patients undergoing CABG. However, although errors and variance in the data undoubtedly exist, we have assumed that these are randomly distributed and should not lead to significant bias in our conclusions. Without a randomized controlled clinical trial, we thought that a retrospective study with compensatory statistical methods constituted a reasonable approach to address this issue.

Why Delayed CABG May Be Preferable
In light of the present findings and similar conclusions by others, we must ask the question of why delayed CABG leads to improved outcomes in the setting of AMI. Intuitively it seems that early reperfusion would lead to the preservation of myocardium, thus limiting infarct size. However, reperfusion injury can lead to increased damage to the vital myocardium beyond the ischemic insult.²⁴ Furthermore, it is known that during the acute phases of an infarct, whole body inflammatory states are increased with increased levels of C-reactive protein as a marker.^25,26 It is possible that reperfusion-induced inflammatory states have systemic manifestations that increase mortality. Beyond biological explanations, on a programmatic level, it is also possible that outcomes are improved by operating in a planned and controlled setting where routine physician, nursing, and ancillary staff are present and rested. Whether these or other unknown factors contribute to reduced mortality by deferring CABG after AMI is still unclear and a subject of considerable interest.

	Conclusions

Top Abstract Introduction Materials and Methods Results Discussion Conclusions Table E1 Table E2 Table E3 References

 
In this analysis, we sought to determine the optimal CABG timing after AMI among patients who underwent surgery during the index hospitalization. We observed a reduced mortality rate when CABG was deferred until hospital day 3, consistent with previous reports. This effect was also observed among high-acuity patients who presented in hemodynamic shock or requiring IABP support. These results suggest that CABG may best be deferred for 3 or more days after admission for AMI under nonurgent clinical circumstances.

	Table E1

Top Abstract Introduction Materials and Methods Results Discussion Conclusions Table E1 Table E2 Table E3 References

 

Exclusion criteria for study

Exclusion criteria ICD-9-CM codes No. of patients excluded (original N = 40,159) Age < 18 y NA 1 Hospital transfers NA 15,276 Incomplete data, including no data on CABG timing available NA 13,261 Diseases of mitral valve (stenosis and insufficiency) 394, 394.0, 394.1, 394.2, 394.9, 424.0 1108 Additional cardiac pathology Diseases of aortic valve (stenosis and insufficiency) 395, 395.0, 395.1, 395.2, 395.9, 424.1, 746.5, 746.6 439 Combination mitral and aortic disease 396, 396.0, 396.2, 396.3, 396.8, 396.9, 746.3, 746.4 184 Pulmonary valve disease 397.1, 424.3, 746.00, 746.01, 746.02, 746.09 2 Tricuspid valve disease 397, 397.0, 424.2, 746.1 69 Malfunctioning mechanical valve 996.02, 996.71 1 Concomitant procedures Mitral valve replacement/repair 35.1, 35.12, 35.2, 35.23, 35.24, 35.33, 35.98, 35.99 15 Aortic valve replacement/repair 35.01, 35.11, 35.21, 35.22 11 Heart transplantation 37.5, 37.51 0 Implantation of removal of ventricular assist device 37.64, 37.65, 37.66 318 Pulmonary valve replacement/repair 35.03, 35.13 0 Tricuspid valve replacement/repair 35.04, 35.14 0 Final study population 9476

ICD-9-CM, International Classification of Diseases, Ninth Revision, Clinical Modification; NA, not available.

	Table E2

Top Abstract Introduction Materials and Methods Results Discussion Conclusions Table E1 Table E2 Table E3 References

 

Independent and dependent variables generated from ICD-9 and ICD-9-CM coding

Primary variable day of CABG Baseline Independent variables ICD-9-CM codes used Charlson index * See below Diabetes mellitus (Y/N) 250, 250.0 250.1, 250.3, 250.7, 250.8, 250.9 COPD 491.2, 491.20, 491.21, 491.22 492, 492.0, 492.8 Hypertension 401, 401.1, 401.91 CRF 585, 588 History of stroke 431, 433, 433.3, 433.8, 433.9, 434, 434.0, 434.1, 434.9 Hyperlipidemia 272.2, 272.4 Obesity 278.0, 278.00, 278.01 PVD 443, 443.8, 443.89, 443.9 Type of AMI:  Anterolateral 410, 410.0, 410.00, 410.01, 410.02, 410.1 410.10, 410.11, 410.12  Posterior 410.6, 410.60, 410.61, 410.62  Inferior 410.4, 410.40, 410.41, 410.42  Lateral 410.5, 410.50, 410.51, 410.52  Subendocardial 410.7, 410.70, 410.71, 410.72 * Charlson Index includes a weighted compilation of 17 baseline comorbidities in multiple systems, including cardiovascular (ischemic disease and congestive heart failure), renal, endocrine (diabetes mellitus), cerebrovascular, peptic ulcer, rheumatologic, hepatic, oncologic (history of cancer or malignancies), and infectious (human immunodeficiency virus/acquired immune deficiency). A total of 1186 unique ICD-9 codes are used for the creation of the Charlson Index.

Acuity variables ICD-CM (for procedures) codes CPR 99.63 IABP before CABG 376.1 Conduction disorder on admission 426, 426.0, 426.1, 426.10, 426.11, 426.12, 426.13, 426.2, 426.3, 426.4, 426.5, 426.50, 426.51, 426.52, 426.53, 426.54, 426.6, 426.8, 426.81, 426.89, 426.9 Ventricular fibrillation on admission 427.4, 427.41, 427.42 Cardiac arrest on admission 427.5, 779.85 Mechanical ventilation before CABG 967, 967.0, 967.1, 967.2, 960.4 Shock 785.51 Atrial fibrillation on admission 427.3, 427.31, 427.32 Ventricular tachycardia on admission 427.1 Coronary stent before CABG 00.45, 00.46, 00.47, 00.48, 00.66, 36.01 36.02, 36.05, 36.06, 36.07 Angiogram before CABG 88.5, 88.50, 88.51, 88.52, 88.53, 88.54, 88.55, 88.56, 88.57

Outcome variables Postoperative LOS Present in database Postoperative stroke 431, 433, 433.3, 433.8, 433.9, 434, 434.0, 434.1, 434.9 Prolonged ventilation (>96 h) 967.2 Postoperative ARF 584, 584.6, 584.7, 584.8, 584.9, 586 587, 588, 5880, 588.1, 588.8, 588.9 Postoperative new onset dialysis 125.5, 389.5, 392.7, 394.2, 399.5 Wound infection 998.5, 998.59

ICD-9-CM, International Classification of Diseases, Ninth Revision, Clinical Modification; CABG, coronary artery bypass graft; AMI, acute myocardial infarction; COPD, chronic obstructive pulmonary disease; PVD, peripheral vascular disease; CRF, chronic renal failure; ARF, acute renal failure; CPR, cardiopulmonary resuscitation; IABP, intra-aortic balloon pump; LOS, length of stay.

	Table E3

Top Abstract Introduction Materials and Methods Results Discussion Conclusions Table E1 Table E2 Table E3 References

 

Table E3 Variables included in propensity analysis to assess the likelihood of undergoing early coronary artery bypass grafting

Category Variable Demographics Age (y) Gender Race Charlson index score * Hypertension (not part of Charlson) MI type Anterolateral infarct Subendocardial infarct Acuity Shock Cardiac arrest on admission Thrombolysis before CABG Mechanical ventilation before CABG Conduction disorder Ventricular fibrillation on admission Ventricular tachycardia on admission Atrial fibrillation on admission Angina on admission Red blood cell transfusion before CABG Angiogram before CABG Coronary stent before CABG * These variables include 17 Charlson index parameters to comprise the 35 variables used to create the propensity score.

MI, Myocardial infarction; CABG, coronary artery bypass grafting.

	Acknowledgments

 
Dr Weiss is the Irene Piccinini Investigator in Cardiac Surgery. Dr Nwakanma is a Hugh R. Sharp Cardiac Surgery Research Fellow. The authors thank the State of California Office of Statewide Health Planning and Development for the provision of data.

	Footnotes

 
Presented at the 33rd Annual Meeting of the Western Thoracic Association, June 27 to 30, Santa Anna Pueblo, New Mexico, Sampson Award Finalist.

	References

Top Abstract Introduction Materials and Methods Results Discussion Conclusions Table E1 Table E2 Table E3 References

 

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