Timing of coronary artery bypass grafting after acute myocardial infarction may not influence mortality and readmissions

Journal Pre-proof Timing of CABG after acute myocardial infarction may not influence mortality and readmissions Valentino Bianco, D.O., MPH, Arman Kilic, M.D., Thomas G. Gleason, M.D., Edgar Aranda-Michel, B.S., Yisi Wang, MPH, Forozan Navid, M.D., Ibrahim Sultan, M.D. PII:

S0022-5223(19)36106-9

DOI:

https://doi.org/10.1016/j.jtcvs.2019.11.061

Reference:

YMTC 15431

To appear in:

The Journal of Thoracic and Cardiovascular Surgery

Received Date: 27 February 2019 Revised Date:

6 November 2019

Accepted Date: 24 November 2019

Please cite this article as: Bianco V, Kilic A, Gleason TG, Aranda-Michel E, Wang Y, Navid F, Sultan I, Timing of CABG after acute myocardial infarction may not influence mortality and readmissions, The Journal of Thoracic and Cardiovascular Surgery (2020), doi: https://doi.org/10.1016/j.jtcvs.2019.11.061. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Copyright © 2019 Published by Elsevier Inc. on behalf of The American Association for Thoracic Surgery

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Timing of CABG after acute myocardial infarction may not influence mortality and

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readmissions

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Valentino Bianco1, D.O., MPH, Arman Kilic1,2, M.D., Thomas G Gleason1,2, M.D., Edgar

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Aranda-Michel1 B.S., Yisi Wang2, MPH, Forozan Navid1,2, M.D., and Ibrahim Sultan1,2, M.D.

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From (1) Division of Cardiac Surgery, Department of Cardiothoracic Surgery, University of

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Pittsburgh and

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(2) Heart and Vascular Institute, University of Pittsburgh Medical Center

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Central Message

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Although patients who undergo CABG within 24 hours more often present in cardiogenic shock,

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there was no significant outcome difference in the cohorts after risk adjustment.

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Key Words/Classifications: CABG, Acute Myocardial Infarction, STEMI, NSTEMI

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Disclosures: Thomas G. Gleason serves on Abbott’s Medical Advisory Board and reports

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research support from Medtronic, Boston Scientific, and Cytosorb. Dr. Arman Kilic serves on

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Medtronic’s Advisory Board.

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Word Count: 3795 (excluding title page, abstract and references)

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Correspondence and Reprint Requests:

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Ibrahim Sultan, MD

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Division of Cardiac Surgery

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5200 Centre Ave, Suite 715

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Pittsburgh, PA 15232

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Tel: 412.623.2027

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Fax: 412.623.3717

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[email protected]

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Graphical Abstract:

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Kaplan-Meier survival estimates show no significant survival advantage up to 5 years for the

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≥24 hr cohort (time from MI to CABG).

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Central Picture:

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Survival at 1 and 5 years was not significantly different between CABG time cohorts

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Perspective Statement

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Coronary artery bypass grafting (CABG) is often delayed in the acute setting in an effort to limit

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associated operative morbidity and mortality. However, delaying surgical revascularization with

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the presumption of increasing patient stability and improving outcomes may not be applicable in

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all cases. The burden of associated comorbid disease may hold greater relevance than the timing

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of CABG.

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Abstract

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Objective: Coronary artery bypass grafting (CABG) is often delayed after acute myocardial

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infarction (AMI) to avoid an increase in postoperative morbidity and mortality. We hypothesized

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that the timing of CABG after AMI may not be consistently associated with postoperative

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outcomes.

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Methods: All patients who underwent isolated CABG at the University of Pittsburgh Medical

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Center from 2011-2017 following an AMI were reviewed. A comparative analysis for time from

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MI presentation to CABG was performed with primary outcomes including all-cause mortality

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and readmission

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Results: A total of 7,048 patients underwent isolated CABG. Of these, 2,058 patients had AMI

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with all relevant variables available for analysis. The study population was divided into two

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CABG timing cohorts including < 24 hrs (n=292) and ≥ 24 hrs (n=1766). Previous PCI,

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cardiogenic shock, and intra-aortic balloon pump (IABP) were more prevalent in the < 24 hrs

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group. Operative mortality was significantly higher in the <24 hrs cohort (7.19% vs 3.79%;

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p=0.01). Diabetes mellitus, peripheral vascular disease, serum creatinine, age, COPD, and

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immunosuppression were significant predictors (p <0.05) of mortality. Following risk adjustment

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with propensity scoring, there was no difference between time cohorts for operative mortality

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(4.15% vs 4.58%; p=0.62). New-onset atrial fibrillation occurred more frequently in the ≥24 hrs

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cohort. There was no difference between groups for the occurrence of major adverse

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cardiovascular and cerebrovascular event (MACCE) readmissions.

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Conclusions: After adjusting for baseline patient characteristics, there was no statistically

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significant difference between timing cohorts for mortality or MACCE readmissions.

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Introduction

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Despite a pronounced improvement in the identification of acute myocardial infarction

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(AMI) and implementation of rapid treatment including both percutaneous coronary intervention

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(PCI) and coronary artery bypass grafting (CABG), there is a trend towards delayed surgical

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intervention to avoid increased risk of morbidity or mortality with urgent CABG. In the setting

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of AMI, CABG outcomes have been well documented [1-6] and both short and long-term

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mortality are dependent on the extent of infarct (transmural vs non-transmural) and the timing of

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surgical revascularization, with patients who have transmural infarcts having better outcomes if

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revascularized within 6 hours [7]. Although percutaneous therapies have taken hold as a primary

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intervention for acute MI, CABG remains a safe and viable option for patients with acute

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coronary syndrome (ACS) and is the appropriate treatment in the event of failed PCI or severe

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multivessel disease [3].

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There is clear evidence that elective CABG can be performed with less risk to the patient in

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comparison to revascularization in the setting of acute MI, which carries a higher risk for

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hospital mortality [8, 9]. It is important to recognize the fundamental differences between

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patients who have successful treatment of the culprit lesion with PCI and then present electively

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for CABG in the following weeks, compared to patients who are admitted to the hospital with

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AMI [5]. It is in the latter patient population that there is a considerable amount of heterogeneity

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as far as timing of surgical revascularization is concerned.

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The factors that place patients with an AMI at higher operative risk are not well established

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and independent predictors of poor outcomes include, but are not limited to, age, female gender,

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and heart failure at presentation [4]. The aim of our current study was to present outcomes from a

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large single center experience with an emphasis on short and long-term mortality, readmissions, 5

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and to define baseline characteristics that predict poor outcomes following CABG based on

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timing after AMI.

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Methods

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Study population

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All patients (n=7048) who underwent isolated CABG at the University of Pittsburgh

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Medical Center from 2011-2017 were reviewed. The study sample size (STEMI + NSTEMI)

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included 2058 cases of AMI which were used in analysis. Discarded cases included those with

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missing independent variables and cases with missing information for readmission and mortality

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status. Both urgent and emergent cases were included. We used conventional guidelines for the

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definition of myocardial infarction including positive EKG findings (ST elevations or

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depressions), elevated troponin, coronary catherization findings, and symptoms (chest and/or

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jaw/arm pain). Perioperative data was retrospectively obtained from a prospectively maintained

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cardiac surgical database. The institutional review board approved use and analysis of the

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database.

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Data Analysis

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Primary stratification was achieved by separating the total CABG patient population into two

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patient cohorts based on timing of CABG (< 24 hours, ≥ 24 hours). The primary outcome was

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all-cause time to death over the study follow-up period. Secondary outcomes included operative

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morbidity as well as readmissions. Readmissions were identified and recorded in our centers

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system-wide database that includes data collected from a total of 40 in-system hospitals

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including over 8,000 hospital beds. At the time of readmission, the top three diagnostic codes

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were reviewed and the most relevant counted as the primary cause of readmission. All

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readmissions up to 5 years were reviewed. In addition, MACCE (Major Adverse Cardiovascular

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and Cerebrovascular Events) related readmission causes included both cardiac readmission and

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stroke-related readmission, whichever occurred first. Only patients that had unplanned

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readmissions to the hospital for >24 hours were included in the analysis. The initial readmission

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for any individual patient was the index readmission used for analysis. Once a patient died or

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was readmitted, he/she was no longer considered at risk for readmission allowing for appropriate

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censoring.

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Baseline characteristics were presented as frequency with percentage for categorical

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variables and median (Q1-Q3) for continuous variables. Normality was checked by Kolmogorov-

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Smirnov test. Chi2 Test (or Fisher’s exact test when 25% cell has expected number less than 5)

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was used for categorical variables and Mann-Whitney U test was used for continuous variables.

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Kaplan-Meier curves for 5 -year overall survival and readmissions were also generated and

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compared using the log-rank test. All-cause mortality was obtained from the review of the

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medical record for patients who died during index hospitalization or during readmissions, phone

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calls when appropriate, and through the Social Security Death Index (obtained from the updated

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Social Security Administration Death Master file, where our health-care system is certified by

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the Social Security Administration as an organization that is exempt from the three-year delay).

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To our knowledge, all unplanned inpatient readmissions were captured as this data is collected

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for all postoperative patients presenting to our health care system which includes 40 hospitals

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and nearly 8,000 inpatient beds. Postoperative patients who present at a non UPMC hospital are

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typically transferred to our hospitals for insurance purposes.

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To address the confounding by observed variables, we used Inverse probability treatment

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weighting (IPTW), a method of propensity score analysis. Using stabilized IPTW weighting, the

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pseudo sample size is only slightly larger than the original data (2068 vs 2058). The inverse

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probability of treatment weight was defined as weight=t/PS + (1-t)/(1-PS). That is each subject’s

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weight was the inverse probability of receiving the treatment that subject received. Simulation

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study showed that stabilized IPTW could minimize the impact of the variance estimate of

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treatment effect. Three models were applied in our analysis. (Please see supplement for more

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details). First the univariable model in which the MI timing group was the only variable. Cox

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proportional assumption was checked by adding the interaction with time. Second, the

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multivariable model. Baseline characteristics (age, gender, race, BMI, and previous disease

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conditions, previous surgery history, admission status, cardiac presentation, creatinine, ejection

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fraction before surgery) were assessed in the univariable Cox Proportional hazard model of time

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to death first and significant variables were adjusted in the multivariable model. Competing risk

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method by using cumulative incidence function was used for modeling time to MACC

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readmission (death was a competing risk). Treatment Effect of MACCE components were

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estimated after that. Finally, patient’s weight was applied to the model to estimate the inverse

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weighted association between MI timing group and outcome and used the robust sandwich

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estimate to estimate the marginal treatment effects. The effect on outcomes was estimated in

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STEMI and NSTEMI cohort separately in the sub-analysis.

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Results

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Baseline Characteristics

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Unadjusted patient baseline characteristics can be found in Supplement 1. Cohorts were risk

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adjusted with propensity scoring using IPTW (n =2058), consisting of the <24 hrs cohort

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(n=288) and the ≥24 hrs cohort (n=1770). There was no significant (p <0.05) difference between

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cohorts for most baseline patient characteristics (Table 1), including age, gender, race, BMI

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(kg/m2), dyslipidemia, diabetes mellitus, hypertension, chronic lung disease, serum creatinine

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(mg/dL), immunosuppression, peripheral artery disease, cerebrovascular disease, history of heart

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failure, previous PCI, prior valve surgery, prior CABG, family history of CAD, NSTEMI,

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STEMI, arrhythmia, number of diseased vessels, LVEF, urgent or emergent surgical status, off-

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pump CABG, BIMA utilization, and Plavix use.

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Operative outcomes

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Unadjusted operative outcomes can be found in Supplement 2. There was no significant

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difference between time cohorts for operative morality, blood product transfusion, prolonged

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ventilation, sternal wound infection, sepsis, pneumonia, reoperation, and permanent stroke

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(Table 2). New-onset atrial fibrillation (32.29% vs 26.61%; p=0.05) occurred more frequently in

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the ≥24 hrs cohort.

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Survival Analysis

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Kaplan-Meier (KM) overall survival estimates at 1 and 5 years (Graphical Abstract / figure 3)

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were not statistically different among cohorts. Following propensity scoring, KM survival

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estimates remained nonsignificant, showing similar survival for both 1 and 5 years (Central

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Figure). On multivariable cox regression analysis, the ≥24 hr cohort was associated with a

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significant reduction in mortality risk [HR 0.63 (0.42, 0.97); p=0.03].(Table 3). Age, diabetes,

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PVD, COPD, immunosuppression, and creatinine level were significantly associate with time to

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death on multivariable analysis.

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Readmission analysis

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There was no statistically significant difference in freedom from MACCE readmissions amongst

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weighted time cohorts (≥24 hrs vs <24 hrs) at 1-year and 5-years (Figure 1) and this trend was

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unchanged when cumulative incidence function was used to compare MACCE readmissions

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(figure 2). On competing risk analysis with cumulative incidence function (Table 4), CABG ≥24

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hrs was not significantly associated with MACCE related readmission. Diabetes, PVD, COPD,

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prior heart failure, and age were significant predictors of MACCE readmissions.

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NSTEMI analysis

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Unadjusted postoperative outcomes can be found in Supplement 3. For risk adjusted cohorts, the

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total NSTEMI population (n=1690) was divided into ≥24 hrs (n=1452) and <24 hrs (n=238)

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groups. There was no significant difference between cohorts for operative mortality, blood

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product transfusion, prolonged ventilation, sternal wound infection, sepsis, pneumonia, and

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permanent stroke. Reoperation (3.88% vs 0.72%; p=0.01) and new-onset atrial fibrillation

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(31.60% vs 25.52%; p=0.06) occurred more frequently in the ≥24 hr cohort (Table 5). On

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multivariable Cox regression analysis (Supplement 4a), CABG ≥24 hrs [HR 0.99 (0.56, 1.76);

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p=0.98] was not significantly associated with time to death or with MACCE readmission [HR

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0.73 (0.51, 1.06); p=0.130] on competing risk model for readmission (Supplement 5a)

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STEMI analysis

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Unadjusted postoperative outcomes can be found in Supplement 3. For risk adjusted cohorts, the

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total STEMI population (n=368) was divided into ≥24 hr (n=318) and <24 hr (n=50) groups.

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Although not statistically significant, operative mortality was higher in the ≥24 hrs cohort (8.4%

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vs 4.00%; p=0.17) (Table 5). Reoperation occurred significantly more in the <24 hr cohort

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(2.40% vs 8.00%; p=0.03). On multivariable Cox regression analysis (Supplement 4b), CABG

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timing >24 hours [HR 0.57 (0.25, 1.29); p=0.18] was not significantly associated with time to

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death. CABG time was not associated with MACCE readmission on competing risk model for

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readmission (Supplement 5b).

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Discussion

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We present one of the largest single center analyses for patients undergoing CABG

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following AMI stratified by different time cohorts. Most large datasets in the literature have

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focused on short-term outcomes or have not included risk of readmissions in the short or long-

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term. Timing after AMI continues to be more relevant today, despite PCI being the main stay of

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therapy, because of increasing number of diabetics in the general population and consistent data

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indicating the superiority of surgical revascularization [10]. Historically, emergent CABG was

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avoided because of higher morbidity and mortality[11]. However, advances in surgical

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techniques and perioperative management of these patients have allowed surgeons to operate on

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higher risk patients with acceptable survival [12-14].

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Prior to propensity scoring, our data shows that significantly more patients in the <24 hrs

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time cohort presented with STEMI, required an IABP, and >1/3 of patients were in cardiogenic

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shock. CABG timing was not a significant independent predictor of time to death or MACCE

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related readmission. Significantly higher incidence of acute hemodynamic compromise appeared

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to be the most relevant determinants of heightened short-term mortality risk in the <24 hr group.

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Yet, on risk-adjusted multivariable analysis neither cardiogenic shock nor IABP placement were

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independent predictors of elevated operative mortality or readmission. Furthermore, although

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there is an apparent association between STEMI and cardiogenic shock, STEMI was not an

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independent predictor of mortality or hospital readmission on multivariable analysis.

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Importantly, our analysis shows that when cardiogenic shock and IABP use are excluded from

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risk adjustment, CABG >24 hrs after MI presentation is significantly associated with a reduction

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in mortality risk on multivariable analysis. Therefore, patients with acute cardiogenic shock who

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present <24 hrs from MI and require IABP placement represent a high-risk cohort. The numeric

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difference in mortality in the STEMI subanalysis despite statistical insignificance does not fall in

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line with historic data that have suggested waiting for more than 24 hours in the setting of a

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STEMI and no ongoing ischemia. This is most likely because of small numbers. However, this

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could be also explained by surgeon selection bias who operated on patients within eight hours of

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STEMI presentation and on going ischemia who have a positive response to revascularization.

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Additionally, all the ‘sicker’ patients were likely delayed and operated on after 24 hours thus

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adding to the selection bias.

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Baseline preoperative comorbidities play a large role in determining patient risk, even when

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time of CABG is controlled for. Risk models have been created that support the influence of

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patient comorbidities in determining outcomes in patients with AMI [15]. In a multinational

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registry report, an increase in age by one decade resulted in an 80% increase in the odds of death

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at 6-month follow-up [16]. Our results support these findings, as age was an independent

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predictor of time to death and MACCE readmission. Patient gender is another important

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determinant of CABG outcomes and it has been found that women generally have a greater

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burden of comorbid disease, present later, and are less likely to undergo revascularization [17,

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18]. Diabetes can significantly increase operative mortality in both STEMI and NSTEMI

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populations [19], which may be due to less aggressive approaches to revascularization [20]. Our

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data showed that diabetes was a predictor of both mortality and readmission. Renal failure is

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another important determinant of risk, which we found to be a predictor of both mortality and all

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cause readmission. Even mild renal disease has been considered a major risk factor for adverse

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cardiovascular events following AMI [21]. Therefore, although our data show an elevated

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operative mortality in the < 24 hrs cohort, the mortality difference diminishes once propensity

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scoring is performed and a thorough multivariable analysis of baseline characteristics is

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considered. The more important determinant of outcomes may be the presence of baseline

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comorbidities.

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A recent review of CABG timing noted that only 7 of 18 studies (39%) reported an

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independent association between early CABG time and increased mortality [22]. Studies for

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both STEMI and NSTEMI patients have shown a lack of association between CABG timing and

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outcomes and it has been suggested that delaying operative intervention in NSTEMI patients

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could result in increased utilization of hospital resources with little patient benefit [23, 24].

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Several studies have shown that timing of CABG after acute MI is not an independent predictor

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of operative mortality [25-27]. Khan and colleagues [28] performed a recent single center study

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for patients with STEMI that were divide into two time cohorts (<24hrs vs >24hrs). There was

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no significant difference between cohorts for morbidity or mortality at 30 days or 1-year post-

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operatively [28]. Similarly, on a separate sub-analysis of STEMI and NSTEMI patients, we

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found no significant association between time of CABG and time to death postoperatively.

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Predominant independent predictors of mortality in both STEMI and NSTEMI patients are

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reflective of the overall AMI population and included multiple comorbidities.

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The lack of a significant association between time of CABG and patient outcomes

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potentially has far-reaching implications and may be indicative of the need to consider foregoing

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delays in performing surgical revascularization in the setting of AMI. Other recent data are

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encouraging and support that delaying CABG beyond one day did not impact mortality [29].

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Our data is novel in that it shows no significant difference in mortality or readmission, whether

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CABG is performed within 24 hours or after 1 day. Therefore, delaying surgical

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revascularization with the presumption of increasing patient stability and improving outcomes

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may not be applicable in all cases, especially when the burden of associated comorbid disease is

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taken into consideration.

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Limitations

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The study is limited by the typical constraints of retrospective study design. There is a possibility

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of selection bias due to the elimination of cases missing independent variables, which may be

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missing due to the emergent nature of a procedure. Although our hospital network has several

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branches, there is a chance that some patients were readmitted at outside centers and were lost to

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follow-up. The use of the ‘top three’ diagnostic codes for readmission diagnosis, is potentially

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limited by human error. On 5-year outcomes, a large percentage of the patients did not have

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follow-up data. Due to the disproportionally large number of patients that had CABG procedures

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after 24 hrs, timing cohorts lacked uniformity in population sizes. Moreover, there is likely

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significant surgeon variability in the decision to take patients with acute STEMI to the OR <24hr

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after presentation. A subset of high-risk patients (e.g. extreme elderly) may have had surgery

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deferred in favor of PCI. Data was not available on how many patients were declined for surgery

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or how many patients died while waiting for surgery. Due to an unknown number of patients

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who initially presented to an outside hospital prior to transfer, details on exact MI timing were

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limited, which limited use of time as a continuous variable. Given that there are subtle but

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relevant differences in surgeon preference regarding when to operate, this is an inherent study

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limitation.

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Conclusions

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The results of the current study indicate that performing early CABG (< 24 hr) confers no

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statistically significant difference in risk adjusted operative mortality compared to delayed

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CABG (≥ 24hr). These findings indicate that in the presence of good clinical and surgical

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judgement, satisfactory outcomes can be achieved for CABG patients with AMI, for all time

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intervals. After adjusting for baseline patient characteristics, mortality and readmissions are

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similar between the two time cohorts. The presence of patient comorbidities plays a large role in

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determining CABG outcomes in the setting of AMI, irrespective of the timing of surgical

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revascularization.

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Sultan, I., et al., Hemiarch Reconstruction Versus Clamped Aortic Anastomosis for Concomitant Ascending Aortic Aneurysm. Ann Thorac Surg, 2018. 106(3): p. 750-756. Castro-Dominguez, Y., K. Dharmarajan, and R.L. McNamara, Predicting death after acute myocardial infarction. Trends Cardiovasc Med, 2018. 28(2): p. 102-109. Eagle, K.A., et al., A validated prediction model for all forms of acute coronary syndrome: estimating the risk of 6-month postdischarge death in an international registry. Jama, 2004. 291(22): p. 2727-33. Bucholz, E.M., et al., Editor's Choice-Sex differences in young patients with acute myocardial infarction: A VIRGO study analysis. Eur Heart J Acute Cardiovasc Care, 2017. 6(7): p. 610-622. Dey, S., et al., Sex-related differences in the presentation, treatment and outcomes among patients with acute coronary syndromes: the Global Registry of Acute Coronary Events. Heart, 2009. 95(1): p. 20-6. Donahoe, S.M., et al., Diabetes and mortality following acute coronary syndromes. Jama, 2007. 298(7): p. 765-75. Elbarouni, B., et al., Temporal changes in the management and outcome of Canadian diabetic patients hospitalized for non-ST-elevation acute coronary syndromes. Am Heart J, 2011. 162(2): p. 347-355.e1. Anavekar, N.S., et al., Relation between renal dysfunction and cardiovascular outcomes after myocardial infarction. N Engl J Med, 2004. 351(13): p. 1285-95. Caceres, M. and D.S. Weiman, Optimal timing of coronary artery bypass grafting in acute myocardial infarction. Ann Thorac Surg, 2013. 95(1): p. 365-72. Ladeira, R.T., et al., Coronary artery bypass grafting in acute myocardial infarction. Analysis of preoperative predictors of mortality. Arq Bras Cardiol, 2006. 87(3): p. 254-9. Parikh, S.V., et al., Timing of in-hospital coronary artery bypass graft surgery for non-ST-segment elevation myocardial infarction patients results from the National Cardiovascular Data Registry ACTION Registry-GWTG (Acute Coronary Treatment and Intervention Outcomes Network Registry-Get With The Guidelines). JACC Cardiovasc Interv, 2010. 3(4): p. 419-27. Applebaum, R., et al., Coronary artery bypass grafting within thirty days of acute myocardial infarction. Early and late results in 406 patients. J Thorac Cardiovasc Surg, 1991. 102(5): p. 74552. Sintek, C.F., T.A. Pfeffer, and S. Khonsari, Surgical revascularization after acute myocardial infarction. Does timing make a difference? J Thorac Cardiovasc Surg, 1994. 107(5): p. 1317-21; discussion 1321-2. Kaul, T.K., et al., Coronary artery bypass grafting within 30 days of an acute myocardial infarction. Ann Thorac Surg, 1995. 59(5): p. 1169-76. Khan, A.N., et al., Outcome of early revascularization surgery in patients with ST-elevation myocardial infarction. J Interv Cardiol, 2015. 28(1): p. 14-23. Nichols, E.L., et al., Optimal Timing From Myocardial Infarction to Coronary Artery Bypass Grafting on Hospital Mortality. Ann Thorac Surg, 2017. 103(1): p. 162-171.

387 388 389 390

17

391 392

Table 1. Baseline characteristics in propensity scored cohorts, stabilized with Inverse probability of treatment weighting (IPTW) (standardized mean difference <=0.10 was considered negligible)

n Age (years) Female Caucasian Race BMI (kg/m2) Dyslipidemia Diabetes Mellitus Hypertension Chronic Lung Disease Serum Creatinine (mg/dL) Immunosuppression Peripheral Arterial Disease Cerebrovascular Disease History of heart failure Previous PCI Previous CABG Previous Valve Surgery Family History of CAD Cardiac presentation NSTEMI 5 STEMI 6 Cardiogenic Shock* Arrhythmia Number of Diseased Vessels none or 1 2 3 Left Ventricular Ejection Fraction (%) Preoperative Intra-Aortic Balloon Pump* Status Elective Urgent Emergent or emergent

MI ≥ 24 hr

MI < 24 hr

P Value

1770 66.00, 58.00-74.00 519(29.31%) 1616(91.32%) 29.53, 25.96-33.30 1496(84.53%)

288 66.00, 58.00-74.00 76(26.39%) 267(92.76%) 29.48, 26.27-32.41 239(82.96%)

NA 0.45 0.31 0.42 0.43 0.49

837(47.27%) 1481(83.70%) 363(20.54%) 1.00, 0.80-1.20 80(4.55%)

143(49.82%) 244(84.73%) 65(22.46%) 0.96, 0.80-1.11 13(4.39%)

0.42 0.66 0.46 0.06 0.91

360(20.34%) 380(21.46%) 431(24.35%) 525(29.64%) 38(2.15%) 7(0.42%) 473(26.70%)

57(19.83%) 56(19.41%) 58(19.99%) 80(27.64%) 9(3.13%) 0(0.00%) 78(27.25%)

0.84 0.43 0.11 0.49 0.39 0.41 0.85 0.82

1452(82.02%) 318(17.98%) 117(6.62%) 277(15.64%)

238(82.60%) 50(17.40%) 31(10.90%) 50(17.40%)

63(3.54%) 348(19.68%) 1359(76.78%)

50.00, 38.00-55.00

Standardized Mean Difference 0.05 0.06 0.05 0.05 0.04 0.05 0.03 0.04 0.10 0.01 0.01 0.05 0.10 0.04 0.06 0.05 0.01 0.01

0.009 0.47 0.73

0.13 0.05

9(3.13%) 49(17.19%) 229(79.68%)

0.55

0.02 0.06 0.07 0.05

48.00, 35.00-55.00

0.44 <.001

0.38

0.42

0.10

269(15.23%)

90(31.27%)

52(2.96%) 1514(85.53%) 204(11.51%)

13(4.39%) 242(84.05%) 33(11.54%)

0.04 .001

18

salvage off pump CABG BIMA utilization Plavix STS PROM%

467(26.13%) 254(14.37%) 286(16.18%) 1.76, 0.86-4.10

73 (25.35%) 41(14.29%) 37(12.71%) 2.09, 1.05-4.18

0.97 0.41 0.13 0.03

0.05 .002 0.09 0.11

BMI-body mass index; PCI –percutaneous coronary intervention; CABG- coronary artery bypass grafting; CAD- coronary artery disease; STEMI –ST segment elevation MI; NSTEMI –non-ST segment MI; MI –myocardial infarction; STS-PROM –Society of Thoracic surgeons predicted risk of mortality; BIMA – bilateral internal mammary artery.

393

*Variables excluded from IPTW propensity score

394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410

Table 2: Operative outcomes stratified by MI timing cohort with stabilized IPTW

411

Operative mortality Blood Product Transfusion

P Value

MI ≥ 24 hr

MI < 24 hr

1770

288

81(4.58%)

12(4.15%)

0.74

676(38.21%)

116(40.27%)

0.50

19

Prolonged Ventilation >24 Hours Deep Sternal Wound Infection Sepsis Pneumonia Permanent Stroke Reoperation New-Onset Atrial Fibrillation

236(13.36%)

33(11.51%)

0.39 0.28

7(0.41%) 17(0.97%) 80(4.49%) 35(1.97%) 64(3.61%)

0(0.00%) 2(0.61%) 14(4.84%) 2(0.67%) 6(2.03%)

0.55 0.79 0.12 0.17

571(32.29%)

77(26.61%)

0.05

412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431

Table 3. Cox proportional hazard, 95% CI and P value for the time to death

432

20

Hazard Ratio Multi-variable model MI timing >= 24 h < 24 h Age Ejection fraction diabetes PVD COPD Immunosuppression creatinine MI timing * time Diabetes * time COPD * time

95% Confidence Interval

0.63 0.42, 0.97 ref ref 1.04 1.03, 1.06 0.87 0.83, 0.92 1.43 1.06, 1.95 1.68 1.23, 2.30 1.86 1.35, 2.55 2.12 1.26, 3.57 1.18 1.05, 1.32 Significant Interaction of variable with time 0.80 0.67, 0.96 1.13 1.01, 1.28 0.54 0.39, 0.75

P Value

0.03 ref <.0001 <.0001 0.02 .001 .0002 .005 .005 0.02 0.04 0.03

433 434 435

*Cox proportional hazard assumption was checked by adding a covariable interaction with time; if significant it was adjusted in the multivariable model.

436 437 438

Other variables those were adjusted in the multivariable model but not significant: cardiac presentation, CVD, dialysis, prior HF, Family history of CAD, number of diseased vessels, previous valve procedure, arrhythmia, BIMA, female, off-pump CABG.

439 440 441 442 443 444 445 446

Table 4. Competing risk model using cumulative incidence function for MACC related readmission

unadjusted MI timing >= 24h <24

Hazard Ratio

95% CI

P

1.17 REF

0.64, 2.13 REF

0.62 REF

21

Multi-variable model MI timing >= 24h <24 Diabetes PVD COPD Previous heart failure Age (per year, increasing) 447 448

0.93 REF 1.30 1.37 1.21 1.59 1.01

0.76, 1.13 REF 1.13, 1.51 1.16, 1.61 1.02, 1.43 1.29, 1.97 1.01, 1.02

0.45 REF .0002 .001 0.02 <.0001 .0006

Other variables those were adjusted in the multivariable model but not significant: previous PCI, arrhythmia

449 450 451 452 453 454 455 456 457 458 459 460 461 462 Table 5. Stabilized IPTW postoperative outcomes for timing cohorts, stratified by type of MI (STEMI vs. NSTEMI).

STEMI (N = 368) MI ≥ 24 hr

MI < 24 hr

Number of Patients

318

50

Operative mortality

27(8.40%)

2(4.00%)

NSTEMI (N = 1690) P-Value

0.17

MI ≥ 24 hr

MI < 24 hr

1452

238

54(3.76%)

10(4.42%)

P-Value

0.52

22

Blood Product Transfusion

131(41.12%)

20(40.00%)

0.97

545(37.57%)

96(40.16%)

0.44

Prolonged Ventilation >24 Hours

49(15.52%)

10(20.00%)

0.36

187(12.89%)

23(9.58%)

0.15

Deep Sternal Wound Infection

0(0.00%)

0(0.00%)

NA

7(0.50%)

0(0.00%)

0.28

Sepsis

2(0.57%)

1(2.00%)

0.48

15(1.06%)

1(0.43%)

0.36

Pneumonia

11(3.31%)

2(4.00%)

0.76

69(4.75%)

12(4.98%)

0.88

Permanent Stroke

5(1.52%)

1(2.00%)

0.89

30(2.06%)

1(0.53%)

0.10

Reoperation

8(2.40%)

4(8.00%)

0.03

56(3.88%)

2(0.72%)

0.01

113(35.43%)

16(31.83%)

0.62

459(31.60%)

61(25.52%)

0.06

New-Onset Atrial Fibrillation

Variables are presented as frequency (percentage) due to them all being categorical variables. STEMI – ST Elevated Myocardial Infarction, NSTEMI – Non-ST Elevated Myocardial Infarction, MI – Myocardial Infarction

463 464 465 466 467 468 469 470 471 472 473 474 475 476

23

477

Figure 1:

478

Weighted Freedom from *MACCE related hospital readmission stratified by CABG timing

479

cohorts, showing no significant difference between groups at 1 and 5 years. *MACCE - Major

480

Adverse Cardiovascular and Cerebrovascular Events.

481 482

Figure 2: Cumulative incidence function for *MACCE hospital readmissions stratified by

483

CABG timing cohorts, showing no significant difference between groups at 1 and 5 years.

484

*MACCE - Major Adverse Cardiovascular and Cerebrovascular Events.

485 486

Figure 3: (Graphical Abstract) Kaplan-Meier (KM) overall survival estimates at 1 and 5 years

487

were similar between the two timed cohorts after acute myocardial infarction

488 489 490 491 492

24

Statistical analysis (IPTW) First, we calculated the propensity score (range from 0.00-1.00) for each patient as the probability of treatment assignment based on selected baseline covariates (we excluded the preop IABP and cardiogenic shock as recommended by the associate statistical editor). More detail is provided in this paper (Deb S, etc. A Review of Propensity-Score Methods and Their Use in Cardiovascular Research. Can J Cardiol. 2016 Feb;32(2):259-65. doi: 10.1016/j.cjca.2015.05.015. Epub 2015 May 23.) Let’s use t denoting the group assignment (t=0 MI>=24h, t=1 MI<24h); let X denotes the vector of covariates. Let PS(t=1|X) denotes the propensity score. The inverse probability of treatment weight is defined as weight=t/PS + (1-t)/(1-PS). That is each subject’s weight is the inverse of probability of receiving the treatment that subject received. The problem here is that a control subject with a PS to 1 can result in a very large weight. That is why we used the stabilized weights with the weights above multiply by the marginal probability of treatment (Pr(t=1)) and control (Pr(t=0)) in the overall sample; that is called the stabilized IPTW. The final stabilized weight is defined as [t Pr(t=1)]/PS + [(1-t) Pr(t=0)]/(1-PS) (Peter Austin, Elizabeth Stuart. Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies. Stat Med. 2015 Dec 10;34(28):3661-79. doi: 10.1002/sim.6607. Epub 2015 Aug 3.) In the following analysis, patient’s stabilized IPTW was applied to the model to estimate the inverse weighted association between MI timing group and outcomes used the robust sandwich estimate to estimate the marginal treatment effects.

Supplement 1. Comparison of baseline preoperative and operative characteristics between MI timing cohorts n Age (years) (median Q1-Q3) Female Caucasian Race BMI (kg/m2) (median Q1-Q3) Dyslipidemia Diabetes Mellitus Hypertension Chronic Lung Disease Serum Creatinine (mg/dL) (median Q1-Q3) Immunosuppression Peripheral Arterial Disease Cerebrovascular Disease History of heart failure Previous PCI Previous CABG

MI ≥ 24 h 1766 66.00 (59.00-74.00) 508 (28.77%) 1613 (91.34%) 29.41 (25.89-33.32) 1549 (87.71%)

MI < 24 h 292 66.00 (57.50-73.00) 87 (29.79%) 262 (89.73%) 29.18 (25.79-34.40) 224 (76.71%)

P Value NA 0.16 0.72 0.37 0.59 <.001

853 (48.30%) 1528(86.52%) 370 (20.95%) 1.00 (0.80-1.20)

107 (36.64%) 221(75.95%) 57 (19.52%) 1.00 (0.80-1.10)

<.001 <.001 0.58 0.16

87(4.93%) 371(21.01%) 394(22.31%) 460(26.05%) 511(28.94%) 35(1.98%)

9(3.08%) 54(18.49%) 49(16.78%) 43(14.73%) 113(38.70%) 6(2.05%)

0.17 0.33 0.03 .001 0.93

Previous Valve Surgery Family History of CAD Cardiac presentation NSTEMI 5 STEMI 6 Cardiogenic Shock Arrhythmia Number of Diseased Vessels 1 or none 2 3 Left Ventricular Ejection Fraction (%) (median Q1-Q3) Pulmonary Artery Systolic Pressure (mmHg) (median Q1Q3) Preoperative Intra-Aortic Balloon Pump Status Elective Urgent Emergent or Emergent salvage STS PROM% off pump CABG Cardiopulmonary Bypass Time (min) (median Q1-Q3) Ischemic Time (min) (median Q1Q3) BIMA utilization Average Waiting days from admit to surgery Plavix used Average hours from Plavix to CABG among people who had Plavix

8(0.45%) 447(25.31%)

1(0.34%) 86(29.45%)

1.00 0.13 <.001

1504(85.16%) 262(14.84%) 59(3.34%) 275(15.57%)

164(56.16%) 128(43.84%) 72(24.66%) 56(19.18%)

40(2.27%) 340(19.25%) 1286(78.48%)

22(7.53%) 67(22.95%) 203(69.52%)

50.00 (38.00-57.00)

50.00 (38.00-55.00)

0.16

32.00 (24.00-40.00)

33.00 (26.00-41.00)

0.51 <.001

205(11.61%)

135(46.23%)

<.001 0.12 <.001

<.001 60(3.40%) 1643(93.04%) 63(3.57%) 1.69 (0.82-3.82) 412(23.33%)

1(0.34%) 120(41.10%) 171(58.56%) 3.56 (1.62-7.71) 104(35.62%)

<.001 <.001

98.00 (78.00-120.00)

99.00 (74.00-121.00)

0.88

68.00 (53.00-88.00) 255(14.44%) 3.78 +/- 3.07 Median=3.00 (2.00-5.00) 301(17.04%) 73.90 +/- 62.47 Median=63.00 (28.00-95.00)

71.00 (50.00-91.00) 46(15.75%) 0.67 +/- 1.96 Median=0.0 (0.00-1.00) 33(11.30%) 13.07 +/- 12.85 Median=7.00 (4.0022.00)

0.91 0.13 <.001 0.01 <.001

Abbreviations: BMI, body mass index; CABG, coronary artery bypass grafting; CAD, coronary artery disease; MI, myocardial infarction; NSTEMI, non-ST elevation myocardial infarction; PCI, percutaneous coronary intervention; PROM, predicted risk of operative mortality; STEMI, ST-elevation myocardial infarction; STS, Society of Thoracic ***Surgeons; h = hours

Supplement 2. Operative outcomes stratified by MI timing cohorts.

Operative mortality Blood Product Transfusion Prolonged Ventilation >24 Hours Deep Sternal Wound Infection Sepsis Pneumonia Permanent Stroke Reoperation New-Onset Atrial Fibrillation

MI ≥ 24 h

MI < 24 h

P Value

67(3.79%) 635(35.96%) 204(11.55%) 8(0.45%) 19(1.08%) 56(3.17%) 32(1.81%) 45(2.55%) 553(31.31%)

21(7.19%) 120(41.10%) 57(19.52%) 0 (0.00%) 6(2.05%) 24(8.22%) 8(2.74%) 11(3.77%) 86(29.45%)

.01 0.09 .001 0.61 0.16 <.001 0.29 0.24 0.52

Operative mortality: 1) all death occurring during hospitalization in or after 30-day (including transfer to other facilities) and 2) all death occurring after discharge but before the 30th postoperative day

Supplement 3. Postoperative outcomes stratified by type of MI

Number of Patients Operative mortality Blood Product Transfusion Prolonged Ventilation >24 Hours Deep Sternal Wound Infection Sepsis Pneumonia Permanent Stroke Reoperation New-Onset Atrial Fibrillation

STEMI (N = 390) MI ≥ 24 h MI < 24 h 262 128 13(4.96%) 8(6.25%) 91(34.73%) 53(41.41%) 35(13.36%) 32(25.00%) 0(0.00%) 0(0.00%) 2(0.76%) 4(3.13%) 9(3.44%) 12(9.38%) 3(1.15%) 3(2.34%) 6(2.29%) 6(4.69%) 71(27.10%) 42(32.81%)

P-Value 0.60 0.20 .004 NA 0.08 0.01 0.37 0.20 0.24

NSTEMI (N = 1668) MI ≥ 24 h MI < 24 h P-Value 1504 164 54(3.59%) 13(7.94%) .007 544(36.17%) 67(40.85%) 0.24 169(11.24%) 25(15.24%) 0.13 8(0.53%) 0(0.00%) 1.00 17(1.13%) 2(1.22%) 0.71 47(3.13%) 12(7.32%) .006 29(1.93%) 5(3.05%) 0.33 39(2.59%) 5(3.05%) 0.61 482(32.05%) 44(26.83%) 0.17

Variables are presented as frequency (percentage) due to them all being categorical variables. STEMI – ST Elevated Myocardial Infarction, NSTEMI – Non-ST Elevated Myocardial Infarction, MI – Myocardial Infarction

Supplement 4A. Cox proportional hazard, 95% CI and P value for the time to death in the NSTEMI

>=24h <24h

Unadjusted HR

95% CI

P

1.03 REF

0.72,1.48 REF

0.86 REF

Multi-variable model HR 0.99 REF

95% CI

P

Inverse weighted HR

95% CI

P

0.56, 1.76 REF

0.98 REF

1.35 REF

0.97,1.87 REF

0.08 REF

Variables selected from univariable model and adjusted in the multivariable model: diabetes, CVD, PVD, COPD, dialysis, previous HF, Family history of CAD, previous CABG, previous valve surgery, arrhythmia, immunosuppression, BIMA, Age, creatinine, status, ejection fraction (per 5 unit) Time dependent covariables: diabetes, COPD, ejection fraction (per 5 unit), status, previous CABG

*----------------------------------------------------------------------------------------------------------------------------*

Table 4B. Cox proportional hazard, 95% CI and P value for CTB in the STEMI Unadjusted HR >=24h <24h

95% CI

P

Multi-variable 95% CI P Inverse 95% CI model weighted HR HR 1.17 0.64, 2.13 0.62 0.57 0.25,1.29 0.18 1.32 0.60, 2.89 REF REF REF REF REF REF REF REF Variables selected from univariable model and adjusted in the multivariable model: diabetes, CVD, COPD, dialysis, previous HF, arrhythmia, immunosuppression, BIMA, previous PCI.

P

0.49 REF

Supplement 5a: NSTEMI MACC readmission

Unadjusted HR

95% CI

P

Multi-variable model

95% CI

P

Inverse weighted HR

95% CI

P

>=24h <24h

HR 1.04 0.81, 1.33 0.79 0.92 0.72, 1.19 0.54 0.98 0.80, 1.20 0.83 REF REF REF REF REF REF REF REF REF Variables selected from univariable model and adjusted in the multivariable model: age, Creatinine, Plavix used, immunosuppression, history of heart failure, hypertension, cvd, pvd, diabetes, race, female gender

Supplement 5b: STEMI MACC readmission

Unadjusted HR

>=24h <24h

95% CI

P

Multi95% CI P Inverse 95% CI VARIABLE weighted HR model HR 0.79 0.56, 1.13 0.20 0.73 0.51, 1.06 0.10 0.91 0.55,1.50 REF REF REF REF REF REF REF REF Variables selected from univariable model and adjusted in the multivariable model: age, HDEF(per 5%), previous valve procedure, family history of CAD, history of heart failure

P

0.71 REF