Author + information
- Received November 10, 2015
- Revision received December 15, 2015
- Accepted December 22, 2015
- Published online March 15, 2016.
- Gabriel C. Brooks, MDa,
- Byron K. Lee, MD, MASa,
- Rajni Rao, MDa,
- Feng Lin, MSb,
- Daniel P. Morin, MD, MPHc,d,
- Steven L. Zweibel, MDe,
- Alfred E. Buxton, MDf,
- Mark J. Pletcher, MD, MPHb,
- Eric Vittinghoff, PhDb,
- Jeffrey E. Olgin, MDa,∗ (, )
- PREDICTS Investigators
- aDepartment of Medicine, Division of Cardiology, University of California San Francisco, San Francisco, California
- bDepartment of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California
- cDepartment of Medicine, Ochsner Medical Center, New Orleans, Louisiana
- dOchsner Clinical School, University of Queensland School of Medicine, New Orleans, Louisiana
- eDepartment of Medicine, Hartford Hospital, Hartford, Connecticut
- fDepartment of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
- ↵∗Reprint requests and correspondence:
Dr. Jeffrey E. Olgin, Department of Medicine, Division of Cardiology, University of California San Francisco, 505 Parnassus Avenue, Box 0214, San Francisco, California 94143-0214.
Background Persistent severe left ventricular (LV) systolic dysfunction after myocardial infarction (MI) is associated with increased mortality and is a class I indication for implantation of a cardioverter-defibrillator.
Objectives This study developed models and assessed independent predictors of LV recovery to >35% and ≥50% after 90-day follow-up in patients presenting with acute MI and severe LV dysfunction.
Methods Our multicenter prospective observational study enrolled participants with ejection fraction (EF) of ≤35% at the time of MI (n = 231). Predictors for EF recovery to >35% and ≥50% were identified after multivariate modeling and validated in a separate cohort (n = 236).
Results In the PREDICTS (PREDiction of ICd Treatment Study) study, 43% of patients had persistent EF ≤35%, 31% had an EF of 36% to 49%, and 26% had an EF ≥50%. The model that best predicted recovery of EF to >35% included EF at presentation, length of stay, prior MI, lateral wall motion abnormality at presentation, and peak troponin. The model that best predicted recovery of EF to ≥50% included EF at presentation, peak troponin, prior MI, and presentation with ventricular fibrillation or cardiac arrest. After predictors were transformed into point scores, the lowest point scores predicted a 9% and 4% probability of EF recovery to >35% and ≥50%, respectively, whereas profiles with the highest point scores predicted an 87% and 49% probability of EF recovery to >35% and ≥50%, respectively.
Conclusions In patients with severe systolic dysfunction following acute MI with an EF ≤35%, 57% had EF recovery to >35%. A model using clinical variables present at the time of MI can help predict EF recovery.
Persistence of severe left ventricular (LV) dysfunction after acute myocardial infarction (MI) has important prognostic implications and is associated with increased morbidity and mortality from both congestive heart failure (HF) and sudden cardiac death. Although implantable cardioverter-defibrillators (ICD) confer a survival benefit in patients with severe LV dysfunction, guidelines recommend implantation of an ICD after a 40-day waiting period (90 days if revascularization occurs) (1) for patients whose ejection fraction (EF) remains ≤35%. This waiting period is based on 2 studies showing no long-term mortality benefit from early implantation of an ICD (2,3). The proportion of patients and factors that predict which patients will continue to have an EF ≤35% 90 days after MI are unknown.
Creatine kinase, troponin, Q waves, dyssynchrony, and wall motion abnormalities measured at the time of acute MI have all been shown to predict LV functional recovery (4–6). Cohorts in which these associations were made included heterogeneous acute MI patients, many of whom had EFs >35% (and often normal or near-normal EFs). Many of these studies occurred prior to the institution of modern HF therapies and rapid revascularization techniques, which may attenuate the inferences of these findings. Taken together, existing data provide limited utility to help us understand the unique risk profile of acute MI patients presenting with severe LV dysfunction. Therefore, it remains a clinical challenge to predict which acute MI patients with severe LV dysfunction will still meet the indications for an ICD at the end of 90 days. In the present study, we define the incidence, identify markers, and develop prediction models for LV recovery to >35% and ≥50% in patients with acute MI and EF ≤35% using data from the PREDICTS (PREDiction of ICd Treatment Study) study.
The model development study samples were drawn from the PREDICTS study, a 60-center international study conducted from July 2008 to May 2011 that followed participants previously randomized in the VEST (Vest Prevention of Early Sudden Death Trial) trial, a randomized, controlled clinical trial enrolling patients 18 years of age or older, admitted with MI and LV systolic dysfunction (EF ≤35%) measured at least 8 h after the MI or percutaneous coronary intervention (PCI). Upon discharge from the hospital, participants were randomized to a LifeVest wearable defibrillator (ZOLL Medical Corporation, Chelmsford, Massachusetts) and optimal medical therapy or optimal medical therapy alone with the primary endpoint of 90-day sudden death mortality.
At the conclusion of VEST trial participation, 90 days after discharge from hospitalization for an index MI, participants were enrolled in the PREDICTS study. In the PREDICTS study, patients were implanted with an ICD based on clinical indications or a Reveal XT (Medtronic, Minneapolis, Minnesota) if the EF recovered to >35% for arrhythmia monitoring. The purpose of the PREDICTS study was to develop a risk stratification algorithm that predicted future ICD shock or sudden death over 5 years in patients who were admitted for an acute MI with an EF ≤35%. Of these 364 participants, 231 had follow-up echocardiograms at 90 days before the study was prematurely terminated. Inclusion criteria for the PREDICTS study was the same as noted above for the VEST trial. Exclusion criteria for the VEST trial and the PREDICTS study included significant valve disease, planned coronary artery bypass graft (CABG) surgery within 2 months, existing ICD, contraindication to eventual ICD, terminal condition, chronic renal failure, chest circumference >56 inches or <26 inches, pregnancy, and discharge to a skilled nursing facility. The PREDICTS study was stopped early due to slower than expected enrollment and termination of funding (from the National Institutes of Health and Medtronic).
After the termination of the PREDICTS study, the VEST trial continued and the VEST Registry was created to follow those enrolled in the VEST trial for 1 year. The VEST registry has the same inclusion/exclusion criteria. Distinct from the PREDICTS study, a 90-day echocardiogram in the VEST study was not mandatory, but rather occurred at the discretion of the treating physician. Of the 509 participants in the VEST registry available at the time of this analysis, 236 had echocardiograms at or near 90 days. This cohort was used for model validation (Online Figure 1).
Baseline echocardiograms were obtained at study sites using standard echocardiographic views and the PREDICTS study Standard Operating Procedure (based on the American Society of Echocardiography guidelines) (7), more than 8 h after MI or acute PCI. EF was calculated by Simpson’s Rule. The PREDICTS study sites underwent a certification process by the PREDICTS study echocardiography core lab, during which the echocardiogram quality and EF calculation methods were verified. Sites were allowed to recruit only after they passed this certification process. The echocardiography core laboratory maintained quality assurance by randomly sampling 50% of the studies. Participants underwent follow-up echocardiograms 90 days after the initial MI systematically (PREDICTS study) or as clinically indicated (VEST registry) as discussed previously.
Risk factors of persistent LV dysfunction
Patient demographics, clinical characteristics (including prior cardiovascular disease and pre-hospitalization medications), characteristics of the MI hospitalization (e.g., electrocardiographic parameters, biomarkers, length of hospital stay, and primary treatment of the MI), baseline echocardiographic parameters, and discharge medications were evaluated as potential predictors of persistent LV dysfunction.
Prolonged hospital stay was defined as a hospital stay >4 days, based on previously published studies demonstrating an association between hospital stays >4 days at the time of acute MI presentation and subsequent poor outcomes (8). Discharge medications were categorized as the following: beta-blockers (carvedilol specifically), angiotensin-converting enzyme inhibitors (ACEIs), angiotensin receptor blockers (ARBs), ACEIs or ARBs, aldosterone receptor blockers, statins, aspirin, and diuretic agents.
For model development, baseline characteristics of study participants were compared according to their EF at 90 days, categorized as ≤35%, 36% to 49%, and ≥50%, using Wilcoxon rank sum test and chi-square tests as appropriate, followed by pairwise tests between categories as well as tests for trend. We used Student t tests to assess the association of baseline characteristics with change in EF.
We developed 2 logistic regression models: one to predict recovery of EF defined as 90-day values of >35% and one for the prediction of 90-day EF ≥50%. First we identified baseline characteristics associated with each recovery measure in single-predictor models at a significance level of p < 0.10. We determined which of the continuous predictors identified in the first step had nonlinear associations with each outcome variable in unadjusted models and used flexible 3-knot restricted cubic spline transformations to achieve a better fit. For each possible candidate logistic model, with 4 to 7 of the identified predictors, we estimated the c-statistic (to measure of discrimination) using 10 repetitions of 10-fold cross-validation to avoid optimism and overfitting. We estimated the Hosmer-Lemeshow goodness of fit statistic using the cross-validated predictions. Among the models with the highest cross-validated c-statistics, we selected the best performing model based on the following criteria: 1) competitive c-statistic; 2) Hosmer-Lemeshow p > 0.10; and 3) simplicity. When ranking the models, if there were other models within 0.03 of the model with the highest c-statistic, we selected the model with the best calibration as measured by the Hosmer-Lemeshow statistics. If more than 1 model was identified with equally high measures of discrimination and calibration, we chose the model that contained the most easily obtainable clinical variables. We then derived point scores based on the selected models for each outcome by categorizing continuous predictors, refitting the models, and rounding the logistic regression coefficients. We estimated the c-statistic and Hosmer-Lemeshow goodness-of-fit statistic for the point scores using 10 repetitions of 10-fold cross-validation.
For model validation, baseline characteristics of participants used in the derivation cohort were compared with those in the validation set and baseline characteristics of participants in the validation set without echocardiograms at 90 days were compared to those with echocardiograms at or near 90 days, using Wilcoxon rank sum and chi-square tests as appropriate. We then applied the models derived for the prediction of EF recovery to the data for registry participants’ data. Predicted risk scores for sustained LV dysfunction were calculated using point scores derived from the PREDICTS study derivation cohort and applied to the VEST registry cohort to estimate discriminative ability.
Per the baseline characteristics of the 231 PREDICTS study participants (Table 1), 40% were routinely taking aspirin prior to the index admission and 25% had a history of MI. Only 13% of participants had a prior history of congestive HF. The EF prior to the acute MI was not known for all patients and thus is not included in this analysis.
Characteristics of the index MI hospitalization are shown in Table 2. The mean EF was 28 ± 6.6%. Most participants (84%) had wall motion abnormalities noted at the time of presentation, with 78% and 73% of participants having apical and anterior wall motion abnormalities, respectively. The majority of the patients presented with ST-segment elevation MI (81%) and another 7% had elevated troponin with a new or presumed new left bundle branch block. Nearly 20% had cardiac arrest or ventricular fibrillation (VF) arrest at the time of presentation for their MI and an additional 7% had sustained ventricular tachycardia or ventricular tachycardia requiring cardioversion. PCI was performed in 84%, 13% of whom were first treated with lytic therapy. Forty percent of the patients required ventilator support and/or circulatory support with an intra-aortic balloon pump.
The mean time from discharge to follow-up echocardiogram was 81.3 ± 32.9 days. The mean EF increased by 12.2 ± 11.9% to a mean of 40.2 ± 11.5% at follow-up. Of the participants, 57% had an EF of >35% and 26% had EF recovery to 50% or greater (Table 2). Only 18.6% had a worse EF at follow-up than at baseline. Univariate analysis demonstrated the following predictors of persistent severe systolic dysfunction: lower baseline EF, elevated baseline (nonfasting) glucose levels, prolonged hospital stay, a prior history of MI, troponin elevation, and a lateral wall motion abnormality (Online Table 1). A history of congestive HF was also associated with persistent EF <35% (Table 2). Analysis of the interaction between multiple wall motion abnormalities demonstrated that there were small multiplicative interactions between anterior or septal and apical wall motion abnormalities. No interaction was found between anterior, septal, or apical and lateral or inferior wall motion abnormalities.
In univariate analysis, EF at the time of MI was directly correlated with EF recovery to ≥50% (Online Table 2). Males had a lower chance of EF recovery to ≥50% (odds ratio [OR]: 0.37; p = 0.006). Notably, those who had VF or cardiac arrest at the time of presentation had higher odds of EF recovery to ≥50% (OR: 2.41; p = 0.03). Increasing level of peak troponin was associated with lower odds of EF recovery to ≥50%. A history of CABG or MI had a negative association with the recovery of EF to ≥50% (Table 1).
Most patients were discharged on guideline-directed medical therapy specific for post-MI (Table 3). Receipt of either beta-blockers or spironolactone was associated with persistent LV dysfunction (p = 0.013 and p ≤ 0.001, respectively). Receipt of a prescription of either furosemide or ACEI or ARBs was associated with a trend toward less EF recovery. Consistent with the concern that confounding by indication explained this apparent association; length of hospital stay was significantly associated with receipt of ACEI, ARBs, diuretic agents, beta-blockers, and aldosterone inhibitors, and the receipt of diuretic agents was significantly associated with acute HF on presentation, history of HF, or lower EF at the time of presentation (Online Table 3).
Predictors of systolic recovery
The model with the highest discrimination and calibration for EF recovery to >35% included EF at the time of MI, prolonged hospital stay, history of MI, lateral wall motion abnormalities, and elevated troponin level. The overall c-statistic for this model was 0.72, increasing to 0.75 after transformation to a point score scale. The calibration of the model, as estimated by the Hosmer-Lemeshow goodness of fit, was 0.34 and improved to 0.99 after transformation into point score scale.
EF on admission showed a strong and independent association with recovery to EF >35%. Compared to those with an admission EF of ≤25%, participants with EF of 26% to 30% and EF of 31% to 35% had increased chance of recovery to EF >35% (OR: 2.77; 95% confidence interval [CI]: 1.34 to 5.70; p < 0.01; and OR: 6.88; 95% CI: 3.26 to 14.5; p < 0.01, respectively). Predictors of hospital discharge within 4 days—lack of lateral wall motion on echocardiogram and no prior history of MI—all had a trend toward a higher odds ratio of EF recovery to >35% (OR: 1.58; 95% CI; 0.86 to 2.89; p = 0.14; OR: 1.46; 95% CI: 0.79 to 2.72; p = 0.23; and OR: 1.52; 95% CI: 0.78 to 2.96; p = 0.22, respectively). A troponin peak of ≤50- and 51- to 500-fold above the upper limit of normal (ULN) had a trend toward higher odds of EF recovery to EF >35% compared to maximum troponin level >500-fold above the ULN (OR: 1.74; 95% CI: 0.82 to 3.69; p = 0.15; and OR: 1.81; 95% CI: 0.91 to 3.62; p = 0.09, respectively) (Online Table 1, top 5 models). After transforming model predictors into point scores, predictor profiles with the lowest score of 0 had a 9% (95% CI: 2.5% to 21.7%) probability of EF recovery to >35%, whereas predictor profiles with a score of ≥7 had an 87% (95% CI: 83.8% to 90.1%) probability of EF recovery to >35% (Table 4, Figure 1; Online Table 4 for probability table).
For EF recovery to ≥50%, the model with the highest discrimination and calibration included EF at the time of MI, history of MI, troponin elevation, and VF and/or cardiac arrest at presentation. The overall c-statistic for this model was 0.79 with a calibration of 0.34, as estimated by the Hosmer-Lemeshow goodness-of-fit calibration statistic. Neither the discrimination nor calibration changed after transformation into a point score scale. EF on admission, VF or cardiac arrest on presentation, and troponin elevation all showed strong and independent associations with recovery to EF ≥50%. Compared to those with an admission EF of ≤25%, participants with EF of 26% to 30% or EF of 31% to 35% had an increased chance of recovery to EF ≥50% (OR: 3.08; 95% CI: 0.93 to 10.24; p = 0.07; and OR: 7.61; 95% CI: 2.48 to 23.33; p < 0.01, respectively). VF or cardiac arrest on presentation was associated with 5.53-fold higher odds of EF recovery to EF ≥50% (95% CI: 2.04 to 14.99; p < 0.01). A troponin peak of ≤50- or 51- to 500-fold above the ULN increased odds of EF recovery to ≥50% (OR: 12.02; 95% CI: 3.53 to 40.9; p < 0.01; and OR: 9.02; 95% CI: 2.82 to 28.83; p < 0.01, respectively) compared to a troponin peak of >500-fold above the ULN. The lack of prior history of MI approached significance for predicting EF recovery to ≥50% (OR: 2.40; 95% CI: 0.85 to 6.78; p = 0.10) (Online Appendix, top 5 models). After transforming model predictors into point scores, predictor profiles with the lowest score of 0 to 2 had a 4% (95% CI: 3% to 7%) probability of EF recovery to ≥50%, whereas predictor profiles with a score of 9 to 11 had a 49% (95% CI: 44% to 54%) probability of EF recovery to ≥50% (Table 5, Figure 2; Online Table 3 for probability table).
Validation in the VEST registry cohort
Characteristics of VEST registry participants, as well as differences between VEST registry and PREDICTS study participants, are depicted in Tables 3 and 6. Echocardiograms were performed 20 days later in the VEST registry compared to the PREDICTS study patients (101 ± 36.9 days vs. 81.3 ± 32.9 days; p < 0.01). The VEST registry participants had a lower mean EF on follow-up (37.2% vs. 40.2%), and more patients in the VEST registry had a decrease in EF at follow-up (30.1% vs. 18.6%). The VEST registry participants were less likely to have a prior history of PCI (22.9% vs. 36.1%; p = 0.02) and there was a trend toward lower prevalence of prior MI, HF, or prior CABG. Registry participants were less likely to have apical wall motion abnormalities (74.7% vs. 78.4%; p = 0.03). The VEST registry participants also had higher B-type natriuretic peptide (BNP) values (3,231 vs. 1,054; p = 0.05), but lower peak troponin (1,061- vs. 1,592-fold increase above the ULN; p < 0.01) and lower low-density lipoprotein cholesterol on presentation (98 mg/dl vs. 109 mg/dl; p = 0.04).
When applied to the VEST registry patients, the prediction models remained significantly predictive, though they performed less well. The c-statistic for the model that predicts partial recovery to EF of ≥35% was 0.66 with a Hosmer-Lemeshow goodness of fit p value of 0.25. The model predicting EF recovery to ≥50% remained robust with a c-statistic of 0.72 with excellent calibration (Hosmer-Lemeshow goodness of fit p value of 0.85).
The incidence of EF recovery in patients presenting with severe LV dysfunction at the time of acute MI has not been well described. In this study of patients with EF ≤35% at the time of MI, 57% of patients had recovered to an EF >35% by 90 days, and 26% had an EF that returned to normal or near normal (≥50%). Systolic function at the time of MI was an independent predictor of EF recovery to >35%. A history of MI, prolonged hospital stay, serum troponin level, and presence of lateral wall motion abnormalities demonstrated large associations with EF recovery to >35% that approached statistical significance (Central Illustration). A model incorporating these variables had fair discrimination and good calibration for predicting EF recovery to >35%.
Independent predictors of EF recovery to ≥50% included systolic function at the time of MI, troponin elevation, and VF and/or cardiac arrest at presentation. A history of MI approached significance for EF recovery to ≥50%. A model incorporating these variables had good discrimination and good calibration for predicting EF recovery to ≥50%.
In a large study involving more than 10,000 registry participants with EF ≤35% at the time of MI, Pokorney et al. (9) demonstrated that only 8% of patients received an ICD within 1 year. Those who received an ICD had 36% lower risk of death within 2 years of their MI compared to those who did not receive an ICD, after adjusting for age, sex, prior MI, prior stroke, and other covariates. A crucial limitation of this study was that measures of EF used to define ICD eligibility were available only at the time of hospitalization for MI. We found that 43% of our participants would continue to be eligible to receive an ICD at 90 days. An 8% ICD implantation rate for primary sudden cardiac death prevention, as was seen in the Pokorney study, suggested a marked underutilization of proven therapy and may explain the higher mortality rate in those without ICD implantation.
Risk scores derived from our prediction models demonstrated that those with the highest risk profile had a 9% and 4% probability of EF recovery to >35% and ≥50%, respectively, whereas those with the lowest risk profile had a 90% and 50% probability of EF recovery to >35% and ≥50%. Randomized studies have shown that alerts to physicians to consider ICDs in post-MI patients can increase appropriate primary prevention ICD implantation rates by 12-fold (10); however, these alerts are rarely used in practice. A risk score predicting those most likely to have a persistently low EF may focus attention on those at highest risk and frame the ICD discussion with the patient at the time of discharge to ensure follow-up. It is unlikely that this risk score will replace a follow-up echocardiogram; however, it is clear from previous studies that follow-up echocardiograms and ICD implantation are underused (9). Regardless of a patient’s risk of persistent severe LV dysfunction, we recommend following current guideline recommendations to delay ICD implantation until an EF of ≤35% is demonstrated 40 days post-acute MI (90 days if revascularization occurs).
Risk factors for persistent LV dysfunction identified in our study largely agree with various findings from prior work. Systolic function and troponin levels at the time of MI have been shown to have strong associations with subsequent functional recovery (4,6,11). Prior studies have reported an association between BNP elevation and adverse remodeling at 4 months (increase in LV end-diastolic volume by 20%), a finding not repeated in our study (12,13). In these prior studies, the mean EF at the time of MI was higher (55% to 46% vs. 28.8% in our study), and mean BNP was lower (195 ± 109 pg/ml and 137 ± 118 pg/ml vs. 1,054 ± 1,735 pg/ml here). These reports may describe a fundamentally different population of patients than our cohort.
In our study, VF or cardiac arrest at presentation predicted near normal functional recovery. This may appear paradoxical given the association of VF/arrest with higher levels of troponin in both the PREDICTS and VEST study cohorts (p < 0.01 and p = 0.04, respectively). Patients who had experienced VF or cardiac arrest may have had myocardial stunning, leading to a low EF assessment at enrollment (though this does not explain higher troponin elevations in these participants). Alternatively, VF may be a marker for ischemia with spontaneous reperfusion (troponin release kinetics differ under conditions of spontaneous reperfusion, nonreperfusion, or when intervention is performed) (14). Animal models demonstrate that spontaneous VF is more likely in ischemia-reperfusion than under ischemia alone (15). One could also speculate that those who experience VF with spontaneous reperfusion are more likely to survive long enough to present to the hospital compared to those who had VF with no reperfusion (making resuscitation less likely). Finally, it should be noted that VF occurred in 20% of the participants in our study, higher than the 11% incidence of VF at the time of MI reported in other studies (16). Our study specifically enrolled MI patients with EF ≤35%, in which one would expect a higher occurrence of VF.
Strengths of this study included the prospective collection of a broad range of clinical data, the multicenter design, data collected soon after an acute MI, a validation cohort with identical inclusion criteria, and baseline data collection to the derivation cohort. An important weakness of the validation cohort (VEST registry) was that follow-up echocardiograms were performed at the discretion of the clinician, rather than as part of a pre-defined study protocol (as was done in the derivation cohort, PREDICTS study); this could be an important source of selection bias. There may be measured and unmeasured confounders that influenced the clinicians’ decision to order the follow-up echocardiogram. Likely confounders that were measured include significant lower peak troponin, less frequent PCI, higher levels of BNP, and fewer apical wall motion abnormalities in the registry. Because the inclusion and exclusion criteria are identical for the PREDICTS study and the VEST registry, the presence of significant differences in covariates may indicate informative censoring. Validation in an external cohort is needed to better estimate the models predictive capacity. Variables and alternative models identified during model selection may have significant predictive power in this and other cohorts.
An important covariate that was not available to us was time to revascularization. All sites in the study were major cardiovascular care centers with on-call interventionalists. In the era of reporting door-to-balloon time measures of quality, it can be assumed that most PCIs were performed within a few hours of presentation. We did not have information regarding LV function prior to the index MI, or the occurrence of staged revascularization after initial hospitalization. These variables, if known, could act as powerful predictors of LV recovery.
Recovery of systolic function to an EF >35% occurs in the majority of patients who present with severe systolic dysfunction at the time of MI. Clinical variables at the time of acute MI can predict the probability of EF recovery to >35% as well as the probability of recovery to near-normal systolic function.
COMPETENCY IN MEDICAL KNOWLEDGE: Most patients with severe LV dysfunction in the acute phase of myocardial infarction exhibit improvement in LV function 90 days later. Prior MI, early ventricular fibrillation or cardiac arrest, peak serum troponin, and EF early after presentation are predictors of later myocardial recovery.
TRANSLATIONAL OUTLOOK: Prospective studies are needed to assess whether earlier implantation of automatic defibrillators in patients with a low likelihood of myocardial recovery improves survival post-MI.
For an expanded Results section as well as a supplemental figure and tables, please see the online version of this article.
The study was supported by grants from the National Institutes of Health/National Heart, Lung, and Blood Institute (NIH/NHLBI) to Dr. Olgin (U01-HL089458) and Dr. Pletcher (U01-HL089145), as well as from Medtronic and ZOLL Medical Corporation. Dr. Morin has served on the speakers bureau for Medtronic Inc., Biotronik, and Zoll; and has received research grant support from Medtronic and Boston Scientific. Dr. Zweibel has served as a consultant for and speaker for Medtronic Inc. Dr. Buxton has received research grant support from Medtronic and Biosense-Webster. Dr. Olgin has received grant support from Zoll and Medtronic. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.
- Abbreviations and Acronyms
- angiotensin-converting enzyme inhibitor
- angiotensin receptor blocker
- B-type natriuretic peptide
- coronary artery bypass graft
- confidence interval
- ejection fraction
- heart failure
- implantable cardioverter-defibrillator
- left ventricular
- myocardial infarction
- odds ratio
- percutaneous coronary intervention
- upper limit of normal
- ventricular fibrillation
- Received November 10, 2015.
- Revision received December 15, 2015.
- Accepted December 22, 2015.
- American College of Cardiology Foundation
- Epstein A.E.,
- DiMarco J.P.,
- Ellenbogen K.A.,
- et al.
- Chia S.,
- Senatore F.,
- Raffel O.C.,
- et al.
- Hackel D.B.,
- Reimer K.A.,
- Ideker R.E.,
- et al.
- Lang R.M.,
- Bierig M.,
- Devereux R.B.,
- et al.
- Gupta A.,
- Gholami P.,
- Turakhia M.P.,
- et al.
- Garcia-Alvarez A.,
- Sitges M.,
- Delgado V.,
- et al.
- Qin H.,
- Walcott G.P.,
- Killingsworth C.R.,
- et al.
- Jabbari R.,
- Engstrom T.,
- Glinge C.,
- et al.