Predicting persistent left ventricular dysfunction following myocardial infarction: the PREDICTS study

Brooks, Gabriel C., Lee, Byron K., Rao, Rajni, Lin, Feng, Morin, Daniel P., Zweibel, Steven L., Buxton, Alfred E., Pletcher, Mark J., Vittinghoff, Eric and Olgin, Jeffrey E. (2016) Predicting persistent left ventricular dysfunction following myocardial infarction: the PREDICTS study. Journal of the American College of Cardiology, 67 10: 1186-1196. doi:10.1016/j.jacc.2015.12.042


Author Brooks, Gabriel C.
Lee, Byron K.
Rao, Rajni
Lin, Feng
Morin, Daniel P.
Zweibel, Steven L.
Buxton, Alfred E.
Pletcher, Mark J.
Vittinghoff, Eric
Olgin, Jeffrey E.
Title Predicting persistent left ventricular dysfunction following myocardial infarction: the PREDICTS study
Journal name Journal of the American College of Cardiology   Check publisher's open access policy
ISSN 1558-3597
0735-1097
Publication date 2016-03-15
Year available 2016
Sub-type Article (original research)
DOI 10.1016/j.jacc.2015.12.042
Open Access Status Not yet assessed
Volume 67
Issue 10
Start page 1186
End page 1196
Total pages 11
Place of publication San Diego, CA, United States
Publisher Elsevier
Language eng
Abstract 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.
Formatted abstract
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.
Keyword Heart failure
Remodeling
Risk assessment
Ventricular ejection fraction
Q-Index Code C1
Q-Index Status Provisional Code
Grant ID U01 HL089145
U01 HL089458
U01-HL089145
U01-HL089458
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
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