Skip to main content
eScholarship
Open Access Publications from the University of California

UCSF

UC San Francisco Previously Published Works bannerUCSF

Predicting Persistent Left Ventricular Dysfunction Following Myocardial Infarction The PREDICTS Study

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.

Many UC-authored scholarly publications are freely available on this site because of the UC's open access policies. Let us know how this access is important for you.

Main Content
For improved accessibility of PDF content, download the file to your device.
Current View