Incremental prognostic value of stress phase entropy over standard PET myocardial perfusion imaging variables.

Eur J Nucl Med Mol Imaging

Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Blvd., Los Angeles, CA, 90048, USA.

Published: October 2023

AI Article Synopsis

  • This study investigates the independent prognostic value of phase variables, specifically stress phase entropy, in predicting mortality compared to traditional PET-MPI variables like myocardial flow reserve (MFR).
  • In a cohort of nearly 4,000 patients, those with abnormal stress phase entropy had a significantly higher annual mortality rate, indicating that it can effectively stratify risk regardless of MFR status.
  • The findings suggest that integrating stress phase entropy into PET-MPI assessments enhances the predictive accuracy for all-cause mortality, beyond what standard measures provide.

Article Abstract

Purpose: Phase analysis can assess left ventricular dyssynchrony. The independent prognostic value of phase variables over positron emission tomography myocardial perfusion imaging (PET-MPI) variables including myocardial flow reserve (MFR) has not been studied. The aim of this study was to explore the prognostic value of phase variables for predicting mortality over standard PET-MPI variables.

Methods: Consecutive patients who underwent pharmacological stress-rest Rb PET study were enrolled. All PET-MPI variables including phase variables (phase entropy, phase bandwidth, and phase standard deviation) were automatically obtained by QPET software (Cedars-Sinai, Los Angeles, CA). Cox proportional hazard analyses were used to assess associations with all-cause mortality (ACM).

Results: In a total of 3963 patients (median age 71 years; 57% male), 923 patients (23%) died during a median follow-up of 5 years. Annualized mortality rates increased with stress phase entropy, with a 4.6-fold difference between the lowest and highest decile groups of entropy (2.6 vs. 12.0%/year). Abnormal stress phase entropy (optimal cutoff value, 43.8%) stratified ACM risk in patients with normal and impaired MFR (both p < 0.001). Among three phase variables, only stress phase entropy was significantly associated with ACM after the adjustment of standard clinical and PET-MPI variables including MFR and stress-rest change of phase variables, whether modeled as binary variables (adjusted hazard ratio, 1.44 for abnormal entropy [> 43.8%]; 95%CI, 1.18-1.75; p < 0.001) or continuous variables (adjusted hazard ratio, 1.05 per 5% increase; 95%CI, 1.01-1.10; p = 0.030). The addition of stress phase entropy to the standard PET-MPI variables significantly improved the discriminatory power for ACM prediction (p < 0.001), but the other phase variables did not (p > 0.1).

Conclusion: Stress phase entropy is independently and incrementally associated with ACM beyond standard PET-MPI variables including MFR. Phase entropy can be obtained automatically and included in clinical reporting of PET-MPI studies to improve patient risk prediction.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10547643PMC
http://dx.doi.org/10.1007/s00259-023-06323-zDOI Listing

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