Spherization indices measured by resting SPECT improve risk stratification in patients with ischemia with non-obstructive coronary artery disease (INOCA)

TL;DR

Resting SPECT imaging helps predict heart risks in patients with non-obstructive coronary disease by measuring heart shape indices.

Contribution

Spherization indices from resting SPECT improve risk prediction in INOCA patients beyond traditional methods.

Findings

Higher shape index (SI) and lower E2 were linked to major adverse cardiac events (MACE) in INOCA patients.

Abnormal SI was an independent predictor of MACE in multivariate analysis.

Adding SI to baseline models improved predictive accuracy for MACE.

Abstract

The prevalence of ischemia with non-obstructive coronary artery disease (INOCA) is substantial, but its risk stratification has been suboptimal. Resting SPECT myocardial perfusion imaging (MPI) could provide useful heart information including spherical indices. We aimed to evaluate the prognostic value of spherical indices in individuals with INOCA.

During a median follow-up of 47.2 ± 20.8 months, 49 (17.2%) patients experienced major adverse cardiac events (MACE). Compared to those without MACE, those with MACE had a higher shape index (SI) (0.60 ± 0.07 vs. 0.58 ± 0.06; P = 0.028) and a lower E2 (eccentricity index calculated by the QPS) (0.81 ± 0.05 vs. 0.83 ± 0.04; P = 0.019). MACE event-free survival analysis revealed significant differences in the SI and E2 among all patients (all log-rank P < 0.01). Multivariate Cox analysis showed abnormal SI (HR: 2.73, 95% CI 1.44–5.18, P = 0.002) and E2 (HR: 1.94, 95% CI 1.08–3.48, P = 0.026) were both independent predictors for MACE when they were put into the same model, respectively. The incorporation of the SI into the baseline model demonstrated a significant improvement in the predictive accuracy for MACEs (P = 0.026), whereas E2 did not exhibit a similar improvement (P > 0.05).

For patients with INOCA, spherical indices (especially the SI) were associated with long-term MACE, which could be a preferable indicator for risk stratification and prognostic prediction.

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