The prevalence and predictors of inducible myocardial ischemia among patients referred for radionuclide stress testing

Review of cardiovascular imaging in the Journal of Nuclear Cardiology 2022: single photon emission computed tomography

In this review, we will summarize a selection of articles on single-photon emission computed tomography published in the Journal of Nuclear Cardiology in 2022. The aim of this review is to concisely recap major advancements in the field to provide the reader a glimpse of the research published in the journal over the last year. This review will place emphasis on myocardial perfusion imaging using single-photon emission computed tomography summarizing advances in the field including in prognosis, non-perfusion variables, attenuation compensation, machine learning and camera design. It will also review nuclear imaging advances in amyloidosis, left ventricular mechanical dyssynchrony, cardiac innervation, and lung perfusion. We encourage interested readers to go back to the original articles, and editorials, for a comprehensive read as necessary but hope that this yearly review will be helpful in reminding readers of articles they have seen and attracting their attentions to ones they have missed.

Development and validation of ischemia risk scores

BACKGROUND

The likelihood of ischemia on myocardial perfusion imaging is central to physician decisions regarding test selection, but dedicated risk scores are lacking. We derived and validated two novel ischemia risk scores to support physician decision making.

METHODS

Risk scores were derived using 15,186 patients and validated with 2,995 patients from a different center. Logistic regression was used to assess associations with ischemia to derive point-based and calculated ischemia scores. Predictive performance for ischemia was assessed using area under the receiver operating characteristic curve (AUC) and compared with the CAD consortium basic and clinical models.

RESULTS

During derivation, the calculated ischemia risk score (0.801) had higher AUC compared to the point-based score (0.786, p < 0.001). During validation, the calculated ischemia score (0.716, 95% CI 0.684- 0.748) had higher AUC compared to the point-based ischemia score (0.699, 95% CI 0.666- 0.732, p = 0.016) and the clinical CAD model (AUC 0.667, 95% CI 0.633- 0.701, p = 0.002). Calibration for both ischemia scores was good in both populations (Brier score  < 0.100).

CONCLUSIONS

We developed two novel risk scores for predicting probability of ischemia on MPI which demonstrated high accuracy during model derivation and in external testing. These scores could support physician decisions regarding diagnostic testing strategies.

Prevalence and predictors of automatically quantified myocardial ischemia within a multicenter international registry

BACKGROUND

The utility of cardiac stress testing depends on the prevalence of myocardial ischemia within candidate populations. However, a comprehensive assessment of the factors influencing frequency of myocardial ischemia within contemporary populations referred for stress testing has not been performed.

METHODS

We assessed 19,690 patients undergoing nuclear stress testing from a multicenter registry. The chi-square test was used to assess the relative importance of features for predicting myocardial ischemia.

RESULTS

In the overall cohort, LVEF, male gender, and rest total perfusion deficit (TPD) were the top three predictors of ischemia, followed by CAD status, age, typical angina, and CAD risk factors. Myocardial ischemia was observed in 13.6 % of patients with LVEF > 55 %, in 26.2 % of patients with LVEF 45 %-54 %, and in 48.3% among patients with LVEF < 45 % (P < 0.001). A similar pattern was noted for rest TPD (P < 0.001). Men had a threefold higher frequency of ischemia versus women (25.8 % vs. 8.4%, P < 0.001). Although the relative ranking of ischemia predictors varied among centers, LVEF and/or rest TPD were among the two most potent predictors of myocardial ischemia within each center.

CONCLUSION

The prevalence of myocardial ischemia varied markedly according to clinical and imaging characteristics. LVEF and rest TPD are robust predictors of myocardial ischemia.

Machine learning to predict abnormal myocardial perfusion from pre-test features

BACKGROUND

Accurately predicting which patients will have abnormal perfusion on MPI based on pre-test clinical information may help physicians make test selection decisions. We developed and validated a machine learning (ML) model for predicting abnormal perfusion using pre-test features.

METHODS

We included consecutive patients who underwent SPECT MPI, with 20,418 patients from a multi-center (5 sites) international registry in the training population and 9019 patients (from 2 separate sites) in the external testing population. The ML (extreme gradient boosting) model utilized 30 pre-test features to predict the presence of abnormal myocardial perfusion by expert visual interpretation.

RESULTS

In external testing, the ML model had higher prediction performance for abnormal perfusion (area under receiver-operating characteristic curve [AUC] 0.762, 95% CI 0.750-0.774) compared to the clinical CAD consortium (AUC 0.689) basic CAD consortium (AUC 0.657), and updated Diamond-Forrester models (AUC 0.658, p < 0.001 for all). Calibration (validation of the continuous risk prediction) was superior for the ML model (Brier score 0.149) compared to the other models (Brier score 0.165 to 0.198, all p < 0.001).

CONCLUSION

ML can predict abnormal myocardial perfusion using readily available pre-test information. This model could be used to help guide physician decisions regarding non-invasive test selection.

Benefit of Early Revascularization Based on Inducible Ischemia and Left Ventricular Ejection Fraction

BACKGROUND

The utility of performing early myocardial revascularization among patients presenting with inducible myocardial ischemia and low left ventricular ejection fraction (LVEF) is currently unknown.

OBJECTIVES

In this study, we sought to assess the relationship between stress-induced myocardial ischemia, revascularization, and all-cause mortality (ACM) among patients with normal vs low LVEF.

METHODS

We evaluated 43,443 patients undergoing stress-rest single-photon emission computed tomography myocardial perfusion imaging from 1998 to 2017. Median follow-up was 11.4 years. Myocardial ischemia was assessed for its interaction between early revascularization and mortality. A propensity score was used to adjust for nonrandomization to revascularization, followed by multivariable Cox modeling adjusted for the propensity score and clinical variables to predict ACM.

RESULTS

The frequency of myocardial ischemia varied markedly according to LVEF and angina, ranging from 6.7% among patients with LVEF ≥55% and no typical angina to 64.0% among patients with LVEF <45% and typical angina (P < 0.001). Among 39,883 patients with LVEF ≥45%, early revascularization was associated with increased mortality risk among patients without ischemia and lower mortality risk among patients with severe (≥15%) ischemia (HR: 0.70; 95% CI: 0.52-0.95). Among 3,560 patients with LVEF <45%, revascularization was not associated with mortality benefit among patients with no or mild ischemia, and was associated with decreased mortality among patients with moderate (10%-14%) (HR: 0.67; 95% CI: 0.49-0.91) and severe (≥15%) (HR: 0.55; 95% CI: 0.38-0.80) ischemia.

CONCLUSIONS

Within this cohort, early myocardial revascularization was associated with a significant reduction in mortality among both patients with normal LVEF and severe inducible myocardial ischemia and patients with low LVEF and moderate or severe inducible myocardial ischemia.