Prognostic value of early left ventricular ejection fraction reserve during regadenoson stress solid-state SPECT-MPI

Artificial intelligence-based fully automated stress left ventricular ejection fraction as a prognostic marker in patients undergoing stress cardiovascular magnetic resonance

AIMS

This study aimed to determine in patients undergoing stress cardiovascular magnetic resonance (CMR) whether fully automated stress artificial intelligence (AI)-based left ventricular ejection fraction (LVEFAI) can provide incremental prognostic value to predict death above traditional prognosticators.

METHODS AND RESULTS

Between 2016 and 2018, we conducted a longitudinal study that included all consecutive patients referred for vasodilator stress CMR. LVEFAI was assessed using AI algorithm combines multiple deep learning networks for LV segmentation. The primary outcome was all-cause death assessed using the French National Registry of Death. Cox regression was used to evaluate the association of stress LVEFAI with death after adjustment for traditional risk factors and CMR findings. In 9712 patients (66 ± 15 years, 67% men), there was an excellent correlation between stress LVEFAI and LVEF measured by expert (LVEFexpert) (r = 0.94, P < 0.001). Stress LVEFAI was associated with death [median (interquartile range) follow-up 4.5 (3.7-5.2) years] before and after adjustment for risk factors [adjusted hazard ratio, 0.84 (95% confidence interval, 0.82-0.87) per 5% increment, P < 0.001]. Stress LVEFAI had similar significant association with death occurrence compared with LVEFexpert. After adjustment, stress LVEFAI value showed the greatest improvement in model discrimination and reclassification over and above traditional risk factors and stress CMR findings (C-statistic improvement: 0.11; net reclassification improvement = 0.250; integrative discrimination index = 0.049, all P < 0.001; likelihood-ratio test P < 0.001), with an incremental prognostic value over LVEFAI determined at rest.

CONCLUSION

AI-based fully automated LVEF measured at stress is independently associated with the occurrence of death in patients undergoing stress CMR, with an additional prognostic value above traditional risk factors, inducible ischaemia and late gadolinium enhancement.

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.

Artificial intelligence for disease diagnosis and risk prediction in nuclear cardiology

Artificial intelligence (AI) techniques have emerged as a highly efficient approach to accurately and rapidly interpret diagnostic imaging and may play a vital role in nuclear cardiology. In nuclear cardiology, there are many clinical, stress, and imaging variables potentially available, which need to be optimally integrated to predict the presence of obstructive coronary artery disease (CAD) or predict the risk of cardiovascular events. In spite of clinical awareness of a large number of potential variables, it is difficult for physicians to integrate multiple features consistently and objectively. Machine learning (ML) is particularly well suited to integrating this vast array of information to provide patient-specific predictions. Deep learning (DL), a branch of ML characterized by a multi-layered convolutional model architecture, can extract information directly from images and identify latent image features associated with a specific prediction. This review will discuss the latest AI applications to disease diagnosis and risk prediction in nuclear cardiology with a focus on potential clinical applications.

The application of artificial intelligence in nuclear cardiology

A decade of unprecedented progress in artificial intelligence (AI) has demonstrated a lot of interest in medical imaging research including nuclear cardiology. AI has a potential to reduce cost, save time and improve image acquisition, interpretation, and decision-making. This review summarizes recent researches and potential applications of AI in nuclear cardiology and discusses the pitfall of AI.

Quantitation of Poststress Change in Ventricular Morphology Improves Risk Stratification

Shape index and eccentricity index are measures of left ventricular morphology. Although both measures can be quantified with any stress imaging modality, they are not routinely evaluated during clinical interpretation. We assessed their independent associations with major adverse cardiovascular events (MACE), including measures of poststress change in shape index and eccentricity index. Methods: Patients undergoing SPECT myocardial perfusion imaging between 2009 and 2014 from the Registry of Fast Myocardial Perfusion Imaging with Next-Generation SPECT (REFINE SPECT) were studied. Shape index (ratio between the maximum left ventricular diameter in short axis and ventricular length) and eccentricity index (calculated from orthogonal diameters in short axis and length) were calculated in end-diastole at stress and rest. Multivariable analysis was performed to assess independent associations with MACE (death, nonfatal myocardial infarction, unstable angina, or late revascularization). Results: In total, 14,016 patients with a mean age of 64.3 ± 12.2 y (8,469 [60.4%] male were included. MACE occurred in 2,120 patients during a median follow-up of 4.3 y (interquartile range, 3.4-5.7). Rest, stress, and poststress change in shape and eccentricity indices were associated with MACE in unadjusted analyses (all P < 0.001). However, in multivariable models, only poststress change in shape index (adjusted hazard ratio, 1.38; P < 0.001) and eccentricity index (adjusted hazard ratio, 0.80; P = 0.033) remained associated with MACE. Conclusion: Two novel measures, poststress change in shape index and eccentricity index, were independently associated with MACE and improved risk estimation. Changes in ventricular morphology have important prognostic utility and should be included in patient risk estimation after SPECT myocardial perfusion imaging.