Prognostic value of heart rate reserve in patients with suspected coronary artery disease undergoing stress myocardial perfusion imaging

Interaction of impaired myocardial flow reserve and extent of myocardial ischemia assessed using 13N-ammonia positron emission tomography imaging on adverse cardiovascular outcomes

BACKGROUND

Myocardial flow reserve (MFR) and the extent of myocardial ischemia identify patients at high risk of major adverse cardiovascular events (MACEs). Associations between positron emission tomography (PET)-assessed extent of ischemia, MFR, and MACEs is unclear.

METHOD

Overall, 640 consecutive patients with suspected or known coronary artery disease undergoing 13N-ammonia myocardial perfusion PET were followed-up for MACEs. Patients were categorized into three groups based on myocardial ischemia severity: Group I (n = 335), minimal (myocardial ischemia < 5%); Group II (n = 150), mild (5-10%); and Group III (n = 155), moderate-to-severe (> 10%).

RESULTS

Cardiovascular death and MACEs occurred in 17 (3%) and 93 (15%) patients, respectively. Following statistical adjustment for confounding factors, impaired MFR (global MFR < 2.0) was revealed as an independent predictor of MACEs in Groups I (hazard ratio [HR], 2.89; 95% confidence interval [CI], 1.48-5.64; P = 0.002) and II (HR, 3.40; 95% CI 1.37-8.41; P = 0.008) but was not significant in Group III (HR, 1.15; 95% CI 0.59-2.26; P = 0.67), with a significant interaction (P < 0.0001) between the extent of myocardial ischemia and MFR.

CONCLUSION

Impaired MFR was significantly associated with increased risk of MACEs in patients with ≤ 10% myocardial ischemia but not with those having > 10% ischemia, allowing a clinically effective risk stratification.

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.

The incremental significance of heart rate recovery as a predictor during exercise-stress myocardial perfusion SPECT imaging in individuals with suspected coronary artery disease

Background

Major adverse cardiac events (MACE) are more likely to occur when abnormal heart rate recovery (HRR). This study aimed to assess the incremental predictive significance of HRR over exercise stress myocardial perfusion single-photon emission computed tomography (MPS) results for MACE in individuals with suspected coronary artery disease (CAD).

Methods

Between January 2014 and December 2017, we continually gathered data on 595 patients with suspected CAD who received cycling exercise stress MPS. HRR at 1, 2, 3, and 4 min were used as study variables to obtain the optimal cut-off values of HRR for MACE. The difference between the peak heart rate achieved during exercise and the heart rate at 1, 2, 3, and 4 min was used to calculate the HRR, as shown in HRR3. Heart rate variations between two locations in time, such as HRR_{2 min-1 min}, were used to establish the slope of HRR. All patients were followed for a minimum of 4 years, with MACE as the follow-up goal. The associations between HRR and MACE were assessed using Cox proportional hazards analyses.

Results

Patients with MACE were older (P = 0.001), and they also had higher rates of hypertension, dyslipidemia, diabetes, abnormal MPS findings (SSS ≥ 5%), medication history (all P < 0.001), and lower HRR values (all P < 0.01). Patients with and without MACE did not significantly vary in their HRR_{4 min-3 min}. The optimal cut-off of HRR1, 2, and 3 combined with SSS can stratify the risk of MACE in people with suspected CAD (all P < 0.001). HRR 1, 2, and 3 and its slope were linked to MACE in multivariate analysis, where HRR3 was the most significant risk predictor. With a global X² increase from 101 to 126 (P < 0.0001), HRR3 demonstrated the greatest improvement in the model's predictive capacity, incorporating clinical data and MPS outcomes.

Conclusions

HRR at 3 min has a more excellent incremental prognostic value for predicting MACE in patients with suspected CAD following cycling exercise stress MPS. Therefore, incorporating HRR at 3 min into known predictive models may further improve the risk stratification of the patients.

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.