Sheriff, Serin Moideen

Abstract Background Ischemic heart disease is the leading cause of death among women. Non-obstructive coronary disease (NOCAD), often identified by a positive stress test and negative coronary angiogram result, is linked to adverse outcomes, particularly among women. Purpose This study evaluated the impact of discordant Single Photon Emission Computed Tomography (SPECT) Myocardial Perfusion Imaging (MPI) stress test and coronary angiogram results on repeat invasive angiography and survival, while assessing sex-related differences. Methods Data were extracted from a single health system for patients who underwent a SPECT MPI stress test, and invasive coronary angiography performed within 3 months from September 2002 to July 2018. Discordance was defined as: Profile A – a positive stress test with negative angiography (epicardial coronary vessel stenosis <50%); Profile B – a negative stress test with positive angiography (at least one epicardial coronary vessel stenosis >=50%). To assess the association between discordance and all-cause mortality, Cox-proportional hazards regression was used to analyze the most recent stress/angiogram pair (adjusting for race, age, and number of prior angiograms). For repeat invasive angiography, where mortality was considered a competing event, the Fine-Gray proportional hazards model for the sub-distribution of a competing risk was used to analyze the first stress/angiogram pair (adjusting for race and age). Hazard ratios (HRs), sub-distribution hazard ratios (SHRs), and corresponding 95% confidence intervals (CIs) were reported. Results 5,179 were included in the final analysis, median age was 67.9 years and 32.7% were female. Approximately 15% and 24% were classified as Profile A and B respectively. Patients with a negative stress/negative angiogram were designated as the reference group. Profile A was not significantly associated with mortality or repeat invasive testing in both sexes. Profile B was associated with the highest risk of death (female aHR: 1.58, P<.001; male aHR: 1.54, P<.001) followed by those with a positive stress/positive angiogram (female aHR: 1.39, P=.009; male aHR: 1.25, P=.05) compared to the reference group. Profile B was significantly associated with repeat invasive testing and a stronger association was seen in females (female aSHR: 9.84, P< .001; male aSHR: 3.42, P<.001). A similar pattern was seen for those with a positive stress/positive angiogram. Conclusion Discordant Profile A showed no significant association with mortality or repeat invasive testing. Discordant Profile B was associated with the highest mortality risk in both sexes and showed a stronger association with repeat invasive testing among females. These findings underscore the importance of careful patient selection for stress testing, the need for improved stress testing options (especially in females), and prompt coronary evaluation in patients with a negative stress test and persistent symptoms or high-risk features.

Background: cardiovascular diseases, including acute myocardial infarction (AMI) and takotsubo cardiomyopathy (TTC), are significant causes of morbidity and mortality worldwide. Timely differentiation of these conditions is essential for effective patient management and improved outcomes. Methods: We conducted a review focusing on studies that applied artificial intelligence (AI) techniques to differentiate between acute myocardial infarction (AMI) and takotsubo cardiomyopathy (TTC). Inclusion criteria comprised studies utilizing various AI modalities, such as deep learning, ensemble methods, or other machine learning techniques, for discrimination between AMI and TTC. Additionally, studies employing imaging techniques, including echocardiography, cardiac magnetic resonance imaging, and coronary angiography, for cardiac disease diagnosis were considered. Publications included were limited to those available in peer-reviewed journals. Exclusion criteria were applied to studies not relevant to the discrimination between AMI and TTC, lacking detailed methodology or results pertinent to the AI application in cardiac disease diagnosis, not utilizing AI modalities or relying solely on invasive techniques for differentiation between AMI and TTC, and non-English publications. Results: The strengths and limitations of AI-based approaches are critically evaluated, including factors affecting performance, such as reliability and generalizability. The review delves into challenges associated with model interpretability, ethical implications, patient perspectives, and inconsistent image quality due to manual dependency, highlighting the need for further research. Conclusions: This review article highlights the promising advantages of AI technologies in distinguishing AMI from TTC, enabling early diagnosis and personalized treatments. However, extensive validation and real-world implementation are necessary before integrating AI tools into routine clinical practice. It is vital to emphasize that while AI can efficiently assist, it cannot entirely replace physicians. Collaborative efforts among clinicians, researchers, and AI experts are essential to unlock the potential of these transformative technologies fully.