Arsanjani, Reza

Abstract Aims Atrial fibrillation (AF) is the most common cardiac arrhythmia. Although lipoprotein(a) [Lp(a)] is known to be a well-established risk factor for atherosclerotic cardiovascular disease, its role in the development of AF, independent of this association, remains unclear. Methods and results Adult patients from the three Mayo Clinic sites with a baseline Lp(a) and without AF history were included. Patients were categorized into two groups based on their Lp(a) levels: high Lp(a) (≥50 mg/dL) and low Lp(a) (<50 mg/dL). Survival probabilities free from incident AF were compared between Lp(a) groups, during a follow-up period up to 15 years, using the Kaplan–Meier curve and the log-rank test. Multivariable Cox regression analysis was also conducted. A total of 75 376 patients were included (median age: 55 years, 59% males), with a median follow-up duration of 8.8 (inter-quartile range: 3.4, 14.8) years. Incident AF was detected in 5738 (7.6%) patients. Survival probability free from incident AF was significantly lower in patients with elevated Lp(a) (86%) compared with those with low Lp(a) (88%, log-rank P < 0.001). Multivariable analysis adjusted for potential risk factors of AF showed a statistically significant association of elevated Lp(a) with an 11% increase in AF risk (adjusted hazard ratio: 1.11, 95% confidence interval: 1.05–1.18). Conclusion Our study suggests that elevated Lp(a) (≥50 mg/dL) is an independent risk factor for incident AF. Future prospective studies are warranted to validate our results and to test if reducing Lp(a) could mitigate the burden of AF.

Lipoprotein(a) [Lp(a)] is a well-established causal risk factor for cardiovascular diseases (CVDs), as reported by multiple Mendelian randomization studies and large epidemiological studies. When elevated Lp(a) is combined with other risk factors, most notably elevated low-density lipoprotein cholesterol (LDL-C), a synergistic atherogenic effect has been reported. However, the current literature is conflicting regarding how Lp(a) interacts in the context of controlled LDL-C levels (e.g., <70 mg/dL) and whether reducing LDL-C can modify the atherogenic effect of Lp(a). In some studies, elevated Lp(a) was still significantly associated with a higher risk of cardiovascular events, despite controlled levels of LDL-C. In contrast, multiple studies have reported attenuation of the cardiovascular risk mediated by elevated Lp(a) with lower LDL-C levels. Moreover, the relationship between Lp(a) and triglycerides, high-density lipoprotein, and very low-density lipoprotein remains unclear. In this literature review, we summarize and discuss the current evidence regarding the interactions between Lp(a) and other lipid molecules, how they contribute to the pathogenesis of CVD, and future perspectives, particularly in the current era where promising targeted Lp(a)-lowering therapies are under development.

Background: Pulmonary hypertension (PH) is a complex condition associated with significant morbidity and mortality. Traditional diagnostic and management approaches for PH often face limitations, leading to delays in diagnosis and potentially suboptimal treatment outcomes. Artificial intelligence (AI), encompassing machine learning (ML) and deep learning (DL) offers a transformative approach to PH care. Materials and Methods: We systematically searched PubMed, Scopus, and Web of Science for original studies on AI applications in PH, using predefined keywords. Out of more than 500 initial articles, 45 relevant studies were selected. Risk of bias was evaluated using PROBAST (Prediction model Risk of Bias Assessment Tool). Results: This review examines the potential applications of AI in PH, focusing on its role in enhancing diagnosis, disease classification, and prognostication. We discuss how AI-powered analysis of medical data can improve the accuracy and efficiency of detecting PH. Furthermore, we explore the potential of AI in risk stratification, leading to treatment optimization for PH. Conclusions: While acknowledging the existing challenges and limitations and the need for continued exploration and refinement of AI-driven tools, this review highlights the significant promise of AI in revolutionizing PH management to improve patient outcomes.

Bioprosthetic aortic valve degeneration (BAVD) is a significant clinical concern following both transcatheter aortic valve replacement (TAVR) and surgical aortic valve replacement (SAVR). The increasing use of bioprosthetic valves in aortic valve replacement in younger patients and the subsequent rise in cases of BAVD are acknowledged in this review which aims to provide a comprehensive overview of the incidence, diagnosis, predictors, and management of BAVD. Based on a thorough review of the existing literature, this article provides an updated overview of the biological mechanisms underlying valve degeneration, including calcification, structural deterioration, and inflammatory processes and addresses the various risk factors contributing to BAVD, such as patient demographics, comorbidities, and procedural variables. The difficulties in early detection and accurate diagnosis of BAVD are discussed with an emphasis on the need for improved imaging techniques. The incidence and progression of BAVD in patients undergoing TAVR versus SAVR are compared, providing insights into the differences and similarities between the two procedures and procedural impacts on valve longevity. The current strategies for managing BAVD, including re-intervention options of redo surgery and valve-in-valve TAVR, along with emerging treatments are discussed. The controversies in the existing literature are highlighted to offer directions for future investigations to enhance the understanding and management of BAVD.

Abstract Aims The increasing need for diagnostic echocardiography tests presents challenges in preserving the quality and promptness of reports. While Large Language Models (LLMs) have proven effective in summarizing clinical texts, their application in echo remains underexplored. Methods and results Adult echocardiography studies, conducted at the Mayo Clinic from 1 January 2017 to 31 December 2017, were categorized into two groups: development (all Mayo locations except Arizona) and Arizona validation sets. We adapted open-source LLMs (Llama-2, MedAlpaca, Zephyr, and Flan-T5) using In-Context Learning and Quantized Low-Rank Adaptation fine-tuning (FT) for echo report summarization from ‘Findings’ to ‘Impressions.’ Against cardiologist-generated Impressions, the models’ performance was assessed both quantitatively with automatic metrics and qualitatively by cardiologists. The development dataset included 97 506 reports from 71 717 unique patients, predominantly male (55.4%), with an average age of 64.3 ± 15.8 years. EchoGPT, a fine-tuned Llama-2 model, outperformed other models with win rates ranging from 87% to 99% in various automatic metrics, and produced reports comparable to cardiologists in qualitative review (significantly preferred in conciseness (P &lt; 0.001), with no significant preference in completeness, correctness, and clinical utility). Correlations between automatic and human metrics were fair to modest, with the best being RadGraph F1 scores vs. clinical utility (r = 0.42) and automatic metrics showed insensitivity (0–5% drop) to changes in measurement numbers. Conclusion EchoGPT can generate draft reports for human review and approval, helping to streamline the workflow. However, scalable evaluation approaches dedicated to echo reports remains necessary.

Background Lipoprotein(a) [Lp(a)] is an independent, causal risk factor for cardiovascular disease. However, it is still unclear whether controlling low-density lipoprotein cholesterol (LDL-C) to optimal levels can attenuate cardiovascular risk mediated by elevated Lp(a), especially in the setting of secondary prevention. Methods Adult patients with a baseline Lp(a) measurement who underwent percutaneous coronary intervention (PCI) and reached their LDL-C target levels (<70 mg/dl) at Mayo Clinic sites between 2006 and 2017 were included. Primary outcomes included major adverse cardiovascular events (MACE) and all-cause mortality. Kaplan–Meier curves were created to compare the survival probabilities among patients with Lp(a) ≥ 50 mg/dl compared with Lp(a) < 50 mg/dl. Multivariable Cox regression analyses were performed to quantify the association of elevated Lp(a) with our relevant outcomes and to control for possible confounders. Results In total, 878 patients (median age: 68 years, and 74% males) who underwent PCI were included for analysis. Of them, 29.7% had elevated Lp(a) ≥ 50 mg/dl. Kaplan–Meier curves did not reveal any significant difference in survival probabilities for elevated Lp(a) for any outcome including MACE (P = 0.91), all-cause mortality (P = 0.26), or the separate MACE components. Similarly, the multivariable analysis showed no significant association for MACE (hazard ratio: 1.07, 95% confidence interval: 0.84–1.37) or all-cause mortality (hazard ratio: 0.98, 95% confidence interval: 0.74–1.30). Conclusion In patients who underwent PCI and have their LDL-C controlled below 70 mg/dl, no significant association was found between elevated Lp(a) ≥ 50 mg/dl and risk for MACE or all-cause mortality.

Introduction: Pulmonary hypertension (PHTN) is common in patients with chronic kidney disease (CKD), however categorization as precapillary or postcapillary is challenging due to the need for invasive right heart catheterization (RHC). Studies have validated transthoracic echocardiography (TTE) to characterize PHTN and ECG-artificial intelligence (AI) to predict diastolic dysfunction risk. The utility of these noninvasive tools in patients with CKD is unknown. Methods: Retrospective analysis of patients with end stage CKD who underwent RHC between 2011 and 2021. Patients with PHTN (pulmonary artery pressure >20mmHg on RHC) were categorized as having precapillary (pulmonary capillary wedge pressure, PCWP, ≤15mmHg and pulmonary vascular resistance, PVR, >2WU) or postcapillary PHTN (PCWP >15mmHg and PVR ≤2WU for isolated postcapillary or PVR >2WU for combined). TTE and ECG within 3 months of RHC were obtained. TTE measurements were used to calculate ePLAR (echocardiographic pulmonary to left atrial ratio); tricuspid regurgitant peak velocity (TRV max ) divided by mitral E/e’. ECGs were analyzed with an AI risk tool for diastolic dysfunction (score 0-3, increasing with increasing risk). Receiver operating curves (ROC) evaluated the performance of these tests. Results: 60 patients were included; 16 (27%) with precapillary and 44 (73%) with postcapillary PHTN. ROC analysis found good accuracy for both ECG-AI diastolic dysfunction risk score to predict precapillary PHTN with AUC 0.724 (sensitivity=60% and specificity=88% for an ECG score value ≤1) and ePLAR with AUC 0.715 (sensitivity=75% and specificity=70% for ePLAR >0.17m/sec). Highest accuracy for detection of precapillary PHTN was reached by combination of ePLAR and ECG-AI tool, with AUC 0.756 (sensitivity=73%, accuracy of 86% for a value ≤1.17). Conclusions: ECG-AI generated diastolic risk score of 0-1 (low risk) and high ePLAR showed good accuracy in characterizing precapillary PHTN in patients with end stage CKD. The combination of these noninvasive tools further increased the diagnostic accuracy, reflecting its potential utility in classification of patients with CKD and PHTN, a crucial determinant of the ideal therapeutic plan in kidney transplant candidates.

Background: ICI-related myocarditis is challenging to diagnose and associated with adverse cardiovascular events and mortality. ICI therapy has also been associated with acute coronary events. There is limited data regarding cardiac troponin T (cTnT) and its association with these cardiovascular outcomes. Methods: All patients treated with ICI at a tertiary institution from 2011 to 2022 were identified, and the charts of those with cTnT measurements were reviewed. In patients with cTnT elevation (cutoff of >14 ng/L), if the Bonaca criteria for myocarditis were met, these patients were labeled as cTnT+/myocarditis+, otherwise as cTnT+/myocarditis-. Patients with non-elevated cTnT values were labeled cTnT-. Medical records were reviewed for baseline characteristics and long-term outcomes, including cardiac death, new heart failure (HF) diagnosis, and MACE (myocardial infarction, TIA/stroke, new HF diagnosis). Kaplan-Meier curves were created to plot the comparison between the three groups. Results: A total of 1669 cancer patients who received ICI therapy had post-ICI cTnT measurements. Mean age was 68.7±11.3, 58.3% were male, with follow-up at 1 year post ICI administration. Of these, 59 patients were cTnT+/myocarditis+, 1077 patients were cTnT+/myocarditis-, and 533 were cTnT-. Survival free of cardiac death, MACE and new HF diagnosis showed a graded response from worst in cTnT+/myocarditis+ patients and best in cTnT- patients; cTnT+/myocarditis- patients had a more favorable but still worse prognosis than cTnT- patients (Figure 1). A cTnT value of 95 ng/L was identified as the best cutoff for the diagnosis of ICI-related myocarditis (AUC 0.82, sensitivity 81.36%, specificity 81.62%). The optimal cTnT cutoff for cardiac death was 576 ng/L (0.70, sensitivity 72%, specificity of 61%) and for MACE 319 ng/L (AUC 0.61, sensitivity 70%, specificity of 52%). Conclusion: In cancer patients receiving ICI therapy cTnT elevations indicate a higher risk of MACE, HF, and cardiac mortality even in patients without myocarditis. Although worst outcomes are seen in those meeting diagnostic criteria for myocarditis.

Background: Atrial fibrillation (AF), the most common clinically managed cardiac arrhythmia, is known to increase the risk of heart failure, stroke, and mortality. Lipoprotein(a) [Lp(a)], a well-established risk factor for atherosclerotic cardiovascular disease (ASCVD), also plays a role in cardiovascular health. However, the influence of Lp(a) on the development of AF, independent of its association with ASCVD, remains unclear. Hypothesis: Elevated Lp(a) may be associated with increased risk of new-onset AF. Methods: A retrospective study was conducted on patients from the three Mayo Clinic sites who had their Lp(a) levels measured and had no prior history of AF. Patients were categorized into two groups based on their serum Lp(a) concentrations: high Lp(a) (≥50 mg/dL) and low Lp(a) (<50 mg/dL). New AF diagnoses were identified during the follow-up period. Survival probabilities between the two Lp(a) groups were compared using the Kaplan–Meier curve and the Log-Rank test. Cox regression analyses, both univariable and multivariable, were performed to evaluate established risk factors and compare the risk of new-onset AF between the high and low Lp(a) groups. Results: A total of 108,930 patients were included (median age: 55 years, 58% males), with a follow-up duration for up to 10 years. Incident AF was detected in 8432 (7.7%) patients. Median serum levels of Lp(a) were significantly higher in patients with AF versus non-AF patients: 15 (IQR 43) vs. 12 (IQR 34) mg/dL, p<0.001. The percentage of patients having elevated Lp(a) was significantly higher in the AF group versus non-AF group: 2119 (25%) vs 21151 (21%), p<0.001. Survival probability for new-onset AF was significantly lower in patients with elevated Lp(a) compared with those with low Lp(a) (log rank p<0.001, Figure 1). In the univariate analysis, high Lp(a) levels were associated with a 17% increase in the risk of AF (HR = 1.17, 95% CIs: 1.12 to 1.23). The multivariate analysis adjusted for well-established risk factors for AF showed a less strong but still significant association, with a 10% increase in AF risk (HR = 1.10, 95% CI: 1.05 to 1.15). Conclusion: Our study suggests that elevated Lp(a) (≥50 mg/dL) seems to be an independent risk factor for new-onset AF. Future prospective studies are still warranted to validate our results.

Background: Secondary prevention after percutaneous coronary interventions (PCIs) is still challenging and lipoprotein(a) [Lp(a)] is believed to be a residual risk factor in this setting. However, the effect of Lp(a) in case of well-controlled low-density lipoprotein cholesterol (LDL-C) is still unclear. The interaction between Lp(a) and LDL-C has been studied in primary prevention but still needs to be investigated in secondary prevention. Hypothesis: Achieving adequate LDL-C control after PCI may reduce the cardiovascular (CV) risk associated with Lp(a). Methods: A retrospective study of consecutive patients who underwent PCI and reached their LDL-C target levels (<70mg/dL) at three academic centers sites between 2006 and 2017. All included patients must have an Lp(a) measurement before the PCI intervention. Clinical outcomes on 10-year follow-up were collected by reviewing the electronic patients' records. The primary outcomes included all-cause mortality (ACM) and major adverse CV events (MACE) including non-fatal MI, ischemic stroke, coronary revascularization, and CV death. Kaplan-Meier curves were created to compare the survival probabilities among patients with high Lp(a) (≥50mg/dL) versus those with low Lp(a) (<50mg/dL). Univariate and multivariate Cox regression analyses were performed to quantify the association of elevated Lp(a) with outcomes and control for potential confounders. Results: In total, 878 patients (median age = 68 years, 74% males) were included in the analysis. Of them, 242 (27.6%) had high Lp(a) ≥50mg/dL. Kaplan Meier analyses did not reveal any significant difference in survival probabilities across the Lp(a) groups for any of the outcomes (Figure 1). The results were consistent in univariate (MACE, HR: 1.08 [95%CIs 0.85-1.35]; ACM, HR: 0.97 [95% CI 0.76-1.23]) and multivariate analyses (MACE, HR: 1.14, [95% CIs 0.90-1.45]; ACM, HR: 1.08 [95% CI 0.84-1.39]). Conclusion: These findings suggest that, in patients who underwent PCI and achieved an LDL-C target of <70 mg/dL, elevated Lp(a) levels were not associated with and increased risk of MACE or mortality. Controlling the LDL-C should still be the cornerstone in secondary prevention after PCI, however, larger prospective studies are still warranted to validate our results.