Awad, Kamal

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: 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.

Background: New-onset atrial fibrillation (AF) is common among kidney transplant (KTx) recipients and is associated with reduced patient survival. Predictors of AF after KTx are not well understood, although AF can be associated with traditional and non-traditional risk factors. While artificial intelligence-enabled electrocardiography (AI-ECG) has shown promise in predicting incident AF, its predictive and prognostic implications in the KTx population have not yet been evaluated. Hypothesis: AI-ECG can predict new-onset AF and carries prognostic implications in patients undergoing KTx. Aims: To evaluate the clinical implications of applying AI-ECG to the preoperative ECGs of recipients of a KTx. Methods: Patients without a history of AF who underwent KTx at three referral centers between 2011 and 2021, with at least one preoperative ECG, were included in this retrospective study. Preoperative ECGs were analyzed using a previously developed AI-ECG algorithm to estimate the probabilities of new-onset AF. Based on these probabilities, patients were categorized into two groups: high and low probability of incident AF. The optimal cut-off value for the AI-ECG tool was determined using ROC analysis. The incidence of new-onset AF and mortality at 5 years post KTx were compared between these two groups using univariate and multivariate Cox regression analyses. Results: In total, 6246 patients were included (mean age 52.9 ±14.3 years, 58.7% males). A pre-transplant AI-ECG probability of AF >10% was identified as the most accurate cutoff point to distinguish between patients at low risk and high risk of incident AF (ROC = 0.72). The study found that a preoperative AI-ECG high risk of AF demonstrated not only a strong association with new-onset AF (HR 2.54, 95%CI 2.02-3.19, p<0.001 on multivariable analysis, Figure 1A ) but was also associated with an increased risk of mortality both on univariate (HR 2.73, 95%CI 2.34-3.18, p<0.001) and multivariate (HR 1.57, 95%CI 1.29-1.91, p<0.001 ) analysis at 5-year follow-up ( Figure 1B ). Conclusion: The application of AI-ECG to a single widely available test may help assess the risk of new-onset AF and mortality in patients undergoing KTx. Accurate risk stratification may identify a high-risk subgroup of transplant recipients who can benefit from closer AF monitoring, risk factor modification, and early management.

The assessment of left atrial strain (LAS) has emerged as an essential component in the evaluation of cardiac function, especially in pathologies such as heart failure and atrial fibrillation. This narrative review aims to outline the available methods for assessing LAS with a major emphasis on speckle-tracking echocardiography techniques. Other imaging modalities, including cardiac magnetic resonance and cardiac computed tomography, also provide important information on LA dynamics but have disadvantages with respect to cost and availability. The current narrative review underlines basic concepts such as the accurate assessment of LAS and discusses the clinical relevance of LAS by pointing out its significant diagnostic and prognostic role in several cardiovascular conditions. The aim of this article is to discuss the current integration of LAS into clinical practice with a view to further improving patient management and treatment strategies.

Abstract Aims Lipoprotein(a) (Lp(a)) levels are known to be mainly genetically determined. However, only scarce data are available on the intra-individual variability of Lp(a) levels across time. Methods and results We included adult patients (≥18 years old) who had baseline and follow-up Lp(a) measurements (between 1997 and 2024) with a minimum of 1 year apart. Patients were categorized into three groups as follows: normal (&lt;30 mg/dL), borderline (30 to 50 mg/dL), and high Lp(a) (≥50 mg/dL). Multivariable logistic regression was conducted to assess the predictors of the intra-individual changes in Lp(a) ≥ 10 mg/dL. A total of 11 669 individuals (median age: 54 years, 60% males) were included in our analysis, with median time between measurements of 4.5 years [interquartile range (IQR): 2.2, 10.6]. The median Lp(a) was 16 mg/dL (IQR: 7, 52) at baseline, compared with 15 mg/dL (IQR: 7, 52) at follow-up. At follow-up, 96.4% of individuals with normal Lp(a) and 89.9% with high Lp(a) remained in their categories, while 51.2% with borderline Lp(a) changed their category. Of the included population, 24.9% had an intra-individual Lp(a) change ≥ 10 mg/dL. Female sex (P &lt; 0.001), history of ASCVD (P = 0.003), statin therapy (P = 0.003), and elevated LDL cholesterol (LDL-C) levels ≥ 100 mg/dL (P &lt; 0.001) were significantly associated with higher odds of intra-individual Lp(a) changes ≥ 10 mg/dL. Conclusion Lipoprotein(a) levels were generally stable over time; however, patients with borderline levels may require more than one Lp(a) measurement, especially if they are females, have a history of ASCVD, have elevated LDL-C levels, or are on statin therapy.

Abstract Aims Lipoprotein(a) [Lp(a)] has demonstrated its association with atherosclerosis and myocardial infarction. However, its role in the development of in-stent restenosis (ISR) after percutaneous coronary intervention (PCI) is not clearly established. The aim of this study is to investigate the association between Lp(a) and ISR. Methods and results A retrospective study of adult patients who underwent successful PCI between January 2006 and December 2017 at the three Mayo Clinic sites and had a preprocedural Lp(a) measurement was conducted. Patients were divided into two groups according to the serum Lp(a) concentration [high Lp(a) ≥ 50 mg/dL and low Lp(a) &lt; 50 mg/dL]. Univariable and multivariable analyses were performed to compare risk of ISR between patients with high Lp(a) vs. those with low Lp(a). A total of 1209 patients were included, with mean age 65.9 ± 11.7 years and 71.8% were male. Median follow-up after baseline PCI was 8.8 [interquartile range (IQR) 7.4] years. Restenosis was observed in 162 (13.4%) patients. Median serum levels of Lp(a) were significantly higher in patients affected by ISR vs. non-affected cases: 27 (IQR 73.8) vs. 20 (IQR 57.5) mg/dL, P = 0.008. The rate of ISR was significantly higher among patients with high Lp(a) vs. patients with low Lp(a) values (17.0% vs. 11.6%, P = 0.010). High Lp(a) values were independently associated with ISR events (hazard ratio 1.67, 95% confidence interval 1.18–2.37, P = 0.004), and this association was more prominent after the first year following the PCI. Conclusion Lipoprotein(a) is an independent predictor for long-term ISR and should be considered in the evaluation of patients undergoing PCI.

Systemic vasculitides are a rare and complex group of diseases that can affect multiple organ systems. Clinically, presentation may be vague and non-specific and as such, diagnosis and subsequent management are challenging. These entities are typically classified by the size of vessel involved, including large-vessel vasculitis (giant cell arteritis, Takayasu’s arteritis, and clinically isolated aortitis), medium-vessel vasculitis (including polyarteritis nodosa and Kawasaki disease), and small-vessel vasculitis (granulomatosis with polyangiitis and eosinophilic granulomatosis with polyangiitis). There are also other systemic vasculitides that do not fit in to these categories, such as Behcet’s disease, Cogan syndrome, and IgG4-related disease. Advances in medical imaging modalities have revolutionized the approach to diagnosis of these diseases. Specifically, color Doppler ultrasound, computed tomography and angiography, magnetic resonance imaging, positron emission tomography, or invasive catheterization as indicated have become fundamental in the work up of any patient with suspected systemic or localized vasculitis. This review presents the key diagnostic imaging modalities and their clinical utility in the evaluation of systemic vasculitis.

Cardiac amyloidosis (CA) is an underdiagnosed form of infiltrative cardiomyopathy caused by abnormal amyloid fibrils deposited extracellularly in the myocardium and cardiac structures. There can be high variability in its clinical manifestations, and diagnosing CA requires expertise and often thorough evaluation; as such, the diagnosis of CA can be challenging and is often delayed. The application of artificial intelligence (AI) to different diagnostic modalities is rapidly expanding and transforming cardiovascular medicine. Advanced AI methods such as deep-learning convolutional neural networks (CNNs) may enhance the diagnostic process for CA by identifying patients at higher risk and potentially expediting the diagnosis of CA. In this review, we summarize the current state of AI applications to different diagnostic modalities used for the evaluation of CA, including their diagnostic and prognostic potential, and current challenges and limitations.