The prognostic value of artificial intelligence to predict cardiac amyloidosis in patients with severe aortic stenosis undergoing transcatheter aortic valve replacement.

Aims: Cardiac amyloidosis (CA) is common in patients with severe aortic stenosis (AS) undergoing transcatheter aortic valve replacement (TAVR). Cardiac amyloidosis has poor outcomes, and its assessment in all TAVR patients is costly and challenging. Electrocardiogram (ECG) artificial intelligence (AI) algorithms that screen for CA may be useful to identify at-risk patients. Methods and results: In this retrospective analysis of our institutional National Cardiovascular Disease Registry (NCDR)-TAVR database, patients undergoing TAVR between January 2012 and December 2018 were included. Pre-TAVR CA probability was analysed by an ECG AI predictive model, with >50% risk defined as high probability for CA. Univariable and propensity score covariate adjustment analyses using Cox regression were performed to compare clinical outcomes between patients with high CA probability vs. those with low probability at 1-year follow-up after TAVR. Of 1426 patients who underwent TAVR (mean age 81.0 ± 8.5 years, 57.6% male), 349 (24.4%) had high CA probability on pre-procedure ECG. Only 17 (1.2%) had a clinical diagnosis of CA. After multivariable adjustment, high probability of CA by ECG AI algorithm was significantly associated with increased all-cause mortality [hazard ratio (HR) 1.40, 95% confidence interval (CI) 1.01-1.96, P = 0.046] and higher rates of major adverse cardiovascular events (transient ischaemic attack (TIA)/stroke, myocardial infarction, and heart failure hospitalizations] (HR 1.36, 95% CI 1.01-1.82, P = 0.041), driven primarily by heart failure hospitalizations (HR 1.58, 95% CI 1.13-2.20, P = 0.008) at 1-year follow-up. There were no significant differences in TIA/stroke or myocardial infarction. Conclusion: Artificial intelligence applied to pre-TAVR ECGs identifies a subgroup at higher risk of clinical events. These targeted patients may benefit from further diagnostic evaluation for CA.

Disproportionately High Aortic Valve Calcium Scores in Atrial Fibrillation: Implications for Transcatheter Aortic Valve Replacement.

AIMS: Doppler mean gradient (MG) can underestimate aortic stenosis (AS) severity in patients with atrial fibrillation (AF) compared to patients in sinus rhythm (SR), potentially delaying intervention in AF. This study compared outcomes in patients with AF and SR following transcatheter aortic valve replacement (TAVR) and investigated delay in TAVR based on computed tomography aortic valve calcium score (AVCS). METHODS AND RESULTS: Patients who underwent TAVR from 2013 to 2017 for native valve severe AS were identified from an institutional database. Baseline characteristics and overall survival were compared between those in SR and AF. There were 820 patients (mean age 81 years; 41.6% female) included. AF was present in 356 patients. Patients in AF were older (82.2 vs. 80.5, p = 0.003), had lower MG compared to SR patients (42.0 vs. 44.9, p = 0.002) with similar indexed aortic valve area (0.4 vs. 0.4, p = 0.17). Median AVCS was higher in AF (males: AF 2850.0 vs. SR 2561.0, p = 0.044; females: AF 1942.0 vs. SR 1610.5, p = 0.025). Projected AVCS assuming same age of diagnosis was similar between AF and SR. Median survival post-TAVR was worse in AF compared to SR (3.2 vs 5.4 years, log rank p < 0.001). AF, lower MG, higher RVSP, dialysis, diabetes, and significant TR were associated with higher mortality (p < 0.05 for all). CONCLUSION: Older age and higher AVCS in patients with AF compared to SR suggests that AS was both underestimated and more advanced at TAVR referral.

A large language model-based generative natural language processing framework fine-tuned on clinical notes accurately extracts headache frequency from electronic health records.

OBJECTIVE: To develop a natural language processing (NLP) algorithm that can accurately extract headache frequency from free-text clinical notes. BACKGROUND: Headache frequency, defined as the number of days with any headache in a month (or 4 weeks), remains a key parameter in the evaluation of treatment response to migraine preventive medications. However, due to the variations and inconsistencies in documentation by clinicians, significant challenges exist to accurately extract headache frequency from the electronic health record (EHR) by traditional NLP algorithms. METHODS: This was a retrospective cross-sectional study with patients identified from two tertiary headache referral centers, Mayo Clinic Arizona and Mayo Clinic Rochester. All neurology consultation notes written by 15 specialized clinicians (11 headache specialists and 4 nurse practitioners) between 2012 and 2022 were extracted and 1915 notes were used for model fine-tuning (90%) and testing (10%). We employed four different NLP frameworks: (1) ClinicalBERT (Bidirectional Encoder Representations from Transformers) regression model, (2) Generative Pre-Trained Transformer-2 (GPT-2) Question Answering (QA) model zero-shot, (3) GPT-2 QA model few-shot training fine-tuned on clinical notes, and (4) GPT-2 generative model few-shot training fine-tuned on clinical notes to generate the answer by considering the context of included text. RESULTS: The mean (standard deviation) headache frequency of our training and testing datasets were 13.4 (10.9) and 14.4 (11.2), respectively. The GPT-2 generative model was the best-performing model with an accuracy of 0.92 (0.91, 0.93, 95% confidence interval [CI]) and R2 score of 0.89 (0.87, 0.90, 95% CI), and all GPT-2-based models outperformed the ClinicalBERT model in terms of exact matching accuracy. Although the ClinicalBERT regression model had the lowest accuracy of 0.27 (0.26, 0.28), it demonstrated a high R2 score of 0.88 (0.85, 0.89), suggesting the ClinicalBERT model can reasonably predict the headache frequency within a range of ≤ ± 3 days, and the R2 score was higher than the GPT-2 QA zero-shot model or GPT-2 QA model few-shot training fine-tuned model. CONCLUSION: We developed a robust information extraction model based on a state-of-the-art large language model, a GPT-2 generative model that can extract headache frequency from EHR free-text clinical notes with high accuracy and R2 score. It overcame several challenges related to different ways clinicians document headache frequency that were not easily achieved by traditional NLP models. We also showed that GPT-2-based frameworks outperformed ClinicalBERT in terms of accuracy in extracting headache frequency from clinical notes. To facilitate research in the field, we released the GPT-2 generative model and inference code with open-source license of community use in GitHub. Additional fine-tuning of the algorithm might be required when applied to different health-care systems for various clinical use cases.

Role of Genetics in Diagnosis and Management of Hypertrophic Cardiomyopathy: A Glimpse into the Future.

Hypertrophic cardiomyopathy (HCM) is the most common inherited cardiomyopathy. It follows an autosomal dominant inheritance pattern in most cases, with incomplete penetrance and heterogeneity. It is familial in 60% of cases and most of these are caused by pathogenic variants in the core sarcomeric genes (MYH7, MYBPC3, TNNT2, TNNI3, MYL2, MYL3, TPM1, ACTC1). Genetic testing using targeted disease-specific panels that utilize next-generation sequencing (NGS) and include sarcomeric genes with the strongest evidence of association and syndrome-associated genes is highly recommended for every HCM patient to confirm the diagnosis, identify the molecular etiology, and guide screening and management. The yield of genetic testing for a disease-causing variant is 30% in sporadic cases and up to 60% in familial cases and in younger patients with typical asymmetrical septal hypertrophy. Genetic testing remains challenging in the interpretation of results and classification of variants. Therefore, in 2015 the American College of Medical Genetics and Genomics (ACMG) established guidelines to classify and interpret the variants with an emphasis on the necessity of periodic reassessment of variant classification as genetic knowledge rapidly expands. The current guidelines recommend focused cascade genetic testing regardless of age in phenotype-negative first-degree relatives if a variant with decisive evidence of pathogenicity has been identified in the proband. Genetic test results in family members guide longitudinal clinical surveillance. At present, there is emerging evidence for genetic test application in risk stratification and management but its implementation into clinical practice needs further study. Promising fields such as gene therapy and implementation of artificial intelligence in the diagnosis of HCM are emerging and paving the way for more effective screening and management, but many challenges and obstacles need to be overcome before establishing the practical implications of these new methods.

Efficacy of Sacubitril-Valsartan on Survival and Cardiac Remodeling in Hypotensive Heart Failure With Reduced Ejection Fraction: A Multicenter Study.

OBJECTIVE: To investigate whether hypotensive patients diagnosed with heart failure and reduced ejection fraction (HFrEF) might benefit from angiotensin receptor-neprilysin inhibitors (ARNis) in real-world practice because patients with baseline systolic blood pressure (SBP) of less than 100 mm Hg have been excluded from landmark trials. PATIENTS AND METHODS: In this multicenter study conducted between January 1, 2013, and December 31, 2021, a total of 7562 symptomatic patients with HFrEF were enrolled and grouped by SBP (hypotension was defined as an SBP of less than 100 mm Hg) and ARNi use as follows: group 1, hypotensive/non-ARNi users (n=484); group 2, hypotensive/ARNi users (n=308); group 3, nonhypotensive/non-ARNi users (n=4560); and group 4, nonhypotensive/ARNi users (n=2210). Inverse probability of treatment weighting was used to balance baseline characteristics for survival analysis. RESULTS: Diverse baseline characteristics and lower rates of medication use were found among non-ARNi users compared with ARNi users. Hypotensive/ARNi users had lower ARNi initiation doses than nonhypotensive/ARNi users. We observed significantly lower mortality, composite heart failure hospitalization, and CV death for hypotensive/ARNi and the other 2 nonhypotensive groups (groups 3 and 4) during a median follow-up of 3.43 years (all P<.05), with a similar effect on reverse remodeling for the hypotensive/ARNi group compared with the hypotensive/non-ARNi group. The event-free survival benefits of ARNi vs renin-angiotensin system inhibitors were consistent with the lower boundary of SBP for clinical benefits found until 88 mm Hg (spline curves) after inverse probability of treatment weighting. CONCLUSION: Patients with HFrEF and hypotension may still benefit from ARNi treatment. Patients with hypotensive HFrEF should not be routinely excluded from ARNi use in a real-world setting.

Cardiac Allograft Vasculopathy: Challenges and Advances in Invasive and Non-Invasive Diagnostic Modalities.

Cardiac allograft vasculopathy (CAV) is a distinct form of coronary artery disease that represents a major cause of death beyond the first year after heart transplantation. The pathophysiology of CAV is still not completely elucidated; it involves progressive circumferential wall thickening of both the epicardial and intramyocardial coronary arteries. Coronary angiography is still considered the gold-standard test for the diagnosis of CAV, and intravascular ultrasound (IVUS) can detect early intimal thickening with improved sensitivity. However, these tests are invasive and are unable to visualize and evaluate coronary microcirculation. Increasing evidence for non-invasive surveillance techniques assessing both epicardial and microvascular components of CAV may help improve early detection. These include computed tomography coronary angiography (CTCA), single-photon emission computed tomography (SPECT), positron emission tomography (PET), and vasodilator stress myocardial contrast echocardiography perfusion imaging. This review summarizes the current state of diagnostic modalities and their utility and prognostic value for CAV and also evaluates emerging tools that may improve the early detection of this complex disease.

Identifying the Causes of Unexplained Dyspnea at High Altitude Using Normobaric Hypoxia with Echocardiography.

Exposure to high altitude results in hypobaric hypoxia, leading to physiological changes in the cardiovascular system that may result in limiting symptoms, including dyspnea, fatigue, and exercise intolerance. However, it is still unclear why some patients are more susceptible to high-altitude symptoms than others. Hypoxic simulation testing (HST) simulates changes in physiology that occur at a specific altitude by asking the patients to breathe a mixture of gases with decreased oxygen content. This study aimed to determine whether the use of transthoracic echocardiography (TTE) during HST can detect the rise in right-sided pressures and the impact of hypoxia on right ventricle (RV) hemodynamics and right to left shunts, thus revealing the underlying causes of high-altitude signs and symptoms. A retrospective study was performed including consecutive patients with unexplained dyspnea at high altitude. HSTs were performed by administrating reduced FiO2 to simulate altitude levels specific to patients' history. Echocardiography images were obtained at baseline and during hypoxia. The study included 27 patients, with a mean age of 65 years, 14 patients (51.9%) were female. RV systolic pressure increased at peak hypoxia, while RV systolic function declined as shown by a significant decrease in the tricuspid annular plane systolic excursion (TAPSE), the maximum velocity achieved by the lateral tricuspid annulus during systole (S' wave), and the RV free wall longitudinal strain. Additionally, right-to-left shunt was present in 19 (70.4%) patients as identified by bubble contrast injections. Among these, the severity of the shunt increased at peak hypoxia in eight cases (42.1%), and the shunt was only evident during hypoxia in seven patients (36.8%). In conclusion, the use of TTE during HST provides valuable information by revealing the presence of symptomatic, sustained shunts and confirming the decline in RV hemodynamics, thus potentially explaining dyspnea at high altitude. Further studies are needed to establish the optimal clinical role of this physiologic method.

Opportunistic screening for coronary artery calcium deposition using chest radiographs - a multi-objective models with multi-modal data fusion.

Background: To create an opportunistic screening strategy by multitask deep learning methods to stratify prediction for coronary artery calcium (CAC) and associated cardiovascular risk with frontal chest x-rays (CXR) and minimal data from electronic health records (EHR). Methods: In this retrospective study, 2,121 patients with available computed tomography (CT) scans and corresponding CXR images were collected internally (Mayo Enterprise) with calculated CAC scores binned into 3 categories (0, 1-99, and 100+) as ground truths for model training. Results from the internal training were tested on multiple external datasets (domestic (EUH) and foreign (VGHTPE)) with significant racial and ethnic differences and classification performance was compared. Findings: Classification performance between 0, 1-99, and 100+ CAC scores performed moderately on both the internal test and external datasets, reaching average f1-score of 0.66 for Mayo, 0.62 for EUH and 0.61 for VGHTPE. For the clinically relevant binary task of 0 vs 400+ CAC classification, the performance of our model on the internal test and external datasets reached an average AUCROC of 0.84. Interpretation: The fusion model trained on CXR performed better (0.84 average AUROC on internal and external dataset) than existing state-of-the-art models on predicting CAC scores only on internal (0.73 AUROC), with robust performance on external datasets. Thus, our proposed model may be used as a robust, first-pass opportunistic screening method for cardiovascular risk from regular chest radiographs. For community use, trained model and the inference code can be downloaded with an academic open-source license from https://github.com/jeong-jasonji/MTL_CAC_classification . Funding: The study was partially supported by National Institute of Health 1R01HL155410-01A1 award.

Echocardiography-Based Deep Learning Model to Differentiate Constrictive Pericarditis and Restrictive Cardiomyopathy.

BACKGROUND: Constrictive pericarditis (CP) is an uncommon but reversible cause of diastolic heart failure if appropriately identified and treated. However, its diagnosis remains a challenge for clinicians. Artificial intelligence may enhance the identification of CP. OBJECTIVES: The authors proposed a deep learning approach based on transthoracic echocardiography to differentiate CP from restrictive cardiomyopathy. METHODS: Patients with a confirmed diagnosis of CP and cardiac amyloidosis (CA) (as the representative disease of restrictive cardiomyopathy) at Mayo Clinic Rochester from January 2003 to December 2021 were identified to extract baseline demographics. The apical 4-chamber view from transthoracic echocardiography studies was used as input data. The patients were split into a 60:20:20 ratio for training, validation, and held-out test sets of the ResNet50 deep learning model. The model performance (differentiating CP and CA) was evaluated in the test set with the area under the curve. GradCAM was used for model interpretation. RESULTS: A total of 381 patients were identified, including 184 (48.3%) CP, and 197 (51.7%) CA cases. The mean age was 68.7 ± 11.4 years, and 72.8% were male. ResNet50 had a performance with an area under the curve of 0.97 to differentiate the 2-class classification task (CP vs CA). The GradCAM heatmap showed activation around the ventricular septal area. CONCLUSIONS: With a standard apical 4-chamber view, our artificial intelligence model provides a platform to facilitate the detection of CP, allowing for improved workflow efficiency and prompt referral for more advanced evaluation and intervention of CP.

Role of lipoprotein(a) concentrations in bioprosthetic aortic valve degeneration.

OBJECTIVES: Lipoprotein(a) (Lp(a)) is associated with an increased incidence of native aortic stenosis, which shares similar pathological mechanisms with bioprosthetic aortic valve (bAV) degeneration. However, evidence regarding the role of Lp(a) concentrations in bAV degeneration is lacking. This study aims to evaluate the association between Lp(a) concentrations and bAV degeneration. METHODS: In this retrospective multicentre study, patients who underwent a bAV replacement between 1 January 2010 and 31 December 2020 and had a Lp(a) measurement were included. Echocardiography follow-up was performed to determine the presence of bioprosthetic valve degeneration, which was defined as an increase >10 mm Hg in mean gradient from baseline with concomitant decrease in effective orifice area and Doppler Velocity Index, or new moderate/severe prosthetic regurgitation. Levels of Lp(a) were compared between patients with and without degeneration and Cox regression analysis was performed to investigate the association between Lp(a) levels and bioprosthetic valve degeneration. RESULTS: In total, 210 cases were included (mean age 74.1±9.4 years, 72.4% males). Median time between baseline and follow-up echocardiography was 4.4 (IQR 3.7) years. Bioprostheses degeneration was observed in 33 (15.7%) patients at follow-up. Median serum levels of Lp(a) were significantly higher in patients affected by degeneration versus non-affected cases: 50.0 (IQR 72.0) vs 15.6 (IQR 48.6) mg/dL, p=0.002. In the regression analysis, high Lp(a) levels (≥30 mg/dL) were associated with degeneration both in a univariable analysis (HR 3.6, 95% CI 1.7 to 7.6, p=0.001) and multivariable analysis adjusted by other risk factors for bioprostheses degeneration (HR 4.4, 95% CI 1.9 to 10.4, p=0.001). CONCLUSIONS: High serum Lp(a) is associated with bAV degeneration. Prospective studies are needed to confirm these findings and to investigate whether lowering Lp(a) levels could slow bioprostheses degradation.

Artificial Intelligence-Based Prediction of Cardiovascular Diseases from Chest Radiography.

Chest radiography (CXR) is the most frequently performed radiological test worldwide because of its wide availability, non-invasive nature, and low cost. The ability of CXR to diagnose cardiovascular diseases, give insight into cardiac function, and predict cardiovascular events is often underutilized, not clearly understood, and affected by inter- and intra-observer variability. Therefore, more sophisticated tests are generally needed to assess cardiovascular diseases. Considering the sustained increase in the incidence of cardiovascular diseases, it is critical to find accessible, fast, and reproducible tests to help diagnose these frequent conditions. The expanded focus on the application of artificial intelligence (AI) with respect to diagnostic cardiovascular imaging has also been applied to CXR, with several publications suggesting that AI models can be trained to detect cardiovascular conditions by identifying features in the CXR. Multiple models have been developed to predict mortality, cardiovascular morphology and function, coronary artery disease, valvular heart diseases, aortic diseases, arrhythmias, pulmonary hypertension, and heart failure. The available evidence demonstrates that the use of AI-based tools applied to CXR for the diagnosis of cardiovascular conditions and prognostication has the potential to transform clinical care. AI-analyzed CXRs could be utilized in the future as a complimentary, easy-to-apply technology to improve diagnosis and risk stratification for cardiovascular diseases. Such advances will likely help better target more advanced investigations, which may reduce the burden of testing in some cases, as well as better identify higher-risk patients who would benefit from earlier, dedicated, and comprehensive cardiovascular evaluation.

Aortic Valve Calcium Score by Computed Tomography as an Adjunct to Echocardiographic Assessment-A Review of Clinical Utility and Applications.

Aortic valve stenosis (AS) is increasing in prevalence due to the aging population, and severe AS is associated with significant morbidity and mortality. Echocardiography remains the mainstay for the initial detection and diagnosis of AS, as well as for grading of severity. However, there are important subgroups of patients, for example, patients with low-flow low-gradient or paradoxical low-gradient AS, where quantification of severity of AS is challenging by echocardiography and underestimation of severity may delay appropriate management and impart a worse prognosis. Aortic valve calcium score by computed tomography has emerged as a useful clinical diagnostic test that is complimentary to echocardiography, particularly in cases where there may be conflicting data or clinical uncertainty about the degree of AS. In these situations, aortic valve calcium scoring may help re-stratify grading of severity and, therefore, further direct clinical management. This review presents the evolution of aortic valve calcium score by computed tomography, its diagnostic and prognostic value, as well as its utility in clinical care.

Challenges and solutions of echocardiography generalization for deep learning: a study in patients with constrictive pericarditis.

Purpose: The inherent characteristics of transthoracic echocardiography (TTE) images such as low signal-to-noise ratio and acquisition variations can limit the direct use of TTE images in the development and generalization of deep learning models. As such, we propose an innovative automated framework to address the common challenges in the process of echocardiography deep learning model generalization on the challenging task of constrictive pericarditis (CP) and cardiac amyloidosis (CA) differentiation. Approach: Patients with a confirmed diagnosis of CP or CA and normal cases from Mayo Clinic Rochester and Arizona were identified to extract baseline demographics and the apical 4 chamber view from TTE studies. We proposed an innovative preprocessing and image generalization framework to process the images for training the ResNet50, ResNeXt101, and EfficientNetB2 models. Ablation studies were conducted to justify the effect of each proposed processing step in the final classification performance. Results: The models were initially trained and validated on 720 unique TTE studies from Mayo Rochester and further validated on 225 studies from Mayo Arizona. With our proposed generalization framework, EfficientNetB2 generalized the best with an average area under the curve (AUC) of 0.96 (±0.01) and 0.83 (±0.03) on the Rochester and Arizona test sets, respectively. Conclusions: Leveraging the proposed generalization techniques, we successfully developed an echocardiography-based deep learning model that can accurately differentiate CP from CA and normal cases and applied the model to images from two sites. The proposed framework can be further extended for the development of echocardiography-based deep learning models.

A Large Language Model-Based Generative Natural Language Processing Framework Finetuned on Clinical Notes Accurately Extracts Headache Frequency from Electronic Health Records.

Background: Headache frequency, defined as the number of days with any headache in a month (or four weeks), remains a key parameter in the evaluation of treatment response to migraine preventive medications. However, due to the variations and inconsistencies in documentation by clinicians, significant challenges exist to accurately extract headache frequency from the electronic health record (EHR) by traditional natural language processing (NLP) algorithms. Methods: This was a retrospective cross-sectional study with human subjects identified from three tertiary headache referral centers- Mayo Clinic Arizona, Florida, and Rochester. All neurology consultation notes written by more than 10 headache specialists between 2012 to 2022 were extracted and 1915 notes were used for model fine-tuning (90%) and testing (10%). We employed four different NLP frameworks: (1) ClinicalBERT (Bidirectional Encoder Representations from Transformers) regression model (2) Generative Pre-Trained Transformer-2 (GPT-2) Question Answering (QA) Model zero-shot (3) GPT-2 QA model few-shot training fine-tuned on Mayo Clinic notes; and (4) GPT-2 generative model few-shot training fine-tuned on Mayo Clinic notes to generate the answer by considering the context of included text. Results: The GPT-2 generative model was the best-performing model with an accuracy of 0.92[0.91 - 0.93] and R2 score of 0.89[0.87, 0.9], and all GPT2-based models outperformed the ClinicalBERT model in terms of the exact matching accuracy. Although the ClinicalBERT regression model had the lowest accuracy 0.27[0.26 - 0.28], it demonstrated a high R2 score 0.88[0.85, 0.89], suggesting the ClinicalBERT model can reasonably predict the headache frequency within a range of ≤ ± 3 days, and the R2 score was higher than the GPT-2 QA zero-shot model or GPT-2 QA model few-shot training fine-tuned model. Conclusion: We developed a robust model based on a state-of-the-art large language model (LLM)- a GPT-2 generative model that can extract headache frequency from EHR free-text clinical notes with high accuracy and R2 score. It overcame several challenges related to different ways clinicians document headache frequency that were not easily achieved by traditional NLP models. We also showed that GPT2-based frameworks outperformed ClinicalBERT in terms of accuracy in extracting headache frequency from clinical notes. To facilitate research in the field, we released the GPT-2 generative model and inference code with open-source license of community use in GitHub.

Using Augmented Mean Arterial Pressure to Identify High Mortality Risk Patients With Moderate Aortic Stenosis.

OBJECTIVE: To study the usefulness of a novel echocardiographic marker, augmented mean arterial pressure (AugMAP = [(mean aortic valve gradient + systolic blood pressure) + (2 × diastolic blood pressure)] / 3), in identifying high-risk patients with moderate aortic stenosis (AS). PATIENTS AND METHODS: Adults with moderate AS (aortic valve area, 1.0-1.5 cm2) at Mayo Clinic sites from January 1, 2010, through December 31, 2020, were identified. Baseline demographic, echocardiographic, and all-cause mortality data were retrieved. Patients were grouped into higher and lower AugMAP groups using a cutoff value of 80 mm Hg for analysis. Kaplan-Meier and Cox regression models were used to assess the performance of AugMAP. RESULTS: A total of 4563 patients with moderate AS were included (mean ± SD age, 73.7±12.5 years; 60.5% men). Median follow-up was 2.5 years; 36.0% of patients died. The mean ± SD left ventricular ejection fraction (LVEF) was 60.1%±11.4%, and the mean ± SD AugMAP was 99.1±13.1 mm Hg. Patients in the lower AugMAP group, with either preserved or reduced LVEF, had significantly worse survival performance (all P<.001). Multivariate Cox regression showed that AugMAP (hazard ratio, 0.962; 95% CI, 0.942 to 0.981 per 5-mm Hg increase; P<.001) and AugMAP less than 80 mm Hg (hazard ratio, 1.477; 95% CI, 1.241 to 1.756; P<.001) were independently associated with all-cause mortality. CONCLUSION: AugMAP is a simple and effective echocardiographic marker to identify high-risk patients with moderate AS independent of LVEF. It can potentially be used in the candidate selection process if moderate AS becomes indicated for aortic valve intervention in the future.

Sinus Node Dysfunction After Percutaneous Transcatheter Closure of Right Coronary Artery-Superior Vena Cava Fistula.

Congenital right coronary artery-superior vena cava (RCA-SVC) fistula is rare and typically does not manifest any symptoms until the fifth decade of life. The present case demonstrates a 48-year-old woman who developed Sinus node dysfunction of unknown cause after Percutaneous coil embolization of the RCA-SVC fistula requiring permanent pacemaker. (Level of Difficulty: Intermediate.).

Using Artificial Intelligence in Predicting Ischemic Stroke Events After Percutaneous Coronary Intervention.

BACKGROUND: Ischemic stroke (IS) is an uncommon but severe complication in patients undergoing percutaneous coronary intervention (PCI). Despite significant morbidity and economic cost associated with post PCI IS, a validated risk prediction model is not currently available. AIMS: We aim to develop a machine learning model that predicts IS after PCI. METHODS: We analyzed data from Mayo Clinic CathPCI registry from 2003 to 2018. Baseline clinical and demographic data, electrocardiography (ECG), intra/post-procedural data, and echocardiographic variables were abstracted. A random forest (RF) machine learning model and a logistic regression (LR) model were developed. The receiver operator characteristic (ROC) analysis was used to assess model performance in predicting IS at 6-month, 1-, 2-, and 5-years post-PCI. RESULTS: A total of 17,356 patients were included in the final analysis. The mean age of this cohort was 66.9 ± 12.5 years, and 70.7% were male. Post-PCI IS was noted in 109 patients (.6%) at 6 months, 132 patients (.8%) at 1 year, 175 patients (1%) at 2 years, and 264 patients (1.5%) at 5 years. The area under the curve of the RF model was superior to the LR model in predicting ischemic stroke at 6 months, 1-, 2-, and 5-years. Periprocedural stroke was the strongest predictor of IS post discharge. CONCLUSIONS: The RF model accurately predicts short- and long-term risk of IS and outperforms logistic regression analysis in patients undergoing PCI. Patients with periprocedural stroke may benefit from aggressive management to reduce the future risk of IS.

Current Management and Future Perspectives in the Treatment of Lp(a) with a Focus on the Prevention of Cardiovascular Diseases.

Lipoprotein(a) [Lp(a)] is a lipid molecule with atherogenic, inflammatory, thrombotic, and antifibrinolytic effects, whose concentrations are predominantly genetically determined. The association between Lp(a) and cardiovascular diseases (CVDs) has been well-established in numerous studies, and the ability to measure Lp(a) levels is widely available in the community. As such, there has been increasing interest in Lp(a) as a therapeutic target for the prevention of CVD. The impact of the currently available lipid-modifying agents on Lp(a) is modest and heterogeneous, except for the monoclonal antibody proprotein convertase subtilisin/kexin type 9 inhibitors (PCSK9i), which demonstrated a significant reduction in Lp(a) levels. However, the absolute reduction in Lp(a) to significantly decrease CVD outcomes has not been definitely established, and the magnitude of the effect of PCSK9i seems insufficient to directly reduce the Lp(a)-related CVD risk. Therefore, emerging therapies are being developed that specifically aim to lower Lp(a) levels and the risk of CVD, including RNA interference (RNAi) agents, which have the capacity for temporary and reversible downregulation of gene expression. This review article aims to summarize the effects of Lp(a) on CVD and to evaluate the available evidence on established and emerging therapies targeting Lp(a) levels, focusing on the potential reduction of CVD risk attributable to Lp(a) concentrations.