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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) < 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., Competing Interests: Conflict of interest: none declared., (© The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Cardiology. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Background: Despite optimal medical therapy and cardiac resynchronization therapy (CRT), significant functional mitral regurgitation (MR) persisted in 30% of the patients and labeled as CRT nonresponders., Aims: We sought to study the impact of transcatheter edge-to-edge repair (TEER) in patients with symptomatic grade III and IV functional MR despite CRT., Methods: A retrospective analysis was conducted of all patients who had prior CRT for at least 6 months and underwent TEER for significant residual functional MR (grade ≥3) and symptomatic heart failure (HF) at our institution. The primary outcomes were the change in New York Heart Association classification (NYHA), MR grade, echo parameters, and NT-ProBNP from baseline to 1-year post-procedure., Results: A total of 28 patients were identified, mean age of 73 ± 6.7 years and 89% males. Procedure success was achieved in all patients. At 1-year follow-up, patients had lower MR grade (median 2, IQR 1 [1,2] vs. 4, IQR 1 [3,4]; p < 0.001), NYHA class (median 2, IQR 1 [2,3] vs. 3, IQR 1 [3,4]; p < 0.001), and NT-ProBNP (7658 ± 11322 vs. 3760 ± 4431; p = 0.035) compared to before the TEER procedure. The left ventricular end-diastolic volume (255 ± 59 vs. 244 ± 66 mm; p = 0.016) and the right ventricular systolic pressure (52 ± 14 mmHg vs. 37 ± 13 mmHg, <0.001) decreased., Conclusion: Patients who remain symptomatic after CRT with severe functional MR had improved functional status and MR grade at 1-year following TEER. There was a signal toward reverse remodeling., (© 2024 Wiley Periodicals LLC.)

Objective: To develop machine learning models using patient and migraine features that can predict treatment responses to commonly used migraine preventive medications., Background: Currently, there is no accurate way to predict response to migraine preventive medications, and the standard trial-and-error approach is inefficient., Methods: In this cohort study, we analyzed data from the Mayo Clinic Headache database prospectively collected from 2001 to December 2023. Adult patients with migraine completed questionnaires during their initial headache consultation to record detailed clinical features and then at each follow-up to track preventive medication changes and monthly headache days. We included patients treated with at least one of the following migraine preventive medications: topiramate, beta-blockers (propranolol, metoprolol, atenolol, nadolol, timolol), tricyclic antidepressants (amitriptyline, nortriptyline), verapamil, gabapentin, onabotulinumtoxinA, and calcitonin gene-related peptide (CGRP) monoclonal antibodies (mAbs) (erenumab, fremanezumab, galcanezumab, eptinezumab). We pre-trained a deep neural network, "TabNet," using 145 variables, then employed TabNet-embedded data to construct prediction models for each medication to predict binary outcomes (responder vs. non-responder). A treatment responder was defined as having at least a 30% reduction in monthly headache days from baseline. All model performances were evaluated, and metrics were reported in the held-out test set (train 85%, test 15%). SHapley Additive exPlanations (SHAP) were conducted to determine variable importance., Results: Our final analysis included 4260 patients. The responder rate for each medication ranged from 28.7% to 34.9%, and the mean time to treatment outcome for each medication ranged from 151.3 to 209.5 days. The CGRP mAb prediction model achieved a high area under the receiver operating characteristics curve (AUC) of 0.825 (95% confidence interval [CI] 0.726, 0.920) and an accuracy of 0.80 (95% CI 0.70, 0.88). The AUCs of prediction models for beta-blockers, tricyclic antidepressants, topiramate, verapamil, gabapentin, and onabotulinumtoxinA were: 0.664 (95% CI 0.579, 0.745), 0.611 (95% CI 0.562, 0.682), 0.605 (95% CI 0.520, 0.688), 0.673 (95% CI 0.569, 0.724), 0.628 (0.533, 0.661), and 0.581 (95% CI 0.550, 0.632), respectively. Baseline monthly headache days, age, body mass index (BMI), duration of migraine attacks, responses to previous medication trials, cranial autonomic symptoms, family history of headache, and migraine attack triggers were among the most important variables across all models. A variable could have different contributions; for example, lower BMI predicts responsiveness to CGRP mAbs and beta-blockers, while higher BMI predicts responsiveness to onabotulinumtoxinA, topiramate, and gabapentin., Conclusion: We developed an accurate prediction model for CGRP mAbs treatment response, leveraging detailed migraine features gathered from a headache questionnaire before starting treatment. Employing the same methods, the model performances for other medications were less impressive, though similar to the machine learning models reported in the literature for other diseases. This may be due to CGRP mAbs being migraine-specific. Incorporating medical comorbidities, genomic, and imaging factors might enhance the model performance. We demonstrated that migraine characteristics are important in predicting treatment responses and identified the most crucial predictors for each of the seven types of preventive medications. Our results suggest that precision migraine treatment is feasible., (© 2024 American Headache Society.)

We present phalloidin-based points accumulation for imaging in nanoscale topography (phalloidin-PAINT), enabling quantitative superresolution imaging of filamentous actin (F-actin) in the cell body and delicate membrane protrusions. We demonstrate that the intrinsic phalloidin dissociation enables PAINT superresolution microscopy in an imaging buffer containing low concentrations of dye-conjugated phalloidin. We further show enhanced single-molecule labeling by chemically promoting phalloidin dissociation. Two benefits of phalloidin-PAINT are its ability to consistently quantify F-actin at the nanoscale throughout the entire cell and its enhanced preservation of fragile cellular structures. In a proof-of-concept study, we employed phalloidin-PAINT to superresolve F-actin structures in U2OS and dendritic cells (DCs). We demonstrate more consistent F-actin quantification in the cell body and structurally delicate membrane protrusions of DCs compared with direct stochastic optical reconstruction microscopy (dSTORM). Using DC2.4 mouse DCs as the model system, we show F-actin redistribution from podosomes to actin filaments and altered prevalence of F-actin-associated membrane protrusions on the culture glass surface after lipopolysaccharide exposure. The concept of our work opens new possibilities for quantitative protein-specific PAINT using commercially available reagents., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Aims: Doppler mean gradient (MG) can underestimate aortic stenosis (AS) severity in patients with atrial fibrillation (AF) compared with patients with 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 with SR and AF. There were 820 patients (mean age 81 years; 41.6% females) included in this study. AF was present in 356 patients. Patients with AF were older (82.2 vs. 80.5, P = 0.003) and had a lower MG compared with patients with SR (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 the same age of diagnosis, was similar between AF and SR. Median survival post-TAVR was worse in AF compared with SR (3.2 vs. 5.4 years, log rank P < 0.001). AF, lower MG, higher right ventricular systolic pressure, dialysis, diabetes, and significant tricuspid regurgitation were associated with higher mortality (P < 0.05 for all)., Conclusion: Older age and higher AVCS in patients with AF compared with those with SR suggest that AS was both underestimated and more advanced at TAVR referral., Competing Interests: Conflict of interest: None declared., (© The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Cardiology. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Background: Conduction abnormality post-transcatheter aortic valve implantation (TAVI) remains clinically significant and usually requires chronic pacing. The effect of right ventricular (RV) pacing post-TAVI on clinical outcomes warrants further studies. Methods: We identified 147 consecutive patients who required chronic RV pacing after a successful TAVI procedure and propensity-matched these patients according to the Society of Thoracic Surgeons (STS) risk score to a control group of patients that did not require RV pacing post-TAVI. We evaluated routine echocardiographic measurements and performed offline speckle-tracking strain analysis for the purpose of this study on transthoracic echocardiographic (TTE) images performed at 9 to 18 months post-TAVI. Results: The final study population comprised 294 patients (pacing group n = 147 and non-pacing group n = 147), with a mean age of 81 ± 7 years, 59% male; median follow-up was 354 days. There were more baseline conduction abnormalities in the pacing group compared to the non-pacing group (56.5% vs. 41.5%. p = 0.01). Eighty-eight patients (61.6%) in the pacing group required RV pacing due to atrioventricular (AV) conduction block post-TAVI. The mean RV pacing burden was 44% in the pacing group. Left ventricular ejection fraction (LVEF) was similar at follow-up in the pacing vs. non-pacing groups (57 ± 13.0%, 59 ± 11% p = 0.31); however, LV global longitudinal strain (-12.7 ± 3.5% vs. -18.8 ± 2.7%, p < 0.0001), LV apical strain (-12.9 ± 5.5% vs. 23.2 ± 9.2%, p < 0.0001), and mid-LV strain (-12.7 ± 4.6% vs. -18.7 ± 3.4%, p < 0.0001) were significantly worse in the pacing vs. non-pacing groups. Conclusions: Chronic RV pacing after the TAVI procedure is associated with subclinical LV systolic dysfunction within 1.5 years of follow-up.

Background: Residual mitral regurgitation (MR) is frequent after transcatheter edge-to-edge repair (TEER). There is controversy regarding the clinical impact of residual MR and its quantitative assessment by transthoracic echocardiography (TTE), which is often challenging with multiple eccentric jets and artifact from the clip. The utility of the velocity time integral (VTI) ratio between the mitral valve (MV) and left ventricular outflow tract (LVOT), (VTI MV/LVOT ), a simple Doppler measurement that increases with MR, has not been assessed post TEER., Methods: Baseline characteristics, clinical outcomes, and TTE data from patients who underwent TEER between 2014 and 2021 across three academic centers were retrospectively analyzed. Post-procedure TTEs were evaluated for VTI MV/LVOT in the first three months after TEER. One-year outcomes including all-cause and cardiac mortality, major adverse cardiac events, and MV reintervention were compared between patients with high VTI MV/LVOT (≥2.5) and low (<2.5)., Results: In total, 372 patients were included (mean age 78.7 ± 8.8 years, 68 % male, mean pre-TEER ejection fraction of 50.5 ± 14.7 %). Follow up TTEs were performed at a median of 37.5 (IQR 30-48) days post-procedure. Patients with high VTI MV/LVOT had significantly higher all-cause mortality (HR 2.10, p = 0.003), cardiac mortality (HR 3.03, p = 0.004) and heart failure admissions (HR 2.28, p < 0.001) at one-year post-procedure. There was no association between raised VTI MV/LVOT and subsequent MV reintervention., Conclusion: High VTI MV/LVOT has clinically significant prognostic value at one year post TEER. This tool could be used to select patients for consideration of repeat intervention., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors. Published by Elsevier Ltd.)

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., (Copyright © 2023 Mayo Foundation for Medical Education and Research. Published by Elsevier Inc. All rights reserved.)

Aims: Pre-emptive prediction to avoid myelosuppression and harmful sequelae is difficult given the complex interplay among patients, drugs and treatment protocols. This study aimed to model plasma and bone marrow concentrations and the likelihood of myelotoxicity following administration of 5-fluorouracil (5-FU) by diverse intravenous (IV) bolus or continuous infusion (cIF) regimens., Methods: Using physicochemical, in vitro and clinical data obtained from the literature consisting of various regimens and patient cohorts, a 5-FU physiologically based pharmacokinetic (PBPK) model was developed. The predicted and observed PK values were compared to assess model performance prior to examining myelotoxicity potential of IV bolus vs. cIF and DPYD wild type vs. genetic variant., Results: The established model was verified by utilizing 5-FU concentration-time profiles of adequate heterogeneity contributed by 36 regimens from 15 studies. The study provided corroborative evidence to explain why cIF (vs. IV bolus) had lower myelotoxicity risk despite much higher total doses. The PBPK model was used to estimate the optimal dosage in patients heterozygous for the DPYD c.1905 + 1G > A allele and suggested that a dose reduction of at least 25% was needed (compared to the dose in wild-type subjects)., Conclusion: A verified PBPK model was used to explain the lower myelotoxicity risk of cIF vs. IV bolus administration of 5-FU and to estimate the dose reduction needed in carriers of a DPYD variant. With appropriate data, expertise and resources, PBPK models have many potential uses in precision medicine application of oncology drugs., (© 2024 British Pharmacological Society.)

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., Competing Interests: Funding Support and Author Disclosures The authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2024 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

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 R 2 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 R 2 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 R 2 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 R 2 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., (© 2024 American Headache Society.)

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.

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.

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., Competing Interests: Conflict of interest: none declared., (© The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Cardiology.)

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 FiO 2 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.

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., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. No commercial re-use. See rights and permissions. Published by BMJ.)

Current management of patients with congenital heart disease has increased their survival into adulthood. This is accompanied by potential cardiac complications, including pulmonary hypertension associated with congenital heart disease (PAH-CHD). PAH-CHD constitutes a challenging subgroup of pulmonary hypertension and requires expert management to improve quality of life and prognosis. Novel agents have shown a significant improvement in morbidity and mortality in patients with pulmonary arterial hypertension. However, the long-term effects of these medications on PAH-CHD patients remain somewhat uncertain, necessitating treatment plans largely founded on the clinical experience of the healthcare providers. The aim of this review is to summarize the current evidence and future perspectives regarding treatment strategies for PAH-CHD to help better guide management of this complex disease.

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.

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.

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 cm 2 ) 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., (Copyright © 2023 Mayo Foundation for Medical Education and Research. Published by Elsevier Inc. All rights reserved.)

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., (© 2023 Society of Photo-Optical Instrumentation Engineers (SPIE).)

Background: Aortic stenosis (AS) is associated with myocardial ischemia through different mechanisms and may impair coronary arterial flow. However, data on the impact of moderate AS in patients with acute myocardial infarction (MI) is limited., Aims: This study aimed to investigate the impact of moderate AS in patients presenting with acute myocardial infarction (MI)., Methods: We conducted a retrospective analysis of all patients who presented with acute MI to all Mayo Clinic hospitals, using the Enterprise Mayo PCI Database from 2005 to 2016. Patients were stratified into two groups: moderate AS and mild/no AS. The primary outcome was all cause mortality., Results: The moderate AS group included 183 (13.3%) patients, and the mild/no AS group included 1190 (86.7%) patients. During hospitalization, there was no difference between both groups in mortality. Patients with moderate AS had higher in-hospital congestive heart failure (CHF) (8.2% vs. 4.4%, p = 0.025) compared with mild/no AS patients. At 1-year follow-up, patients with moderate AS had higher mortality (23.9% vs. 8.1%, p < 0.001) and higher CHF hospitalization (8.3% vs. 3.7%, p = 0.028). In multivariate analysis, moderate AS was associated with higher mortality at 1-year (odds ratio 2.4, 95% confidence interval [1.4-4.1], p = 0.002). In subgroup analyses, moderate AS increased all-cause mortality in STEMI and NSTEMI patients., Conclusion: The presence of moderate AS in acute MI patients was associated with worse clinical outcomes during hospitalization and at 1-year follow-up. These unfavorable outcomes highlight the need for a close follow-up of these patients and for timely therapeutic strategies to best manage these coexisting conditions., (© 2023 Wiley Periodicals LLC.)

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.

Migraine is a highly prevalent and disabling neurological disorder. However, information about migraine management in real-world settings is limited to traditional health information sources. In this paper, we (i) verify that there is substantial migraine-related chatter available on social media (Twitter and Reddit), self-reported by those with migraine; (ii) develop a platform-independent text classification system for automatically detecting self-reported migraine-related posts, and (iii) conduct analyses of the self-reported posts to assess the utility of social media for studying this problem. We manually annotated 5750 Twitter posts and 302 Reddit posts, and used them for training and evaluating supervised machine learning methods. Our best system achieved an F 1 score of 0.90 on Twitter and 0.93 on Reddit. Analysis of information posted by our 'migraine cohort' revealed the presence of a plethora of relevant information about migraine therapies and sentiments associated with them. Our study forms the foundation for conducting an in-depth analysis of migraine-related information using social media data., (©2023 AMIA - All rights reserved.)

Cells have emerged as a promising new form of drug delivery carriers owing to their distinguished advantages such as naturally bypassing immune recognition, intrinsic capability to navigate biological barriers, and access to hard-to-reach tissues via onboarding sensing and active motility. Over the past two decades, a large body of work has focused on understanding the ability of cell carriers to breach biological barriers and to modulate drug pharmacokinetics and pharmacodynamics. These efforts have led to the engineering of various cells for tissue-specific drug delivery. Despite exciting advances, clinical translation of cell-based drug carriers demands a thorough understanding of the pressing challenges and potential strategies to overcome them. Here, we summarize recent advances and new concepts in cell-based drug carriers and their clinical translation. We also discuss key considerations and emerging strategies to engineering the next-generation cell-based delivery technologies for more precise, targeted drug delivery., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

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.

Background: Post-transcatheter aortic valve replacement (TAVR) patient outcome is an important research topic. To accurately assess post-TAVR mortality, we examined a family of new echo parameters (augmented systolic blood pressure (AugSBP) and arterial mean pressure (AugMAP)) derived from blood pressure and aortic valve gradients., Methods: Patients in the Mayo Clinic National Cardiovascular Diseases Registry-TAVR database who underwent TAVR between 1 January 2012 and 30 June 2017 were identified to retrieve baseline clinical, echocardiographic and mortality data. AugSBP, AugMAP and valvulo-arterial impedance (Zva) (Zva) were evaluated using Cox regression. Receiver operating characteristic curve analysis and the c-index were used to assess the model performance against the Society of Thoracic Surgeons (STS) risk score., Results: The final cohort contained 974 patients with a mean age of 81.4 ± 8.3 years old, and 56.6% were male. The mean STS risk score was 8.2 ± 5.2. The median follow-up duration was 354 days, and the one-year all-cause mortality rate was 14.2%. Both univariate and multivariate Cox regression showed that AugSBP and AugMAP parameters were independent predictors for intermediate-term post-TAVR mortality (all p < 0.0001). AugMAP1 < 102.5 mmHg was associated with a 3-fold-increased risk of all-cause mortality 1-year post-TAVR (hazard ratio 3.0, 95%confidence interval 2.0-4.5, p < 0.0001). A univariate model of AugMAP1 surpassed the STS score model in predicting intermediate-term post-TAVR mortality (area under the curve: 0.700 vs. 0.587, p = 0.005; c-index: 0.681 vs. 0.585, p = 0.001)., Conclusions: Augmented mean arterial pressure provides clinicians with a simple but effective approach to quickly identify patients at risk and potentially improve post-TAVR prognosis.

Background: Pulmonary vein stenosis (PVS) is an uncommon but known cause of morbidity and mortality in adults and children and can be managed with percutaneous re-vascularization strategies of pulmonary vein balloon angioplasty (PBA) or pulmonary vein stent implantation (PSI)., Aim: To study the safety and efficacy outcomes of PBA vs PSI in all patient categories with PVS., Methods: We performed a literature search of all studies comparing outcomes of patients evaluated by PBA vs PSI for PVS. We selected all published studies comparing PBA vs PSI for PVS with reported outcomes of restenosis and procedure-related complications in all patient categories. In adults, PVS following atrial fibrillation ablation and in children PVS related to congenital etiology or post-procedural PVS following total or partial anomalous pulmonary venous return repair were included. The patient-centered outcomes were risk of restenosis requiring re-intervention and procedural-related complications. The meta-analysis was performed by computing odds ratios (ORs) using the random effects model based on underlying statistical heterogeneity., Results: Eight observational studies treating 768 severe PVS in 487 patients met our inclusion criteria. The age range of patients was 6 months to 70 years and 67% were males. The primary outcome of the re-stenosis requiring re-intervention occurred in 196 of 325 veins in the PBA group and 111 of 443 veins in the PSI group. Compared to PSI, PBA was associated with a significantly increased risk of re-stenosis (OR 2.91, 95%CI: 1.15-7.37, P = 0.025, I 2 = 79.2%). Secondary outcomes of the procedure-related complications occurred in 7 of 122 patients in the PBA group and 6 of 69 in the PSI group. There were no statistically significant differences in the safety outcomes between the two groups (OR: 0.94, 95%CI: 0.23-3.76, P = 0.929), I 2 = 0.0%)., Conclusion: Across all patient categories with PVS, PSI is associated with reduced risk of re-intervention and is as safe as PBA and should be considered first-line therapy for PVS., Competing Interests: Conflict-of-interest statement: All the authors received no financial support for the research, authorship, and/or publication of this article., (©The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved.)

Echocardiography is an integral part of the diagnosis and management of cardiovascular disease. The use and application of artificial intelligence (AI) is a rapidly expanding field in medicine to improve consistency and reduce interobserver variability. AI can be successfully applied to echocardiography in addressing variance during image acquisition and interpretation. Furthermore, AI and machine learning can aid in the diagnosis and management of cardiovascular disease. In the realm of echocardiography, accurate interpretation is largely dependent on the subjective knowledge of the operator. Echocardiography is burdened by the high dependence on the level of experience of the operator, to a greater extent than other imaging modalities like computed tomography, nuclear imaging, and magnetic resonance imaging. AI technologies offer new opportunities for echocardiography to produce accurate, automated, and more consistent interpretations. This review discusses machine learning as a subfield within AI in relation to image interpretation and how machine learning can improve the diagnostic performance of echocardiography. This review also explores the published literature outlining the value of AI and its potential to improve patient care.

Aims: Increased left ventricular (LV) wall thickness is frequently encountered in transthoracic echocardiography (TTE). While accurate and early diagnosis is clinically important, given the differences in available therapeutic options and prognosis, an extensive workup is often required to establish the diagnosis. We propose the first echo-based, automated deep learning model with a fusion architecture to facilitate the evaluation and diagnosis of increased left ventricular (LV) wall thickness., Methods and Results: Patients with an established diagnosis of increased LV wall thickness (hypertrophic cardiomyopathy (HCM), cardiac amyloidosis (CA), and hypertensive heart disease (HTN)/others) between 1/2015 and 11/2019 at Mayo Clinic Arizona were identified. The cohort was divided into 80%/10%/10% for training, validation, and testing sets, respectively. Six baseline TTE views were used to optimize a pre-trained InceptionResnetV2 model. Each model output was used to train a meta-learner under a fusion architecture. Model performance was assessed by multiclass area under the receiver operating characteristic curve (AUROC). A total of 586 patients were used for the final analysis (194 HCM, 201 CA, and 191 HTN/others). The mean age was 55.0 years, and 57.8% were male. Among the individual view-dependent models, the apical 4-chamber model had the best performance (AUROC: HCM: 0.94, CA: 0.73, and HTN/other: 0.87). The final fusion model outperformed all the view-dependent models (AUROC: HCM: 0.93, CA: 0.90, and HTN/other: 0.92)., Conclusion: The echo-based InceptionResnetV2 fusion model can accurately classify the main etiologies of increased LV wall thickness and can facilitate the process of diagnosis and workup.

Right ventricular (RV) pacing is the main treatment modality for patients with advanced atrioventricular (AV) block. Chronic RV pacing can cause cardiac systolic dysfunction and heart failure (HF). In this review, we discuss studies that have shown deleterious effects of chronic RV pacing on systolic cardiac function causing pacing-induced cardiomyopathy (PiCM), heart failure (HF), HF hospitalization, atrial fibrillation (AF) and cardiac mortality. RV apical pacing is the most widely used and studied. Adverse effects of RV pacing appear to be directly related to pacing burden and are worse in patients with pre-existing left ventricular (LV) dysfunction. Chronic RV pacing is also associated with heart failure with preserved ejection fraction (HFpEF). Mechanisms, risk factors, clinical and echocardiographic features, and strategies to minimize RV pacing-induced cardiac dysfunction are discussed in light of the latest data. Studies on biventricular (Bi-V) pacing upgrade in patients who develop RV PiCM, use of alternate RV pacing sites, de novo Bi-V pacing, and physiologic pacing using HIS bundle pacing (HBP) and left bundle area (LBBA) pacing in patients with an anticipated high RV pacing burden are discussed., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

This study aimed to investigate the association between drunk riding, unhelmeted riding, unlicensed riding, and running-off-road (ROR) crashes. Multiple logistic regression was used to calculate the adjusted odds ratio (AOR) by using the National Taiwan Traffic Crash Dataset for 2011-2016. The results revealed that unhelmeted riding was associated with 138% (AOR = 2.38; CI (confidence interval) = 2.34-2.42) and 47% (AOR = 1.47; CI = 1.45-1.49) higher risks of drunk riding and unlicensed riding, respectively. The risk of unhelmeted riding increased with blood alcohol concentrations (BACs), and riders with the minimum BAC (0.031-0.05%) had nearly five times (AOR = 4.99; CI = 4.74-5.26) higher odds of unlicensed riding compared with those of riders with a negative BAC. Unhelmeted riding, drunk riding, and unlicensed riding were associated with 1.21 times (AOR = 1.21; CI = 1.13-1.30), 2.38 times (AOR = 2.38; CI = 2.20-2.57), and 1.13 times (AOR = 1.13; CI = 1.06-1.21) higher odds of ROR crashes, respectively. The three risky riding behaviours (i.e., unhelmeted riding, drunk riding, and unlicensed riding) were significantly related to ROR crashes. The risk of unhelmeted riding and ROR crashes increased with BACs.

Objective: To investigate the effectiveness and safety of angiotensin receptor-neprilysin inhibitors (ARNIs) in real-world patients with heart failure with reduced ejection fraction (HFrEF) and advanced chronic kidney disease (estimated glomerular filtration rate [eGFR] < 30 mL/min per 1.73 m 2 ), which have been excluded from the landmark trials., Patients and Methods: This study examined 3281 patients pooled from two multicenter HFrEF cohorts, and 661 patients with baseline eGFR less than 30 mL/min per 1.73 m 2 were further analyzed (the Taiwan Society of Cardiology - Heart Failure with reduced Ejection Fraction (TSOC-HFrEF) registry: May 1, 2013 to October 31, 2014, and the Treatment with Angiotensin Receptor neprilysin inhibitor fOr Taiwan Heart Failure patients (TAROT-HF) study: March 1, 2017, to December 31, 2018). Propensity score matching was performed to adjust for confounders. At 1-year follow-up, all-cause mortality, total heart failure hospitalizations, renal function, and left ventricular ejection fraction (LVEF) were used as the endpoints., Results: After propensity score matching, 510 patients (age, 69.8±13.9 years; male, 61.0%; mean LVEF, 29.8±7.3%; mean eGFR, 19.8±9.0 mL/min per 1.73 m 2 ) were included in the final analysis, including 278 patients receiving ARNI treatment (ARNI group) and 232 patients not on ARNI treatment (non-ARNI group). Baseline characteristics were comparable between the two groups. At 1 year, eGFR and LVEF measurements were significantly higher in the ARNI group than in the non-ARNI group (25.0±17.1 mL/min per 1.73 m 2 vs 21.4±17.5 mL/min per 1.73 m 2 ; P=.04; and 40.1±12.9% vs. 33.1±10.8%, P<.001, respectively). The ARNI group had significantly lower risks of 1-year all-cause mortality (19.4 vs 30.9 per 100-person year; P=.02), and total HF rehospitalizations (70.0 vs 110.4 per 100-person year; P=.01) than non-ARNI users., Conclusion: Our results show the effectiveness of ARNIs in HFrEF patients with advanced chronic kidney disease in a real-world setting., (Copyright © 2022 Mayo Foundation for Medical Education and Research. Published by Elsevier Inc. All rights reserved.)

Background: Prior studies have reported conflicting results of persistent iatrogenic atrial septal defect (iASD) and its impact following a transcatheter edge-to-edge repair (TEER) procedure. This study aims to evaluate the incidence of iASD and its clinical and hemodynamic impact after a TEER., Methods: Consecutive patients who underwent a TEER procedure from June 2014 to September 2020 at the Mayo Clinic were identified. The presence of iASD was retrospectively identified on post-procedure transthoracic echocardiography (TTE) to group patients into an iASD+ group and an iASD- group for comparison of prognosis and cardiac function., Results: A total of 316 patients were included; the mean age was 79.1 ± 9.1 years, and 67.7% were male. Persistent iASD was diagnosed in 108 (34.2%) patients. There was no difference concerning all-cause mortality, heart failure hospitalization, and stroke/TIA between groups at follow-up (median follow-up of 9 months). Post-procedure TTE demonstrated no differences regarding right ventricle (RV) and left ventricle (LV) dimensions and function, including TAPSE (15.2 ± 4.6 vs. 15.4 ± 5.5 mm, p = 0.875), and LV ejection fraction (51.1 ± 14.0% vs. 51.3 ± 13.9%, p = 0.933). However, patients with iASD had higher RV systolic pressure (48.7 ± 14.4 vs. 45.5 ± 14.5 mmHg, p = 0.042) compared with patients without iASD., Conclusion: Notwithstanding higher RV pressures, patients with persistent iASD had no hemodynamic compromise and an equal prognosis compared with those without a residual atrial defect after a TEER procedure. These findings support the mid-term safety of procedures in which an interatrial defect needs to be created and would argue against the need for interventional closure.

Objective: Heart failure (HF) imposes a substantial burden and the prevalence of HF is high in patients with chronic kidney disease (CKD). HF results in multiple hospital admissions, but whether HF subtypes worsen long-term outcomes and renal function in patients with CKD remains inconclusive., Methods: The study comprised 10 904 patients with CKD aged ≥20 years who underwent echocardiography between 1 January 2011 and 31 December 2018. The patients were stratified into four groups: non-HF, HF with reduced ejection fraction (HFrEF), HF with mildly reduced ejection fraction (HFmrEF) and HF with preserved ejection fraction (HFpEF). The primary end points were all-cause mortality, major adverse cardiovascular events (MACEs) and adverse renal outcomes., Results: In inverse probability of treatment weighting-adjusted method, the risk of all-cause mortality and MACEs relative to the non-HF group was greatest in the HFrEF group (HR 3.18 (95% CI 2.57 to 3.93) and HR 3.83 (95% CI 3.20 to 4.59)), followed by the HFmrEF (HR 2.75 (95% CI 2.22 to 3.42) and HR 3.08 (95% CI 2.57 to 3.69)) and HFpEF (HR 1.85 (95% CI 1.59 to 2.15) and HR 2.43 (95% CI 2.16 to 2.73) groups. In addition, the HFrEF group had the greatest risks of end-stage renal disease (HR 2.58 (95% CI 1.94 to 3.44)) compared with other groups., Conclusions: HF is associated with subsequent worse clinical outcomes, which may be more pronounced in patients with HFrEF, followed by those with HFmrEF and those with HFpEF relative to non-HF group., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background. Clinical medicine relies heavily on the synthesis of information and data from multiple sources. However, often simple feature concatenation is used as a strategy for developing a multimodal machine learning model in the cardiovascular domain, and thus the models are often limited by pre-selected features and moderate accuracy. Method. We proposed a two-branched joint fusion model for fusing the 12-lead electrocardiogram (ECG) signal data with clinical variables from the electronic medical record (EMR) in an end-to-end deep learning architecture. The model follows the joint fusion scheme and learns complementary information from ECG and EMR. Retrospective data from the Mayo Clinic Health Systems across four sites for patients that underwent percutaneous coronary intervention (PCI) were obtained. Model performance was assessed by area under the receiver-operating characteristics (AUROC) and Delong's test. Results. The final cohort included 17,356 unique patients with a mean age of 67.2 ± 12.6 year (mean ± std) and 9,163 (52.7%) were male. The joint fusion model outperformed the ECG time-domain model with statistical margin. The model with clinical data obtained the highest AUROC for all-cause mortality (0.91 at 6 months) but the joint fusion model outperformed for cardiovascular outcomes - heart failure hospitalization and ischemic stroke with a significant margin (Delong's p < 0.05). Conclusion. To the best of our knowledge, this is the first study that developed a deep learning model with joint fusion architecture for the prediction of post-PCI prognosis and outperformed machine learning models developed using traditional single-source features (clinical variables or ECG features). Adding ECG data with clinical variables did not improve prediction of all-cause mortality as may be expected, but the improved performance of related cardiac outcomes shows that the fusion of ECG generates additional value., (© 2022 Institute of Physics and Engineering in Medicine.)

Background: The 2016 American Society of Echocardiography guidelines have been widely used to assess left ventricular diastolic function. However, limitations are present in the current classification system. The aim of this study was to develop a data-driven, unsupervised machine learning approach for diastolic function classification and risk stratification using the left ventricular diastolic function parameters recommended in the 2016 American Society of Echocardiography guidelines; the guideline grading was used as the reference standard., Methods: Baseline demographics, heart failure hospitalization, and all-cause mortality data were obtained for all adult patients who underwent transthoracic echocardiography at Mayo Clinic Rochester in 2015. Patients with prior mitral valve intervention, congenital heart disease, cardiac transplantation, or cardiac assist device implantation were excluded. Nine left ventricular diastolic function variables (mitral E- and A-wave peak velocities, E/A ratio, deceleration time, medial and lateral annular e' velocities and E/e' ratio, and tricuspid regurgitation peak velocity) were used for an unsupervised machine learning algorithm to identify different phenotype clusters. The cohort average of each variable was used for imputation. Patients were grouped according to the algorithm-determined clusters for Kaplan-Meier survival analysis., Results: Among 24,414 patients (mean age, 63.6 ± 16.2 years), all-cause mortality occurred in 4,612 patients (18.9%) during a median follow-up period of 3.1 years. The algorithm determined three clusters with echocardiographic measurement characteristics corresponding to normal diastolic function (n = 8,312), impaired relaxation (n = 11,779), and increased filling pressure (n = 4,323), with 3-year cumulative mortality of 11.8%, 19.9%, and 33.4%, respectively (P < .0001). All 10,694 patients (43.8%) classified as indeterminate were reclassified into the three clusters (n = 3,324, n = 5,353, and n = 2,017, respectively), with 3-year mortality of 16.6%, 22.9%, and 34.4%, respectively. The clusters also outperformed guideline-based grade for prognostication (C index = 0.607 vs 0.582, P = .013)., Conclusions: Unsupervised machine learning identified physiologically and prognostically distinct clusters on the basis of nine diastolic function Doppler variables. The clusters can be potentially applied in echocardiography laboratory practice and future clinical trials for simple, replicable diastolic function-related risk stratification., (Copyright © 2022 American Society of Echocardiography. Published by Elsevier Inc. All rights reserved.)

Background: Most studies have focused on injuries sustained by intoxicated drivers themselves, but few have examined the effect of drunk driving on injury outcomes among VRUs (vulnerable road users) in developing countries. This study aims to evaluate the effect of drunk driving on fatal injuries among VRUs (pedestrians, cyclists, or motorcyclists)., Methods: The data were extracted from the National Taiwan Traffic Crash Dataset from January 1, 2011, to December 31, 2019. Crashes involving one motorized vehicle and one VRU were considered. This study examines the effect of drunk driving by estimating multivariate logistic regression models of fatal injuries among VRUs after controlling for other variables., Results: Among 1,416,168 casualties, the fatality rate of VRUs involved in drunk driving was higher than that of general road users (2.1% vs. 0.6%). Drunk driving was a significant risk factor for fatal injuries among VRUs. Other risk factors for fatal injuries among VRUs included VRU age ≥ 65 years (adjusted odds ratio [AOR]: 5.24, 95% confidence interval [CI]: 5.53-6.07), a nighttime accident (AOR: 4.52, 95% CI: 4.22-4.84), and being hit by a heavy-duty vehicle (AOR: 2.83, 95% CI: 2.26-3.55). Subgroup analyses revealed a linear relationship between driver blood alcohol concentration (BAC) and the risk of fatal injury among motorcyclists. Motorcyclists exhibited the highest fatality rate when they had a BAC ≤ 0.03% (AOR: 3.54, 95% CI: 3.08-4.08)., Conclusion: Drunk driving was associated with a higher risk of fatality for all VRUs. The risk of fatal injury among motorcyclists was linearly related to the BAC of the drunk drivers. Injuries were more severe for intoxicated motorcyclists, even those with BAC ≤ 0.03%, which is within the legal limit., (© 2022. The Author(s).)

Background: Nonbacterial thrombotic endocarditis is characterized by the presence of organized thrombi on cardiac valves, often associated with hypercoagulable states. There is a paucity of data regarding the predictors of mortality in patients with nonbacterial thrombotic endocarditis. Our primary aim was to identify predictors of in-hospital mortality in patients with nonbacterial thrombotic endocarditis., Methods: A systematic literature review of all published cases and case series was performed until May 2018 according to Preferred Reporting Items for Systematic Review and Meta-analyses statement guidelines. We applied random forest machine learning model to identify predictors of in-patient mortality in patients with nonbacterial thrombotic endocarditis., Results: Our search generated a total of 163 patients (mean age, 46 ± 17 years; women, 69%) with newly diagnosed nonbacterial thrombotic endocarditis. The in-hospital mortality rate in the study cohort was 30%. Among the patients who died in the hospital, initial presentation of pulmonary embolism (12.2 vs. 2.6%), splenic (38.7 vs. 10.5%), and renal (40.8 vs. 9.6%) infarcts were higher compared to patients alive at the time of discharge. Higher rates of malignancy (71.4 vs. 39.4%, P = .0003) and lower rates of antiphospholipid syndrome (8.1 vs. 48.2%, P = .0001) were noted in deceased patients. Random forest machine learning analysis showed that older age, presence of antiphospholipid syndrome, splenic infarct, renal infarct, peripheral thromboembolism, pulmonary embolism, myocardial infarction, and mitral valve regurgitation were significantly associated with increased risk of in-hospital mortality., Conclusion: Patients admitted with nonbacterial thrombotic endocarditis have a high rate of in-hospital mortality. Factors including older age, presence of antiphospholipid syndrome, splenic/renal infarct, lower limb thromboembolism, pulmonary embolism, myocardial infarction, and mitral valve regurgitation were significantly associated with increased risk of in-hospital mortality in patients with nonbacterial thrombotic endocarditis.

Background: Freon™ is a halogenated hydrocarbon often used as a refrigerant. When inhaled recreationally, it has the desired effects of euphoria and intoxication. Toxic effects include cardiovascular and neurologic insults such as arrhythmias and seizures, and less well-described toxicities include airway and lung injury. The treatment in general is primarily supportive., Clinical Features: We present the case of a 42-yr-old previously healthy male who developed acute bronchiolitis and pneumonitis following inhalation of Freon leading to severe respiratory failure. He was supported by veno-venous extracorporeal membrane oxygenation and managed with high-dose corticosteroids., Conclusion: To our knowledge, this is the first case report of an inhaled Freon exposure resulting in acute lung injury refractory to conventional therapy that was salvaged by vv-ECMO as a bridge towards a full recovery., (© 2022. Canadian Anesthesiologists' Society.)

Objective: To compare the artificial intelligence-enabled electrocardiogram (AI-ECG) atrial fibrillation (AF) prediction model output in patients with migraine with aura (MwA) and migraine without aura (MwoA)., Background: MwA is associated with an approximately twofold risk of ischemic stroke. Longitudinal cohort studies showed that patients with MwA have a higher incidence of developing AF compared to those with MwoA. The Mayo Clinic Cardiology team developed an AI-ECG algorithm that calculates the probability of concurrent paroxysmal or impending AF in ECGs with normal sinus rhythm (NSR)., Methods: Adult patients with an MwA or MwoA diagnosis and at least one NSR ECG within the past 20 years at Mayo Clinic were identified. Patients with an ECG-confirmed diagnosis of AF were excluded. For each patient, the ECG with the highest AF prediction model output was used as the index ECG. Comparisons between MwA and MwoA were conducted in the overall group (including men and women of all ages), women only, and men only in each age range (18 to <35, 35 to <55, 55 to <75, ≥75 years), and adjusted for age, sex, and six common vascular comorbidities that increase risk for AF., Results: The final analysis of our cross-sectional study included 40,002 patients (17,840 with MwA, 22,162 with MwoA). The mean (SD) age at the index ECG was 48.2 (16.0) years for MwA and 45.9 (15.0) years for MwoA (p < 0.001). The AF prediction model output was significantly higher in the MwA group compared to MwoA (mean [SD] 7.3% [15.0%] vs. 5.6% [12.4%], mean difference [95% CI] 1.7% [1.5%, 2.0%], p < 0.001). After adjusting for vascular comorbidities, the difference between MwA and MwoA remained significant in the overall group (least square means of difference [95% CI] 0.7% [0.4%, 0.9%], p < 0.001), 18 to <35 (0.4% [0.1%, 0.7%], p = 0.022), and 35 to <55 (0.5% [0.2%, 0.8%], p < 0.001), women of all ages (0.6% [0.3%, 0.8%], p < 0.001), men of all ages (1.0% [0.4%, 1.6%], p = 0.002), women 35 to <55 (0.6% [0.3%, 0.9%], p < 0.001), and men 18 to <35 (1.2% [0.3%, 2.1%], p = 0.008)., Conclusions: Utilizing a novel AI-ECG algorithm on a large group of patients, we demonstrated that patients with MwA have a significantly higher AF prediction model output, implying a higher probability of concurrent paroxysmal or impending AF, compared to MwoA in both women and men. Our results suggest that MwA is an independent risk factor for AF, especially in patients <55 years old, and that AF-mediated cardioembolism may play a role in the migraine-stroke association for some patients., (© 2022 American Headache Society.)