bluuebunny/crossref_metadata_2025_split huggingface dataset:
Treatment of Post-COVID-19 POTS by inhibiting FcRn: a phase 2 randomized, placebo controlled, double-blind, proof of concept study with efgartigimod
Authors: Chung T H , Vernino S , Raj S , Fedorowski A , Konig M F , Rohane P , Ulrichts P , Van Middendorp J , Azar A 
Published: October 2024

Abstract

Background
While the underlying pathophysiology is unknown, post–COVID-19 postural orthostatic tachycardia syndrome (POTS) may be related to immune dysfunction and autoimmunity precipitated by SARS-CoV-2 infection. Various autoantibodies, including those targeting G-protein coupled receptors (GPCRs), have been observed in patients with POTS. Binding of these autoantibodies to adrenergic receptors, which are prototypical GPCRs, has been hypothesized to modulate receptor activity, increase sympathetic tone, and cause tachycardia. Moreover, IgG anti-GPCR autoantibodies, acting as partial agonists, are thought to decrease the effect of peripheral vasomotor control, leading to an increased sympathetic response to upright posture that can result in postural tachycardia in the absence of hypotension.

Purpose
We hypothesize that a reduction in autoantibody levels by efgartigimod, a FcRn-antagonist, will ameliorate the underlying immune-mediated pathogenesis and lead to clinical improvements in patients who developed POTS following COVID-19 infection. We propose a phase II multicenter, randomized, placebo-controlled, double-blind, proof-of-mechanism study initiated to evaluate the safety and efficacy of efgartigimod in adults with post–COVID-19 POTS.

Methods
Adult patients diagnosed with new-onset of POTS following SARS-CoV-2 infection are eligible for participation in the study. Prior COVID-19 must be confirmed by documentation of historical PCR test, and POTS diagnosis must meet consensus criteria. Study subjects must have moderate to severe autonomic symptoms (COMPASS-31 score ≥ 35 points at screening). Approximately 42 patients will be randomly allocated (2:1) to receive weekly intravenous efgartigimod or placebo for 24 weeks. At the end of the treatment period, participants who complete the study may roll over into an open-label extension (OLE).
The co-primary endpoints are change from baseline to week 24 in the COMPASS-31 and Malmo POTS Symptom Score as well as safety and tolerability outcomes. Key secondary endpoints include assessments of disease activity, fatigue, cognitive function, walking capacity and quantitative autonomic testing. Additional exploratory endpoints include intraepidermal small fiber densities (optional), sudomotor innervation (optional) and peripheral blood biomarkers to define histopathological and immune determinants associated with treatment response.

Conclusions
This phase II study of efgartigimod will evaluate the effect of FcRn inhibition on disease pathology and the potential for therapeutic benefit in patients with post–COVID-19 POTS.
The association between mammographic breast density, epicardial adipose tissue volume and coronary artery disease
Authors: Aldous E , Goel V , Yeong C , Vasanthakumar S , Tu H , Salib A , Xu E , Nerlekar N 
Published: October 2024

Abstract

Background
Dysfunctional adipose tissue is strongly linked to cardiometabolic disease. Epicardial adipose tissue (EAT) which is measurable on cardiac computed tomography (CT) is well correlated with cardiovascular disease (CVD), however it is not reported clinically. Breast mammography mandates reporting of breast density (BD), which is also a marker of breast adipose tissue quantity with less dense breasts indicating greater adipose tissue volume. Given the widespread use of screening mammography, this presents an opportunity to employ BD as a possible surrogate for CVD risk.

Purpose
To evaluate the correlation between BD, EAT and coronary artery disease (CAD).

Methods
Single-centre, retrospective, cross-sectional study, including women who had both clinically indicated coronary computed tomography angiogram (CCTA) and mammography. Epicardial adipose tissue volume (EATv) was quantified using semi-automated software (QFAT 2.0). BD was visually assessed by standard 4-level Breast Imaging-Reporting and Data System (BI-RADS) grade with grade A-B representing low density, and C-D representing high density. CAD was categorised as presence/absence of any coronary artery plaque, and CAD severity was quantified using the Coronary Artery Disease Reporting and Data Systems (CAD-RADS) score, with CAD-RADS 1-2 representing mild disease, CAD-RADS 3 moderate disease and CAD-RADS 4 severe disease. CAD analyses were adjusted for modifiable (hypertension, dyslipidaemia, diabetes, body mass index) and non-modifiable (age, past smoking, family history, breast cancer) risk factors. Logistic regression was performed with results presented as Odds Ratio (OR) and [95% Confidence Intervals].

Results
Among 153 patients (mean age 62±10), 103 (67.3%) had low BD (high breast adiposity). Compared with high BD, low BD patients were older, had greater rates of hypertension, had significantly higher mean EAT (106.6 ± 43.0 vs 81.0 ± 31.6ml, p&lt;0.001) (Figure 1), and body mass index (30.5 ± 6.3 vs 26.4 ± 5.4kg/m², p&lt;0.001). Adjusted for age and hypertension, EATv remained independently predictive of low BD (OR:1.02 [1.01-1.03], p=0.006). Low BD made up a higher proportion of mild (76.5%), moderate (73.9%) and severe (80.0%) disease. Low BD was strongly associated with presence of CAD (prevalence 75% vs 48%, OR 3.21 [1.58-6.53], p=0.001) independent of EATv (p=0.25), and independent of modifiable (OR: 2.69 [1.24-5.92], p=0.012) and non-modifiable (OR: 2.42 [1.04-5.85], p=0.047) risk factors (Figure 2).

Conclusions
Low breast density is associated with higher EATv and independently associated with CAD presence beyond EATv and other risk factors. Consequently, mammographic breast density may act as an early risk identification tool for CAD in women.
First-in-human study to evaluate the safety, pharmacokinetics (PK), and pharmacodynamics (PD) of RBD4059, a GalNAc-siRNA drug targeting coagulation factor XI (FXI), in healthy subjects
Authors: Gabrielsen A , Feng Z , Fu J , Ueckert S , Egnell L , Wikstrom J , Wei W , Chen G , Friend R , Zhan H , Liang Z , Gao S , Gan L 
Published: October 2024

Abstract

Background
Patients at risk of thrombosis/thromboembolism, require effective and safe anti-coagulation with high compliance. Inhibiting FXI has emerged as a promising strategy with potential for lower bleeding risk compared to currently approved anti-coagulants. Here we report on the safety, PK and PD effects of the GalNAc-conjugated siRNA RBD4059, specifically targeting and repressing FXI protein synthesis in the liver.

Purpose
Investigate safety, PK, and PD of first-in-human dosing of RBD4059 in healthy subjects.

Methods
A randomized, single-blind, placebo-controlled, single ascending dose (SAD) phase I study in healthy subjects (NCT05653037), in cohorts of eight randomized 3:1 to receive escalating doses of RBD4059 (50, 150, and 400 mg) or placebo administered subcutaneously.

Results
As of January 25, 2024, 24 subjects (with follow-up to week 24) with demographics and baseline clinical characteristics shown in Table 1 were enrolled. RBD4059 was well tolerated (Table 2), all related AEs were grade 1. There was no evidence of any clinically relevant bleeding events. Plasma exposure of RBD4059 tended to increase in proportion to the dose. Maximum concentration in plasma (Cmax) was reached in 2 to 4 h and then declined in line with one-compartment kinetics. The mean plasma half-life (t1/2) ranged from 3.2 to 7.0 h. RBD4059 treatment resulted in dose dependent and sustained reductions in FXI activity and FXI antigen (Figure 1A, B, D). Observed mean maximum percentage change from baseline in FXI activity from the 50 mg, 150 mg, and 400 mg SAD cohorts were 67.5%, 81.0%, and 85.8%, respectively, recovering with a maintained effect observed on D141 (50mg: 50.6% inhibition; 150mg: 68.2% inhibition). The reduction in FXI antigen showed a very similar pattern as the reduction in FXI activity, and reductions were accompanied by a concomitant increase in APTT. (Figure 1C, D). PKPD modelling supports that RBD4059 may be dosed every 3 months or less frequent with a &gt;90% FXI reduction throughout the dosing interval, and will be tested in the upcoming phase 2 study.

Conclusion(s)
RBD4059 demonstrated favorable safety, predictable PK, and pronounced durable PD effect in the dose range of 50 mg - 400 mg. This first-in-human SAD study supports the further development of RBD4059 as an anticoagulant that can maintain a high level of PD effect with a once every 3 month or less frequent dosing regimen, with potential for enhanced compliance in chronic anti-coagulant therapy.Table 1 & Table 2Figure 1.Panels A-D
Interethnic validation of an artificial intelligence algorithm for prediction of atrial fibrillation using sinus rhythm electrocardiogram
Authors: Lee J H , Choi J , Kim J , Cho Y , Oh I 
Published: October 2024

Abstract

Background
Previous research has demonstrated acceptable diagnostic accuracy of artificial intelligence (AI)-enabled sinus rhythm (SR) electrocardiogram (ECG) interpretation for identifying paroxysmal atrial fibrillation (AF). However, interethnic validations of the AI algorithms have not been widely conducted.

Purpose
We aimed to develop our own AI model for the identification of paroxysmal AF based on SR ECGs in the Korean population and to validate its diagnostic performance in Brazilian citizens.

Methods
We trained a Transformer-based vision network on 90% of a dataset comprising 808,194 ECGs from 121,282 patients at Seoul National University Bundang Hospital (2003-2020). The remaining 10% of the dataset was used for internal validation. The model was trained to compute a risk score for paroxysmal AF or new-onset AF within 2 years. External validation was conducted using non-AF ECGs from the CODE 15% dataset provided by the Telehealth Network of Minas Gerais, Brazil.

Results
In internal validation, our AI model achieved an Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.911 (95% CI: 0.902 - 0.921), with a sensitivity of 78.1% and a specificity of 89.0%. Subgroup analyses showed an AUROC of 0.893 (95% CI: 0.876 - 0.909) for patients in routine health checkups or outpatient settings, and 0.854 for patients with "Normal ECG" interpretations. In external validation with the CODE 15% dataset, the AI model exhibited an AUROC of 0.884 (95% CI: 0.869-0.900), which increased to 0.906 (95% CI: 0.893-0.919) when adjusted for age and sex (Figure 1). In the subset of patients with "Normal ECG" interpretations, the AUROC was 0.826 (95% CI: 0.769-0.883), increasing to 0.861 (95% CI: 0.814-0.908) after applying the same adjustments.

Conclusions
Our AI-powered SR ECG interpretation model demonstrated excellent diagnostic performance in predicting paroxysmal AF, with valid performance in the Brazilian population as well. This suggests that the model has potential for broad application across different ethnic groups.Figure 1
Prospective evaluation of an AI algorithm for real-time LVEF assessment in acute coronary syndrome using left coronary angiograms: the CathEF multicenter study
Authors: Perrin N P , Theriault-Lauzier P , Sarshoghi A , Ly S , Knafo M , Grandchamp A , Xu Y A N X U , Lessard M G , Corbin D , Tastet O , Gallo R , So D , Marquis-Gravel G , Tanguay J F , Avram R 
Published: October 2024

Abstract

Background
Acute assessment of Left Ventricular Ejection Fraction (LVEF) is critical at time of percutaneous coronary intervention to optimize clinical management. The CathEF artificial intelligence algorithm offers a novel approach for real-time, intra-procedural LVEF assessment using routinely obtained left coronary artery angiograms without additional dye use[1]. Our objective was to evaluate the real-time application of the CathEF algorithm for LVEF measurement during coronary angiogram procedures in patients with acute coronary syndrome (ACS) and to compare its performance with transthoracic echocardiography (TTE) and left ventriculography.

Methods
The CathEF study is a prospective multi-center study that recruited ACS patients undergoing coronary angiography at two institutions from July 2022 to July 2023. Using the CathEF algorithm and the PACS-AI software, we analyzed 2 to 4 left coronary angiogram videos per procedure for LVEF assessment to compare to ventriculography (if indicated) and echocardiography performed during the same hospitalization. Operators were blinded to the CathEF AI-generated results. The primary measure was the algorithm's area under the receiving-operating characteristic curve (AUC) in identifying LVEF &lt;40% or ≥40% compared to TTE-LVEF.

Results
240 patients (32% female, average age 66 ± 12 years) were enrolled, with 207 undergoing TTE during index hospitalization (coronary angiogram to TTE mean delay of 0.8±6.3 days). The CathEF algorithm analyzed 881 coronary angiogram videos, averaging 3.12±1.66 per patient. Indications for angiography included unstable angina (18%), NSTEMI (47%), and STEMI (35%). The algorithm took under one minute to apply. CathEF’s performance yielded an AUC of 0.90 (95% CI, 0.840-0.96) and an MAE of 6.81% ± 0.43, with a Spearman correlation of 0.52.

Conclusions
This study provides proof-of-concept for prospective deployment of an AI algorithm at point-of-care with reliable LVEF measurement in real-time during ACS. This innovation holds the potential to ensure all ACS patients can receive prompt and appropriate care based on their LVEF.CathEF algorithm applied to an angiogramPACS-AI : Platform used to apply CathEF
Interpretable artificial intelligence-based transthyretin amyloid cardiomyopathy detection in patients with severe aortic stenosis
Authors: Shiri I , Balzer S , Bernhard B , Nakase M , Tomii D , L Leib Z , Dobner S , Rominger A , Caobelli F , Valenzuela W , Lanz J , Pilgrim T , Windecker S , Stortecky S , Graeni C 
Published: October 2024

Abstract

Background
The concurrent presence of transthyretin amyloid cardiomyopathy (ATTR-CM) in candidates for transcatheter aortic valve implantation (TAVI) with severe aortic stenosis (AS) is frequently observed.

Objective
This study aims to develop a multimodal artificial intelligence-based model for ATTR-CM detection in patients with severe AS.

Methods
A total of 263 prospectively enrolled (57% males, 83±4.6 years) patients with severe AS were enrolled. All underwent DPD-scintigraphy, and coexisting ATTR-CM was confirmed in 27 (10.3%). 4D-cardiac computed tomography (CT), electrocardiogram, echocardiography, angiography, clinical, and laboratory data were assessed. Specifically, in the 4D-CT, LV myocardial radiomics features as well as global- and segmental myocardial strain. All features underwent z-score normalization, and those with high correlation were eliminated. Different feature selection and machine learning (ML) techniques were used for classification in single- and multimodality scenarios. Eighty percent of the dataset was used for pre-processing, feature extraction, and tuning, with the remaining 20% for evaluating the final model. The model's effectiveness was assessed with accuracy, F1 score, and ROC-AUC across 100 random seeds to establish a reliable classifier. The SHAP (SHapley Additive exPlanations) model is used to interpret the output of ML models.

Results
Among the different algorithms used, Logistic Regression offers the most effective detection of ATTR-CM, surpassing other models with AUC scores of 0.85. CT strain (AUC: 0.85), Radiomics (AUC: 0.76), and clinical features (AUC: 0.70) alone and in combination yielded the highest performance (i.e., AUC: &gt;70), while the remaining modalities had AUC scores ranging from 0.52 to 0.56.

Conclusion
The study shows that AI is effective for non-invasively detecting ATTR-CM in severe aortic stenosis patients, offering a potential alternative to costly DPD-scintigraphy or invasive biopsy.
Impact of a clinician-to-clinician electronic consultation program in heart failure patients with previous hospitalization. Implications for heart failure multidisciplinary management
Authors: Garcia Vega D , Mazon-Ramos P , Portela-Romero M , Rodriguez-Manero M , Rey-Aldana D , Sestayo-Fernandez M , Cinza-Sanjurjo S , Gonzalez-Juanatey J R 
Published: October 2024

Abstract

Background
A clinician-to-clinician electronic consultation (e-consultation) programme may not only improve the accessibility to care but may also impact patient outcomes, particularly in heart failure (HF) patients with a previous episode of hospitalization (HFH), group of patients associated with a worse outcome.

Purpose
To evaluate the impact of an outpatient care management programme that includes a clinician-to-clinician e-consultation on delay time in care, hospital admissions, and mortality in a high-risk group of patients with heart failure (HF) and previous episodes of HF hospitalization (HFH).

Materials and methods
We selected 6444 HF patients who visited the cardiology service at least once between 2010 and 2021. Of these, 4146 were attended in e-consultation, and 2230 had previous HFH (Fig 1a). Using an interrupted time series regression model, we analysed the impact of incorporating e-consultation into the healthcare model in the group of patients with HFH and evaluated the elapsed time to cardiology care, HF, cardiovascular (CV), and all-cause hospital admissions and mortality, calculating the incidence relative risk (iRR). We performed a multivariate logistic regression for each of these outcomes in both groups.

Results
Patients with HFH had a higher prevalence of men (P &lt; 0.001) but had a similar age (P = 0.267) compared to patients without HFH. Patients with HFH had a higher prevalence of diabetes (P &lt; 0.001), ischaemic heart disease (P = 0.036), and peripheral arterial disease (P = 0.008) (Fig 1b). In the group of patients with HFH, the introduction of e-consult substantially decreased waiting times to cardiology care (8.6 [8.7] vs. 55.4 [79.9] days, P &lt; 0.001) (Fig 2a). In that group of patients, after e-consult implantation, hospital admissions for HF were reduced (iRR [95%CI]: 0.837 [0.840–0.833]), 0.900 [0.862–0.949] for CV and 0.699 [0.678–0.726] for all-cause hospitalizations (Fig 2b). There was also lower mortality (iRR [95%CI]: 0.715 [0.657–0.798] due to HF, 0.737 [0.764–0.706] for CV and 0.687 [0.652–0.718] for all-cause) (Fig 2c). The improved outcomes after e-consultation implementation were significantly higher in the group of patients with previous HFH. The multivariate analyses showed a higher risk of hospitalizations in men, patients with HFH, and those who required more emergency department assistance (Fig 2d).

Conclusions
In patients with HFH, an outpatient care programme that includes an e-consultation significantly reduced waiting times to cardiology care and was safe, with a lower rate of hospital admissions and mortality in the first year. Our results may have implications for optimize the heart failure care organization.Figure 1Figure 2
Distribution of microvascular endothelial function in different clinical and non-clinical settings in the united states and china
Authors: Naghavi M , Messahli L , Naghavi Y , Gul A , Gul M , Yen A , Tanaka H , Metcalfe R , Kleis S 
Published: October 2024

Abstract

Background
Despite the fact that microvascular (endothelial) dysfunction is associated with various diseases from cardiovascular to kidney, lung, liver, and other medical conditions, it has not been extensively studied in various clinical and non-clinical settings as blood pressure has. Digital Thermal Flowmetry (DTF) of microvascular endothelial function is a new and automated technique based on monitoring fingertip temperature fall and rebound during reactive hyperemia. Here we report distributions of microvascular function across (1) CVD patients, (2) wellness and internal medicine clinics, (3) college students, and (4) community-based healthy volunteers in China.

Methods
A total of 7,907 endothelial function test results were collected from various settings. Blood pressure and heart rate were measured before the tests. The tests were conducted using FDA-approved automated VENDYS devices (Endothelix Inc., Palo Alto, CA). Adjusted maximum temperature rebound was reported as Vascular Reactivity Index (VRI) and compared across different settings.

Results
VRI in CVD patients, wellness and internal medicine clinics, college students, and Chinese volunteers were (1.25±0.34) (1.53±0.5) (1.86±0.5) (1.95±0.44.) respectively P&lt;0.01. Age was weakly correlated with VRI with the equation age=-0.01 VRI+2.01 (r=0.17 p&lt;0.001)

Conclusions
To our knowledge, this is the largest database of finger-based endothelial function testing. VRI showed distinct distributions across various clinical and non-clinical settings with CVD patients exhibiting the lowest and Chinese healthy volunteers the highest values. The VRI trend mimics the statistically expected risk trend with CVD patients having the highest and Chinese healthy volunteers having the lowest CVD events risk.Figure 1.
The role of digital health intervention in improving physical activity in cardiac rehabilitation center: a randomised controlled trial
Authors: Kizilkilic S E , Ramakers W , Falter M , Scherrenberg M , Bonneux C , Xu L , Pieters Z , De Pauw M , Hansen D , Coninx K , Dendale P 
Published: October 2024

Abstract

Background
Physical activity plays a crucial role in cardiac rehabilitation for secondary prevention. Despite its importance, participation rates in cardiac rehabilitation programs remain low. Telemedicine, particularly telerehabilitation, presents a viable solution by offering remote access to cardiac rehabilitation services. This innovative approach has the potential to significantly enhance patient outcomes.

Purpose
This study aimed to assess the effectiveness of digital health intervention in improving physical activity in cardiac rehabilitation center.

Methods
A prospective randomised controlled trial was conducted, enrolling 80 eligible cardiac rehabilitation patients. Participants were 1:1 randomised into two groups: a 12-week center-based cardiac rehabilitation program (control group), and a 12-week center-based cardiac rehabilitation program combined with a digital health intervention (intervention group). This digital health intervention consists of smartphone, web, and tablet applications to monitor physical activity outside of rehabilitation sessions and motivate patients to engage in more physical activities.

Results
Of the initial 80 patients, 70 completed the study, and the results are presented in Table 1. In the intervention group, there was a statistically significant improvement in peak rate of oxygen consumption (VO2 peak) (p &lt; 0.01) and peak power output (W peak) (p &lt; 0.01), as measured by cardiopulmonary exercise testing between the end and the beginning of the study at the significance level of 0.05. Similar improvements were also observed in the control group (p = 0.0002 for VO2 peak and p &lt; 0.01 for W peak). After 12 weeks of intervention, significant between-group differences were found: We observe a greater improvement in VO2 peak, on average 1.39 (95% CI: 0.23-2.54) ml/kg, and in W peak, on average, 12.29 (95% CI: 4.12-20.45), higher in the intervention group compared to the control group. Regarding physical activity, the intervention group showed a significant improvement in step count (P = 0.002), measured by an accelerometer, whereas no significant improvement was observed in the control group (P = 0.9714). There is a statistically significant difference in the change of step count between the intervention and control group at the significance level of 0.05. We observe a greater improvement, on average 14 788 steps (95% CI: 4 246 – 25 331), in the intervention group compared to the control group.

Conclusion
This study shows that a combination of a digital health intervention based telerehabilitation with a center-based cardiac rehabilitation is more effective in increasing patient’s exercise capacity and physical activity compared with a center-based cardiac rehabilitation.Table 1:results