Browse Articles
Discover research articles across all indexed journals
Performance and Safety of the Extravascular Implantable Cardioverter Defibrillator Through Long-Term Follow-Up: Final Results From the Pivotal Study
BACKGROUND: Substernal lead placement of the extravascular implantable cardioverter defibrillator (EV ICD) permits both defibrillation at thresholds similar to those seen with transvenous implantable cardioverter defibrillators and effective anti-tachycardia pacing (ATP) while avoiding the vasculature and associated complications. The global Pivotal study has shown the EV ICD system to be safe and effective through 6 months, but long-term experience has yet to be published. Our aim was to report the performance and safety of the EV ICD system throughout the study. METHODS: The EV ICD Pivotal study was a prospective, global, single-arm, premarket clinical study. Individuals with a Class I or IIa indication for a single-chamber implantable cardioverter defibrillator per guidelines were enrolled. Freedom from major system- or procedure-related complications and appropriate and inappropriate therapy rates were assessed through 3 years with the Kaplan-Meier method. ATP success was calculated from simple proportions. RESULTS: An implantation was attempted in 316 patients (25.3% female; 53.8±13.1 years of age; 81.6% primary prevention; left ventricular ejection fraction, 38.9±15.4%). Of 299 patients with a successful implantation, 24 experienced 82 spontaneous arrhythmic episodes that were appropriately treated with ATP only (38, 46.3%), shock only (34, 41.5%), or both (10, 12.2%) for a Kaplan-Meier–estimated rate of first any appropriate therapy of 9.2% at 3 years. ATP was successful in 77.1% (37/48) of episodes, and ATP use significantly increased from discharge to last follow-up visit ( P <0.0001). Shock therapy was successful in 100% (27/27) of discrete, spontaneous ventricular arrhythmias. The inappropriate shock rates at 1 and 3 years were 9.8% and 17.5%, respectively, with P-wave oversensing the predominant cause. No major intraprocedural complications were reported, and the estimated freedom from system- or procedure-related major complications was 91.9% at 1 year and 89.0% at 3 years. The most common major complications were lead dislodgement (10 events; n=9 patients, 2.8%), postoperative wound or device pocket infection (n=8, 2.5%), and device inappropriate shock delivery (n=4, 1.3%). Twenty-four system revisions were performed as a result of major complications related to the EV ICD system or procedure. CONCLUSIONS: From implantation to study completion, the EV ICD Pivotal study demonstrated that a single integrated system with an extravascular lead placed in the substernal space maintains high ATP success, effective defibrillation, and a consistent safety profile. REGISTRATION: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT04060680.
A pediatric emergency prediction model using natural language process in the pediatric emergency department
Abstract This study developed a predictive model using deep learning (DL) and natural language processing (NLP) to identify emergency cases in pediatric emergency departments. It analyzed 87,759 pediatric cases from a South Korean tertiary hospital (2012–2021) using electronic medical records. Various NLP models, including four machine learning (ML) models with Term Frequency-Inverse Document Frequency (TF-IDF) and two DL models based on the KM-BERT framework, were trained to differentiate emergency cases using clinician transcripts. Gradient Boosting, among the ML models, performed best with an AUROC of 0.715, AUPRC of 0.778, and F1-score of 0.677. DL models, especially the fine-tuned KM-BERT model, showed superior performance, achieving an AUROC of 0.839, AUPRC of 0.879, and F1-score of 0.773. Shapley-based explanations provided insights into model predictions, underlining the potential of these technologies in medical decision-making. This study demonstrates the potential of advanced DL techniques for NLP in emergency medical settings, offering a more precise and efficient approach to managing healthcare resources and improving patient outcomes.
Kidney Decline Predicts Worse Heart Failure Outcomes
Retraction Note: Serum melatonin levels and in a sample of Iranian patients with migraine
Independence of Lipoprotein(a) and Low-Density Lipoprotein Cholesterol–Mediated Cardiovascular Risk: A Participant-Level Meta-Analysis
BACKGROUND: Low-density lipoprotein cholesterol (LDL-C) and lipoprotein(a) (Lp[a]) levels are independently associated with atherosclerotic cardiovascular disease (ASCVD). However, the relationship between Lp(a) level, LDL-C level, and ASCVD risk at different thresholds is not well defined. METHODS: A participant-level meta-analysis of 27 658 participants enrolled in 6 placebo-controlled statin trials was performed to assess the association of LDL-C and Lp(a) levels with risk of fatal or nonfatal coronary heart disease events, stroke, or any coronary or carotid revascularization (ASCVD). The multivariable-adjusted association between baseline Lp(a) level and ASCVD risk was modeled continuously using generalized additive models, and the association between baseline LDL-C level and ASCVD risk by baseline Lp(a) level by Cox proportional hazards models with random effects. The joint association between Lp(a) level and statin-achieved LDL-C level with ASCVD risk was evaluated using Cox proportional hazards models. RESULTS: Compared with an Lp(a) level of 5 mg/dL, increasing levels of Lp(a) were log-linearly associated with ASCVD risk in statin- and placebo-treated patients. Among statin-treated individuals, those with Lp(a) level >50 mg/dL (≈125 nmol/L) had increased risk across all quartiles of achieved LDL-C level and absolute change in LDL-C level. Even among those with the lowest quartile of achieved LDL-C level (3.1–77.0 mg/dL), those with Lp(a) level >50 mg/dL had greater ASCVD risk (hazard ratio, 1.38 [95% CI, 1.06–1.79]) than those with Lp(a) level ≤50 mg/dL. The greatest risk was observed with both Lp(a) level >50 mg/dL and LDL-C level in the fourth quartile (hazard ratio, 1.90 [95% CI, 1.46–2.48]). CONCLUSIONS: These findings demonstrate the independent and additive nature of Lp(a) and LDL-C levels for ASCVD risk, and that LDL-C lowering does not fully offset Lp(a)-mediated risk.
Prediction and control for the transmission of brucellosis in inner Mongolia, China
Targeting Sphingosine-1-Phosphate Signaling to Prevent the Progression of Aortic Valve Disease
BACKGROUND: Aortic valve disease (AVD) is associated with high mortality and morbidity. To date, there is no pharmacological therapy available to prevent AVD progression. Because valve calcification is the hallmark of AVD and S1P (sphingosine-1-phosphate) plays an important role in osteogenic signaling, we examined the role of S1P signaling in aortic stenosis disease. METHODS: AVD progression and its consequences for cardiac function were examined in a murine wire injury–induced AVD model with and without pharmacological and genetic modulation of S1P production, degradation, and receptor signaling. S1P was measured by liquid chromatography-mass spectrometry. Calcification of human valvular interstitial cells and their response to biomechanical stress were analyzed in the context of S1P signaling. Human explanted aortic valves from patients undergoing aortic valve replacement and cardiovascular magnetic resonance imaging were analyzed for S1P by liquid chromatography-mass spectrometry. RESULTS: Raising S1P concentrations in mice with injury-induced AVD by pharmacological inhibition of its sole degrading enzyme S1P lyase vastly enhanced AVD progression and impaired cardiac function resembling human disease. In contrast, low S1P levels caused by SphK1 (sphingosine kinase 1) deficiency potently attenuated AVD progression. We found S1P/S1PR2 (S1P receptor 2) signaling to be responsible for the adverse S1P effect because S1PR2-deficient mice were protected against AVD progression and its deterioration by high S1P. It is important to note that pharmacological S1PR2 inhibition administered after wire injury successfully prevented AVD development. Mechanistically, biomechanical stretch stimulated S1P production by SphK1 in human valvular interstitial cells as measured by C17-S1P generation, whereas S1P/S1PR2 signaling induced their osteoblastic differentiation and calcification through osteogenic RUNX2/OPG signaling and the GSK3β-Wnt-β-catenin pathway. In patients with AVD, stenotic valves exposed to high wall shear stress had higher S1P content and increased SphK1 expression. CONCLUSIONS: Increased systemic or local S1P levels lead to increased valvular calcification. S1PR2 antagonists and SphK1 inhibitors may offer feasible pharmacological approaches to human AVD in prophylactic, disease-modifying or relapse-preventing manners.
Digital framework for georeferenced multiplatform surveillance of banana wilt using human in the loop AI and YOLO foundation models
Cardiorespiratory Fitness Assessment in Stem Cell Transplant Recipients: Getting to the Heart of the Problem
Author Correction: IoT-based automated system for water-related disease prediction
Letter by Yan et al Regarding Article, “Randomized, Multicenter Study to Assess the Effects of Different Doses of Sildenafil on Mortality in Adults With Pulmonary Arterial Hypertension”
Machine learning and AVO class II workflow for hydrocarbon prospectivity in the Messinian offshore Nile Delta Egypt
Abstract This study presents a comprehensive workflow to detect low seismic amplitude gas fields in hydrocarbon exploration projects, focusing on the West Delta Deep Marine (WDDM) concession, offshore Egypt. The workflow integrates seismic spectral decomposition and machine learning algorithms to identify subtle anomalies, including low seismic amplitude gas sand and background amplitude water sand. Spectral decomposition helps delineate the fairway boundaries and structural features, while Amplitude Versus Offset (AVO) analysis is used to validate gas sand anomalies. The entire seismic volume is classified into facies domains using machine learning, which isolates target features from seismic background data. The study area, covering 1850 km 2 , includes major structures such as the Rosetta fault and Nile Delta offshore anticline, with reservoirs consisting of layered sandstones and mudstones. Over 90 wells, including exploration and development wells, have been drilled in the area. Seismic amplitude data, including full and partial offset stacked, were analyzed to classify gas, water, and shale zones. The workflow’s performance is demonstrated through the successful identification of the low-amplitude Swan-E Messinian anomaly, characterized as a high-risk gas prospect. Machine learning techniques, specifically neural network models, were trained to differentiate seismic features such as low-amplitude gas sand from background-amplitude water sand and shale. By iterating over multiple attributes and validating the models on blind test sets and on a blind section, which excluded a known shallow gas field, the workflow significantly improved the ability to detect potential hydrocarbon reservoirs characterized by low seismic amplitude. The results show that this integrated approach reduces exploration risk, quantifies the chance of success, and enhances decision-making in well placement and hydrocarbon exploration. This method is particularly useful for identifying low seismic amplitude anomalies, which are often challenging to detect with conventional seismic analysis. (1) This study developed a workflow to detect low seismic amplitude gas fields in near-field exploration. (2) It uses a machine learning algorithm to classify and explore low-seismic-amplitude gas sand reservoirs. (3) This approach helps estimate the likelihood of success and reduces the risk associated with hydrocarbon exploration wells.
SLICE-CEA CardioLink-8: A Randomized Trial of Evolocumab on Carotid Artery Atherosclerotic Plaque Characteristics in Asymptomatic High-Risk Carotid Stenosis
A copy number variation detection method based on OCSVM algorithm using multi strategies integration
Hidden artistic complexity of Peru’s Chancay culture discovered in tattoos by laser-stimulated fluorescence
Tattoos were a prevalent art form in pre-Hispanic South America exemplified by mummified human remains with preserved skin decoration that reflects the personal and cultural representations of their times. Tattoos are known to fade and bleed over time and this is compounded in mummies by the decay of the body, inhibiting the ability to examine the original art. Laser-stimulated fluorescence (LSF) produces images based on fluorescence emitted from within the target. Here, we present the first results of applying LSF to tattoos on human remains from the pre-Columbian Chancay culture of coastal Peru. We find that the preserved skin fluoresced strongly underneath the black tattoo ink yielding a high-contrast image that virtually eliminates the ink bleed, revealing the exceptionally fine details of the original artwork. The level of detail and precision of the artwork was found to be higher than associated pottery, textiles, and rock art suggesting special effort was expended by the Chancay on at least some of their tattoos. This suggests artistic complexity in pre-Columbian Peru was at a higher level than previously known, expanding the degree of artistic development found in South America at this time. LSF expands the scope of tattoo analysis and the level of detail this can yield providing a new technique to gain further insights into this important art form.
Response by Hoeper et al to Letter Regarding Article, “Randomized, Multicenter Study to Assess the Effects of Different Doses of Sildenafil on Mortality in Adults with Pulmonary Arterial Hypertension”
Bias-corrected serum creatinine from UK Biobank electronic medical records generates an important data resource for kidney function trajectories
Abstract Loss of kidney function is a substantial personal and public health burden. Kidney function is typically assessed as estimated glomerular filtration rate (eGFR) based on serum creatinine. UK Biobank provides serum creatinine measurements from study center assessments (SC, n = 425,147 baseline, n = 15,314 with follow-up) and emerging electronic Medical Records (eMR, “GP-clinical”) present a promising resource to augment this data longitudinally. However, it is unclear whether eMR-based and SC-based creatinine values can be used jointly for research on eGFR decline. When comparing eMR-based with SC-based creatinine by calendar year ( n = 70,231), we found a year-specific multiplicative bias for eMR-based creatinine that decreased over time (factor 0.84 for 2007, 0.97 for 2013). Deriving eGFR based on SC- and bias-corrected eMR-creatinine yielded 454,907 individuals with ≥ 1eGFR assessment (2,102,174 assessments). This included 206,063 individuals with ≥ 2 assessments over up to 60.2 years (median 6.00 assessments, median time = 8.7 years), where we also obtained eMR-based information on kidney disease or renal replacement therapy. We found an annual eGFR decline of 0.11 (95%-CI = 0.10–0.12) versus 1.04 mL/min/1.73m 2 /year (95%-CI = 1.03–1.05) without and with bias-correction, the latter being in line with literature. In summary, our bias-corrected eMR-based creatinine values enabled a 4-fold increased number of eGFR assessments in UK Biobank suitable for kidney function research.
Preventing Allogeneic Stem Cell Transplant–Related Cardiovascular Dysfunction: ALLO-Active Trial
BACKGROUND: Allogeneic stem cell transplantation (allo-SCT) is an efficacious treatment for hematologic malignancies but can be complicated by cardiac dysfunction and exercise intolerance impacting quality of life and longevity. We conducted a randomized controlled trial testing whether a multicomponent activity intervention could attenuate reductions in cardiorespiratory fitness and exercise cardiac function (co-primary end points) in adults undergoing allo-SCT. METHODS: Sixty-two adults scheduled for allo-SCT were randomized to a 4-month activity program (activity; n=30) or usual care (UC; n=32). Activity comprised a multicomponent exercise training (3 days.week -1 ) and sedentary time reduction (≥30 minutes.day -1 ) program and was delivered throughout hospitalization (≈4 weeks) and for 12 weeks after discharge. Physiological assessments conducted before admission and at 12 weeks after discharge included cardiopulmonary exercise testing to quantify peak oxygen uptake ( V ˙ o 2 p e a k ), exercise cardiac magnetic resonance imaging for peak cardiac (CI peak ) and stroke volume (SVI peak ) index, echocardiography-derived left ventricular ejection fraction and global longitudinal strain, and cardiac biomarkers (cTn-I [troponin-I] and BNP [B-type natriuretic peptide]). RESULTS: Fifty-two participants (84%) completed follow-up (25 activity and 27 UC); median (interquartile range [IQR]) adherence to the activity program was 74% (41%–96%). There was a marked decline in V ˙ o 2 p e a k in the UC program (−3.4 mL‧kg -1 ‧min -1 [95% CI, −4.9 to −1.8]) that was attenuated with activity (−0.9 mL‧kg -1‧ min -1 [95% CI, −2.5 to 0.8]; interaction P =0.029). Activity preserved exercise cardiac function, with preservation of CI peak (0.30 L‧min -1 ‧m -2 [95% CI, −0.34 to 0.41]) and SVI peak (0.6 mL.m -2 [95% CI, −1.3 to 2.5]), both of which declined with UC (CI peak , −0.68 L‧min -1 ‧m -2 [95% CI, –1.3 to −0.32]; interaction P =0.008; SVI peak , −2.7 mL.m -2 [95% CI, −4.6 to −0.9]; interaction P= 0.014). There were no treatment effects of activity on cardiac biomarkers or echocardiographic indices. CONCLUSIONS: Intervening during and after allo-SCT with a multicomponent activity program during and after allo-SCT is beneficial for preserving a patient’s cardiorespiratory fitness and exercise cardiac function. These results may have important implications for cardiovascular morbidity and mortality after allo-SCT. REGISTRATION: URL: https://anzctr.org.au/ ; Unique identifier: ACTRN12619000741189.