Cardiovascular Monitoring Research Articles

Nonlinear associations between cardiovascular stress reactivity and emotional reactivity and emotion regulation among adolescents.

Cardiovascular stress reactivity (CVR) is considered as a physiological pathway linking emotional reactivity and emotion regulation with psychopathology. However, the associations between CVR and emotional reactivity and emotion regulation remain underexplored, with limited evidence showing that either excessive or blunted CVR is associated with emotional reactivity and emotional regulation. Recently, moderate CVR has been theoretically hypothesized to be related to optimal outcomes; however, whether CVR is nonlinearly associated with emotional reactivity and emotion regulation still needs to be investigated. Parents of 341 junior school students reported their children's emotional reactivity and emotion regulation on the Emotion Questionnaire, and the students were invited to participate in a mental arithmetic task with continuous cardiovascular monitoring indexed by heart rate (HR) and systolic and diastolic blood pressure (SBP, DBP). Results did not reveal any linear relationships between CVR and emotional reactivity or emotion regulation. However, quadratic associations between HR, SBP reactivity and emotional reactivity and emotion regulation were found even after controlling for sex, age and BMI. Specifically, there was a U-shaped association between HR, SBP reactivity, and emotional reactivity, while there was an inverted U-shaped association between HR, SBP reactivity, and emotion regulation. These findings suggest that moderate to high rather than exaggerated or blunted CVR reflects adaptive emotional reactivity and better emotion regulation among adolescents.

Substantiation and Effectiveness of Remote Monitoring System Based on IoMT Using Portable ECG Device.

Cardiovascular disease is a major global health concern, with early detection being critical. This study assesses the effectiveness of a portable ECG device, based on Internet of Medical Things (IoMT) technology, for remote cardiovascular monitoring during daily activities. We conducted a clinical trial involving 2000 participants who wore the HiCardi device while engaging in hiking activities. The device monitored their ECG, heart rate, respiration, and body temperature in real-time. If an abnormal signal was detected while a physician was remotely monitoring the ECG at the IoMT monitoring center, he notified the clinical research coordinator (CRC) at the empirical research site, and the CRC advised the participant to visit a hospital. Follow-up calls were made to determine compliance and outcomes. Of the 2000 participants, 318 showed abnormal signals, and 182 were advised to visit a hospital. The follow-up revealed that 139 (76.37%) responded, and 30 (21.58% of those who responded) sought further medical examination. Most visits (80.00%) occurred within one month. Diagnostic approaches included ECG (56.67%), ECG and ultrasound (20.00%), ultrasound alone (16.67%), ECG and X-ray (3.33%), and general treatment (3.33%). Seven participants (23.33% of those who visited) were diagnosed with cardiovascular disease, including conditions such as arrhythmia, atrial fibrillation, and stent requirements. The portable ECG device using the patch-type electrocardiograph detected abnormal cardiovascular signals, leading to timely diagnoses and interventions, demonstrating its potential for broad applications in preventative healthcare.

Advancing Wearable Cardiovascular Monitoring: Integration of Flexible Piezoresistive Sensors for Robust Pulse Waveform Detection

Wearable, flexible piezoresistive pressure sensors have diverse applications, spanning electronic skin, robotic limbs, and cardiovascular monitoring. Recent advancements in wearable health monitoring devices utilizing these sensors show promise for continuous cardiovascular monitoring. However, a substantial gap exists between laboratory development and market availability for pressure sensor based pulse monitoring. Challenges include the lack of effective testing schemes ensuring robust wearable operation and the necessity of bulky signal acquisition equipment, limiting adaptability. This study addresses these challenges by integrating the outputs of multiple flexible, skin-mountable piezoresistive sensors to create a robust wearable platform. This platform encompasses both sensing and miniaturized signal acquisition for arterial pulse waveform monitoring. The single piezoresistive sensor in our design exhibits sensitivity (1.4 kPa-1 at 2 – 8 kPa). This robust signal allows for the use of simple, compact signal collection devices mounted on a wristband. Moreover, the capability of the the system allows for detection of the three distinct peaks associated with the full pulse waveform, including the systolic, diastolic, and reflected diastolic peaks. In validation, a comparison between the wrist pulse waveform obtained with the compact signal collection device and a standard sourcemeter (Keithley 2460) confirms the capability of our wearable system to resolve the three distinct peaks. This work contributes to bridging the gap between research and practical applications, offering a potential solution for widespread adoption of continuous cardiovascular monitoring technology in diverse settings.

Refined matrix completion for spectrum estimation of heart rate variability.

Heart rate variability (HRV) is an important metric in cardiovascular health monitoring. Spectral analysis of HRV provides essential insights into the functioning of the cardiac autonomic nervous system. However, data artefacts could degrade signal quality, potentially leading to unreliable assessments of cardiac activities. In this study, we introduced a novel approach for estimating uncertainties in HRV spectrum based on matrix completion. The proposed method utilises the low-rank characteristic of HRV spectrum matrix to efficiently estimate data uncertainties. In addition, we developed a refined matrix completion technique to enhance the estimation accuracy and computational cost. Benchmarking on five public datasets, our model shows effectiveness and reliability in estimating uncertainties in HRV spectrum, and has superior performance against five deep learning models. The results underscore the potential of our developed matrix completion-based statistical machine learning model in providing reliable HRV spectrum uncertainty estimation.

SiamQuality: a ConvNet-based foundation model for photoplethysmography signals

Objective. Physiological data are often low quality and thereby compromises the effectiveness of related health monitoring. The primary goal of this study is to develop a robust foundation model that can effectively handle low-quality issue in physiological data. Approach. We introduce SiamQuality, a self-supervised learning approach using convolutional neural networks (CNNs) as the backbone. SiamQuality learns to generate similar representations for both high and low quality photoplethysmography (PPG) signals that originate from similar physiological states. We leveraged a substantial dataset of PPG signals from hospitalized intensive care patients, comprised of over 36 million 30 s PPG pairs. Main results. After pre-training the SiamQuality model, it was fine-tuned and tested on six PPG downstream tasks focusing on cardiovascular monitoring. Notably, in tasks such as respiratory rate estimation and atrial fibrillation detection, the model’s performance exceeded the state-of-the-art by 75% and 5%, respectively. The results highlight the effectiveness of our model across all evaluated tasks, demonstrating significant improvements, especially in applications for heart monitoring on wearable devices. Significance. This study underscores the potential of CNNs as a robust backbone for foundation models tailored to physiological data, emphasizing their capability to maintain performance despite variations in data quality. The success of the SiamQuality model in handling real-world, variable-quality data opens new avenues for the development of more reliable and efficient healthcare monitoring technologies.

Monitoring of cardiovascular emergencies in the emergency department

In the emergency department, patients with potential or confirmed cardiovascular diseases constitute a significant portion of the overall patient population. Monitoring for cardiovascular surveillance of these patients, until and during the diagnostics and acute therapy often presents an interdisciplinary and interprofessional challenge. This is partly due to the limited number of monitoring spaces in emergency departments. Therefore, it is crucial to establish a differentiated indication for cardiovascular monitoring. Despite limited monitoring resources, ensuring high patient safety is paramount. The correct approach holds significant prognostic importance. For patients requiring extended monitoring, especially using invasive systems, close personnel monitoring is essential, in addition to appropriate staffing and medical equipment. The overarching goal for such patients is to ensure prompt transfer to a suitable destination unit. The provision of an intensive care bed for further care within one hour is aimed for according to the directive of the Federal Joint Committee on staged emergency care in hospitals. Often, at the beginning of the emergency department visit, a definitive diagnosis is not yet established - this is addressed accordingly with symptom-oriented considerations. The present review article focuses on the practical Implementation and modalities of monitoring, as well as its application in a selection of cardiovascular diagnoses in the emergency department.

Mechanical Structure Design of Pressure Sensors with Temperature Self-compensation for Invasive Blood Pressure Monitoring

Abstract Invasive blood pressure (IBP) is a fundamental part of basic cardiovascular monitoring. Conventional piezoresistive pressure sensors are limited in usage due to the high cost associated with equipment and intricate fabrication processes. Meanwhile, low-cost strain gauge pressure sensors have poor performance in the gauge factor (GF) and temperature insensitivity. Here, we report a mechanical structure design for diaphragm pressure sensors (DPSs) by introducing a compensation grid to overcome the aforementioned challenges. A simplified model is established to analyze the mechanical deformation and obtain the optimal design parameters of the DPS. By rationally arranging the placement of sensitive grids to eliminate the discrepancy of relative resistance changes within four arms of the Wheatstone full-bridge circuit, the appropriate GF and high temperature insensitivity are simultaneously achieved. The blood pressure sensor with the DPS is then fabricated and characterized experimentally, which demonstrates an appropriate GF (ΔU/U0)/P = 3.56 × 10−5 kPa−1 and low temperature coefficient of voltage (ΔU/U0)/ΔT = 3.4 × 10−7 °C−1. The developed mechanical structure design offers valuable insights for other resistive pressure sensors to improve the GF and temperature insensitivity.

Close Cardiovascular Monitoring during the Early Stages of Treatment for Patients Receiving Immune Checkpoint Inhibitors.

Background: There is an unmet medical need for the early detection of immune checkpoint inhibitor (ICI)-induced cardiovascular (CV) adverse events due to a lack of adequate biomarkers. This study aimed to provide insights on the incidence of troponin elevations and echocardiographic dynamics during ICI treatment in cancer patients and their role as potential biomarkers for submyocardial damage. In addition, it is the first study to compare hs-TnT and hs-TnI in ICI-treated patients and to evaluate their interchangeability in the context of screening. Results: Among 59 patients, the mean patient age was 68 years, and 76% were men. Overall, 25% of patients received combination therapy. Although 10.6% [95% CI: 5.0-22.5] of the patients developed troponin elevations, none experienced a CV event. No significant changes were found in 3D left ventricular (LV) ejection fraction nor in global longitudinal strain f (56 ± 6% vs. 56 ± 6%, p = 0.903 and -17.8% [-18.5; -14.2] vs. -17.0% [-18.8; -15.1], p = 0.663) at 3 months. There were also no significant changes in diastolic function and right ventricular function. In addition, there was poor agreement between hs-TnT and hs-TnI. Methods: Here, we present a preliminary analysis of the first 59 patients included in our ongoing prospective clinical trial (NCT05699915) during the first three months of treatment. All patients underwent electrocardiography and echocardiography along with blood sampling at standardized time intervals. This study aimed to investigate the incidence of elevated hs-TnT levels within the first three months of ICI treatment. Elevations were defined as hs-TnT above the upper limit of normal (ULN) if the baseline value was normal, or 1.5 ≥ times baseline if the baseline value was above the ULN. Conclusions: Hs-TnT elevations occurred in 10.6% of the patients. However, no significant changes were found on 3D echocardiography, nor did any of the patients develop a CV event. There were also no changes found in NT-proBNP. The study is still ongoing, but these preliminary findings do not show a promising role for cardiac troponins nor for echocardiographic dynamics in the prediction of CV events during the early stages of ICI treatment.

Enhanced ECG Signal Classification

The increasing amount of medical data emphasizes the urgent need for efficient methods in classifying electrocardiogram (ECG) signals. While current approaches are valuable, they struggle to achieve both high sensitivity and specificity, limiting their effectiveness in timely cardiac diagnosis. These challenges underscore the importance of more robust methodologies to improve the accuracy of ECG signal classification. To tackle these issues, this research suggests a comprehensive approach using machine learning techniques. Our framework incorporates various algorithms such as Support Vector Machines (SVM), XGBoost, K-Nearest Neighbors (KNN), Logistic Regression, and an ensemble classifier. This ensemble method aims to leverage the strengths of individual models, enhancing the overall classification performance. The application of this approach shows promising results, with increased sensitivity and specificity in categorizing ECG signals. The versatility of our proposed framework has significant potential for various applications, contributing to advancements in cardiovascular health monitoring and diagnosis.

CPSS: Fusing consistency regularization and pseudo-labeling techniques for semi-supervised deep cardiovascular disease detection using all unlabeled electrocardiograms

Background and objective:Deep learning usually achieves good performance in the supervised way, which requires a large amount of labeled data. However, manual labeling of electrocardiograms (ECGs) is laborious that requires much medical knowledge. Semi-supervised learning (SSL) provides an effective way of leveraging unlabeled data to improve model performance, providing insight for solving this problem. The objective of this study is to improve the performance of cardiovascular disease (CVD) detection by fully utilizing unlabeled ECG. Methods:A novel SSL algorithm fusing consistency regularization and pseudo-labeling techniques (CPSS) is proposed. CPSS consists of supervised learning and unsupervised learning. For supervised learning, the labeled ECGs are mapped into prediction vectors by the classifier. The cross-entropy loss function is used to optimize the classifier. For unsupervised learning, the unlabeled ECGs are weakly and strongly augmented, and a consistency loss is used to minimize the difference between the classifier’s predictions for the two augmentations. Pseudo-labeling techniques include positive pseudo-labeling (PL) and ranking-based negative pseudo-labeling (RNL). PL introduces pseudo-labels for data with high prediction confidence. RNL assigns negative pseudo-labels to the lower-ranked categories in the prediction vectors to leverage data with low prediction confidence. In this study, VGGNet and ResNet are used as classifiers, which are jointly optimized by labeled and unlabeled ECGs. Results:CPSS has been validated on several databases. With the same number of labeled ECGs (10%), it improves the accuracies over pure supervised learning by 13.59%, 4.60%, and 5.38% in the CPSC2018, PTB-XL, and Chapman databases, respectively. CPSS achieves comparable results to the fully supervised method with only 10% of labeled ECGs, which reduces the labeling workload by 90%. In addition, to verify the practicality of CPSS, a cardiovascular disease monitoring system is designed by heterogeneously deploying the trained classifiers on an SoC (system-on-a-chip), which can detect CVD in real time. Conclusion:The results of this study indicate that the proposed CPSS can significantly improve the performance of CVD detection using unlabeled ECG, which reduces the burden of ECG labeling in deep learning. In addition, the designed monitoring system makes the proposed CPSS promising for real-world applications.

Blood pressure changes during ketamine infusion for the treatment of depression

ObjectiveThis study aimed to examine blood pressure changes during ketamine infusion for depression and exploring the factors associated with these changes. MethodThis study is a retrospective chart-review of patients with depression undergoing ketamine infusion at Yale Psychiatry Hospital during a 7-year period. Blood pressure (BP) was recorded every 10 min during the infusion. Surges in systolic and diastolic blood pressure (SBP, DBP), along with severe hypertension events, were analyzed in relation to patient demographics. ResultsA total of 138 patients received a total of 2342 infusions. SBP and DBP peaked at 40 min after the start of the infusion with a mean change of 16.0 (SD: 11.2) and 11.0 mmHg (SD: 8.45) respectively. Severe hypertension was observed in 17 patients (12.5%) and 23 infusion sessions (0.98%), occuring more frequently during the first three infusions (43.4%). Age (OR = 1.04 [1.02,1.05], p-value <0.001) was significantly associated with a surge in SBP and all patients with a past medical history of hypertension experienced a BP surge during their infusions. ConclusionKetamine infusions can cause significant blood pressure increases, particularly in older and hypertensive patients, highlighting the need for careful cardiovascular monitoring to mitigate risks during treatment.

Rheumatic diseases in the elderly: Challenges and management

Abstract The elderly demographic is seeing a gradual rise as a proportion of the overall global population, whilst also experiencing high rates of musculoskeletal issues, requiring specialized care. The management of rheumatic diseases in the elderly presents unique challenges due to the complex interplay among physiological changes, cognitive decline, and altered immune responses. Such conditions can be categorized into those progressing from earlier life stages (such as rheumatoid arthritis, systemic lupus erythematosus, spondyloarthritis, and gout) and those predominantly emerging in old age (such as polymyalgia rheumatica, giant cell arteritis, osteoarthritis, crystal arthropathies, and osteoporosis), each with distinct characteristics and management needs. These diseases often present atypically with nonspecific symptoms and altered serological markers, complicating diagnosis and management due to increased toxicity risks and comorbidities. Comprehensive, multidisciplinary care that includes cardiovascular health monitoring, bone density maintenance, gastrointestinal tolerance management, infection prevention, and mental health support are essential to improve outcomes and quality of life for elderly patients with rheumatic diseases.

A Residual U-Net Neural Network for Seismocardiogram Denoising and Analysis During Physical Activity.

Seismocardiogram (SCG) signals are noninvasively obtained cardiomechanical signals containing important features for cardiovascular health monitoring. However, these signals are prone to contamination by motion noise, which can significantly impact accuracy and robustness of the measurements. A deep learning model based on the U-Net architecture is proposed to recover SCG signals contaminated by motion noise induced by walking. The model performance was evaluated through qualitative visualization, as well as quantitative analyses. Quantitative analyses included distance-based comparisons before and after applying our model. Analyses also included assessments of the model's efficacy in improving the performance of downstream tasks related to health parameter estimation during walking. Experimental findings revealed that the denoising model improved similarity to clean signals by approximately 90%. The performance of the model in enhancing heart rate estimation demonstrated a mean absolute error of 1.21 BPM and a root-mean-squared error (RMSE) of 1.97 BPM during walking after denoising with 9.16 BPM and 10.38 BPM improvements, respectively, compared to without denoising. Furthermore, the RMSEs of aortic opening and aortic closing time estimation after denoising for one dataset with catheter ground truth were 7.29 ms and 19.71 ms during walking, respectively, with 50.33 ms and 51.91 ms RMSE improvements compared to without denoising. And for another dataset with ICG-derived PEP ground truth, the RMSE of aortic opening time estimation after denoising was 10.21 ms during walking, with 38.74 ms RMSE improvement compared to without denoising. The proposed model attenuates motion noise from corrupted SCG signals while preserving cardiac information. This development paves the way for improved ambulatory cardiac health monitoring using wearable accelerometers during daily activities.

Dolutegravir-based Treatment in HIV-infected Diabetics Requires Close Cardiovascular Monitoring: A Point of Concern

Dolutegravir-based Treatment in HIV-infected Diabetics Requires Close Cardiovascular Monitoring: A Point of Concern

Carfilzomib Induced Cardiac Tamponade - A Case Report

Carfilzomib, a highly selective proteasome inhibitor, is commonly used in the treatment of multiple myeloma and AL amyloidosis. While its efficacy is well-established, there is increasing recognition of its association with cardiovascular adverse events, including hypertension, heart failure, and arrhythmias. However, cases of carfilzomib-induced pericardial disease remain rare. Here, we present a case of a 78-year-old female with multiple myeloma who experienced two episodes of hemorrhagic pericardial effusion following carfilzomib therapy, highlighting the importance of vigilant cardiovascular monitoring during treatment.

Major cardiovascular events in long-term multiple myeloma survivors: a Korean case–control study (the CAREMM-2105 study)

PurposeDespite improvements in multiple myeloma (MM) survival rates, data on cardiovascular outcomes in long-term survivors remain lacking.MethodsThis retrospective case–control study utilized the Korean National Health Insurance Service database (2009–2020) to compare the incidence of cardiovascular disease (CVD) between patients with MM and a matched control group, focusing on long-term (> 5 years) survivors. A preliminary case cohort (n = 15,402 patients with MM) and a matched control cohort (n = 123,216 patients without MM) were established based on birth year and sex. Following 1:1 propensity score matching, the final matched cohorts each comprised 15,402 participants.ResultsThe case and control cohorts were comparable in mean age (66.2 ± 11.5 years vs. 66.1 ± 11.3 years), sex, age distribution, and comorbidities. By the 8-year follow-up, the cumulative incidence of CV events (12.5% vs. 22.1%) and CVD risk were significantly lower in the case cohort. The 5-year landmark analysis revealed significant differences in CVD incidence between the cohorts (7.8% [case cohort] vs. 9.8% [control cohort]), with variations across age groups and sex, highlighting a significantly higher CVD risk among patients aged < 50 years in the case cohort (P < 0.001).ConclusionsThese findings underscore the need for vigilant CVD monitoring in MM long-term survivors, particularly those aged < 50 years at first diagnosis.Implication for Cancer SurvivorsThis study highlights the importance of integrating cardiovascular monitoring and risk management into long-term care for MM survivors, with a focus on younger patients and personalized interventions.

The Impact of Kidney Stones on Congestive Heart Failure Risk.

Introduction Heart failure (HF) represents a substantial global health concern, evidenced by its high prevalence, significant mortality rates, and considerable economic impact worldwide. Within this broader context, congestive heart failure (CHF) emerges as a critical subset, affecting millions and leading to high rates of morbidity and mortality. Recent explorations have started to uncover a potential link between kidney stonesand broader systemic health problems, including coronary artery disease. This association suggests that kidney stones might also indicate an increased risk for cardiovascular diseases such as CHF. However, the exploration into the direct relationship between kidney stones and CHF is still in its nascent stages, creating a significant gap in understanding the full cardiovascular implications of kidney stone disease. Methods Utilizing data from the National Health and Nutrition Examination Survey (NHANES) for the period of March 2017 to March 2020, we conducted a logistic regression analysis to assess the relationship between kidney stones and CHF. This analysis adjusted for key variables such as age, gender, race, and educational attainment, aiming to isolate the impact of kidney stones on CHF risk among 8,521 participants. Results Our findings revealed a higher incidence of CHF among individuals with a history of kidney stones (7%) compared to those without (3%). Logistic regression analysis further highlighted kidney stones as an independent risk factor for CHF, with an odds ratio (OR) of 1.857, significant at p < 0.01. These results underline the importance of considering kidney stones in the broader context of cardiovascular health risks, particularly CHF, as their presence significantly elevates the risk compared to the general population without kidney stones. Additional demographic analyses indicated significant influences of age, gender, race, and educational level on the risk of CHF, emphasizing the complex interplay between these factors and heart health. Conclusion The study confirms the association between a history of kidney stones and an increased risk of CHF, suggesting the need for heightened cardiovascular monitoring for patients with such a history. It also brings to light the significant role demographic factors play in CHF risk, advocating for targeted interventions to mitigate these disparities. Our research supports a broader view of patient care that includes consideration of urological conditions as potential risk factors for heart failure. Further exploration into the mechanisms linking kidney stones and cardiovascular health is recommended to inform more effective prevention and treatment strategies.

The Pulse of Progress: Smart Wearables and the Evolution of Cardiovascular Monitoring Technologies

This paper explores the transformative role of smart wearables in the evolution of cardiovascular monitoring technologies. With cardiovascular diseases (CVDs) as the leading cause of mortality globally, there's a critical need for continuous, real-time health monitoring outside traditional clinical settings. Smart wearables, ranging from fitness trackers to specialized health-monitoring devices, have emerged as pivotal tools in this regard. They offer personalized, preventive, and real-time management of health, leveraging advancements in technology to provide a continuous stream of health data previously inaccessible. This systematic review draws upon recent studies to examine the effectiveness of wearable technologies in detecting, predicting, and managing CVDs.

Angiogenesis inhibitors and cardiovascular outcomes in cancer: Real-world, population-based, matched cohort study.

e24024 Background: Angiogenesis inhibitors (AI) have vascular side effects and are now recognized forcardiovascular adverse events (CVAEs). Clinical trial outcomes can underestimate treatment-related adverse events due to restrictive eligibility criteria, limited sample size, and failure to anticipate specific toxicities. In contrast, observational studies have small sample sizes and insufficient adjustment of confounding covariates. In addition, prospective high-quality evidence in real-world settings is scarce. We evaluated the incidence of CVAEs in real-world populations on AI therapy. Methods: This large retrospective cohort study was conducted using the TriNetX database and included adult patients ( &gt; 18 years) with new cancer diagnoses. We performed 1:1 propensity score matching (PSM) for demographics, BMI, nicotine dependence, comorbidities, cancer type, and medications to similar controls as cancer patients never treated with AIs. The primary outcome was the new incidence of CVAEs-like heart failure (HF), major adverse cardiovascular events (MACE; coronary artery intervention myocardial infarction, or unstable angina), arrhythmia, ischemic heart disease (IHD), venous thromboembolism (VT), and cerebrovascular events (CVE cerebral infarction, carotid intervention, stroke, transient ischemic attack). Sensitivity analysis assessed statistical robustness. Hazard ratios (HR) were calculated to compare the AI therapy association with incident CVAEs. Results: We identified 1,170,667 patients with diagnoses of colorectal, cervical, kidney, liver, nonsmall cell lung, or ovarian cancer between 2010 and 2022. Among these, 59,890 had an AI treatment history, and 110,777 did not, with a median follow-up time of 3.1 years. After PSM, both cohorts (59545 each) were well-matched. In the adjusted analysis, AI therapy was associated with higher risks of MACE (HR, 1.23; 95% CI 1.16 -1.31), HF (HR, 1.16; 95% CI 1.08 - 1.25), and VT (HR 2.07; 95% CI 1.95 - 2.19), IHD (HR 1.23; 95% CI 1.16- 1.31), arrhythmias (HR 1.19; 95% CI 1.12-1.27), CVEs (HR, 1.30; 95% CI 1.21- 1.40) at 1 year of exposure. These outcomes were still significantly higher “increased,” suggesting causation; this is an association at follow-up by increasing the lag exposure. Sensitivity analysis results were consistent, and all statistically significant associations remained unchanged. Conclusions: Angiogenesis inhibitor treatment was associated with a significantly higher risk of CVAEs and various malignancies. The increased risk ranges from approximately 1.16 to 2.07 fold, indicating a substantial elevation in risk. These findings highlight the importance of risk stratification and cardiovascular monitoring during and after AI therapy. There is an unmet need to define strategies for CVAE prevention in this high-risk population.

Evaluation of vasovagal tonus index and electrocardiographic parameters in horses using a new modified base apex lead method.

Vasovagal tonus index (VVTI) serves as a straightforward assessment tool for autonomic function during both physiological and pathological conditions, including pregnancy, in horses. Obtaining VVTI through a modified base apex lead system could be a practical and comfortable solution. In this study, we assessed VVTI in horses with respect to training status and electrocardiographic measurements utilizing a novel modified base apex lead technique. A total of 12 Thai native crossbred horses and 12 Arabian horses, all free of cardiac abnormalities, were enrolled in the study. Animals underwent electrocardiogram (ECG) and VVTI using both the base-apex lead method and its modified version. 25 mm/s and 10 mm/mV ECG recordings provided standard bipolar limb leads. The amplitudes and durations of P waves, QRS complexes, PR interval, QT interval, and T duration were assessed. The T wave's shape was examined. Each recording's R-R interval was utilized to assess heart rate. Twenty consecutive beats were used to compute the variability of heart rate (VVTI). The P wave amplitude was the only significant difference (p < 0.05) between the base apex lead method and the modified base apex lead method, with no variations in heart rate, P duration, PR interval, T duration, and QRS duration and amplitude. Both methods showed mainly biphasic T wave patterns. The VVTI values of all horses did not differ significantly between the base apex and modified base apex methods. There was no significant difference in VVTI between Thai crossbred horses and Arabian horses in either method. This study provided the first evidence that VVTI can be evaluated using the modified base apex lead system and may be useful for cardiovascular function monitoring in horses.