Two-channel portable fluorescence meter for risk stratification of cardiovascular diseases

2019 ◽  
Author(s):  
Vladimir N. Grishanov ◽  
Dmitriy V. Kornilin ◽  
Petr A. Lebedev ◽  
Dmitry E. Kopaev ◽  
Darya Yu. Pimenova ◽  
...  
Keyword(s):  
Cardiovascular Diseases ◽  
Risk Stratification

Chromogranin-A Levels Measured with Automated Immunoassay

2015 ◽  
Vol 30 (1) ◽  
pp. 132-135 ◽  
Author(s):  
Damien Gruson ◽  
Thibault Lepoutre ◽  
Françoise Smits
Keyword(s):  
Cardiovascular Diseases ◽  
Risk Stratification ◽  
Immunosorbent Assay ◽  
Reference Values ◽  
Chromogranin A ◽  
Turnaround Time ◽  
Enzyme Linked Immunosorbent Assay ◽  
Neuroendocrine Neoplasms ◽  
Automated Assay ◽  
Automated Immunoassay

Measurement of chromogranin-A (CgA) levels is relevant for the diagnosis of neuroendocrine neoplasms. The use of CgA testing for risk stratification of cardiovascular diseases is also increasing. The objective of our study was to determine the performances and reference values of a novel automated assay for CgA testing. The new method was compared with an enzyme-linked immunosorbent assay. Our results showed that the performances of the automated assay were satisfactory and that the agreement between the two methods was excellent. The automation of CgA testing also reduced the turnaround time of analysis and, therefore, might contribute to a faster delivery of the results to physicians.

scholarly journals A Bayesian Framework for Estimating the Risk Ratio of Hospitalization for People with Comorbidity Infected by the SARS-CoV-2 Virus

2020 ◽  
Author(s):  
Xiang Gao ◽  
Qunfeng Dong
Keyword(s):  
Public Health ◽  
Cardiovascular Diseases ◽  
General Population ◽  
Risk Stratification ◽  
Large Scale ◽  
Chronic Respiratory Disease ◽  
Health Policies ◽  
Public Health Policies ◽  
Infected People ◽  
The U.S

Estimating the hospitalization risk for people with certain comorbidities infected by the SARS-CoV-2 virus is important for developing public health policies and guidance based on risk stratification. Traditional biostatistical methods require knowing both the number of infected people who were hospitalized and the number of infected people who were not hospitalized. However, the latter may be undercounted, as it is limited to only those who were tested for viral infection. In addition, comorbidity information for people not hospitalized may not always be readily available for traditional biostatistical analyses. To overcome these limitations, we developed a Bayesian approach that only requires the observed frequency of comorbidities in COVID-19 patients in hospitals and the prevalence of comorbidities in the general population. By applying our approach to two different large-scale datasets in the U.S., our results consistently indicated that cardiovascular diseases carried the highest hospitalization risk for COVID-19 patients, followed by diabetes, chronic respiratory disease, hypertension, and obesity, respectively.

P485Re-interpretation of variants of uncertain significance in inherited cardiovascular diseases-A pilot study

EP Europace ◽  
2020 ◽  
Vol 22 (Supplement_1) ◽  
Author(s):  
V Novelli ◽  
D Mazza ◽  
M Cammarano ◽  
R Quarta ◽  
M Curra ◽  
...  
Keyword(s):  
Cardiovascular Diseases ◽  
Risk Stratification ◽  
Allele Frequency ◽  
Published Data ◽  
Variants Of Uncertain Significance ◽  
Improve Risk Stratification ◽  
Genetic Test Results ◽  
Uncertain Significance ◽  
Inherited Cardiac Conditions ◽  
Suspected Diagnosis

Abstract Background- Identification of variants of uncertain significance (VUSs) poses relevant challenges in counseling and managing patients. They have an unknown impact on health, making the genetic tests clinically irrelevant. Recent studies demonstrate that a routine reclassification analysis enables to reclassify from 20% to 80% of this type of variant, improving risk stratification. Purpose- We investigated whether, in the context of inherited cardiac conditions, a review of the updated literature, including new functional data, allele frequency (GnomAD) and segregation analysis may help in the variant reclassification. Methods- Retrospective review of all VUSs in genes associated with inherited cardiac conditions identified in our cardiogenetic clinic between 2016 and 2018. Results- Thirty-one VUSs, classified using ACMG guidelines, were identified in 26 cases with a confirmed or suspected diagnosis of inherited cardiovascular diseases, including Long QT syndrome, Brugada syndrome, Arrhythmogenic Cardiomyopathy and Hypertrophic Cardiomyopathy). Twentyfour variants were identified in well-defined causative genes (SCN5A, KCNQ1, KCNH2, KCNE1, DSP, DSG2, MYH7, TPM1, TNNI3, TNNT2, CACNA1C, MYL3) while the remaining variants were identified in minor genes with limited evidence to support their disease causation as, ANK2 and AKAP9 gene. Preliminary results of the reclassification analysis showed that two variants were downgraded to likely benign (LB) applying the BS1 criterion (allele frequency) and 4 variants were upgraded to likely pathogenic (LP) according to novel published data and family segregation studies. Moreover, further studies to assess cosegregation in other variants are still ongoing. Conclusion- Based on our experience, 25% of variants of uncertain significance in well-defined causative genes, identified in patients with a confirmed or suspected diagnosis of inherited cardiovascular disease, were reclassified. These findings suggest that re-evaluation of genetic test results should be performed routinely in all diagnostic labs, in order to improve risk stratification and identification of family members at high risk.

scholarly journals Cardiovascular Risk Factors from Another Point of View

2021 ◽  
Vol 6 (1) ◽  
pp. 27-31
Author(s):  
Péter Balázs Oltean ◽  
István Kovács ◽  
Roxana Hodas ◽  
Theodora Benedek
Keyword(s):  
Atrial Fibrillation ◽  
Cardiovascular Risk ◽  
Cardiovascular Diseases ◽  
Risk Stratification ◽  
Inflammatory Markers ◽  
Computed Tomographic Angiography ◽  
Point Of View ◽  
Major Adverse Cardiovascular Events ◽  
Imaging Methods ◽  
Cardiovascular Risk Stratification

Abstract Cardiovascular diseases remain the main cause of death in western societies. This contributes to the appearance of new diagnostic and treatment methods addressed to reduce the burden of cardiovascular diseases. In the last decades new imaging methods have emerged; furthermore, routine biomarkers were found to be useful in cardiovascular risk stratification. Data reviewed in this article emphasize the multifactorial etiology of cardiovascular disease. The authors describe the role of inflammation in the precipitation and progression of atherosclerosis and atrial fibrillation. Affordable and well-known inflammatory markers can be used alone or in combination with new imaging methods for a better cardiovascular risk stratification. Coronary computed tomographic angiography findings and inflammatory markers are capable to identify patients with high risk of major adverse cardiovascular events or atrial fibrillation. Furthermore, they also have an important role in the choice of treatment strategy and follow-up.

scholarly journals INNOVATIONS IN DIAGNOSTICS AND TREATMENT OF PATIENTS WITH HYPERTRIGLYCERIDEMIA

2017 ◽  
Vol 2 (1) ◽  
pp. 43-51
Author(s):  
VV V Simerzin ◽  
OV V Fatenkov ◽  
IV V Gagloeva ◽  
MA A Galkina ◽  
YaA A Panisheva ◽  
...  
Keyword(s):  
Acute Pancreatitis ◽  
Risk Factor ◽  
Cardiovascular Diseases ◽  
Risk Stratification ◽  
Independent Risk Factor ◽  
High Level ◽  
Innovative Techniques

This article presents innovative techniques for diagnostics, risk stratification and treatment of patients with hypertriglyceridemia. Hypertriglyceridemia is not only known as an independent risk factor of cardiovascular diseases, but also its high level can become a cause of acute pancreatitis.

scholarly journals Photoplethysmography and Deep Learning: Enhancing Hypertension Risk Stratification

Biosensors ◽  
2018 ◽  
Vol 8 (4) ◽  
pp. 101 ◽  
Author(s):  
Yongbo Liang ◽  
Zhencheng Chen ◽  
Rabab Ward ◽  
Mohamed Elgendi
Keyword(s):  
Blood Pressure ◽  
Signal Processing ◽  
Feature Extraction ◽  
Deep Learning ◽  
Cardiovascular Diseases ◽  
Risk Stratification ◽  
Physiological Parameter ◽  
National Committee ◽  
Hypertension Risk ◽  
Arterial Blood

Blood pressure is a basic physiological parameter in the cardiovascular circulatory system. Long-term abnormal blood pressure will lead to various cardiovascular diseases, making the early detection and assessment of hypertension profoundly significant for the prevention and treatment of cardiovascular diseases. In this paper, we investigate whether or not deep learning can provide better results for hypertension risk stratification when compared to the classical signal processing and feature extraction methods. We tested a deep learning method for the classification and evaluation of hypertension using photoplethysmography (PPG) signals based on the continuous wavelet transform (using Morse) and pretrained convolutional neural network (using GoogLeNet). We collected 121 data recordings from the Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) Database, each containing arterial blood pressure (ABP) and photoplethysmography (PPG) signals. The ABP signals were utilized to extract blood pressure category labels, and the PPG signals were used to train and test the model. According to the seventh report of the Joint National Committee, blood pressure levels are categorized as normotension (NT), prehypertension (PHT), and hypertension (HT). For the early diagnosis and assessment of HT, the timely detection of PHT and the accurate diagnosis of HT are significant. Therefore, three HT classification trials were set: NT vs. PHT, NT vs. HT, and (NT + PHT) vs. HT. The F-scores of these three classification trials were 80.52%, 92.55%, and 82.95%, respectively. The tested deep method achieved higher accuracy for hypertension risk stratification when compared to the classical signal processing and feature extraction method. Additionally, the method achieved comparable results to another approach that requires electrocardiogram and PPG signals.

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