Risk markers of incident atrial fibrillation in patients with coronary heart disease

Tomasdottir, Maria; Held, Claes; Hadziosmanovic, Nermin; Westerbergh, Johan; Lindbäck, Johan; Aylward, Philip E.; Budaj, Andrzej; Cannon, Christopher P.; Engdahl, Johan; Granger, Christopher B.; Köenig, Wolfgang; Manolis, Athanasios; Oldgren, Jonas; Stewart, Ralph; Svennberg, Emma; Vinereanu, Dragoş; White, Harvey D.; Siegbahn, Agneta; Wallentin, Lars; Hijazi, Ziad

doi:10.1016/j.ahj.2020.12.016

Cited by 9 publications

(5 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Studies proved that BMI is a good predictor of cardiovascular disease 39 . Patients with high BMI are more likely to develop AF 40 . However, in this study, we did not find a correlation between plasma GDF‐15 level and BMI.…”

Section: Discussioncontrasting

confidence: 80%

See 1 more Smart Citation

Plasma growth differentiation factor‐15 in patients with “lone” atrial fibrillation

Qian²,

et al. 2022

Clinical Laboratory Analysis

View full text Add to dashboard Cite

Growth differentiation factor-15 (GDF-15) is a member of the transforming growth factor β superfamily. 1 Its expression is correlated with diverse stimuli, including oxidation, inflammation, hypoxia, tissue injuries, and so on. 2,3 Cardiovascular disease is also closely related to GDF-15 production. 4 As a popular cardiovascular disease, atrial fibrillation (AF) poses high burden globally. Moreover, about five million new cases are diagnosed annually. Thus, substantial efforts are made toward the mechanism and treatment of AF. 5 Studies found that plasma GDF-15 level is high in patients with AF. 6,7 GDF-15 involves in left atrial remodeling and acts as a predictor of AF

show abstract

Section: Discussioncontrasting

confidence: 80%

“…39 Patients with high BMI are more likely to develop AF. 40 However, in this study, we did not find a correlation between plasma GDF-15 level and BMI.…”

Section: Discussioncontrasting

confidence: 76%

Plasma growth differentiation factor‐15 in patients with “lone” atrial fibrillation

Qian²,

et al. 2022

Clinical Laboratory Analysis

View full text Add to dashboard Cite

show abstract

“…allows for a more comprehensive understanding of the dataset's structure and composition. During our analysis, we employed two different datasets: Dataset 1, also known as the heart dataset [20] , and Dataset 2, referred to as the CHD dataset [21] .…”

Section: Experimental Scenariomentioning

confidence: 99%

A Clinical Data Analysis Based Diagnostic Systems for Heart Disease Prediction Using Ensemble Method

Kumar,

Singh,

Kumar

2023

Big Data Min. Anal.

View full text Add to dashboard Cite

The correct diagnosis of heart disease can save lives, while the incorrect diagnosis can be lethal. The UCI machine learning heart disease dataset compares the results and analyses of various machine learning approaches, including deep learning. We used a dataset with 13 primary characteristics to carry out the research. Support vector machine and logistic regression algorithms are used to process the datasets, and the latter displays the highest accuracy in predicting coronary disease. Python programming is used to process the datasets. Multiple research initiatives have used machine learning to speed up the healthcare sector. We also used conventional machine learning approaches in our investigation to uncover the links between the numerous features available in the dataset and then used them effectively in anticipation of heart infection risks. Using the accuracy and confusion matrix has resulted in some favorable outcomes. To get the best results, the dataset contains certain unnecessary features that are dealt with using isolation logistic regression and Support Vector Machine (SVM) classification.

show abstract

“…Tomasdottir M, et al [27] Risk markers of incident atrial fi brillation in patients with coronary heart disease R: 13,153…”

Section: Kitzman Dw Et Al [213]mentioning

confidence: 99%

Heart Failure (HF): Recent innovations in clinical therapy and critical profiles of acute and chronic forms

Perrotta¹

2022

J Cardiovasc Med Cardiol

View full text Add to dashboard Cite

Background and objectives: Heart failure is a pathological condition characterized by the inability of the heart to pump (cardiac output) an adequate amount of blood to meet the metabolic needs of the body or, at any rate, to be able to do so only at the expense of increased filling pressures in one or more cardiac chambers and the upstream venous circulation. The research group states here the primary objective of expanding the indications contained in the ESC 2021 with the results of the last year on the subject of treatment profiles, to facilitate a better understanding of the overall clinical picture and contribute to the next edition of the guidelines. Materials and methods: Systematic literature review in the English language from January 1, 2021, to September 30, 2022, on PubMed/MedLine, Web on Science, and Cochrane archive. Our search strategy retrieved 8,317 articles. We excluded books and papers, meta-analyses, reviews, and systematic reviews and selected only 46 studies most significant about the clinical trial and randomized controlled trial. Results: The recent ESC 2021 guidelines are accurate and timely, and have confirmed their usefulness from a functional perspective, including concerning certain aspects that were represented as a “grey area”. Early risk prediction plays a key role in the subsequent management of patients, and to optimize risk prediction and intensity of management, one should take into account that: a) biomarkers have improved the understanding of the pathophysiology of heart failure and may therefore help to adjust the intensity of management in AHF; b) among the wide variety of biomarkers currently available, NT-proBNP and cTn seem the most promising in this indication; c) among the risk scores described, those combining demographic and clinical parameters with biomarkers in a model with routinely available rapid variables seem the most promising tools; d) in addition to biomarkers, age, systolic blood pressure, respiratory rate, oxygen saturation, creatinine, electrolytes, and blood urea nitrogen are the most commonly used predictor variables in the risk scores described; e) among the models selected, the MEESSI-AHF risk score appears to be currently the most promising tool for predicting the risk of AHF; f) during the management of decompensated patients (and in later stages), the psychological aspect is completely ignored, thus promoting the worsening of psychological symptoms (the need is as evident during the acute episode as it is in the daily management of chronic heart failure). Conclusions: The scientific literature search enriched the structure of ESC 2021, suggesting its implementation, with other findings related to new drug therapies such as Sotagliflozin, Hydrochlorothiazide Apabetalone, Alprostadil, Empagliflozin, Sacubitril/Valsartan, Dapagliflozin, Sodium-glucose co-transporter-2 inhibitors, and biomarkers such as Urinary sodium (UNa+), IL-6 levels and rh-brain natriuretic peptide (rhBNP), as well as the use of mindful breathing session, osteopathic manipulative treatment, electrical muscle stimulation, low-level tragus stimulation, venoarterial extracorporeal membrane oxygenation, oral nutritional supplements, and the correlative hypothesis between heart failure and intestinal dysbiosis, also concerning the psychological profile. However, these clinical studies suffer from some limitations that will necessarily have to be taken into account, such as the limited size of the population sample selected or the conflict of interest determined by the fact that some research is funded by the same pharmaceutical company producing the drug used that do not necessarily represent a negative limitation on the results obtained from studies.

show abstract

Risk markers of incident atrial fibrillation in patients with coronary heart disease

Cited by 9 publications

References 35 publications

Plasma growth differentiation factor‐15 in patients with “lone” atrial fibrillation

Plasma growth differentiation factor‐15 in patients with “lone” atrial fibrillation

A Clinical Data Analysis Based Diagnostic Systems for Heart Disease Prediction Using Ensemble Method

Heart Failure (HF): Recent innovations in clinical therapy and critical profiles of acute and chronic forms

Contact Info

Product

Resources

About